diff --git a/examples/mexgen/RAG.ipynb b/examples/mexgen/RAG.ipynb index 446086e..3eaa2a7 100644 --- a/examples/mexgen/RAG.ipynb +++ b/examples/mexgen/RAG.ipynb @@ -135,8 +135,8 @@ "metadata": {}, "outputs": [], "source": [ - "model_type = \"flan-t5\"\n", - "# model_type = \"granite-hf\"\n", + "# model_type = \"flan-t5\"\n", + "model_type = \"granite-hf\"\n", "# model_type = \"vllm\"" ] }, @@ -145,7 +145,15 @@ "execution_count": 5, "id": "96d1c5e0-8de8-401a-9eb4-35140d8af7b8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading checkpoint shards: 100%|██████████████████████████████████████| 2/2 [00:09<00:00, 4.72s/it]\n" + ] + } + ], "source": [ "if model_type == \"flan-t5\":\n", " model_name = \"google/flan-t5-large\"\n", @@ -266,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "217abab4-77cb-49f6-977a-540ebfd42b76", "metadata": {}, "outputs": [], @@ -290,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "c8cd8126-45b4-484c-9cc4-6e5418551f9c", "metadata": {}, "outputs": [], @@ -300,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "ef099a4d-d6f9-472b-a9b4-ca560d17bfab", "metadata": {}, "outputs": [], @@ -320,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "0c31ef9a-2e67-4182-8674-6e567bf9e0a5", "metadata": {}, "outputs": [], @@ -363,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "ace2cda5-ce5e-4ff5-a2b9-e7822e4f65e8", "metadata": {}, "outputs": [], @@ -532,7 +540,9 @@ { "data": { "text/plain": [ - "{'max_new_tokens': 64}" + "{'max_new_tokens': 64,\n", + " 'chat_template': True,\n", + " 'system_prompt': 'You are an AI assistant that provides a *very short* answer to the user *solely based on the provided documents*, aiming to answer the user as correctly as possible. If no documents are provided, answer from your memory.'}" ] }, "execution_count": 12, @@ -575,7 +585,7 @@ { "data": { "text/plain": [ - "['idaho']" + "[' The coldest state in the contiguous USA is Alaska.']" ] }, "execution_count": 13, @@ -602,18 +612,11 @@ "id": "4d73858e-0d2f-493b-8186-e03ee133055f", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Token indices sequence length is longer than the specified maximum sequence length for this model (740 > 512). Running this sequence through the model will result in indexing errors\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "['Alaska']\n" + "[' Alaska is the coldest state of the contiguous USA, with the lowest official temperature recorded at Prospect Creek at -62°F (-52°C).']\n" ] } ], @@ -755,7 +758,7 @@ { "data": { "text/plain": [ - "['Alaska']" + "[[' Alaska is the coldest state of the contiguous USA, with the lowest official temperature recorded at Prospect Creek at -62°F (-52°C).']]" ] }, "execution_count": 19, @@ -784,20 +787,12 @@ { "data": { "text/html": [ - "Answer the user Question using the following Context: \n", - "\n", - "Context:\n", - "\n", - " Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \n", - "\n", - " Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \n", - "\n", - " Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \n", - "\n", - " Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \n", - "\n", - " Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \n", - "\n", + "Documents: \n", + " Document 0: Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \n", + " Document 1: Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \n", + " Document 2: Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \n", + " Document 3: Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \n", + " Document 4: Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \n", " \n", "\n", "Question: What is the coldest state of the contiguous USA?" @@ -852,34 +847,34 @@ " \n", " \n", " \n", - " Answer the user Question using the following Context: \\n\\nContext:\\n\\n\n", + " Documents: \\n\n", " n\n", " -0.000000\n", " \n", " \n", - " Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \\n\\n\n", + " Document 0: Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \\n\n", " p\n", - " 0.377750\n", + " 1.127079\n", " \n", " \n", - " Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \\n\\n\n", + " Document 1: Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \\n\n", " p\n", - " -0.010540\n", + " 2.366256\n", " \n", " \n", - " Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \\n\\n\n", + " Document 2: Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \\n\n", " p\n", - " 0.017820\n", + " 0.994951\n", " \n", " \n", - " Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \\n\\n\n", + " Document 3: Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \\n\n", " p\n", - " -0.050607\n", + " 1.003484\n", " \n", " \n", - " Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \\n\\n\n", + " Document 4: Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \\n\n", " p\n", - " -0.059520\n", + " 0.906972\n", " \n", " \n", " \\n\\nQuestion: What is the coldest state of the contiguous USA?\n", @@ -893,12 +888,12 @@ "text/plain": [ " unit_types prob\n", "units \n", - "Answer the user Question using the following Co... n -0.000000\n", - " Alaska / Alaska Alaska (; ; ; ) is a U.S. stat... p 0.377750\n", - " Alaska / the 90s °F (the low-to-mid 30s °C), w... p -0.010540\n", - " Alaska / Southeast Alaska is a mid-latitude oc... p 0.017820\n", - " Alaska / the action of the sea is directed\". A... p -0.050607\n", - " Alaska / but is not connected by road to the r... p -0.059520\n", + "Documents: \\n n -0.000000\n", + "Document 0: Alaska / Alaska Alaska (; ; ; ) is... p 1.127079\n", + "Document 1: Alaska / the 90s °F (the low-to-mi... p 2.366256\n", + "Document 2: Alaska / Southeast Alaska is a mid... p 0.994951\n", + "Document 3: Alaska / the action of the sea is ... p 1.003484\n", + "Document 4: Alaska / but is not connected by r... p 0.906972\n", "\\n\\nQuestion: What is the coldest state of the ... n -0.000000" ] }, @@ -975,7 +970,7 @@ { "data": { "text/plain": [ - "array([False, True, False, True, False, False, False])" + "array([False, True, True, False, False, False, False])" ] }, "execution_count": 23, @@ -1052,20 +1047,12 @@ { "data": { "text/html": [ - "Answer the user Question using the following Context: \n", - "\n", - "Context:\n", - "\n", - " Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \n", - "\n", - " Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \n", - "\n", - " Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \n", - "\n", - " Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \n", - "\n", - " Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \n", - "\n", + "Documents: \n", + " Document 0: Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America. The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait. To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean. The Pacific Ocean lies to the south and southwest. It is the largest state in the United States by area and the seventh largest subnational division in \n", + " Document 1: Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \n", + " Document 2: Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \n", + " Document 3: Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \n", + " Document 4: Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \n", " \n", "\n", "Question: What is the coldest state of the contiguous USA?" @@ -1120,94 +1107,79 @@ " \n", " \n", " \n", - " Answer the user Question using the following Context: \\n\\nContext:\\n\\n\n", + " Documents: \\n\n", " n\n", " -0.000000\n", " \n", " \n", - " \n", - " n\n", - " -0.000000\n", - " \n", - " \n", - " Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America.\n", + " Document 0: Alaska / Alaska Alaska (; ; ; ) is a U.S. state in the northwest extremity of North America.\n", " s\n", - " 0.625500\n", + " 0.480711\n", " \n", " \n", " The Canadian administrative divisions of British Columbia and Yukon border the state to the east, its most extreme western part is Attu Island, and it has a maritime border with Russia (Chukotka Autonomous Okrug) to the west across the Bering Strait.\n", " s\n", - " 0.058528\n", + " 0.715425\n", " \n", " \n", " To the north are the Chukchi and Beaufort seas—the southern parts of the Arctic Ocean.\n", " s\n", - " 0.047288\n", + " 0.226573\n", " \n", " \n", " The Pacific Ocean lies to the south and southwest.\n", " s\n", - " 0.004326\n", + " 0.119170\n", " \n", " \n", - " It is the largest state in the United States by area and the seventh largest subnational division in\n", + " It is the largest state in the United States by area and the seventh largest subnational division in \\n\n", " s\n", - " 0.074129\n", + " 0.176745\n", " \n", " \n", - " \\n\\n\n", - " n\n", - " -0.000000\n", - " \n", - " \n", - " Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below . Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter. The highest and lowest recorded temperatures in Alaska are both in the Interior. The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States. The lowest official Alaska temperature is in Prospect Creek on January 23, \\n\\n\n", - " p\n", - " 0.041008\n", - " \n", - " \n", - " \n", - " n\n", - " -0.000000\n", - " \n", - " \n", - " Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts.\n", + " Document 1: Alaska / the 90s °F (the low-to-mid 30s °C), while in the winter, the temperature can fall below .\n", " s\n", - " 0.069577\n", + " 0.856947\n", " \n", " \n", - " On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year.\n", + " Precipitation is sparse in the Interior, often less than a year, but what precipitation falls in the winter tends to stay the entire winter.\n", " s\n", - " 0.022369\n", + " 0.403527\n", " \n", " \n", - " Juneau averages over of precipitation a year, and Ketchikan averages over .\n", + " The highest and lowest recorded temperatures in Alaska are both in the Interior.\n", " s\n", - " 0.070767\n", + " 0.193359\n", " \n", " \n", - " This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months.\n", + " The highest is in Fort Yukon (which is just inside the arctic circle) on June 27, 1915, making Alaska tied with Hawaii as the state with the lowest high temperature in the United States.\n", " s\n", - " 0.006302\n", + " 0.558523\n", " \n", " \n", - " The climate of Anchorage and south central Alaska is mild by Alaskan standards due\n", + " The lowest official Alaska temperature is in Prospect Creek on January 23,\n", " s\n", - " 0.020253\n", + " 1.080584\n", " \n", " \n", - " \\n\\n\n", + " \\n\n", " n\n", " -0.000000\n", " \n", " \n", - " Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \\n\\n\n", + " Document 2: Alaska / Southeast Alaska is a mid-latitude oceanic climate (Köppen climate classification: \"Cfb\") in the southern sections and a subarctic oceanic climate (Köppen \"Cfc\") in the northern parts. On an annual basis, Southeast is both the wettest and warmest part of Alaska with milder temperatures in the winter and high precipitation throughout the year. Juneau averages over of precipitation a year, and Ketchikan averages over . This is also the only region in Alaska in which the average daytime high temperature is above freezing during the winter months. The climate of Anchorage and south central Alaska is mild by Alaskan standards due \\n\n", + " p\n", + " 1.521426\n", + " \n", + " \n", + " Document 3: Alaska / the action of the sea is directed\". Alaska is the northernmost and westernmost state in the United States and has the most easterly longitude in the United States because the Aleutian Islands extend into the Eastern Hemisphere. Alaska is the only non-contiguous U.S. state on continental North America; about of British Columbia (Canada) separates Alaska from Washington. It is technically part of the continental U.S., but is sometimes not included in colloquial use; Alaska is not part of the contiguous U.S., often called \"the Lower 48\". The capital city, Juneau, is situated on the mainland of the North American continent \\n\n", " p\n", - " 0.071507\n", + " 1.693132\n", " \n", " \n", - " Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \\n\\n\n", + " Document 4: Alaska / but is not connected by road to the rest of the North American highway system. The state is bordered by Yukon and British Columbia in Canada, to the east, the Gulf of Alaska and the Pacific Ocean to the south and southwest, the Bering Sea, Bering Strait, and Chukchi Sea to the west and the Arctic Ocean to the north. Alaska's territorial waters touch Russia's territorial waters in the Bering Strait, as the Russian Big Diomede Island and Alaskan Little Diomede Island are only apart. Alaska has a longer coastline than all the other U.S. states combined. Alaska is the \\n\n", " p\n", - " -0.010955\n", + " 1.598298\n", " \n", " \n", " \\n\\nQuestion: What is the coldest state of the contiguous USA?\n", @@ -1221,24 +1193,21 @@ "text/plain": [ " unit_types prob\n", "units \n", - "Answer the user Question using the following Co... n -0.000000\n", - " n -0.000000\n", - "Alaska / Alaska Alaska (; ; ; ) is a U.S. state... s 0.625500\n", - "The Canadian administrative divisions of Britis... s 0.058528\n", - "To the north are the Chukchi and Beaufort seas—... s 0.047288\n", - "The Pacific Ocean lies to the south and southwe... s 0.004326\n", - "It is the largest state in the United States by... s 0.074129\n", - "\\n\\n n -0.000000\n", - " Alaska / the 90s °F (the low-to-mid 30s °C), w... p 0.041008\n", - " n -0.000000\n", - "Alaska / Southeast Alaska is a mid-latitude oce... s 0.069577\n", - "On an annual basis, Southeast is both the wette... s 0.022369\n", - "Juneau averages over of precipitation a year, a... s 0.070767\n", - "This is also the only region in Alaska in which... s 0.006302\n", - "The climate of Anchorage and south central Alas... s 0.020253\n", - "\\n\\n n -0.000000\n", - " Alaska / the action of the sea is directed\". A... p 0.071507\n", - " Alaska / but is not connected by road to the r... p -0.010955\n", + "Documents: \\n n -0.000000\n", + "Document 0: Alaska / Alaska Alaska (; ; ; ) is... s 0.480711\n", + "The Canadian administrative divisions of Britis... s 0.715425\n", + "To the north are the Chukchi and Beaufort seas—... s 0.226573\n", + "The Pacific Ocean lies to the south and southwe... s 0.119170\n", + "It is the largest state in the United States by... s 0.176745\n", + "Document 1: Alaska / the 90s °F (the low-to-mi... s 0.856947\n", + "Precipitation is sparse in the Interior, often ... s 0.403527\n", + "The highest and lowest recorded temperatures in... s 0.193359\n", + "The highest is in Fort Yukon (which is just ins... s 0.558523\n", + "The lowest official Alaska temperature is in Pr... s 1.080584\n", + " \\n n -0.000000\n", + "Document 2: Alaska / Southeast Alaska is a mid... p 1.521426\n", + "Document 3: Alaska / the action of the sea is ... p 1.693132\n", + "Document 4: Alaska / but is not connected by r... p 1.598298\n", "\\n\\nQuestion: What is the coldest state of the ... n -0.000000" ] }, @@ -1358,7 +1327,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_get_probs batch size = 10\n" + "toma_get_probs batch size = 9\n" ] } ], @@ -1392,7 +1361,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 30, @@ -1401,7 +1370,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/examples/mexgen/question_answering.ipynb b/examples/mexgen/question_answering.ipynb index 6bd0122..f20cf73 100644 --- a/examples/mexgen/question_answering.ipynb +++ b/examples/mexgen/question_answering.ipynb @@ -379,8 +379,8 @@ } ], "source": [ - "output_orig = wrapped_model.generate(\"\".join(prompt), **model_params)\n", - "print(output_orig)" + "output_orig = wrapped_model.generate(\"\".join(prompt), text_only=False, **model_params)\n", + "print(output_orig.output_text)" ] }, { @@ -555,7 +555,7 @@ { "data": { "text/html": [ - "Context: In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million. Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium. In 2003, Beyonce starred opposite Cuba Gooding, Jr., in a musical comedy as Lilly, a single mother whom Gooding's character falls in love with. The film received mixed reviews from critics but grossed $30 million in the U.S. Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film. Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts. \n", + "Context: In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million. Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium. In 2003, Beyonce starred opposite Cuba Gooding, Jr., in a musical comedy as Lilly, a single mother whom Gooding's character falls in love with. The film received mixed reviews from critics but grossed $30 million in the U.S. Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film. Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts. \n", "\n", "Question: What movie did Beyonce star in in 2003? \n", "\n", @@ -618,32 +618,32 @@ " \n", " In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million.\n", " s\n", - " -0.467680\n", + " -0.303321\n", " \n", " \n", " Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium.\n", " s\n", - " 0.099590\n", + " -0.090525\n", " \n", " \n", " In 2003, Beyonce starred opposite Cuba Gooding, Jr., in a musical comedy as Lilly, a single mother whom Gooding's character falls in love with.\n", " s\n", - " 6.336826\n", + " 5.442492\n", " \n", " \n", " The film received mixed reviews from critics but grossed $30 million in the U.S.\n", " s\n", - " -0.202770\n", + " 0.024841\n", " \n", " \n", " Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film.\n", " s\n", - " 0.197400\n", + " 0.030438\n", " \n", " \n", " Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts.\n", " s\n", - " -0.021606\n", + " 0.049272\n", " \n", " \n", " \\n\\nQuestion:\n", @@ -668,12 +668,12 @@ " unit_types prob\n", "units \n", "Context: n 0.000000\n", - "In July 2002, Beyonce continued her acting care... s -0.467680\n", - "Beyonce released \"Work It Out\" as the lead sing... s 0.099590\n", - "In 2003, Beyonce starred opposite Cuba Gooding,... s 6.336826\n", - "The film received mixed reviews from critics bu... s -0.202770\n", - "Beyonce released \"Fighting Temptation\" as the l... s 0.197400\n", - "Another of Beyonce's contributions to the sound... s -0.021606\n", + "In July 2002, Beyonce continued her acting care... s -0.303321\n", + "Beyonce released \"Work It Out\" as the lead sing... s -0.090525\n", + "In 2003, Beyonce starred opposite Cuba Gooding,... s 5.442492\n", + "The film received mixed reviews from critics bu... s 0.024841\n", + "Beyonce released \"Fighting Temptation\" as the l... s 0.030438\n", + "Another of Beyonce's contributions to the sound... s 0.049272\n", "\\n\\nQuestion: n 0.000000\n", "What movie did Beyonce star in in 2003? n 0.000000\n", "\\n\\nAnswer: n 0.000000" @@ -859,7 +859,7 @@ { "data": { "text/html": [ - "Context: In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million. Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium. In 2003 , Beyonce starred opposite Cuba Gooding , Jr. , in a musical comedy as Lilly , a single mother whom Gooding 's character falls in love with . The film received mixed reviews from critics but grossed $30 million in the U.S. Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film. Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts. \n", + "Context: In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million. Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium. In 2003 , Beyonce starred opposite Cuba Gooding , Jr. , in a musical comedy as Lilly , a single mother whom Gooding 's character falls in love with . The film received mixed reviews from critics but grossed $30 million in the U.S. Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film. Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts. \n", "\n", "Question: What movie did Beyonce star in in 2003? \n", "\n", @@ -922,22 +922,22 @@ " \n", " In July 2002, Beyonce continued her acting career playing Foxxy Cleopatra alongside Mike Myers in the comedy film, Austin Powers in Goldmember, which spent its first weekend atop the US box office and grossed $73 million.\n", " s\n", - " -0.233123\n", + " -0.030021\n", " \n", " \n", " Beyonce released \"Work It Out\" as the lead single from its soundtrack album which entered the top ten in the UK, Norway, and Belgium.\n", " s\n", - " 0.320631\n", + " 0.085883\n", " \n", " \n", " In\n", " w\n", - " -0.052855\n", + " 0.091946\n", " \n", " \n", " 2003\n", " w\n", - " 0.045652\n", + " 1.777418\n", " \n", " \n", " ,\n", @@ -947,27 +947,27 @@ " \n", " Beyonce\n", " w\n", - " -0.084887\n", + " 0.143749\n", " \n", " \n", " starred\n", " w\n", - " -0.034127\n", + " 0.203287\n", " \n", " \n", " opposite\n", " w\n", - " -0.236251\n", + " 0.015318\n", " \n", " \n", " Cuba\n", " w\n", - " 0.078328\n", + " 0.023138\n", " \n", " \n", " Gooding\n", " w\n", - " 0.216411\n", + " 0.310289\n", " \n", " \n", " ,\n", @@ -977,7 +977,7 @@ " \n", " Jr.\n", " w\n", - " 0.022668\n", + " 0.025616\n", " \n", " \n", " ,\n", @@ -987,32 +987,32 @@ " \n", " in\n", " w\n", - " 0.471464\n", + " 0.034352\n", " \n", " \n", " a\n", " w\n", - " -0.190492\n", + " 0.021783\n", " \n", " \n", " musical\n", " w\n", - " 6.583380\n", + " 2.658460\n", " \n", " \n", " comedy\n", " w\n", - " -1.930192\n", + " 3.001379\n", " \n", " \n", " as\n", " w\n", - " 0.956929\n", + " 1.735643\n", " \n", " \n", " Lilly\n", " w\n", - " -0.279934\n", + " -0.632696\n", " \n", " \n", " ,\n", @@ -1022,57 +1022,57 @@ " \n", " a\n", " w\n", - " -0.081362\n", + " -0.069837\n", " \n", " \n", " single\n", " w\n", - " -0.014852\n", + " -0.020159\n", " \n", " \n", " mother\n", " w\n", - " 0.129198\n", + " 0.060091\n", " \n", " \n", " whom\n", " w\n", - " 0.116678\n", + " 0.016289\n", " \n", " \n", " Gooding\n", " w\n", - " 0.075750\n", + " 0.010448\n", " \n", " \n", " 's\n", " w\n", - " 0.212793\n", + " 0.017195\n", " \n", " \n", " character\n", " w\n", - " 0.066415\n", + " -0.010148\n", " \n", " \n", " falls\n", " w\n", - " -0.131812\n", + " -0.004816\n", " \n", " \n", " in\n", " w\n", - " -0.056521\n", + " -0.005142\n", " \n", " \n", " love\n", " w\n", - " 0.015170\n", + " 0.009327\n", " \n", " \n", " with\n", " w\n", - " -0.189587\n", + " -0.004349\n", " \n", " \n", " .\n", @@ -1082,17 +1082,17 @@ " \n", " The film received mixed reviews from critics but grossed $30 million in the U.S.\n", " s\n", - " 0.058848\n", + " -0.005575\n", " \n", " \n", " Beyonce released \"Fighting Temptation\" as the lead single from the film's soundtrack album, with Missy Elliott, MC Lyte, and Free which was also used to promote the film.\n", " s\n", - " 0.456628\n", + " 0.037958\n", " \n", " \n", " Another of Beyonce's contributions to the soundtrack, \"Summertime\", fared better on the US charts.\n", " s\n", - " -0.202139\n", + " 0.083051\n", " \n", " \n", " \\n\\nQuestion:\n", @@ -1117,41 +1117,41 @@ " unit_types prob\n", "units \n", "Context: n 0.000000\n", - "In July 2002, Beyonce continued her acting care... s -0.233123\n", - "Beyonce released \"Work It Out\" as the lead sing... s 0.320631\n", - "In w -0.052855\n", - "2003 w 0.045652\n", + "In July 2002, Beyonce continued her acting care... s -0.030021\n", + "Beyonce released \"Work It Out\" as the lead sing... s 0.085883\n", + "In w 0.091946\n", + "2003 w 1.777418\n", ", n 0.000000\n", - "Beyonce w -0.084887\n", - "starred w -0.034127\n", - "opposite w -0.236251\n", - "Cuba w 0.078328\n", - "Gooding w 0.216411\n", + "Beyonce w 0.143749\n", + "starred w 0.203287\n", + "opposite w 0.015318\n", + "Cuba w 0.023138\n", + "Gooding w 0.310289\n", ", n 0.000000\n", - "Jr. w 0.022668\n", + "Jr. w 0.025616\n", ", n 0.000000\n", - "in w 0.471464\n", - "a w -0.190492\n", - "musical w 6.583380\n", - "comedy w -1.930192\n", - "as w 0.956929\n", - "Lilly w -0.279934\n", + "in w 0.034352\n", + "a w 0.021783\n", + "musical w 2.658460\n", + "comedy w 3.001379\n", + "as w 1.735643\n", + "Lilly w -0.632696\n", ", n 0.000000\n", - "a w -0.081362\n", - "single w -0.014852\n", - "mother w 0.129198\n", - "whom w 0.116678\n", - "Gooding w 0.075750\n", - "'s w 0.212793\n", - "character w 0.066415\n", - "falls w -0.131812\n", - "in w -0.056521\n", - "love w 0.015170\n", - "with w -0.189587\n", + "a w -0.069837\n", + "single w -0.020159\n", + "mother w 0.060091\n", + "whom w 0.016289\n", + "Gooding w 0.010448\n", + "'s w 0.017195\n", + "character w -0.010148\n", + "falls w -0.004816\n", + "in w -0.005142\n", + "love w 0.009327\n", + "with w -0.004349\n", ". n 0.000000\n", - "The film received mixed reviews from critics bu... s 0.058848\n", - "Beyonce released \"Fighting Temptation\" as the l... s 0.456628\n", - "Another of Beyonce's contributions to the sound... s -0.202139\n", + "The film received mixed reviews from critics bu... s -0.005575\n", + "Beyonce released \"Fighting Temptation\" as the l... s 0.037958\n", + "Another of Beyonce's contributions to the sound... s 0.083051\n", "\\n\\nQuestion: n 0.000000\n", "What movie did Beyonce star in in 2003? n 0.000000\n", "\\n\\nAnswer: n 0.000000" @@ -1238,7 +1238,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_get_probs batch size = 4\n" + "toma_get_probs batch size = 5\n" ] } ], @@ -1257,7 +1257,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_get_probs batch size = 17\n" + "toma_get_probs batch size = 22\n" ] } ], @@ -1291,7 +1291,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 29, @@ -1300,7 +1300,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/examples/mexgen/summarization.ipynb b/examples/mexgen/summarization.ipynb index e98df7c..32d28e8 100644 --- a/examples/mexgen/summarization.ipynb +++ b/examples/mexgen/summarization.ipynb @@ -148,7 +148,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading checkpoint shards: 100%|██████████████████████████████████████| 2/2 [00:07<00:00, 3.79s/it]\n" + "Loading checkpoint shards: 100%|██████████████████████████████████████| 2/2 [00:09<00:00, 4.51s/it]\n" ] } ], @@ -348,13 +348,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']\n" + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an 8% online growth and expansion into 11 new countries, with plans to extend mobile payment services globally, while benefiting from steady economic growth in Spain and expanding intern']\n" ] } ], "source": [ - "output_orig = wrapped_model.generate(document, **model_params)\n", - "print(output_orig)" + "output_orig = wrapped_model.generate(document, text_only=False, **model_params)\n", + "print(output_orig.output_text)" ] }, { @@ -446,11 +446,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_generate batch size = 132\n", - "toma_generate batch size = 128\n", - "toma_generate batch size = 64\n", - "toma_generate batch size = 64\n", - "toma_generate batch size = 4\n" + "toma_generate batch size = 132\n" ] }, { @@ -464,8 +460,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']\n", - "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by an 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and expand its global presence.', \" Zara, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the six months ending 31 July, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, under the strategic leadership of Chairman Pablo Isla who emphasized technological advancements\", ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by an 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and expand its global presence.', ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the first half of its fiscal year, driven by a 6-8% expansion of new store space, significant online growth, and technological advancements like mobile payments and a unified InWallet app across all brands.', \" Inditex, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 40 countries, particularly benefiting from steady economic growth in Spain.\"]\n", + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an 8% online growth and expansion into 11 new countries, with plans to extend mobile payment services globally, while benefiting from steady economic growth in Spain and expanding intern']\n", + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and seamless integration of online and physical stores, with plans to further develop technological initiatives.', ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, under the strategic leadership of Chairman Pablo Isla who emphasized technological advancements and', ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by an 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and expand its global presence.', ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the first half of its fiscal year, driven by a 6-8% expansion of new store space, significant online growth, and technological advancements like mobile payments and a unified InWallet app across all brands.', \" Inditex, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales from €9.4bn to €10.5bn, with significant growth in online sales and expansion into 40 countries, particularly benefiting from steady economic growth in Spain.\"]\n", "NLI scalarizer ref->gen\n", "toma_call batch size = 132\n", "NLI scalarizer gen->ref\n", @@ -517,7 +513,7 @@ { "data": { "text/plain": [ - "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']" + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an 8% online growth and expansion into 11 new countries, with plans to extend mobile payment services globally, while benefiting from steady economic growth in Spain and expanding intern']" ] }, "execution_count": 14, @@ -546,24 +542,24 @@ { "data": { "text/html": [ - "The world's biggest clothing retailer posted net earnings of €1.26bn (£1.1bn) in the six months to 31 July - up 8% on the same period last year. \n", - "Sales jumped from €9.4bn to €10.5bn, an increase of 11%. \n", - "The group's clothes can now be bought online in around 40 countries, it said. \n", - "Inditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka. \n", + "The world's biggest clothing retailer posted net earnings of €1.26bn (£1.1bn) in the six months to 31 July - up 8% on the same period last year. \n", + "Sales jumped from €9.4bn to €10.5bn, an increase of 11%. \n", + "The group's clothes can now be bought online in around 40 countries, it said. \n", + "Inditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka. \n", "How Zara's founder became the richest man in the world - for two days\n", - "Chairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period. \n", - "It also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\". \n", - "This will encompass online apps for all of its brands and a specific app for the whole group called InWallet. \n", - "Mr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\" \n", - "Tom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important. \n", - "\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said. \n", - "\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online. \n", - "\"So there is a big opportunity there for them to expand online into new territories.\" \n", - "The company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales. \n", - "That country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency. \n", - "All of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries. \n", + "Chairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period. \n", + "It also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\". \n", + "This will encompass online apps for all of its brands and a specific app for the whole group called InWallet. \n", + "Mr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\" \n", + "Tom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important. \n", + "\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said. \n", + "\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online. \n", + "\"So there is a big opportunity there for them to expand online into new territories.\" \n", + "The company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales. \n", + "That country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency. \n", + "All of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries. \n", "In a call with analysts, it said it would open 6-8% of new store space over course of the year. \n", - " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year." + " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year." ], "text/plain": [ "" @@ -640,143 +636,143 @@ " 1.000000\n", " 1.000000\n", " 1.000000\n", - " -0.168489\n", - " -0.258453\n", + " 1.000000\n", + " 1.000000\n", " \n", " \n", " \\nSales jumped from €9.4bn to €10.5bn, an increase of 11%.\n", " s\n", - " 0.464572\n", - " 0.424606\n", - " 0.182319\n", - " 1.000000\n", - " 1.000000\n", + " -0.759372\n", + " 0.209952\n", + " 0.113165\n", + " 0.178240\n", + " 0.243746\n", " \n", " \n", " \\nThe group's clothes can now be bought online in around 40 countries, it said.\n", " s\n", - " -0.678671\n", - " -0.371478\n", - " -0.077795\n", - " -0.848492\n", - " -0.902917\n", + " -0.808849\n", + " 0.178138\n", + " 0.047222\n", + " 0.768345\n", + " 0.806839\n", " \n", " \n", " \\nInditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka.\n", " s\n", - " 0.786595\n", - " 0.094391\n", - " 0.558204\n", - " -0.817181\n", - " -0.782678\n", + " 0.879383\n", + " 0.231298\n", + " 0.547190\n", + " 0.295272\n", + " 0.324798\n", " \n", " \n", " \\nHow Zara's founder became the richest man in the world - for two days\\nChairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period.\n", " s\n", - " 0.902068\n", - " 0.182539\n", - " 0.411495\n", - " -0.536248\n", - " -0.589693\n", + " 0.576931\n", + " 0.249219\n", + " 0.383890\n", + " 0.053929\n", + " -0.008015\n", " \n", " \n", " \\nIt also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\".\n", " s\n", - " 0.372554\n", - " -0.045436\n", - " 0.263193\n", - " -0.807955\n", - " -0.818814\n", + " 0.159763\n", + " 0.135365\n", + " 0.276763\n", + " 0.055919\n", + " 0.058838\n", " \n", " \n", " \\nThis will encompass online apps for all of its brands and a specific app for the whole group called InWallet.\n", " s\n", - " -0.301760\n", - " 0.040993\n", - " -0.066632\n", - " 0.469033\n", - " 0.533445\n", + " -0.421199\n", + " 0.041748\n", + " -0.082515\n", + " 0.004378\n", + " -0.014728\n", " \n", " \n", " \\nMr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\"\n", " s\n", - " 0.318680\n", - " -0.106296\n", - " 0.365367\n", - " -0.767757\n", - " -0.868582\n", + " -0.173751\n", + " 0.112717\n", + " 0.356537\n", + " 0.182602\n", + " 0.122337\n", " \n", " \n", " \\nTom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important.\n", " s\n", - " -0.882482\n", - " -0.320363\n", - " -0.308789\n", - " -0.816193\n", - " -0.824716\n", + " -0.974988\n", + " 0.123459\n", + " -0.272267\n", + " 0.156446\n", + " 0.170738\n", " \n", " \n", " \\n\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said.\n", " s\n", - " 0.936409\n", - " 0.039259\n", - " 0.392456\n", - " -0.575641\n", - " -0.552495\n", + " 0.144039\n", + " 0.089828\n", + " 0.298620\n", + " -0.065742\n", + " -0.086818\n", " \n", " \n", " \\n\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online.\n", " s\n", - " -0.113354\n", - " 0.076609\n", - " -0.241972\n", - " -0.023411\n", - " -0.005748\n", + " -0.397169\n", + " -0.080152\n", + " -0.252495\n", + " -0.261450\n", + " -0.312519\n", " \n", " \n", " \\n\"So there is a big opportunity there for them to expand online into new territories.\"\n", " s\n", - " 0.057621\n", - " 0.137399\n", - " -0.048943\n", - " 0.321005\n", - " 0.308903\n", + " -1.147384\n", + " 0.230585\n", + " -0.086955\n", + " 0.163171\n", + " 0.185499\n", " \n", " \n", " \\nThe company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales.\n", " s\n", - " 0.851514\n", - " 0.165589\n", - " 0.491583\n", - " 0.323525\n", - " 0.387338\n", + " 0.340592\n", + " 0.231468\n", + " 0.404248\n", + " 0.402877\n", + " 0.426029\n", " \n", " \n", " \\nThat country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency.\n", " s\n", - " -0.590795\n", - " -0.295202\n", - " -0.117127\n", - " -0.770218\n", - " -0.767762\n", + " 0.129581\n", + " 0.102772\n", + " 0.015495\n", + " 0.240929\n", + " 0.281816\n", " \n", " \n", " \\nAll of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries.\n", " s\n", - " 0.618455\n", - " 0.087252\n", - " 0.348640\n", - " 0.251070\n", - " 0.269188\n", + " -0.024213\n", + " 0.170660\n", + " 0.438374\n", + " 0.353669\n", + " 0.380591\n", " \n", " \n", " \\nIn a call with analysts, it said it would open 6-8% of new store space over course of the year.\n", " s\n", - " 0.568610\n", - " 0.155764\n", - " 0.315034\n", - " -0.179051\n", - " -0.138836\n", + " -0.615888\n", + " 0.117855\n", + " 0.012849\n", + " 0.114480\n", + " 0.097671\n", " \n", " \n", " \\n\n", @@ -790,11 +786,11 @@ " \n", " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year.\n", " s\n", - " -0.372237\n", - " 0.015519\n", - " -0.301576\n", - " 0.204164\n", - " 0.205304\n", + " -0.196459\n", + " 0.016528\n", + " -0.250643\n", + " -0.409811\n", + " -0.476984\n", " \n", " \n", "\n", @@ -804,65 +800,65 @@ " unit_types nli_logit \\\n", "units \n", "The world's 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False, False,\n", " False, False, False, False, False, False, False, False, False])" ] }, @@ -998,20 +994,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "toma_generate batch size = 195\n", - "toma_generate batch size = 128\n", - "toma_generate batch size = 64\n", - "toma_generate batch size = 64\n", - "toma_generate batch size = 64\n", - "toma_generate batch size = 3\n", - "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']\n", - "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by an 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and expand its global presence.', \" Zara, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as part of its technological drive led by Chairman Pablo Isla.\", ' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the first half of its fiscal year, driven by a strong online drive, expansion into 11 new countries, and increased international presence of its brands, particularly in Spain, outpacing European rivals H&M and Next.', \" Zara's parent company, Inditex, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and expansion into 11 new countries, as the company continues to invest in technology and open new stores, particularly in Spain, amid steady economic growth in\", \" Zara, the world's largest clothing retailer, reported net earnings of €1.26bn, with a 11% sales increase from €9.4bn to €10.5bn, driven by its robust online expansion and strong performance in Spain, as it continues to invest in technology, including mobile payments and a unified online platform for all brands.\"]\n", + "Subtree ['eight', 'brands', 'including', 'Pull&Bear', ',', 'Massimo', 'Dutti', 'and', 'Bershka'] not contiguous!\n", + "toma_generate batch size = 204\n", + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an 8% online growth and expansion into 11 new countries, with plans to extend mobile payment services globally, while benefiting from steady economic growth in Spain and expanding intern']\n", + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.', \" Zara, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn, driven by robust online sales and expansion into 11 new countries, with plans to roll out mobile payment and digital apps across all brands, while also benefiting from steady economic growth in Spain.\", \" Inditex, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales, expansion into 40 more countries, and the introduction of mobile payment systems in Spanish stores, aiming to extend these services globally.\", \" Inditex, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales, expansion into new markets, and robust performance in Spain, outpacing European rivals H&M and Next due to favorable economic conditions\", \" Zara, the world's largest clothing retailer, reported a 8% increase in net earnings to €1.26bn for the first half of its fiscal year, driven by a 11% rise in sales to €10.5bn, with significant growth in online sales and international expansion, particularly in Spain, despite stiff competition from rivals H&M and Next.\"]\n", "NLI scalarizer ref->gen\n", - "toma_call batch size = 195\n", + "toma_call batch size = 204\n", "NLI scalarizer gen->ref\n", - "toma_call batch size = 195\n", + "toma_call batch size = 204\n", "summ scalarizer\n", - "toma_get_probs batch size = 195\n" + "toma_get_probs batch size = 204\n" ] } ], @@ -1044,7 +1036,7 @@ { "data": { "text/plain": [ - "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an online drive, expansion into 11 new countries, and steady economic growth in Spain, outpacing European rivals H&M and Next.']" + "[' Inditex, the parent company of Zara, reported a 8% increase in net earnings to €1.26bn and a 11% rise in sales to €10.5bn for the six months ending 31 July, driven by an 8% online growth and expansion into 11 new countries, with plans to extend mobile payment services globally, while benefiting from steady economic growth in Spain and expanding intern']" ] }, "execution_count": 20, @@ -1073,24 +1065,24 @@ { "data": { "text/html": [ - "The world's biggest clothing retailer posted net earnings of €1.26bn (£1.1bn) in the six months to 31 July - up 8% on the same period last year . \n", - " Sales jumped from €9.4bn to €10.5bn, an increase of 11% . \n", - "The group's clothes can now be bought online in around 40 countries, it said. \n", - "Inditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka. \n", + "The world's biggest clothing retailer posted net earnings of €1.26bn (£1.1bn) in the six months to 31 July - up 8% on the same period last year . \n", + "Sales jumped from €9.4bn to €10.5bn, an increase of 11%. \n", + "The group's clothes can now be bought online in around 40 countries, it said. \n", + " Inditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka . \n", "How Zara's founder became the richest man in the world - for two days\n", - "Chairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period. \n", + "Chairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period. \n", "It also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\". \n", - "This will encompass online apps for all of its brands and a specific app for the whole group called InWallet. \n", - "Mr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\" \n", - "Tom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important. \n", - "\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said. \n", - "\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online. \n", - "\"So there is a big opportunity there for them to expand online into new territories.\" \n", - "The company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales. \n", - "That country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency. \n", - "All of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries. \n", + "This will encompass online apps for all of its brands and a specific app for the whole group called InWallet. \n", + "Mr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\" \n", + "Tom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important. \n", + "\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said. \n", + "\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online. \n", + "\"So there is a big opportunity there for them to expand online into new territories.\" \n", + "The company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales. \n", + "That country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency. \n", + "All of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries. \n", "In a call with analysts, it said it would open 6-8% of new store space over course of the year. \n", - " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year." + " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year." ], "text/plain": [ "" @@ -1163,38 +1155,38 @@ " \n", " The world's biggest clothing retailer\n", " nsubj\n", - " 1.000000\n", - " 0.106474\n", - " 0.584114\n", - " -0.721895\n", - " -0.785352\n", + " -0.442970\n", + " -0.099926\n", + " -0.059958\n", + " -0.240193\n", + " -0.183453\n", " \n", " \n", " posted\n", " ROOT\n", - " -1.616351\n", - " -0.856041\n", - " 0.314072\n", - " -1.913796\n", - " -1.909047\n", + " -0.129547\n", + " 0.197238\n", + " -0.099478\n", + " 0.060759\n", + " 0.089610\n", " \n", " \n", " net earnings of €1.26bn (£1.1bn)\n", " dobj\n", - " 0.508601\n", - " 1.000000\n", - " 0.749068\n", + " -1.556081\n", + " 0.978237\n", + " 0.548432\n", " 1.000000\n", " 1.000000\n", " \n", " \n", " in the six months to 31 July\n", " prep\n", - " 0.776250\n", - " 0.430682\n", - " 0.058211\n", - " -0.268173\n", - " -0.162354\n", + " 0.368621\n", + " 0.419872\n", + " -0.044429\n", + " -0.088184\n", + " -0.020920\n", " \n", " \n", " -\n", @@ -1208,11 +1200,11 @@ " \n", " up 8% on the same period last year\n", " advmod\n", - " 0.360540\n", - " 0.201041\n", - " 0.540846\n", - " -0.142716\n", - " -0.140645\n", + " 0.825911\n", + " 1.000000\n", + " 0.480190\n", + " 0.721515\n", + " 0.632897\n", " \n", " \n", " .\n", @@ -1224,6 +1216,24 @@ " 0.000000\n", " \n", " \n", + " \\nSales jumped from €9.4bn to €10.5bn, an increase of 11%.\n", + " s\n", + " -2.082431\n", + " -0.035347\n", + " -0.186891\n", + " -0.014012\n", + " 0.041359\n", + " \n", + " \n", + " \\nThe group's clothes can now be bought online in around 40 countries, it said.\n", + " s\n", + " -1.848802\n", + " 0.252483\n", + " -0.152765\n", + " 0.877237\n", + " 0.802476\n", + " \n", + " \n", " \\n\n", " n\n", " 0.000000\n", @@ -1233,40 +1243,49 @@ " 0.000000\n", " \n", " \n", - " Sales\n", + " Inditex\n", " nsubj\n", - " -0.274059\n", - " 0.325036\n", - " -0.224990\n", - " -0.028301\n", - " 0.077366\n", + " 0.621883\n", + " 0.232645\n", + " 1.000000\n", + " -0.184108\n", + " -0.214304\n", " \n", " \n", - " jumped\n", + " operates\n", " ROOT\n", - " -0.086474\n", - " 0.089315\n", - " -0.149052\n", - " 0.669745\n", - " 0.593084\n", + " 0.302204\n", + " 0.349993\n", + " 0.105573\n", + " 0.248764\n", + " 0.302329\n", " \n", " \n", - " from €9.4bn\n", + " eight brands\n", + " dobj\n", + " 0.971319\n", + " 0.311736\n", + " 0.636319\n", + " 0.086158\n", + " 0.124065\n", + " \n", + " \n", + " in 90 countries\n", " prep\n", - " 0.510297\n", - " -0.184605\n", - " 0.098164\n", - " -0.127199\n", - " -0.015523\n", + " 0.300766\n", + " 0.277287\n", + " 0.482949\n", + " 0.093760\n", + " 0.131249\n", " \n", " \n", - " to €10.5bn, an increase of 11%\n", + " including Pull&Bear, Massimo Dutti and Bershka\n", " prep\n", - " -0.008494\n", - " 0.701980\n", - " 0.117139\n", - " 0.288908\n", - " 0.417433\n", + " -1.047984\n", + " 0.268103\n", + " 0.403236\n", + " 0.205738\n", + " 0.116170\n", " \n", " \n", " .\n", @@ -1278,130 +1297,112 @@ " 0.000000\n", " \n", " \n", - " \\nThe group's clothes can now be bought online in around 40 countries, it said.\n", - " s\n", - " -0.365596\n", - " -0.656207\n", - " -0.087184\n", - " -0.224039\n", - " -0.182479\n", - " \n", - " \n", - " \\nInditex operates eight brands in 90 countries including Pull&Bear, Massimo Dutti and Bershka.\n", - " s\n", - " 0.510964\n", - " 0.058316\n", - " 1.000000\n", - " -1.156331\n", - " -1.039227\n", - " \n", - " \n", " \\nHow Zara's founder became the richest man in the world - for two days\\nChairman and chief executive Pablo Isla emphasised the firm's investment in technology, saying the firm had expanded its online stores to 11 new countries in the period.\n", " s\n", - " 0.571232\n", - " 0.403418\n", - " 0.757583\n", - " -0.673680\n", - " -0.686417\n", + " 0.043983\n", + " 0.440579\n", + " 0.464072\n", + " 0.328390\n", + " 0.275410\n", " \n", " \n", " \\nIt also launched mobile phone payment in all its Spanish stores, with the objective of \"extending the service to other countries\".\n", " s\n", - " 0.237210\n", - " -0.097768\n", - " 0.484087\n", - " -1.038979\n", - " -0.975200\n", + " 0.241369\n", + " 0.328979\n", + " 0.409995\n", + " 0.184377\n", + " 0.193284\n", " \n", " \n", " \\nThis will encompass online apps for all of its brands and a specific app for the whole group called InWallet.\n", " s\n", - " -0.192134\n", - " 0.088210\n", - " -0.122555\n", - " 0.603147\n", - " 0.635328\n", + " -0.492164\n", + " 0.127164\n", + " -0.139276\n", + " 0.147447\n", + " 0.143949\n", " \n", " \n", " \\nMr Isla said: \"Both our online and bricks-and-mortar stores are seamlessly connected, driven by platforms such as mobile payment, and other technological initiatives that we will continue to develop.\"\n", " s\n", - " 0.192191\n", - " -0.214986\n", - " 0.664587\n", - " -0.920382\n", - " -0.972651\n", + " -0.769050\n", + " 0.277042\n", + " 0.526606\n", + " -0.003886\n", + " -0.036231\n", " \n", " \n", " \\nTom Gadsby, an analyst at Liberum, said the firm's \"online drive\" was important.\n", " s\n", - " -0.572602\n", - " -0.675622\n", - " -0.575378\n", - " -0.982668\n", - " -0.920406\n", + " -1.086961\n", + " 0.240946\n", + " -0.429545\n", + " 0.152901\n", + " 0.185139\n", " \n", " \n", " \\n\"I expect over the years they may find they don't have to open as many stores to maintain their strong growth rate as the online channel will become increasingly important,\" he said.\n", " s\n", - " 0.596223\n", - " 0.084480\n", - " 0.721840\n", - " -0.740238\n", - " -0.658017\n", + " 0.218032\n", + " 0.169816\n", + " 0.435118\n", + " -0.188105\n", + " -0.164832\n", " \n", " \n", " \\n\"And while Zara is available in many of the territories in which they operate [online], most of their other brands aren't readily available outside Europe online.\n", " s\n", - " -0.072174\n", - " 0.164850\n", - " -0.445056\n", - " -0.030105\n", - " -0.006846\n", + " -2.271954\n", + " 0.102717\n", + " -0.361450\n", + " -0.145796\n", + " -0.116563\n", " \n", " \n", " \\n\"So there is a big opportunity there for them to expand online into new territories.\"\n", " s\n", - " 0.036688\n", - " 0.295659\n", - " -0.090021\n", - " 0.412792\n", - " 0.367900\n", + " -0.535650\n", + " 0.349627\n", + " -0.076690\n", + " 0.213354\n", + " 0.198559\n", " \n", " \n", " \\nThe company also said it had benefited from steady economic growth in Spain, where Inditex gets about a fifth of its sales.\n", " s\n", - " 0.542170\n", - " 0.356319\n", - " 0.904162\n", - " 0.416033\n", - " 0.461316\n", + " 0.231958\n", + " 0.415129\n", + " 0.543749\n", + " 0.310050\n", + " 0.306031\n", " \n", " \n", " \\nThat country's clothing market grew at an average of 3% in the three-months to the end of July, according to the Spanish statistics agency.\n", " s\n", - " -0.375242\n", - " -0.632490\n", - " -0.215306\n", - " -0.980383\n", - " -0.902879\n", + " -0.376289\n", + " 0.291986\n", + " -0.093117\n", + " 0.464798\n", + " 0.475905\n", " \n", " \n", " \\nAll of the group's brands increased their international presence during the period, with 83 new stores opened in 38 countries.\n", " s\n", - " 0.393162\n", - " 0.185929\n", - " 0.641166\n", - " 0.316148\n", - " 0.312922\n", + " 0.579023\n", + " 0.514682\n", + " 0.550269\n", + " 0.565154\n", + " 0.566206\n", " \n", " \n", " \\nIn a call with analysts, it said it would open 6-8% of new store space over course of the year.\n", " s\n", - " 0.363273\n", - " 0.338824\n", - " 0.579605\n", - " -0.216823\n", - " -0.149992\n", + " 1.000000\n", + " 0.598608\n", + " 0.361614\n", + " 0.323148\n", + " 0.292496\n", " \n", " \n", " \\n\n", @@ -1415,11 +1416,11 @@ " \n", " The firm's strong performance sets it apart from European rivals H&M and Next, which have blamed unseasonal weather for below-forecast results this year.\n", " s\n", - " -0.237008\n", - " 0.033395\n", - " -0.554685\n", - " 0.262542\n", - " 0.244516\n", + " -5.948836\n", + " -0.613289\n", + " -0.644638\n", + " -1.314049\n", + " -1.210691\n", " \n", " \n", "\n", @@ -1428,99 +1429,102 @@ "text/plain": [ " unit_types nli_logit \\\n", "units \n", - "The world's biggest clothing retailer nsubj 1.000000 \n", - "posted ROOT -1.616351 \n", - "net earnings of €1.26bn (£1.1bn) dobj 0.508601 \n", - "in the six months to 31 July prep 0.776250 \n", + "The world's biggest clothing retailer nsubj -0.442970 \n", + "posted ROOT -0.129547 \n", + "net earnings of €1.26bn (£1.1bn) dobj -1.556081 \n", + "in the six months to 31 July prep 0.368621 \n", "- n 0.000000 \n", - "up 8% on the same period last year advmod 0.360540 \n", + "up 8% on the same period last year advmod 0.825911 \n", ". n 0.000000 \n", + "\\nSales jumped from €9.4bn to €10.5bn, an incre... s -2.082431 \n", + "\\nThe group's clothes can now be bought online ... s -1.848802 \n", "\\n n 0.000000 \n", - "Sales nsubj -0.274059 \n", - "jumped ROOT -0.086474 \n", - "from €9.4bn prep 0.510297 \n", - "to €10.5bn, an increase of 11% prep -0.008494 \n", 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"\\n\"I expect over the years they may find they d... 0.169816 0.435118 \n", + "\\n\"And while Zara is available in many of the t... 0.102717 -0.361450 \n", + "\\n\"So there is a big opportunity there for them... 0.349627 -0.076690 \n", + "\\nThe company also said it had benefited from s... 0.415129 0.543749 \n", + "\\nThat country's clothing market grew at an ave... 0.291986 -0.093117 \n", + "\\nAll of the group's brands increased their int... 0.514682 0.550269 \n", + "\\nIn a call with analysts, it said it would ope... 0.598608 0.361614 \n", "\\n 0.000000 0.000000 \n", - "The firm's strong performance sets it apart fro... 0.033395 -0.554685 \n", + "The firm's strong performance sets it apart fro... -0.613289 -0.644638 \n", "\n", " summ bart \n", "units \n", - "The world's biggest clothing retailer -0.721895 -0.785352 \n", - "posted -1.913796 -1.909047 \n", + "The world's biggest clothing retailer -0.240193 -0.183453 \n", + "posted 0.060759 0.089610 \n", "net earnings of €1.26bn (£1.1bn) 1.000000 1.000000 \n", - "in the six months to 31 July -0.268173 -0.162354 \n", + "in the six months to 31 July -0.088184 -0.020920 \n", "- 0.000000 0.000000 \n", - "up 8% on the same period last year -0.142716 -0.140645 \n", + "up 8% on the same period last year 0.721515 0.632897 \n", ". 0.000000 0.000000 \n", + "\\nSales jumped from €9.4bn to €10.5bn, an incre... -0.014012 0.041359 \n", + "\\nThe group's clothes can now be bought online ... 0.877237 0.802476 \n", "\\n 0.000000 0.000000 \n", - "Sales -0.028301 0.077366 \n", - "jumped 0.669745 0.593084 \n", - "from €9.4bn -0.127199 -0.015523 \n", - "to €10.5bn, an increase of 11% 0.288908 0.417433 \n", + "Inditex -0.184108 -0.214304 \n", + "operates 0.248764 0.302329 \n", + "eight brands 0.086158 0.124065 \n", + "in 90 countries 0.093760 0.131249 \n", + "including Pull&Bear, Massimo Dutti and Bershka 0.205738 0.116170 \n", ". 0.000000 0.000000 \n", - "\\nThe group's clothes can now be bought online ... -0.224039 -0.182479 \n", - "\\nInditex operates eight brands in 90 countries... -1.156331 -1.039227 \n", - "\\nHow Zara's founder became the richest man in ... -0.673680 -0.686417 \n", - "\\nIt also launched mobile phone payment in all ... -1.038979 -0.975200 \n", - "\\nThis will encompass online apps for all of it... 0.603147 0.635328 \n", - "\\nMr Isla said: \"Both our online and bricks-and... -0.920382 -0.972651 \n", - "\\nTom Gadsby, an analyst at Liberum, said the f... -0.982668 -0.920406 \n", - "\\n\"I expect over the years they may find they d... -0.740238 -0.658017 \n", - "\\n\"And while Zara is available in many of the t... -0.030105 -0.006846 \n", - "\\n\"So there is a big opportunity there for them... 0.412792 0.367900 \n", - "\\nThe company also said it had benefited from s... 0.416033 0.461316 \n", - "\\nThat country's clothing market grew at an ave... -0.980383 -0.902879 \n", - "\\nAll of the group's brands increased their int... 0.316148 0.312922 \n", - "\\nIn a call with analysts, it said it would ope... -0.216823 -0.149992 \n", + "\\nHow Zara's founder became the richest man in ... 0.328390 0.275410 \n", + "\\nIt also launched mobile phone payment in all ... 0.184377 0.193284 \n", + "\\nThis will encompass online apps for all of it... 0.147447 0.143949 \n", + "\\nMr Isla said: \"Both our online and bricks-and... -0.003886 -0.036231 \n", + "\\nTom Gadsby, an analyst at Liberum, said the f... 0.152901 0.185139 \n", + "\\n\"I expect over the years they may find they d... -0.188105 -0.164832 \n", + "\\n\"And while Zara is available in many of the t... -0.145796 -0.116563 \n", + "\\n\"So there is a big opportunity there for them... 0.213354 0.198559 \n", + "\\nThe company also said it had benefited from s... 0.310050 0.306031 \n", + "\\nThat country's clothing market grew at an ave... 0.464798 0.475905 \n", + "\\nAll of the group's brands increased their int... 0.565154 0.566206 \n", + "\\nIn a call with analysts, it said it would ope... 0.323148 0.292496 \n", "\\n 0.000000 0.000000 \n", - "The firm's strong performance sets it apart fro... 0.262542 0.244516 " + "The firm's strong performance sets it apart fro... -1.314049 -1.210691 " ] }, "execution_count": 22, @@ -1606,15 +1610,18 @@ "output_type": "stream", "text": [ "toma_get_probs batch size = 9\n", - "toma_get_probs batch size = 13\n", - "toma_get_probs batch size = 10\n", - "toma_get_probs batch size = 15\n", - "toma_get_probs batch size = 10\n", "toma_get_probs batch size = 14\n", "toma_get_probs batch size = 11\n", - "toma_get_probs batch size = 13\n", + "toma_get_probs batch size = 16\n", + "toma_get_probs batch size = 2\n", + "toma_get_probs batch size = 9\n", + "toma_get_probs batch size = 15\n", + "toma_get_probs batch size = 11\n", + "toma_get_probs batch size = 16\n", + "toma_get_probs batch size = 1\n", "toma_get_probs batch size = 11\n", - "toma_get_probs batch size = 15\n" + "toma_get_probs batch size = 16\n", + "toma_get_probs batch size = 1\n" ] } ], @@ -1651,7 +1658,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 25, @@ -1660,7 +1667,7 @@ }, { "data": { - "image/png": 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69et3wzH9/f3ZvHkzMTExODs7Y29vj0qluofvSkJColYgCHDlgChozqwDQ5G4XyaHoO6ilyaktxQkXEvJ0eXw2r7X2Je8D5mVgMrBCTkyvCY+h0wmIyupkK3fnwXA3lVDhxEh+DVwvsWotZcaFzehoaGcOHECrVbL6tWrefrpp9m9e3eFAqdNmzblvDpt27YlLCyMRYsW8e6771Y4/syZM5k+fXrZdn5+Pj4+PtX/RmoBo0aNoqSkhJYtW6JQKJg2bRrPPPMMAEuWLOG9995jxowZJCcn4+LiQuvWrenfv/9Nx5wwYQK7du2iefPmFBYWsnPnTjp37nwP3o2EhEStQJsEJ34WRU1u/LX9zsHQbCQ0HgF2njVnn8QtOZp2lA/+fJHLZGFhacmrbV6lXe92yFRqVC6igHHxtqFRJy+s7NU07eF73y1D/RuZUMsS1rt3707dunVZtGhRpY5/9NFHUSqV/Pzzz5U6Pj8/H3t7e7RaLXZ2duVe0+l0xMfHExAQgKXl/eeGe9CRvh8JiXuEQQfn/xQFzcWdwNXLhNoWGg6Gpk+CT0spOLiWYzKbWBy9mKO/fMkzG42cCHeg09xlBDsEE3csg8j1lxgwtSn2rpqaNrVS3Oz6/W9q3HPzb8xmc7kYmZthMpmIjo6mb9++d9kqCQkJiQccQYCUKFHQRP92tYnlVfw7iMtOYQPu+wSLh4Wskize3PoSDVdEMv2UKE67auvgUODKH8tOkHQ+F4DjW67QZWS9mjT1rlCj4mbmzJn06dMHX19fCgoKWLlyJbt27WLz5s2AuMzi5eXFnDlzAHjnnXdo3bo1QUFB5OXl8cknn3DlyhXGjx9fk29DQkJC4v6lMBNO/SKKmoyz1/bb+0DTJ6DJ41JdmmpEW2LgjxPJPNna765lpx5KPcSin6YzanUuHnkgyGQ4jJvIZf8+bPnoBGaTgEIlJ6K3H816+t4VG2qaGhU3GRkZjBo1itTUVOzt7WncuDGbN2+mR48egBggK5df6xCRm5vLhAkTSEtLw9HRkYiICA4cOHDDAGQJCQkJiQowGeDCVlHQxG4Cs1Hcr7QUvTNNR0JAJ5BLHXqqC22JgSX74/l+XzwFOiOe9hq613ev1jlMZhOLor4m85tvmLHPjEIA3F3hfx+yNUqgMC4JAP/GLrR/NPi+WY66HWpU3Hz//fc3ff3ffYzmzZvHvHnz7qJFEhISEg8wGefFqsEnf4GijGv7vSJEQdNwKGgcasy8B5U9sZn8b+VxCnSiiAxxt8GymgN2M4ozeGXPK1yMO8Jnh0VhY923N16z3+bYnmwKc+Oxc7Gk/fAQAhq7VOvctZFaF3MjISEhIVGN6LRiJ+6oFZB87Np+a1doMkIUNW5hNWffQ0CYpx0Gk5kQdxumdQuhT0MP5PLqW5Lal7yPWXtnkavPxcrRmvznh+ChaYD3CDEbNrynNSq1gkadvVCq7+8sqMoiiRsJCQmJBw2zGeJ3i8tO5zaA8Wr/PblSrEXTdCQE9wCFVLPq3whmM2nrNrNrYw5m5GhUJqxtZNg4WGDrZoOtlxP2fm7Y1fVEafnfmj7aEgNL918mNr2Ar0aGA+Bqa8HaKe0IdbetVlFjMBtYtO8zLOctw6+RDPfm9ZnuPJvYP/PItFHxqMmMXCFHqVY8sLE1N6LK4mbPnj20bdsWpbL8qUajkQMHDtCxY8dqM05CQkJCogrkXoYTK8W6NNqEa/vd6ouCpvFjYONaY+bVZgRBoGjPHlLnf8lBTR8K7PwByBOAgquPROBYIVAIQhwWxiIsZSVYqYzIrGXss7Jmk1FFkVkcc0xsOs1DxLiaMM+bpy5XlbSiNBZ8P5HeK2JxyYd6uT5kOb3C8Y3pgNiqqCBHh71r1XoUPihUWdx06dKF1NRU3Nzcyu3XarV06dJFKtUvISEhcS8pLYKzf4hemst7r+23tIeGw8QU7jrNpJo0N6H4yBEy5s2n5Phxzoc8ToGdP2qFiZbhMkqyCijMLaGo0EyxXkaJ2RKdwhZBrkSvsiUfG46pjRyVG9Ff7RXqbJLRVqfk0GeniTIcwhJRAFlZybBxUGPrZo2tpyP2/m7Y1a2D2qZqgb27Lm3j+Psv8cR+HYJcxYVGg0l27YT5fD5ypYzwnn6E9/ZD9ZAsQVVElcWNIAgVpq9lZ2djbS3VP5CQkJC46wgCJB4Wg4NPr4XSgqsvyKBuF9FLU68/qKRilzej5PQZMufPp2jfPgBSfTqSUqc9yKDXlHB8b9B+IEtbwqHDcew9Es8arQyDTMwqcy410KGghDCDEpPaDkEuo1RlSym25AMUX32kACdKgCvAFVTGQjRCMRqVESsrsLFXY+t6VQD5uWJX1xMLexsMJgPf/Tkbv3lr6JMGOgsHjrd9DZ1gBWbwre9Eh8dCcHB/OL0111NpcTNkyBBAdHWNHj0aC4tra40mk4lTp07Rtm3b6rdQQkJCQkIkPxVOrYKonyD7wrX9jv5i1eAmI8DhwWwvU53o4+LI/PwLCrZuFXcolQiDniZWGwFGgVYDAssJm+JSI0cu57I/Lou9sZmcS/tHTCrg6r1+XVdr+jf2pEOwK018HFDIoCglm/xLqeQnZFGQnk9BdglFBSaKdTJKTBboFDaYFWoMShsM2JQXQKnAKR3iWlgiSmMRVoWX6XjqDxRmMKosEDoPwFJjjdwgp+3gAELa+dy12jn3G5UWN/b29oDoubG1tUWjueZGU6vVtG7dmgkTJlS/hRJ3xOjRo8nLy2PdunU1bYqEhMTtYCyF2L/FbKe4bSBcDehQWUH9R8T+Tr5tpZo0laA0KYmsBV+i3fAHmAWQybAfOADrMZNYtywFk1GPf2MXmvTw4XhCLvsvZLEvLovjCbkYTOU7FdXzsKVdkAuh7rb0buiBnea/wdm23q7YervidQN7zGYzJWk5aC+loU3IpDDtHwFkvCqA1OjkNpgUFhiV1uQ7NOBsvafxSD9MTMgISvUOoBcAE9tWxLFraTQaoQiNshQrDVjbqbB1scLW0wE7P1fsA+ugcane2J/aSqXFzZIlSwCxS/SLL74oLUFJSEhI3E1ST4lxNKd+hZKca/t924jLTg0eAQvbGjPvfsKQkUH2N9+Q+9tqlAodfl1yKVD1xH7KW6jrBrHhi5MU5uqR2SpZb6Hn1fe2UaA3lhtDRlmHLd5/pCEjW/vdsV1yuRzrOi5Y13GhTgWv6016ln8/nfXaQ/hpW1I/ty+ZbuGYfUPxFLQUl5RQYlJRIrfBpLDEqNRQgIYCAN3VRwZw1oC4FpaCwqhDIxSiURiw0ghY26mwcdZg52mPva+4BGbpYl+ugO79SJVjbt566627YUetRRAESowlNTK3RqmptItx9erVvP3228TFxWFlZUWzZs1o1qwZy5YtAygbR+rqLSFRiynOEfs6Ra2AtFPX9tt6im0Qmo4El6Cas+8+w5ibS87335Oz4icEnQ6lxoR/3yJUqlJUrpf5I1/DuY8O0SP7R3xs83mV/sReEFcp7CyVuNtZkpRbQonBhAAEudkwrVswfRvd/S7oVzIusGPWGMKPgqnJKIyaRgDYOFrQ4tGG1A0vn9RTkqVFG5eCNiGLwrQ8CrKKKco3UFQCJUY1Opk1RqUGk9KSQiwpBNADmVcf501AGpCGwqTD0lyERlGKlaWAjZ0SG2cNth722Pm64hDogaW7Y60WQFUWNwEBATe94F66dOmODKptlBhLaLWyVY3MHflEJFaqWweGpaam8vjjj/Pxxx8zePBgCgoK2Lt3L6NGjSIhIYH8/Pwyz5uTk9PdNltCQqIqmE1wcQdELYeYv8F0NeVGoYbQvtDsKTFIWP7wZr5UFVNhETk/LiPnhyWYCwsBsIoIwa3+WVSGQtJkbjyaNA7Li6d5rKSUcNe1qOQ6/pBt44zPSIqbT+bZtZe5kCGeG+Rmw9RuwfRr5ImiGuvU3IhtO77HNPsL3DXdiGzRE7NCjVwuo2kPHyL6+KO2/O+lW+Nij8bFHo/WNx63JCuf/PgU8q9kkp+qpTCriEKtgeISKDGq0MmsMSitMCksKVJYUgRQCmRdfcSYgXQgHbmpFEtzIRq5HmtLAWs7BTZOGmw97LDzccE+0BMb75orO1BlcfP888+X2zYYDERFRbFp0yZeeuml6rJLogqkpqZiNBoZMmQIfn6iq7RRI1HlazQa9Ho9Hh4eNWmihITEv8mKu9oKYRUUpF7b79FYFDSNhoGVdDNSFcx6Pbk//0z2om8x5Ypdr/Pr+LM7oh0jrX5AY8jmitmNJ0pfQ2d2Y5jOAr2g5oDrB7RXL8cyNYqIhB8g43dm2Q7mR+veTOzR5J6JmpLSYtZ9MJ4Gv0URHzCUJO+uAHiFOtJxRAhOnncWDqJxsUPjYod7ixt3AdfnFqC9lEp+Qib5KXmiAMorpbgEig0qdDIrDEprzAo1xQonioFsA5B99XEBIAsbw3me/n74Hdl7J1RZ3EybNq3C/V999RVHjx69Y4NqGxqlhsgnImts7srQpEkTunXrRqNGjejVqxc9e/Zk2LBhODo63mULJSQkqoS+AM6sFbOdEg9d269xEgvsNRsJHo1qzr77FMFgIPf3NaR9+RWyrEwAUmxcWVavF0nervxkMQdPWQ6Jsjr80vBrXgsKJntdIvn5JTjVtedUWAdmHvZj06AC7A58BJnneEy3lOHWfyGz+Q7kFUXEVC8X449zduozNL1QBICNfQz2rn1oNSiIoAi3e5YFZeFoi1uELW4RITc8pjS/CO3FVLRXMihIzaMw86oAKhYoNijRYYWVsvSe2Hsjqq39Qp8+fZg5c2bZ8seDgkwmq9TSUE2iUCjYunUrBw4cYMuWLSxYsIDXXnuNyMiaEWUSEhLXIQhwZb8oaM6uA0OxuF8mh6AeoqAJ6QNKdY2aeT9yJbOQsz+txuGXpTjkpiMDMjQO/BTag22+zXGys+JbzVd4FuRgcArBZ8yfvGTjxuZvT5OZUcxpBzhalE3+NrGq7/K8Rvxv8n6xF9fOD5Bpk8C57l1/H+tj/mDjb/vpoBlCqWo5hmefouszM0EA2T3wGFUVtZ01rs2CcG124/gvs7FmC/pWm7hZvXq1FM9Rg8hkMtq1a0e7du1488038fPzY+3atajVaqlqtIRETZCXCCd/FjOeci9f2+8cLFYNbjICbKXl4qqQXajnwMVsDsRlUrBzJ30P/0FAvrikl6e2Zk39Hmi796dVvTrMCHIhxN0GWWlr2PIaqq5vgLUL+/+6xIqzKRy1N6ID0EGgqzXTugXTv3EdkMug8XBoMFhsNOpwXU+mza+J1Z4bDKmW1PtCbTbzNi1FcdCbxrpeaJ1AOb8rTbpdDZypfbqm0siVNRsjVmVx06xZs3LuMUEQSEtLIzMzk6+//rpajZOoHJGRkWzfvp2ePXvi5uZGZGQkmZmZhIWFodPp2Lx5MzExMTg7O2Nvb49KJTXLk5C4KxhK4PxfYrbTpV2UJQ+rbaHhEFHUeLeQWiFUkuJSI4fjczhwMZt9F7I4m5pPk8wLjD77N/Vyxd5ZJWoNST2HUGfcaD4IqYNKIYeCNLC9miZvYQMDPgcg7nQWE3efo+jqiv/1ouY/MTUKFfheF52bEgUHvxSf75sHXd+AkF63/V2e3LSRU8ticLJvJu6wMtLtsUaEtpQEb3VQZXHzyCOPlNuWy+W4urrSuXNn6tW7cZCSxN3Dzs6OPXv2MH/+fPLz8/Hz82Pu3Ln06dOH5s2bs2vXLpo3b05hYaGUCi4hUd0IAqQcFwVN9O+g1157zb+DKGjCBoK6di9v1waMJjMnk7Tsj8ti/7+K54XmXOGDc5tolilWZhbUFtg/+SQhz4wn3MHh2iBJx2DFYGjzLHR6Gb3RhIVSQUGOjt1LzxFsUpBmK+fVwfUZ0MSr8oHCzkHQ5XU4sADST8PPj4lCtdubEFD5htGGklLWvbWIrNxgzPZNQDDh0dqCASM6otZU22LKQ49MEATh1oc9OOTn52Nvb49Wq8XOrnylRp1OR3x8PAEBAVhaSj1ZahvS9yNRqyjMgFO/iLE0meeu7bf3gaZPiA9H/xoz735AEATiMgrZH5fFvrhsIi9l/6d4Xkshh7GxW/A7dzVhRaXCcfhwXCZNROn6r1TjhEhYMRRKCzB5t2RJ0AIW7k3k+1ERXFh5kYwrBdj6WPPYjAgsKkinrhTFOXDgC4hcdC1+KqATPPI12Hvf9FRtfCzHpj7DJedn0Vs6oTZepsvUdgQ1Dr09Wx4ybnb9/je39e2aTCbWrl3LuXPiH3T9+vUZNGgQSqWkOiUkJB5gTAa4sEUUNBc2g/nqhVhpKXpnmo0E/45SK4SbkKotYX9cNgfixNYGGQX6cq/ba1S0retMV9tSmmz7FdO2zaJ3TC7H/pFHcJkyBbV3BQ0NLu+Dn4aDoYhUhwiGp04lMe4iAPNWnablFSMW1koemdj49oUNiOn53WdDq8mwdy4c/QEyz4sZbzegIFdH4sal6D/+Ak+9gDl/FZk92vDozGkoFdJ1825Q5U/1zJkzDBgwgPT0dEJDRbX50Ucf4erqyoYNG2jYsGG1GykhISFRo5TkwcGv4NgSKMq8tt+ruShoGgwBjUNNWVer0ZYYOHTpmpi5mFlU7nULpZwW/k60C3KhfZALwfJicr/5hrzffy9LhrDt3RvXqc9hERhY8SQXdyL8/DgyYwmRssY8nfYsOuT4OGkY6u2CYk8myGT0HNcAO5fKldi4Jbbu0PdjaPss5MRfW3Y0m2HbWxAxGpN9ACe2JRC5IY6gc4fx0Qtc9FXj/fEEujftVT12SFRIlcXN+PHjadiwIceOHSuro5Kbm8vo0aN55plnOHDgQLUbKSEhIVEj6Asg8hsxzkJ3NZbG2g2aPCZ24XaT4gz/jd5o4viVvKtLTVmcSsrDfF3wg1wGjbzsy8RMuJ8jlioFxpwcsr/9hssrVyKUijVSrDt2wHXaNDQNGtx4wgtbEVaNRGbSs8PUlMmG53G2t+O5bsF0dLLjj/lRmJHRamAgvvWdbzzO7eLgWz6j6vTvcOALEnftYbf+BbRFdoCc6JBGpISnM2j2MhxtXKrfDolyVFncnDhxgqNHj5YrEOfo6Mj7779PixYtqtU4CQkJiRrBUAJHFotZMcXZ4j7XMOj8KtTrJ2bSSABgNgucTc0vEzNHLuegM5jLHRPoYk27IBfaBbnQJtAZe6trn5+poIDMhUvJWboUc7EYw6KJiMDtheexat78hvMaTWbkMhlybSIyk54E1868mTeJ1/uGMbyFD+YSE79+cASzUSCgiQsRve680WVlKLQMZZ/hEy7miDVgNPI8ZG6/4Ny/A49F/HnPivE97FRZ3ISEhJCenk6DfynpjIwMgoKkhm4SEhL3MUY9HP8R9nwKhWniPqe60GWWWPdE6u8EQEJ2MfuuZjQduJhFbrGh3OsuNha0D3IuEzR1HCpeCircu4+UF1/EpBW9Ypb16+P6wvNYt29/QxFgMgv8cTKZz7ddYHrPUAY2Hwt23rj7dWSbXIWlSoHZZOaPxWcozNXj4G5F99H170kxvNjDaexcno3REASCGa+snfQO+wFLimHzXtDmQKdXpYKN94Aqi5s5c+YwdepUZs+eTevWYg2AQ4cO8c477/DRRx+Rn59fduytopklJCQk7jVGkxmjWcBSdZ1QMRnh5ErY/TFoE8V99r7Q+RVoPAIe8qDPf4rn/eOdScotKfe6tVpB60BRzLQPdiHYzeaWHoqiQ4dIevZZBL0edWAgrtOmYduzxw3PM5sF/opOZf62WPyz96A1B7P8oAUDm9SBkJ5YXHfsoXWXSI7JRWmhoM/ERvckxVoQBAzHd2IsdcU+Px7vK79yaqgzpv7fwe65YrmAS7vF+jgSd50qf+P9+/cHYPjw4WW/hP9kkw8YMKBsWyaTSZVxJSQkahUms8DoJUeIjM9mZCs/nuscgPPlP2HXHMi5JB5k6wkdX4Rmox7qO+yohFw2RqeyLy6bc6n55V5TymWE+zpeFTPONPZ2EIvnVZLi48dJnDwFQa/HpksXvD+fj0xd8WdtNgtsOZvGvK0XiEkvYKh8Dx+rF5FjE4LVE5v+c3zcsQyitooF/rqNCsOpzp01m7wZRXl6Ui9q8Q9QEvPyNOT7jhJuF0ii0xUyP36W59pNFK+TIf3E4o42bteK/um0YnxO87F3zb6HmSqLm507d94NOyQkJCTuOssOXGZfXBYgkHboV3KPrcZZliS+aOUCHaaLFxtVNWXU3IdcyS7iw7/P8/fptHL763nY0j7IhXbBLrT0d8La4va8ISXR0SROeAahpATrtm3xmj/vhsIG4NU1p/j1qPgdPW25m9l8iwwB15DWYONQ7tic1CJ2/CiWKGnaw5egCLfbsvFWmExmoncmcXhDPCajiWbRH2OfcQWDAna3z2PgzFXUd70uc1gmg7D+5Qc5+JVYSVnirlDl386AgAB8fHz+4zoUBIHExER8fX1vcKaEhIREzZGQXcwnm8/TRR7FB/br8SyJBUArWLFcMRiXts8xtGVolTwQDxJ5xaV8sT2O5YcuYzAJyGUwoEkduoW507auMy42Frce5Bbozp8nYfwEzEVFWLVogfdXXyK3KD+uIAgYzULZ9zCgSR02RqfxeeBRusUvEg9qMQH6fFyunlBpiZG/v4nGoDfhFepAm0dukDZ+hyTH5rJnVSw5KWJKe7FTNpdt9TiZ4dikjjw3fC42aptbD+ToD+Gj7oqNErcpblJTU3FzK6+Ic3JyCAgIkJaiJCQkah2CILDi52X8JFtMuDoOSkBQ2xAbOIoXrrTlbK4c/rzEokPpvNQrlD4NPR6arJZSo5kfD15mwY44tCViYHCnEFdm9Q0j1MO22ubRx8WRMHYcZq0WTdOmeC9ciFxzzUMmCAL747L5bGsMHUNceb57CADtg1w40i0WzY6PxQPbPAs93yvX00kQBLYvO0deejE2jhb0HNcQeTWL1CKtngO/xxF7WOwgrtRApP+fHLHfhqufiilt3+DVho9X/vem6RPVap9Eeaosbv6Jp/k3hYWFUkl8CQmJ2kfCITLWv8Gs7MMgB7PSEnmricjaTiPU2pl1RjM/H07gi+0XiM8qYspPx2ni48CrvevRpu5dqItSSxAEgU2n0/hw03muZIsp2PU8bJnVN4yOIa63OLtqlF65QsKYsZhycrCsXx+fbxehsLkWC3PoUjafbYnl8OUcABJzS5jSOQi1Uo7s6PdodrwuHth+utjL6V/XoOObr3DpRCZypYxezzTEyq56Y6WMBhO/vn+E4vxSQKBO6n4u22zgSFMd/nb+fNrpU0KdpBYKtYlKi5vp06cDIJPJeOONN7CyutYEzmQyERkZSdOmTavdQInKsXr1at5++23i4uKwsrKiWbNmrF+/nn79+tG0aVPmz59fduwjjzyCg4MDS5cuBcDf35/x48cTGxvLmjVrcHZ2ZsGCBbRp04bx48ezfft2AgMD+eGHH2h+te7E0qVLef7551mxYgUzZswgMTGRvn378uOPP/Lbb7/x1ltvodVqeeqpp5g3bx4KhZRCK1E9mMwm0ovTSSxIJKkgiaTCJFKLUvGz86OLTxdCHUPFG7CUKNjxPsRtxR3QC0ou+AzDs7Q++YsPYJ+5GYfBg1FrNDzd1p+hEd58t+cS3+29xMnEPB7/7hCdQ115pXc9wjwfrMzPqIRc3v/rHEev5ALgamvBiz1DGBbhU/lGkpWkNCmZK6PHYMzMxCI4GJ/vF6O4mkl77EoOc7fEcuCiWEtIrZTzREtfpnSui1p51fNStyvY1oGIp6HTK/8RNonncohcLwaDd3wsBI8A+2q1H0CpUtCghQMxW88QEvUDdgUJFAbJGOg3kNfavYmVSmqKWtuotLiJiooCRLUfHR2N+roAMLVaTZMmTXjxxRer38IaRhAEhJKSWx94F5BpNJVycaampvL444/z8ccfM3jwYAoKCti7dy9V6Yk6b948PvjgA9544w3mzZvHU089Rdu2bRk7diyffPIJr7zyCqNGjeLMmTNlNhUXF/PFF1+watUqCgoKGDJkCIMHD8bBwYGNGzdy6dIlhg4dSrt27Xjsscdu+3OQePgoLC0kqTBJFC8FSaKQubqdUpiCUTBWeN7XJ77Gw9KZzqXQJfkcLXQ6ZCj41diR7a6j+LSOhrSrN2q6k6fI+mIBjk88juPIkdg4O/NCjxCebO3Hgh0XWBmZwK6YTHbHZjK4qRfTe4bg7Xh/X8QSc4r5eHMMG06mAGCpkvNMx7pM7Bh42wHCN8OQnk7CmDEYU1NRBwTgu+QHlFcLwH635xLvbxSDf1UKGSNa+DKlS1087f8VzO0UCJP3iz2d/kV+dglbFp9BECCsrSf129epFruL80s5sCaO+u08qRPsSP7WrWg+eY2I/EJKlQJL+ljQZvKbvBc8+KFZvrzfqPRv8z9ZUmPGjOHzzz9/aGrYCCUlxIRH1MjcocePIbO69T/T1NRUjEYjQ4YMwc9PrMLZqFGjKs3Vt29fJk6cCMCbb77JwoULadGiBY8++igAr7zyCm3atCE9PR0PDw8ADAYDCxcupG7dugAMGzaM5cuXk56ejo2NDfXr16dLly7s3LlTEjcS5TCZTWQUZ5BUmHTNA3PVC5NUkESuPvem56vkKrxsvPC29cbbxht3a3dOJR/kYPpR0nTZrAJWebqhEeQYC4IoKgpnZYQ76VOnAmDTpQv6CxcwJCWR9fVCdGfO4rPoG0D0YrwzqCFj2wXwyZYY/jqVypqoZP48lcqoNn78r0sQjtb3V4q4tsTA1zvjWLL/MqUmMzIZDAv3ZkbPUDzs7044gTEri4TRYzAkJqLy8cF36RJwvCZQejXwYO7WGAY38+J/XYKuCUdBgO1vg08rCO0j7qtA2BgNJjYtOo2uyICrry0dHw+5Y6FhNpk5vSeZyPWXKNWZyLqST/vSPyn4/XeUwCUPWD8ygFceXUBdh7p3NJfE3aXKUn3JkiV3ww6JO6BJkyZ069aNRo0a0atXL3r27MmwYcPKtci4FY0bNy577u7uDpQXSP/sy8jIKBM3VlZWZcLmn2P8/f2xsbEpty8jI+P23pjEfU2RoUgULPkJJBWllPO+JBcmYzRX7H35BydLJ7xtvPGy9cLH1gdvG2+8bb3xsfXBzcoNuezqskXuFbH43sk16BCItLRgp1cYuxQmskvzwC4WtV0sU8/8ymu+Vjir3bD+4GXq2vlQsHUb2T/8gOOop8rmNWZmUnrlCn4REXz1RDjPdMjjw7/Pc/BSNov3xfPLkUQmda7L2HYBaNS1e7nVYDKzMjKB+dtiy6oItwtyZlbfMBrUqf7lm38w5uaSMHYcpfHxKD09Kfn0a57dkohamcKCx5sB4OtsxaGZ3XCwuk4oCgJselXs56WwgKnHwd77P+MLgsCen2PJTCjA0lpF74kNUaru7LtIjctj98+xZCcXAuDmZ0v9jkoynluHBfBHaxmlY4fwedvX0Cgf3lIB9wtVFjddu3a96es7duy4bWNqIzKNhtDjx2ps7sqgUCjYunUrBw4cYMuWLSxYsIDXXnuNyMhI5HL5f5anDAbDf8ZQqa71evnn7qeifWazucJz/jmmon3XnyPx4GAWzGQUZ5SLfUksSCS5IJmkwiRydDkMKCjk7awc0pUK+vh4lZ07KVeLi1nArLFHYeWK2tYTa3sf7O0DcHYJxcOrJdaqWxRfy08R2yQc/xHM4u+0ZUgfOnWZRSfPxjy/6jh/xEfi5hGHh8clLhLHq4MKsDQUUPLHAIIcguji04XOX87C1+VaTZKc5SvI/vZbLBs3xnnsGBr36MHKCa3YcyGLD/8+z7nUfD7ZHMOPBy/zfPcQHo3wRlnL0scFQWDr2XQ+/Ps8l7LElOUgNxtm9a1Hl1C3u7qUYsrPJ3HcePSxsST71uP3R6fz988xACjkMmb1rVe29FRO2JjNsHEGHP1B3O7zUYXCBuDsvhTOHUhFJoOe4xtg53z7YqM4v5SDa+I4f0isOWNhpaT1I3XJC4hnyoEX8e8rIGg0DH/8HfoH9r/FaBK1hSqLmyZNmpTbNhgMnDhxgtOnT/P0009XaayFCxeycOFCLl++DECDBg1488036dOnzw3P+e2333jjjTe4fPkywcHBfPTRR/Tt27eqb6PSyGSySi0N1TQymYx27drRrl073nzzTfz8/Fi7di2urq6kpqaWHWcymTh9+jRdunSpQWsl7kfSi9L58eyPXNJeKvO+GMz/Fcr/4Gcw8EZ2LipApbZlQqMJZctIzVZPQZV3AfLzgcTyJ9q4w4ux17bXToa8K6BxFJcnlJaQdhoSD8M/sTeBXaDr6+AVATIZO89nsO5EKnKZL4tCwmncpSVJhUnsTtzNzsSdHEs/RlxeHGnp2SyN+hE7Gxs6+3Sms09nQoylyNRqdKdOkfz8C6i8vXEaPZoOQwbT4bn2/HEyhU+3xJCUW8LMNdEs3nuJl3vXo2d991oRfxGdpOW9v84SGS9mHjlbq3mhRwgjWvjcdRFmKiwiccIzxF3JYGWb0exyb4hwUaxu3K+xJ893C/5vTA2A2QQbpkLUCkAGg76EZk9WOEdavJY9v4i/H60GBeIT9t8lq6qQdD6nTNiENrXHZ99XHI2x58P4bQC4tgzj006f4m/vf0fzSNxbqixu5s2bV+H+2bNnU1hYWKWxvL29+fDDDwkODkYQBJYtW8agQYOIior6T2NOgAMHDvD4448zZ84c+vfvz8qVK3nkkUc4fvw4DRs2rGCGh4PIyEi2b99Oz549cXNzIzIykszMTMLCwrC2tmb69On89ddf1K1bl88++4y8vLyaNlniPiO5MJlxm8eRXJhcbr9SpsTTxrPcspG3rTc+Vp4Er5mCQkiFgI64D/6WqXae105s+5zYw6k4B0pyxJ//PLf5V1XZlOOQeb5iw+QqGLUe/NuJ20v6IaSfoa7ekjVqa6zzbTD/L4PUVn54j+vFkx1e4Mn6T6LVazl4dANxK90olZUS6fMna0rW8PuF37GsY0nXd9vQP0qB66ZjGJKSSH/vPTIXLMB5zGgemTSJPo08WHEogS93XOBiZhETlx8j3NeBmX3DaOF/Zxfb2yUlr4RPNsewNkr8jiyUcsa1D2By57rYWt79LubmkhKSJk9mV1op73R/GfPVZcPeDTyY1j34xhlnJiOsnwKnfgGZHAYvgsbDKzy0OL+UTYtOYzYKBDZ1Jfw2On0LgkB+Vgn2ruJNa3ALd1IvavEqvYDhi5cwFxVTJw7kExU8GjaCl1q8hIXizgsYStxbqi08/sknn6Rly5Z8+umnlT7nn15U//D++++zcOFCDh06VKG4+fzzz+nduzcvvfQSAO+++y5bt27lyy+/5JtvvrmzN3AfY2dnx549e5g/fz75+fn4+fkxd+5c+vTpg8Fg4OTJk4waNQqlUskLL7wgeW0kqkRSQRJjN48ltSgVX1tfxjQcI4oZW2/crdxRyiv4N7LjPUg9CZb28Mg3cL2wATGtt7L0fB+ilkPsJjDqxH0aJ3D0A8eAa8IGoDgLmS4XX8CjSEH8dlcQ5MizziKLvAQdXgDA3sKe3jE/cdLKkX0F4+kYP5xByS0JdlpCqSaWTEUyUwKcsRgPI+N96LYnB1WmFkP8WchLxMLKiXHt/Hm0uTff7r7E4n2XOJ6Qx6PfHKR7mBsv965HiHv1FcC7GYV6Iwt3xbF4bzx6o7gEPLiZFy/2CsXrBt24q5vS4hLSnnuO4iNHaGrvhIOlkvBAF57vHkJDr1vE9kQtF4WNXAlDF4vd1yvAbDKzZfFpivLETt/dng6rkqfMbDJz8XgmxzZfoTBHx4h3mxNXFMvJ+AM4/PUrbkfF4nznveCHIbZ81OUdevv3rvT4ErULmVCVfOGbsHz5cl555RVSUlJu63yTycRvv/3G008/TVRUFPXr1//PMb6+vkyfPp3nn3++bN9bb73FunXrOHnyZIXj6vV69Hp92XZ+fj4+Pj5otdr/ZHzpdDri4+MJCAiQChLWQqTv596TmJ/I2C1jSStKw9/On+97fY+b1S369SQcgiV9QDDDsCXQcMjtTV5aBIe/g/3zoeRq9pR7Q+jymphFU8GF7djZWF5ZvhMPYw6vHf0bUjPR1HXH739tkVlooNt1HZm/74U58Sini3oSWfgEpYIY41PPcgdNXNfwbJPGnMk+A8DSpHSCL8mwdDSgthGrsBfnWpN9zg7n1s7kz9jGFzvi+OVIIv3Zi6s8nyA/X3o2D8PJtc61JTUL+3ItA+4Eo8nMqiOJzN8WS1ZhKQAtA5x4vV8Yjb0dqmWOW5GqLeHLbbFEHYzm0z/fR25lhe/i79CFNsSpshll/yxJhfT5b/+l69j/exwntiagslAw7NXmOHlWriGm0WDi/ME0jm2+TGG2eC0wKQxsCfseq9yzPLvBhGs+mGSwrqOatKFteaX1LHzsfCpnv8Q9Iz8/H3t7+wqv3/+myp6bIUPK/6MSBIHU1FSOHj3KG29UvZV7dHQ0bdq0QafTYWNjw9q1aysUNgBpaWllWTv/4O7uTlrajZuPzZkzh7fffrvKdklIPOwk5CcwdvNY0ovTCbAP4Pue3+NqVYnKtdtmi8Km8YjbEzYGHRxbCnvnQtHVTDvnYOgyC+o/ckNxoDOYeHFjCvFmL2ZcjoTUTBSuLnj98Csyd1GQCYLA/tVxhLX1xHncZuRmM431WoIyMjm0MZ1zJw2c13XlQmonWgW483I3e+KKItFvn0N63STsTOBoAjWQc1ZNYaKMwoQc1Gef5NVx4xnzbFtkP75PkO40JCM+rkdlDbOSrwmzPZ9CbrzoibJyuvrT+dpz19D/iDhBENgVk8kHG89xIUMMBQhwsebVPvcu7icjX8fXuy6yMvIKpSYBlI5Ee9aj/5xXsAoP55ZRikY9yBSgUIJcAYO+uunhcccyOHG103fXUWGVEjZJ2Sns3XKarEgzcp0otEqUhUR77uaM+z5ctEW8tdKEXACdhwPK2S/ySseBqOR3fwlP4u5TZXFjb1/exSiXywkNDeWdd96hZ8+eVTYgNDSUEydOoNVqWb16NU8//TS7d+++ocCpKjNnziyrrgzXPDcSEhI35rL2MuM2jyOjJIO69nVZ3GsxLhqXyp08YiXsfF8sk18VTAYxoHTPJ5B/VRU4+EHnmdDoUfFCeBPmbYslPquIp5P24x99EJRKvD//HJX7NU/T8c1XOLk9kfMHUxn1flvUGiVoHNH4OtB5YjD1r+Sz5+dYMtKL+OZEIl+eTqJfo2AmPbKbOq4KDqYcZGfCDo4k7sLNKpeeRwWanQFOnyX5heno3R1w7xhAlp8fSRmpKHQ5OMoKcZQVYo0OwcKuvPi4sBUSD1X8hpSW8Hp6uV1nU/L5YOO5q53NwdFKxbRuwYxs7XdPGn5mFer5ZtdFlh+6UrYE1jDrEk9d2Ea/917EunXrWw9SWgy/jBRF3OBFori5CdkphWy/2um72Q06fQuCwCXtJY5nHCcqPYrjGccpzCxlxIlZyFFSoM7hZJ0dFAYm07hOQwa6vUy4ezgW2YtBBiEzZ5VrByFx/1PjdW7UajVBQUEAREREcOTIET7//HMWLVr0n2M9PDxITy//x359UbmKsLCwwMJCCgaTkKgsl7SXGL95PJklmQQ5BLG452KcNVXosWTlBP3mVm3SxMOwZgLkXha37byg40tixozi1nfSp5Ly+G7PJVyK83js5J8AuM+aiVV4eNkxCWeyOXS1TH+bwXVRa5QU6Aysi0rmp8gEnu8eQu+GHgx7tTkxcTls3Xqek4l5/HEihYzIDGzq2TOuRzPea98Nk2DiZOZJdnXdxXvnttJgVwK9jgvYpeeR91sUqXUsOT93PDbmJqzYYyI2vRA1BoLUZsYcTWRIuLfY5qDVRAjuUT6wuiQHirNFcXOV9Hwdx1e8xsokV/aZG6JWKBjTzp8pXYKw19wbT0NcRgEDv9xPcam4LNdQVsATe3+iaW48Pp/Px6ZDh1sPUloEKx+Dy3tBZQVZseAWdsPD9SVGNi06jVFvwivUkdZXO30bTAbO5pzlePpxjmcc50TGCcx5SurkB3HeXRSLcis5iXVP4OplT9NWvozxeAuLTfuwDumA6mrjZ+Hdd5BJrWEeSG47oPjYsWOcOyeq6QYNGtCsWbNqMchsNpeLkbmeNm3asH379nIxN1u3bqVNmzbVMreExMPOpbxLjN08lmxdNsGOwSzuuRgny0pk/2iTxQtW48cqjIW5KblXxAteSQ5Yu0GHGRAxGlSVi6sqNZp5efUpzAK0bVMf30cXUrRvL46PP37NvMwStnx/BgSo384T6towc000608kl12sfz2aSO+GHsjlMsJCnFkf0o7TyVp+Wn0O73PFlJ4oYt75o2gaOrB0XEsi3COIcI+A5jO4NPASe+K2krt2LU23XWFzWCkbT4lJDnU83RlmW4/d+WGczfTmpdWn+G7vJV7pXY+uDW5evr9Ib2TRnkts3nOAv+Xf00ctkKIOwLLDszi17gyquytsTGahrNdUXVcbAl2tkctkjMk6Tuiqhcjkcrw+/QTb7t1vPZguH1YOh4SDoLaFkb/dVNgIZoHtS8+Sl16MtaMax35FfHXqK46nHyc6Kxq9SbxOOBd50Tx5EIHZTZEBnVo2Jzy4EY1dG2OjFguKGnNzSX35DXK2b8e6fXt8vl2ETC6XhM0DTJXFTUZGBiNGjGDXrl04ODgAkJeXR5cuXVi1ahWurpXvJjtz5kz69OmDr68vBQUFrFy5kl27drF582YARo0ahZeXF3PmzAFg2rRpdOrUiblz59KvXz9WrVrF0aNH+fbbb6v6NiQkJP5FXG4c47aMI0eXQ6hjKN/1/A5Hy0pUuTabYd1kiN8NOZfE2JjKUloEq0aKwsazKYz+Cyxsbnna9Xyz+yLn0wpwslbz1oD62NhYYNOhfdnrhlITfy+KRl9sROVqyQJtNse/uFT2el1Xa55s7ceQ8P8WjGvoZc+MoQ3Y8uM5chMK6aRTYT6vI/FsDr4NnBEEgVKTmUD7QAIjJkLERLJnZqJN2I0+bR8HUg5Q91gq4zYk0zRoBxtbWRLtFMYlbRjjlmfQ0tebV/rUI8Kv/OdsMgusPpbI3C2xZBTocUfB344D6G3YRp3SeNg+Aw5+AM3HQYvxYOv+H9vvhHydgSX7LrM2Kok/p3bAxkKJTCZj6egWmL75kpxV3wPg+f772FWmzlhJHvw0DJKOiEHVT60B7+Y3PDyjOIPt60+Qc1KJWW5ihfeHpB+8cu0AAUJKmtIqvS/Wadfeu19DZ9qGtikXk1O4dx8ps2ZiyswClQpr6Wb4oaDK4ua5556joKCAM2fOEBYmqu6zZ8/y9NNPM3XqVH7++edKj5WRkcGoUaNITU3F3t6exo0bs3nzZnr06AFAQkIC8uuCB9u2bcvKlSt5/fXXmTVrFsHBwaxbt+6hrnEjIVEdxObGMmHLBHJ0OYQ5hfFtj29xsHSo3MmRC0Vho7KCRhXXJ6kQQYB1UyA9GqxdYcRPVRY2F9ILWLA9lsfPb6XDpJE425RfghYEgZ3Lz5OdVIjGVsUmJxPHk/JRymX0aujBk638aB3odFPviYu3LY/PbEFsZBr7VsehKzSwYcFJApq4oG7uzKwt5xjXPoAnWvlia6nC2dqVR8KG8UjYMHRGHWdiX0WQbSYiTiAiroQ4j+NsaHWCQ6EKonUBjPgljLYeHXmjd0eC3GzYeyGT9/86x/m0AgB8nax4tU84fRo+iUynFSsyH/5WrBO052PYNw+eXA2Bnav02VVEkd7I0gOX+XbPJbQlYoHGtceTeKqNv/h5LvuenO9FYeMxezYOgx+59aDFObB8MKSeELPGnloLda55+gVBID4/nuPpx4nKiOJ4+nFIsqbfuUnIgL3+v5FucwVvG2/C3cNppIrAuM0NbaKYISaTQVBzd8J7+eLifS393qzXk/HpXHKXLwdAXbcuXp9+gmXYjb1FEg8OVU4Ft7e3Z9u2bbRo0aLc/sOHD9OzZ89aXyDuZqlkUqpx7Ub6fu4OMTkxTNgygVx9LvWd6/Ntj2+xt6hk36H0M/BtZzCVQv950Hxs5SfeOxe2vyMW4nt6A/hV7Y7aZBYYuvAAXns28uzJNSgcHKi7dQsKW1sMJjPbzqazOjKBvloV6Re1DHq+KdGlOi5mFvFoc2/cbKv+O1RaYuTwX/Gc2pGEYBYosVHwpaIQZGBrqeTJ1n6Maef/n7FLL18me+lSctesQVYqioYMe/irhZxNzWUIMhlmvRsONCE9yxmz3hVruSfTujbgqTZ+WCj/tXxiMsL5DXDwazFuZfpZUF/1VmiTwdbjloG611NSamL5oct8s/sSOUWiaKjras3z3UPo18gTuVxG9uLFZHwqxlK5z3wVp8pWpL+8H5Y/Aha2MGo9Btd6nMs5R1RGFMfSj3Ei40S5Rqm2OieGRr+IpdGa0pAMwoY40My1Ge7WooemVGfkx1kHMBrMhLX1pGl3X+xdy9fzKU1KImnyFPQXLgDg+MQTuL30IvJKtrSRqJ3c1VRws9n8n/5BIPYZknoISUjcX5zPOc/4LePR6rU0dG7INz2+qbywMejg9/GisAnpDRFjKj9x7GbY/q74vO/HVRY2AEsPXEZ/4gSTotcD4DxhPBlmJT9vjWXV4QQyCsSYjHb9wnhkYCB1gh3wutmAlUCtUdJ+WDBhbT3Z+8sFmvT0xbekmEW7L3Ixo4iFOy/y/b54hkV480yHQPxdRMGh9vfHc/ZsXKdOJXflz+T+9BNuubkMS/MhycWT09lRyC0yyGcrmuuM/C29Dkd3BhBgX/7hbOmMrMFgseBdfuo1YSMIsGKoWOyw9WRoOvKW3rBCvZGun+4q+7wCXKyZ1i2YAU3qlMXb5Py4vEzYuL7wQuWFDVDk1ZQrPd7ghD6T7SfncyrzFDqTrtwxFgoLGrk0oplTODYbG1JiFHDzs6X/M+2JjUzn6MZ0+k4We2KpLZX0Gt8QZ28brOwqrqWjdHJCKC1F4exMnQ/ex6ZTp0rbK/FgUGXPzaBBg8jLy+Pnn3+mTp06ACQnJzNy5EgcHR1Zu3btXTG0upA8N/cv0vdTvZzNPsuELRPIL82nsUtjFvZYiJ365ndD5dg0Cw59JS4pTT4INpWMt8u6AN91BX2+KIgGzK+y7QnZxYyYs4GPt32Gsy4fXbsuLOw4hu0xmZjMAkoBHGzVDG/uwxOtfPF2rP7+cIIgXNdQVuDXFWeIic5kHSXkKgTqedjy97QOFS55mXU6tOvWow4IwLpVS7R6LT/v/R2rJSs53MmeU3aZ5bwZ/8ZWbUuAfQCB9oGi4LETRY+3wYBycTfQacUDLewhYhS0nAgO10pgmM0Ccvk1u6b+HEVUYi5TuwYzuJlXuR5Uub/8StpbbwHgMmUyrlOn3vRzySzO5Gz8NmLSj7Ot6AoxuTGYhfI3vvYW9jRza0a4Wzjh7uHUd6qPUq5kx4/nOH8wDQtrJaGtPIiJTENfJPYPGzi1KT71bxzcbszORuHoiOxqKIM+Lg6FoyNK5ypk+knUau6q5+bLL79k4MCB+Pv7l9WLSUxMpGHDhqxYseL2LJa4Yzp37kzTpk2ZP39+TZsicR9wJusME7ZOoKC0gMaujfmm+zfYqqvQLiDnkhhrAzDwy8oLG50Wfn5cFDa+baDPx1W2XRAEXlt9nOkHluKsy0ceWJcnnXtQdE4s+Nfaz5FuKRAU4ETH7iEolHen/sv1osVkNFN4MgeXIoEJcg0p7kqatPYrO6ak1MSxK7m0C3JGJpMht7TEccRjZefbW9gz9FwxWceSaXEsGetOHVE/+SgpwY7E518mXhvPJe0l4rXxJBcmU1BawKnMU5zKPFXOJqVcSWhwY4aVGOiWegHHomw4sADh4NfI6g/E0G4Gq5PsWbjrIj+ObVnmWXp7YANsLJX/qZWjXb+etNmzAXAaOxaX554r97ogCFzOv1y2xBSVEYUhN57vUzOoJwis83TDrFLhZeNFuFs4zdxFQRNgH4BcVn6u03uSOX9QLMhq1Js5tSNJ/GxcNTTr6YtncMUeRcFkomDrVtJmv43zpIk4jx4NgMXVEiMSDydVFjc+Pj4cP36cbdu2cf682MwuLCyM7pVJBZS475g9ezbr1q3jxIkTNW2KRDURnRnNxK0TKTAU0NS1KQu7LyxLma00ToFiw8rL+yC0kv13zCb4fQJkXxDr2Az/EZSVLNF/FUEQ+HjTeRquW0KDnMtgY0PA11/S/2g+GrWCJ1r6kLglmfOZqVwqzqR5H39sne6+l0+lVjDs5ebs/fUCCWey8Uw1ULQukQtKC4Ii3Pj1aCJv/XGGhl52TOpUlz4NPcuWfP7BpnMn9BcuULB1K0W791C0ew+O9etTd9xY7Ho9j0wp/rvWm/Rcyb9SJnbitfFc1ooCSGfScabwCmeAd1yt6GAj5yltPq11ejizlslnfNmmawXAnK2HeGdgQ9ys3HCsoFVC/qZNpMycBYJQFrNiFIyczzovFsvLiCIqI4ocXU7ZOV4GI0vT0vEymsjV2PNKq1mEBvbEw/rGtcgA0i5p2bMqpmzbZDTj4mNDRG9/Apu5lvMy/UPp5cvkrVuHdv0fGFNTASjYtBmnp56SUrwlbq/OjUwmo0ePHmVZTRIPHoIgYDKZatoMiWrmZOZJJm2dRKGhkHC3cL7u/jXWqtuszBrQUXxUlp3vw4XNYnG6ET/9t/v3TSjUG1kblcyy/ZdJSMvlozzxrr6o9RBwceejYf4AnN6dxPkDqchk0HN8g3sibP7Bwd2K/s825nJ0Nvt+jSU/S8eWxWc4syeZwiANlio5p5PzeXZlFH7OMUzoEMiwCG8sVeKFWNOoEd5ffE7plSvkLFtG3pq16M6eJWXGi2R9+RWBf6xHplJhobAgxDGEEMeQcvObBTNpRWllgideG8/FvEs8e1mDa5E3/YVzbDO2RKYoQO2yi8CMzfy02MzfTm64OAaVxfME2gfiezId46w5YDaj79uB3/s7ErVlAqeyTlFiLCk3r1quppFrI7pY+TEicgUWRhM41cXx6T/oZP/f9HoQm1imxedTkK3DJ8yJTd+eRjCDylKBm68t4b398AmrOItN++df5K5cScnx42X75HZ2OD7xOK5TpkjC5h6iM+pIKkgiyLH2ecmqrXHm/cKDGnPTuXPnspT45cuXo1KpmDx5Mu+88w4ymYzly5fz+eefExMTg7W1NV27dmX+/Pm4Xa3UuWvXLrp06cLGjRt5/fXXiY6O5ttvv2XMmPJBokuWLGH0VbfvveZ+/n5qAycyTjBp2ySKDEVEuEfwdbevsVJVMRYldgs41xUfVeHMWvhttPh8yHfQuHIp4+dS81lx6ArropIpKr0mtr3lRsbFxlLsEoGdIYO+L7Sm1NKBdfOiMJsE2gyuS3gvv6rZWI0YS01EbU3g2KYrCGaBEW+0RLBVsezAZZYdvExesZgx5WKjZmz7ACZ3qvufC7kxN5fcn38md8VP2Hbriue775Z7Tel48xpEgiAwZOEBohLyALDTyOncqBQvr0uk58cy+9Cv2JmMFMtkrLexZoW9LQkqFU0umXl5tRmVCfY1kLOgvwzhOs+Jndqu3BJTfef6qHMuw7IBUJgGLiFi9ptteW9NflYJCWdzSDybQ9L5HEp1JlQWClx8bEiN0+LoYcXAaU2xcSz/ty2YTCCXl30+ae+8S+7KlSCXY92+HQ6DB2PTtStyqRL9PUNv0rM6djWLoxdjobBgw+AN96Qn112NuXnYEAQBY2nNZIEp1fIqNcFbtmwZ48aN4/Dhwxw9epRnnnkGX19fJkyYgMFg4N133yU0NJSMjAymT5/O6NGj2bhxY7kxXn31VT799FMCAwOxtLRkxowZbNq0iW3btgH/7S0mcX9wPP04k7dNpthYTEuPlizouqDqwiYnHlaPETNyxm4Cz8aVOy8tWqxnA9Dm2UoLm61n05nw49GybQ9rJWlFRpRyeKlIS5JLBAD5KjdWzz+D3EqD2QR1w91o1tO3Sm+tulGqFbToF0BoKw9SL2px9BC9Yy/0CGGwrws7MvP4ft9lkvNKiErIq/DvXOnoiOuUKTiPHYu55Jq3pOTMGa6MeBy7AQOw7dEdy3r1UHp4IJPJ+OdeVSaTIZPJ6BTiyqXMIp7pGMjotv5YW1z9l28sBZd2mA9+hVXGWR4vKGREQRHxlh4IewyUmlQcCpXzZX8ZnrZeZUIm3C2cQIfA8vEymbGwtC8UZYJbfXG58jqv3KmdiZzamYQ2o7zHx8JaicZGRWqcFpWFgj6TGpUTNtcvO3l98jFWzcWifw6PDUdVxxO7AQPL9Q2TuPuUmkr5/cLvLD61mIwSMcbN09qTpIIkAuwDati68kji5hYYS818O213jcz9zOedUFlU3sXq4+PDvHnzkMlkhIaGEh0dzbx585gwYQJjx16rPxIYGMgXX3xBixYtKCwsxMbmWrzFO++8U2650cbGBqVSedP+XRK1m6NpR5myfQolxhJaebZiQdcFaJRVrPdhMsLaiVBaKAYCuzeo3HlF2bDqCTAUQ92u0P3tGx56OauIjAI9LQPEjJgOwS642KhpFeDMwEbuaKdO4ZytFx4Dh5J8xBPk0KZhCWdOFJCvdINSsFAa6TIy5J50xq4Mdi4a7FyufdaZCQX8Nf8EHgF2rH4sgkN5BQS7XQvkTsotZsH2OCZ0DCTITfy7lFtaIr/OU1m4axeCwYB2zRq0a9aIx9jbUeITSKTMmfrPTaBDxyYATOgQyLj2Adha/uuuWqmGZk8ibzpSLMB4aCGy2E0E6lKhE+QVNKL3Gz/TV6Uuqy9zQ2zcwK4Ogo0nWV1XkrCvmAbtDVjaiHOW6kxoM0qQy2W4B9rhW98Jn/rOaDOK2frDWQC6jQ7D0cMaU0EB+Zs2oV27rtyyk/bPP8vEjWVoKJahoVX5GiSqgV2Ju3jv0HukF4v9HT2sPZjQaAKDgwajqkT/t3uNJG4eIFq3bl3un3qbNm2YO3cuJpOJEydOMHv2bE6ePElubm5ZTaKEhIRyHdibN79xSXSJ+48jaUf43/b/UWIsoY1nG77o+gWWyttY0ts3DxIjxZ5AlejkDIhdvn97GvISwDEAhv1Q1tnbZBYwmMzojWYOXszmp8gr7L2QRV1Xa7ZN74RMJsNSpWDfK12xVCn4edzLNE2LJSTrCon7QknQNMJNSCH82SdpVKRj/cwNpJc6ozcq2TD9V/rO7oOVZ+1LAc5LL0apVpB2KZ81Hx+jQQcvgoKviYfFe+P55Wgivx5LpEeYO5M61yXct/zyk+v//odNu3bk/vobutOn0V28iFmbj4X2BB2Brw90KxM3xk1/UXTgAKWh9bCsF4pFvXoona5Lp5bJILAzJcWupC46j4NXBg5BpdjN+BaHf1LHc6+I1acryIgr0upJPFdCguxzki4WUHJSLJpn56whuIX4vkJauOPkaY13qKPYhR2x0/fOn8QA4vBevvgHW5H80ssUbN2KoLtaA+dfy04SNYtGqSG9OB03jRsTGk9gSPAQ1IqqJQTcSyolbvLz8ys94K3Wwe43lGo5z3xeMwWglOrqSWHV6XT06tWLXr168dNPP+Hq6kpCQgK9evWitLS03LHW1rcZXCpRKxAEAeNV4XAw+Qgv755JqayEdnXaMb/LfNK1JnKL8zCYzJQazZSazBiMZgwmAaPZzKCm1yrIbT6TxoX0ApzyTjPi1BzkwO+ez3N0p5ZS40k+HNqoLHV44a6L7I7NwGASro2tTcegG4qBEWwe3BR7jXiRnrU2mpWRCf+xXSYTWw3klxixtxLvBC1VCg58/wtN928AQP/IKBKyGoFgpt3TYgl/lbUlw754lCOf/8nRMyrS5XVY9foueo0OwatDo7v5cVeZ4BbueAY5cGBNHBeOpHNmTzJxx9JpPagu9dvXYVDTOqTklbDlbHrZo2WAE5M71aVzqGvZzYtlkyZcsvbms60xRMdn4VuQTmhRGn2tCnl7yrXstcJ9+8nfsAH4o2yf0tUVi7B6WIbWw3nCeAypqSSOG4dJW0q+Tzccps9H7uByzeitb0LM39D4UWg9BdwbkBF5iB3rc8nOKR/norJQ4BXqiKXttTv5f3uv9CVG/v4mGgoL8G7kRauBgchkUHLiBIJOh7puXRwGPyItO9UgBrOBPy/+SbGxmJFhIwFo6dGSTzp9QhefLlgoan98U6XEjYODwy3dvP8UtHrQMmxkMlmVloZqksjIyHLbhw4dIjg4mPPnz5Odnc2HH35YVpvo6NGjFQ3xH9Rq9QP3nT5ojF92hEOXcsrESjlk0+jbbSvzuszDQmHB7D8OszMm84ZjDWxSp+xv/Y8TKeyMjucv9UzkchN/mlozI6YekAjAO4MalImbi5mFHLqU86/RrK4+oNQxuGyv6l9pvS42ah5t7sMTLX3xcSofB5Rz+iyaeR8AENtpIEVpHqAEP8s0VD6tyM8qKbtwtpjWH89dJ9myPI4SlSN/LE+h1akEwv/X75af4b3ExtGCnuMa0KBDHfb+Ekt2chG7V8Zw8XgGA6c15dtRzYnLKGDR7kusO5HM4fgcDsfn0NzPkd8mtUEmk/Hq79H8clT8Hiws1HTt3J6Jneri8q/eWg6PDsMiMADdufPoYs5juJKAMTMTY2YmRfsPYNu3D4njxmPSalF6eqD28SVv4xYs69XDIiQUuaWavIxSEvJ7YHswlsCotuDfAauEy2TnfAGAq68tPvWd8K3vhEeg/U3rChnzC4h8dTF1j27DWpdB0Mc7kV/9HfJ4/TUUjo5YNmpUa5YVHzaMZiN/XfqLRacWkViQiJXSir4BfXG0dEQmk9Hbv5JlH2oBlRI3O3fuvNt2SFQDCQkJTJ8+nYkTJ3L8+HEWLFjA3Llz8fX1Ra1Ws2DBAiZNmsTp06d597rMi5vh7+9PfHw8J06cwNvbG1tbWyykrIRahc5gplBvrPA1GUrmd5lf5j52tbXA21GDWiFHpZCjUsrKnquVcrG6r0K8sLQNcmZg/koC0tPJV7kSF/EOUy0crp4nL1en5YlWvnQKcUWlkGORdxHVtlmozDpUzZ5A3XI0DlbX7uRf7BXK891DUCnlqBTi/BVdzEz5+VyY+D/sjKWcqxNKYIsu7D+qRm4qpfnY9mz8+hTGUjMDpjbBzU/0GHt3bsJjIV5sfHsTGfI6HIxWkTZ9BT3eHYbKunZl2HmFODJ8VgtO70km8o946oa7lX0OQW62fPJoE6b3DOGHffGsjEwg3M+x7PUOIS6sjUrmiVa+TOlcFze7it+bdcuWWLdsWbZtKixCHxuLPuY8+osXSZo4CVNODhb1w5DJ5WjXriVrwyZyHeuR41iPHNdG6FQTAPBxTCZQdhQu78UG6Bu6Ho9Rb6Nxdrjp+xRMJoojI8lbuw7tpi24GsQ2D8jlEH8enMWlcKlFQs1hMpvYGL+RRacWcSVf7L7uZOnE2IZjb28ZuxYgpYJfx/2caty5c2caNGiA2Wxm5cqVKBQKJk+ezHvvvYdMJuPnn39m1qxZpKamEh4ezsyZMxk4cCBRUVE0bdq0LBU8NzcXBweHsnH1ej0jR45k+/bt5OXlSangtYDdsZmcSMhjYqdALFUKUvJKMJjMqBRyojKP8Pr+mRiEEjr5tOezzp9gobwDMWoshV0fiB2nK9N1Oj9VbKRZmAb1+sPw5eJFrIoIgkD00+NRHT5AusYB9cIlxHx3mmKVE/Wc0ilwCSU5Ng8HdyuGvdocC035+zSz0cTut1dzNsMZZHIcjOn0fak9jqE+N5ixZinOL8XSRlVWrO5ydBbajBIadfZCrpCjLTYgIOBgJYpUs1kgo0CPh/3t/R0YUlK4/OSTGFNSsQgOwvfHHyk+dpxNG4vILLKC67KhZGYDTiWJ1B/bi8bNBDjyPem/H0Pwaoll/YZY1AvFIiiowlTswr37SH3zzbIiewBFVh5Y9RtI8LMjpWWnWsCpzFO8vv914rXxADhYODCm4RhGhI6oekblXaYqqeC3LW6Ki4tJSEj4T8xG48aVTA+tIR5UcfMwIH0/UGo003v+Hi5lFfFC9xCmdb+23LMnaQ/P73weg9lAN99ufNLxk3ubxWDUw9J+kHQEXMNg/FaxE/RtUFJq4s2pcxm2/xf2PPMmnUoFjlywQ2UsIqRjAGcOZKCyUDDs1eY4ed44Tuz8z7vYvb0Io1KDylhI137OBA1ud7vv8J5gLDWx8u1ICrJ1ONWxpuOIELxCbl7TpioY0jM4P3oKGTp7dB4h9PpiLEpXMVh4/fwoks7n4uhhhVegNR4aLQ7ai6hUlDXLFASBC63bYNJqrw2qUGARGIhFvXpYRYTjOGIEAPoLF7g0YCAyG1tSnMNJcm6FT5/WdHkqrNrej8SdkVyYTP81/bFSWTGm4Rger/f47Rf2vMvcVXGTmZnJmDFj+Pvvvyt8vbbHZ0ji5v5F+n7guz2XeH/jOVxs1Ox4sTN2V1N8dyXu4oVdL2A0G+nh14OPOn50+0W1BAFO/gyNHoXKiiNBgD+ehagVYGkPE3ZWvdDfdczZeI5Fey7hr4HfJrRm3Rv7KVXZEOBWTHyGeDfZZ2IjApvduqdV1qlLbPz8CAUqVxDMNPHOpe1rQ5HfhkfpXmA2C5zbn8LBdRfLmkYGt3Cn7ZAgbBxv3wuXcSWfc7svE787hiLVtUyy0R+1w9peHDc7uRC1RnnTys6CyYR2wwb0586ji4lBf+5cOaFj1aY1fkuWlG1rd+xm8z4FmSmluPnbMWRGOApV7fzsH3TMgpntCds5nXWaFyJeKNu/J2kP4W7hVW/Dco+5q0X8nn/+efLy8oiMjKRz586sXbuW9PR03nvvPebOnXvbRktISNycjAIdn28XU21f7lWvTNjsSNjBjN0zMJqN9PTryYcdP7yzaqFHFsPGF+H4chj9V+WWlQ5/JwobmRyGLbltYVOamMi5nFK+23sJgDeGN+f0wm2UqlyxNGhJzHUEzET08auUsAFwaRzIiHnubH5tLQmGOpxMdibjfyvp8+4ANC61ryilXC6jQQcv6jZzI/KPS5zem8yFI+nEn8qiRV9/mnTzqVIz0JQLeRz+8xLJMXniDpUzCGbcfazwbepZLt7J2evWFzeZQoHDI4/AI+K2IAgY09PRnT+P/vx5VJ6eZccKgsDRBFcyU9LQ2Kro/UxDSdjUAIIgsDNxJ1+f+JqYXDEFv09AH+o51QOgo3cV2qjcJ1RZ3OzYsYP169fTvHlz5HI5fn5+9OjRAzs7O+bMmUO/frUrM0FC4kHhk00xFOqNNPa2Z1iE2LNn25VtvLT7JYyCkT7+ffigwwco5XdQviozBra8Lj6vP6hywiZ+L2x6VXze/W0I6nZbU5uLikiYPIXilCwCWo6hYecWNFcbWZlmDwqwcbMjK9eMbwMnWg4IrNLYajtr+n3+BEfnb+DoeQ2p8jqsemUrfcbXx6NN/VsPUANY2qjo9EQo9dvXYc+qWNIuaTm49iKufrb41HO69QBXKcguITkmD5lgwi3jOO76izT97BVs692+Z+16ZDIZKg8PVB4e2HbuXO61M3uSOX8oTez1Ne7e9vqSEEXNnqQ9fHXiK87lnAPAWmXNyLCReFp73uLs+5sq/xcsKioq60fk6OhIZmYmISEhNGrUiOPXVZSUkJCoPk4m5vHbMbFZ5FsDGiCXy9hyeQsv73kZk2Cib0Bf3m///p0JG2MprJkARp1YTbjlM7c+J/eKWKhPMEGj4dD2uduaWhAEUl5/HUNcHCZLOwQnJ97sX599r/+GWVEHB2M6g98axtGNVwjv5Vdhl+hbIZfLaTl9EB5bj7F1VQLFKifW/pBA2+gEmjxTe1NcXX1tGfJSOLGRaaTEacsJG6PBhFJ1rVSFrtDA6T3JWDtYENZWvHgFhtkQrDuOa9TvWFvJ8PtxGRbB1SNsbkbaJS17fxU9ja0H18W7CoJM4s5JzE/k5T0vczr7NCAW4Xsy7ElG1R+Fg6VDzRp3D6jyf8LQ0FBiYmLw9/enSZMmLFq0CH9/f7755hs8PR8MJfiQJZDdNzzM38ucv8W7rsHNvIjwc2RT/CZe3fsqJsHEgMABvNvuXRSVqRp8M3Z9AKknQeMIg76+tdemtAhWjYTibPBsAgO/ECvx3QY5P/xAwd+bMMgUvN9iFM8/1hbD2Xgul3iADNoODkRtqaLtkDvvPuzbI4LhoT5sfG8rWQpP9h2HtJd+otu7w1Ba1s4yBzKZjNDWnoS2vvY/tkir55f3j9Cok7iEdXpPMucOpGAsNWPrZEloK3cwlJIy9Tl8IiOR29nh+8NSLIKDbzJT9VCk1bNpUTRmk0DdcFea9ajZXl8PIy5WLqQWpaJRani83uOMbjAaR8vqC0yv7VRZ3EybNo3Uq2l9b731Fr179+ann35CrVazdOnS6rbvnqJSiXEKxcXFaDRV7L0jcdf5JzNPobg/iipWJx8Nbcwnm2N4tU89Nl7ayMx9MzELZgbVHcTbbd++c2FzeT/smy8+H/AF2N3iRkUQYP2zkB4N1q4wYiWobu9vpujAATLmfgbAokYD8WrfkgGNPVkzeQfI6mBNAf59utzW2DfC1teNYV89xq63fuN8jjtxBZ5kP/s7/WZ2wr6u160HqAWcP5hKSX4phzfEc3hDfNl+Fx8bmnb3xVxqIGXqVIojI5FbW+O7+Dssw+5+lpLJZGbL4jMUaUtx9LCi66gwqSjfXUYQBCLTIvnz4p+80+4d5DI5GqWGTzt9SoB9AM6a2teK5G5zx3VuiouLOX/+PL6+vri4uNz6hBrmVtHWqamp5OXl4ebmhpWVlfRHWUswm82kpKSgUqnw9fV9aL+XDRc38Pr+1zELZgYHDWZ229nlOzTfDmYzLGwDmeeh6ZPwyFe3PmfvZ7D9bZAr4ekN4Nf2tqY2JCcTP3QYprw8tvg2Z3HrkWyd0Zni3VH8vbFEFFEyGV2erEf99nVua45bcebH7ezdo8ektERtKKD7YHcC+re+K3NVJ4IgEHcsg/2r4yjK0+PbwImmPXzxDnUEo5Gk51+gcPt2ZBoNvou/wyoi4p7Yte+3C5zcnojKUsGjrzYv64gucXc4knaEr058xbH0YwB82ulTevn3qmGr7g53NVvq31hZWREeHn6nw9Qa/ul+nZGRUcOWSPwbuVz+0AmbjAIdbrZiEOYfF//g9X2vIyAwNHgob7Z5886FDYjLTyNWwo53oc+Htz4+dgtsf0d83ufj2xY2ABmfzcOUl0ecozdfNRnK2/3r42ar5qc/roDSDWQyvEIdqNfm7nWlbzCqG24NL7DxqygKVS78/UcBzU7/TquXB9fadHEQl6qCm7sT0MQFXaEBG0fx90QwmUh55RVR2KjV+Hz91T0TNheOpHNyu9gWovvT9SVhcxc5ln6Mr098zeG0wwCo5WoeDX2UcLcH53p8J1TZc2MymVi6dCnbt28nIyOjrLv0P+zYsaNaDaxuKqv8TCYTBoPhHlomcSvUanWtvthUN1eyi+g5bw8DmtShZcNLvHf4LQQEhocM57XWr1WPsKkqWRfgu66gz4eI0TDg8zsazlhQwG+jpzPPqyN1GwazckIrTi3ewr5j4hKxlZ2SEW+0RmN797sP63ML2PTGepKMoofIS5FMn3cHYeF0/zQDFsxmUmfOQrt+PahU+Hy54J61NchOLmT1R0cxlpoJ7+1Hm0fuftDyw0h+aT4zds3gUOohAFRyFUODhzK+0Xjcrd1vcfb9zV313EybNo2lS5fSr18/GjZs+MDeRSsUiocytkOi9vDeX+fQG82cSk1gc9FbIBN4LPQxXmv1WvX83eUlQl4C+FeyYq9OCz8/Lgobn9bQ55M7NmH1uVxmBj+CpUrOh0MbYSrRc+iwERQqZAj0+1/TeyJsACwcbRnwxRMc/mQ9xy/akIwXq17aRO9JTXBvEXpPbLgTBEEg7e13RGGjUOD12dx7Jmz0xQb+/iYaY6kZnzBHWg2sWqq+ROWxVdlSZChCKVcyOGgwExpNwNPmwUjmqU6qLG5WrVrFr7/+St++fe+GPRISEsDeC5lsPZuOXCaQZDkfhUzgiXpP8GrLV6tH2JhNsHYSXNkP/T+D5mNvcbwZ1jwD2RfAzgseWw7K2xMdxcejKDl5ktLBj/H+X2IW2Is9Q/FztmbTW39gVIiF5DoMDShriHmvkMvltH5lMO4bD7P991QKVS6s/fYi7c8k0HB0j3tqS1UQBIH0OXPI++UXkMmo89FH2PW4N/YKZoFtS8+hzSzBxsmCHuMa3FaqvkTFnM46zdIzS5ndZjY2ahtkMhlvtXkLG7UNXjb3R/B7TVBlcaNWqwkKuvN0TAkJiYoxmMy8veEsAAqH/SgsMngy7ElebvFy9XlKD34JV/aByhoCKnF3v/N9iN0ESkt4bAXY3F7DQ0NGBknTpmLKzGL78RQKrJvQxMeBMe0CKM7I5XKKEhTg5lBKox41d/cf0Lclw+ul8NecXeQoPNh9CNJifqbL7GEoLO5hv65KIAgCmfPmk/vjcgA833sP+/73rpjqsU2XuXwqC4VSTp+JjdDY3BtP24PO2eyzfH3ia3Yn7QYgyCGISU0mARDqVPs9iTVNlRftZ8yYweeff/5Q1xyRkLibLD94hbiMQmSKQixct/F0/aerV9iknoLt74rPe8+5dauEM2th76fi8wFfgNftBSwKpaUkP/8Cpsws9N7+fGlRD5VCxifDGqOQyzg4fxMmhSUaQx6D3qje1O/bwS6wDo8uGEqwXRoAMbnu/PbsrxRcSathy8qTtXAh2d9+C4D7m2/gMHTIPZv7yulsIq+moXd6IuSee9puxsnMk2QWZ9a0GVXmfM55pu6YymN/PsbupN3IZXIG1h1I3wBptaQqVNlzs2/fPnbu3Mnff/9NgwYNymrD/MOaNWuqzTgJiYeN7EI9H285A8hQu25hbJPHeSH8heoTNoYSsQqx2QD1+kP4qJsfnxYN66aIz9s8C00eu+2p0z/8iJLjx5HZ2DKz2ZPoFBa80CWYYDcb0k/EE5vlBHJo3dkBtXXtKKantLSg58dP4Pn9ZvYfMpOt8OSXdw7RY7g3fr2a17R5ZH//A1lfLADA7ZVXcHriiXs2tzazhK0/nAEBGnSoQ1jbu5OqXxWMZiPbE7bz49kfOZV5irENx5ZrEFmbMQtmXt7zMpsvbwZAhoy+gX2Z1HgS/vb+NWvcfUiVxY2DgwODBw++G7ZISDz07L8ch95UhNxCy8T2jXghfFr1Bu1vfUusZ2PjLnphbjZ2UTasegIMxRDYRewbdZvkrV1H7sqVAGwcMJFzJQ6EutsyuXNdorYkcHjtBcxyFU6mNOqNHHHb89wtGo3rhWuj82xaFE2Rypm/fs+h+el1NH9hYI1l8OX89BMZn4hB3a7PT8N5zOh7Nreh1MSmb6PRFxtxD7Cjw/CQezZ3RRSUFrDmwhpWnltJSlEKIGYRlZpKy44xC+aayTCsJHKZHJVchQwZvf17M6nJJAIdpMDs2+WOi/jdb1QllUxC4l5iFsyM3TyWoymnaeLUgRWDPqteYZMSBd92Fp+P/B2Cu9/4WJMRVgyG+D3g6A8TdoLV7fUGKjlzhiuPP4FQWkrBY08zXN8IuQzWTmmHY4GJDQtOwtX/QgOG2uPb497UZLkdSrLy2fTmH6SYRS+FjyqFXu89goX9rbtpVyd5q1eT+vobADhPmojb88/fs7kFQWD70nPERIqdvofPalFWY6cm+OL4F/x07ieKjcUAOFo4Mjx0OCPqjcBF40KuLpevTnxFji6Hzzp/VmN2/ptL2kssOrmISU0mEWAfAEBKYQrFhmKCHKW41oq4p0X8JCQkqoffYn7jWPoxrCw0fNz9xeovs+DZFAZ9BdlxNxc2IHYGj98jBhyP+Pm2hQ2APiYWwWTCsmNHxqojQF/K+A6BBGgs+HXekTJh4ylPxrdH19ue516gcbFj0JdPcHDOGk4m2JNoqMMv0/+i79TmuDS5N3VdtBs2kPrGmwA4Pf00rtOm3ZN5/+H07mRiItOQyWX0HN/wngsbQRDK/W0UGYooNhZT174uT9V/in6B/bBUXrMpV5/L6tjVmAQTR9KO0MKjxT21999cyb/CNye/YWP8RsyCWCfuo44fAVDHpuaX9h4UKiVuwsPD2b59O46OjjRr1uym/3SlzuASElXnt6jzfHBgE4I1TAufdndSPGUyaPbkrY+L+gkiF4rPhywC9/p3NK3DkMGo/f346Hwpqaey8Xe24rmOdflr/gn0xUbxILORjpNvv9LxvUQul9PutWG4rzvAzg1ZFKhc+X3BeTp2uULYyLsrzvI3byHl1ZkgCDg8PgK3V1+5p7XGUi9q2Xe103ebwXXFVg/3CIPZwNbLW/nx7I+82vJVmro1BWBUg1F09O5I2zptK/wsAu0DGRYyjF9ifuGTI5+wqv+qGlmeSsxP5JtT3/DXpb8wCSYAOvt0ZnSD0ffcloeBSombQYMGYWEhBvg98sgjd9MeCYmHjmK9kTfWnUanH0ZwiC+P13u8eie4tBs8GlXO+5J0FP58Xnze6RUIG3Db0wqlpcjUYlrwSXs/lp0SK6p+MLgRB3+5QHZSITLBjCCTU9cuE5sGHTmTdYbzOeeJyY3Bz86PkWEjAdCb9Ky5sAZHC0ccLR1xsHAo+6lW1EzqcdAjbXFpkMRfH+0lT+nOjr2Qdm4VHd8ahkJV/U7xgl27SJ4xA0wm7IcMweONN+6psCnS6tn0bTRms0BQhBtNu/vck3m1ei2rY1fz8/mfSS9OB+Cncz+ViRsvG69b3gxMaTqFvy79xbmcc/x56U8G1h14t80ux6dHPmXFuRVloqaTdycmN51MA+cG99SOh4kajbmZM2cOa9as4fz582g0Gtq2bctHH31EaOiNc/iXLl3KmDFjyu2zsLBAp9NVak4p5kaitjHp1w1sOi5Hrspl47Q21HOpxuWN7IvwTQewtIexf4vxMzeiIE2MySlIhdB+Yj2b2wyWzd+0iczPv8B7wReYfQPo/fkermQX80QrX550dWbnivOAwAXnYyQ4nMLgn0dCSWKZmx5gTMMxTI+YDkByYTK9f+9d4VzWKmuGhw4vO7bEWMKCqAU4WjjiYOkg/rxODNlb2KOUV5/4MBbr2PbG71wsEqvEuppT6ftmT2y8XattjqIDB0icNBmhtBS7fv2o8/FHyO5hBXWTycz6eVGkxmlx9LRm2CsRqC3vblTDlfwrrDi7gvUX11NiLAHA2dKZEfVGMDx0OE6WVVsq/eH0D8w7Ng83Kzf+HPwnGuXtdbG/Hb499S0LohbQ3qs9U5pMoZFro3s294PEfRNzs3v3bv73v//RokULjEYjs2bNomfPnpw9exZr6xs3XLOzsyMmJqZs+0FtASHx4BOdmsymKBMgZ3BrqlfYmIxiVWFDkVibxv4md9pGPfzypChsXOvB4G9uW9joYmNJmfUaQnExuX+sZ25QA5INR3DyyaRBSHvqBgwm7lgaCWevsD1ELDyHGAuKo4UjIU4hhDqG0svvWmdjGTK6+3YnV59Lni6PXH0uWr0Wk2CiyFCEjGv/A7JLsll+dvkN7RsSPIS324qZX8WGYl7Z88o1EfQvMeRp7XnLfj1KK0t6zx1J1MKNHDouJ1PhyS9v7aXnyEB8uja9rc/weoqPHCFxyv8QSkux7dGdOh/OuafCBuDA73GkxmlRWyroM7HhXRc2giAwedtkEgvEJpwhjiE8Vf8p+gb0vW1P3ciwkfxy/hdSilJYdmZZWUG86iatKI3F0Yvp5N2JDt4dyuZu5dmKJq5N7sqcEv+lyr+hjo6OFYoJmUyGpaUlQUFBjB49+j/elYrYtGlTue2lS5fi5ubGsWPH6Nix4w3Pk8lkZd27JSTuZyat2gyCO7Z2qczpfYuaM1Vl76eQfBQs7OGRhSC/wQVREOCvGZB0RPTwjFgJlrfn1dTlZXPumdFYFhdzMciaN52WY8g1ofECA7A7WcfIBsNxyr9EosyJRikt6NS9BfU9GxLqFIqrxrXC/y91bOowr8u8cvvMgpmC0gLy9Hnl7sItlZaMaTimTATl6fLI018TRI4W1+JEcnQ57EradcP3MzR4KLPbzgagsLSQJzc++R8hZG9hj6OlI3WH12VQI4FNS2IoVjmwYVUaLaP/oPm0218CKTlxgsSJkxB0Oqw7dqDO3LnIVPe2QnLskTRO7UgCoNvou9Pp22AysOXKFnr49UCtUCOTyRgZNpIDKQd4qv5TtPJodcc3sRYKC16IeIGX9rzEb7G/Ma7hOFSK6vssM4ozWBy9mNWxqzGYDURnRdPeqz0ymQxrlbUkbO4xVRY3b775Ju+//z59+vShZcuWABw+fJhNmzbxv//9j/j4eCZPnozRaGTChAlVGlur1QLg5HRzd2NhYSF+fn6YzWbCw8P54IMPaNCg4rVLvV6PXq8v287Pz6+STRISd4sFBzaTnO4OmPloSIvqjR1JPAK7Pxaf9/8MHG7itTmyGKKWg0wOw364ZcVik9lEQkECMbkxxObEolFqmNB4AoLZTMarr2OZlkumHXzQT4dBJkMwWeAsr0t/i640CPKlKCWLM1c0oIQZNkOIaNH/tt6iXCbH3sIeewv7cvtdNC5lS1T/xmg2lsU9ANiqbXmrzVui+NHlio/rvEPXe21y9blc1F4EbcX2DAsZxlvt32JEcB3WvLOWT1otYJnRhrrzf+TloTNp6lO1FPeSM2dImPAM5uJirFq3xvuLL5Cr7218UXZyITuXnwcgorcfgU2rb6kNIE+Xx2+xv/Hz+Z/JLMnEJJjK4mGeqPdEWcxVddHLvxepRak8EvRItQmbzOJMfjj9A7/G/EqpWayrE+Eewf+a/k9aVahBqhxzM3ToUHr06MGkSeVdeosWLWLLli38/vvvLFiwgG+//Zbo6OhKj2s2mxk4cCB5eXns27fvhscdPHiQCxcu0LhxY7RaLZ9++il79uzhzJkzeHt7/+f42bNn8/bb/y0+JsXcSNQkuSV5tPxoHQadK03qalk/oRory+oLxDib3Hho9CgMXXzjY+P3wo+DQDBBj3egXcVpxWsurOFU5ilic2OJy4sri4EAMaBz09BNZH75FVlffolJpSDq7Uc5rqrLusMCDip33veqQ/yRDBp39abk6FEu5Htga8hi5DdD7krw7d2gxFhCdGZ0OfHzjyjK0+fR2adzWTB4Qu4V+v1RXrS1sGvG5DbP0dy9+S0verrYWBJGPY0pLw9NRAS+332L3Mrqrr23itAXG/h1zlHyM0vwqe9E/2ebVFtDzEvaS6w4u4INFzegM4nxkm4aN6ZFTLvnwb53woqzK5h/fD56k3gDHe4WzpSmU2jp0VISNneBqsTcVFnc2NjYcOLEif80z4yLi6Np06YUFhZy8eJFGjduTFFRUaXHnTx5Mn///Tf79u2rUKTcCIPBQFhYGI8//jjvvvvuf16vyHPj4+MjiRuJGuW1fa+x5tQZ5Ln92DH1cdxtq7EI3ObXxMaY9j4waR9oHCo+Li9BDCAuzkZoMJTkXm8TkxdLbE4s+aX5vNLylbJDH//zcU5nny7btlRYEuwYTIhjCGFOYfRN9yBponjD4zlnDultutF/wV4MJoEPGvmTuzcdmQw6DvBgz7okBLmS7l0UhD5Wiaad9yFGs5HM4kwOb9jMisQdxLidRJCJwdJNXZvyWuvXqOdUr8Jz9fHxXHlqFKasLCwbNcJ3yQ8obO5ekUCzWaAoT09+ZgnarBLys0rIz9KRcSUfbUYJtk6WDJ/VAkubO/d06E16Xtj5AnuT95btC3MKY1SDUfTy61Wty0S3QhAETmaepIlrk9sWIluvbGX6ruk0cW3ClKZTaOPZRhI1d5G7GlDs5OTEhg0beOGF8v06NmzYULacVFRUhK2tbaXHfPbZZ/nzzz/Zs2dPlYQNgEqlolmzZsTFxVX4uoWFRVkau4REbWBf8j7+uPgHKmsZPw5rUb3CBqDDDDEwuPnYGwub0mKMqx7nT7mOdb4BxBjOUrj2WmM+pUzJCxEvlC2V9a/bn9Z1WhPqFEqoYyi+tr4orovhMbpmY9WiBRbBwdgOGsTTCw9gMAkM8nZGeyADgNaP1OXihr0I8jq4mFMJfrSaU95rEUq5Ek8bTwY9PpoOZzrz81eb2Ot3kvNuhziZcRIrWcWZOqWJiSSMHoMpKwuLevXw/e7bahE2+mID+Vk6tJkl5GeL4iU/q4T8zBIKcnSYTRXf4yrVcvpManRHwub6tgcWCgtKTaXIkNHZpzOj6o8iwj3ingsCQRB4bsdz7E7azVfdvqKj941jPP8hT5fH0jNL8bD2YEQ9sUVIN99u/NDrh0p54yTuLVUWN2+88QaTJ09m586dZTE3R44cYePGjXzzzTcAbN26lU6dbn1HJggCzz33HGvXrmXXrl0EBARU1RxMJhPR0dH07St1TJWo/RQZipi9bw4gZlD8U6ujWrFyEmNnboQgwPr/8bI5ja2uzoAJDEUo5UqCHIIIcRSzlYxmY5m4+XfsgyAI5G/Zgk3HjsgtLVE6O+P7w/cgCHy/P56TiXm4K5U0vmJAZxKoG+6GizKHg0axAmu7JxrWWE+me41TA38mfPw0/q//ztmkXiTbXeDwzL24vG2LlbsTnx75lECHQHpbNSdl9FiM6emog+ri+8P3KBwcKjWHyWimIEdHQZbuOu/LNRFTVizxBsgVMmydLbFz0Vx9WGLvosEj0B5rh9u7OcwuyebXmF9ZG7eWlf1W4qJxAeClFi+hUWrwtfO9rXGrA5lMhr+dP7vZzdyjc2lbp+0NywNo9VqWnVlW1uLBydKJgXUHYqWyQi6T13jFY4mKua06N/v37+fLL78sS8cODQ3lueeeo23bqlUYnTJlCitXrmT9+vXlatvY29uj0Yh3NqNGjcLLy4s5c8QLwjvvvEPr1q0JCgoiLy+PTz75hHXr1nHs2DHq1791JVWpzo1ETTJ986es2e2Pq+cJdk2cibW6mjJPzGaI2wbBPW7YDFMQBIyCEdWBL2HbbHZbW/N6HV+ebjyeDl4dCLQPrNSyQGliImlvv0PRvn3/6Wt0JbuIXvP3UFpq5hULR4wZOpzqWDPkxWasnfYb2QpPvFXJDFrwVPW87/uMY19s4PBpNWa5Co0hl/pPWPNM0ssICDgXyRlwwEDvHC9Clv6Eys2t7DxBENAVGq4Jl0zdVQ+M+LwwV8et/pNrbFVl4sXeVRQwds4a7Fw1WDtYVFs8TVxuHMvPLefPi3+WBdhOC5/G+Ebjq2X86iK/NJ9+a/qRp8/jjdZvMDx0+H9eX352OSvOrqDQUAhAPad6TGkyhc4+nSVPTQ1w1+vctGvXjnbt2t2WcdezcKFY4r1z587l9i9ZsoTRo0cDkJCQUO4OLzc3lwkTJpCWloajoyMREREcOHCgUsJGQqImOZJ6hPWRajBrCNR0qj5hA3D4W9j0CjR+DIZ8W+4lQRA4mHqQL6O+pKuVD+N3LwKgY6e32dTsCaxVlbNDMBjIXrKUrK++QtDrkalUyDXXglwFQeDV36PRGcz0cXPAFKdDrVHSZ2Ij4v84QLbCE5nZSIdJHarvfd9nREwdgNvOE2xdcYkSlSOHfy3gsaY92SzbRra1iaU9FKyRF9P7wI+0Ku2OMVtOflYJ2iwdRr3ppmMrVHJRuLiU98DYuWiwdba8q7VpBEFgf8p+lp9dzoGUA2X7Gzo3ZFSDUXT3u0UvsxrATm3HpCaT+PDwh3x14iv6BvTFRi0uAW65vIXZB2ZTYCgAxDo7U5pMoatvV0nU3CdUynOTn59fppJulUpd270hkudGoibQGXX0WPYyiRd6oZCb2fViN3ycqin7JeMcLOoEJj30/RRaXivBcDz9OF9EfcGx9GMAeBjN/J2YhDL8aRjw+Q29PP+mOCqKtDffQn9B7Ctk1bo1Hm+9icV1S8k/H05g5ppoLFVytjzfCVmmGMvhHWLPiklrKFS5EGKfRo+PqjEz7D6lMCmTje9uJlMmLtPJDdmc9orhhNc28i2zAVAbLelz/hk8C66m5svAxsGinGi5XsRY2alr7MKbp8uj++ru6E165DI5XX26MqrBKJq6Nq3VYsBgNjBk/RAu519mXMNxPB/xPACxubEM/WMoQQ5BTG4yme5+3WukH5VEeao9W0qhUJCamoqbmxtyubzCX9Z/OrWaTDe/u6hpJHEjURN8HDmPhX+6IhgcmdjJl5l9qqn8ulEP33WD9GgI6gEjfwOZjDNZZ1gQtYD9KfsBUMvVDNeZGJd6BRevFvD0n6CsXM2U3FW/kDZ7NgAKBwfcXn0F+0GDyv0fSNWWMOiTPdj+n727js+qbh84/rlr3d0budHd3SVpoKggghiIgRgoilgoKGCgoKiUiqiUSLeEdMdGj3X3duf5/XHrfPZjxI33GHG9X6+9nu3c33POdc6D7OIb17fYwkMDohnZrmrpZ/s/+52/TriiNRXx6LutcAn2tc+z3+bMRhPb3vmNE+n/1o7ROEFCxDH+8l5NgTqHz8PnExDog4evE87eWpxukcURGcUZbL60mftr3l967OO9H2NWzDxc62HC3G1bGFKZNsdv5rnNzwGwe8huXHTWf3QcTDtIA/8GktTcQuw+LLVp06bSlVCbN2/+7xEKcRc5lnGMb7dfRDHWxMdNxfNdatnv4pvetSY2Lr7QfyaoVMw7Po+P930MWFc9Daw+gFHnjxCUuAHcQ+CBBded2AC4tm2LysUFj549CXh5HFrvsjtB56QWMnvmAR7O0KJSqXiwwb+bGOpzCzh42Aw6qFulRBKb/6HRaen07mDqHTyNUeeKd5Q/jq5aVKrOWJRnuZR/iUiPSMD6j8cH/3iQmt41GVF3BFGeUZUSc2xWLPNPzGfV+VWYLCZq+9Yu3fxxXLNxlRLTf9UxvCOtgluxN2Uv2frs0uSmUUCjSo5M/BfXldz8s/LJZDKxdetWHn/8cZuXbAtxNzKajby2+UP0GfcC8Hbfhrg42Gnuw7mtsPMLAJS+n6Nyt1bTbRfWjhkHZtArqhdPN3ia8H3z4PQG0DjCgz+A+9X3SjIkJFCwaTM+Q62Tfh3CQqm2ZnWZCa4AaRfzOLA2nrMH0/BXAFR4hrliKDCBh7WHYfeMVeh1fjgbc2j+wo1VIr7T+TWqcdkxtUpdmtiAtRfhROYJTmSeYPmZ5XSP6s7IeiOvWCvHniyKhT8T/mTBiQXsTtlderyhf0NMlquvwrodqFQqZnSaQWx2LIEuV/9vQ9w+bPpbVqvVMnXqVIYOtfMeOELcoeYcm8OZZAXQ0DDCnb71g+1zYZMBlj9LklbNrGpNUWXvYRJ9AKjqWZX19623Lr3dPxe2TbWe0+8z6waaV6AYjWTOnUvGzC9RSkpwjI7GtYW13MP/JjbZKYVsWxRHwqns0mNntWYi2wTz8IN1Soer8uPTOJHgBhpo3MwZnauTfZ79LtQ4sDELey9kztE5bLm0hbUX1rL2wlrahbbjifpPVFgvw6W8Szyz8Rku5F0AQKPS0C2yG4/WfpT6/vUr5J6VwUXnIj01dxib/wnZuXNntm7dSlRUVAWEI8Sd43T2ab4+8jU6TxPj2z9Mm5BGdptcmW7I5eu6Hfg1aSsmQzLqM8t4usHTBLlaN5T1UzvB0qfg8E/WE1qOhgYPXvF6RQcPkjLxbfRxcYB1wrAuMKDctg5OWpLO5KBWq8jx1/FbUR5+oW58eH/tMs+347P1mDXBeBjTqD/y/nKvJa5fA/8GfN75c+Ky4/j26LesubCGPxP/5M/EP5nXcx6NA6+cuNrCaDaWlgQIcgui2FSMu86de2vey5CYIQS72SlBF6IC2Zzc9OrVi9dee42jR4/SpEkTXF3LLiPt1+/22RdEiIpitpiZuHMiJovJWom1QS+7JDbZJdl8d+w7fjr1U+l+Ni2CW/Bsw2dLExtSjsIvwyHztHUzzI6vW6sWlxdnXh5p06aR8/NiUJTLJgybDGZO7kwmM7GAjg9bh0BcvRzpOqw2Zy0Gnlp6BLUWvr23Pg7afydeph84zbm8AFBDq3vCUWuvsCO5sFlN75p81P4jRjcczXfHviMuO65Mr8O53HNEeUTZPBH2ROYJFpxYwOH0w6wYsAKtWotOrePTzp8S5RF13SUDhLgV2FzE72pVRWW1lBBW847P48PNf+DqZGbVkK8IcCm/F8QWe5L38NyGpyi0GAHrnIcxjcbQPNg6dISiwL7vYM1467Jw92C491uIKr8mlaIonB90L/qTJwHwHDSodMJwSaGRY1sTOLI5geJ86/0GT2iGX5h1W5W8EiPdp20jJa+EJ9tXZXzvspOklz6zkCRLCAFKEvfPfuQ/P7u4MrPFXLoVRqGxkO6/dsfX2ZeR9UbSq0ovdOorF2Y0W8xsSdjCghMLSssFAMzuNpvWIbYVZRWiolVoET+LxXLDgQlxN4jPi+fTfd9QkjyGErMrsYkqAi6fM2qzmKTjaIwl1DIrjOnwIW1r9Pu3N6gkF1Y8ByeWWX+u0R0GzALXK69OUqlU+I4YQcbMmQRNehvX5s3Jzyrh8C+nOb49qbRonLuPEw27hePp/29dnsmrTpGSV0KUrwsvdK1Z5roX1+4jyRICioU2w2QeQ0X73z2+4rLjUFA4n3ueN7a/wcyDMxledzgDqg/ASfvvnKciYxFLzyzlh5M/cCn/EmBdWdejSg8erf1o6QooIW5XN7T9wu1Mem5ERbIoFkasHcH2I0EYs9pSI8CNVc+3Q6exbYhAb9azOHYxe1P28mmnT1Fd2A4LBnJRrRDRfDSq7u/82zhxv3UYKuciqLXQ9W3rHJv/18v6z4Rhh7AwPHr1sh5TFDAaUTk4kBiXzYoZh7BYrH8l+Ia60qh7JNWbBqD5n/h3ns1gyDfWVTOLRrWkZdV/EyiLxcKiUYvI1gYR6ZjEPZ9Kr83NVmAoYHHcYuYfn09mibUgoK+TL4/WfpTB0YNxc3DjaPpRhqyyFlP0cPDggegHeDD6QQJdZbWQuHVV+PYLW7du5eOPP+bk393ZtWvX5uWXX6Zdu7u3rLoQAL/G/cru+IsYswYC8Fbf2jYlNkaLkaWnlzL7yGzSiqy7ae84uZi2K14Gi5HIWgOtyQtYh6H++hLWTwSLEbwi4L7vIazpZdf93wrDGl9fXNu2RePujr7QhJObteZNUBVPnNx1eAW40LhHJBF1fC6bJ1RsMDN+yVEAHm4RUSaxATi5cDPZ2iDUZgNtnrn25rnC/twc3Hi87uMMiRnCsjPL+P7Y9yQVJvHpgU/pHNEZNwc36vnXo1+1ftT3q0/fan1La7sIcaewOblZuHAhw4cPZ9CgQTz3nLWq444dO+jSpQtz585lyBAprS7uTimFKXyybxr61MGAhm61A2lXw/+a54F17sMf5//gq0NfkVCQAECgSyBPxTxMi3XvWYedwprDgK+sPTJFWbDsGYhbbb1Arb7Q7wtw9ip73XImDPuPG0f8+RIOroujKNfAkEktUatVaHRqHpzQHGf3Kxf4m7Y+louZRQR7OvFar7I1Vsx6I3u3ZoHOl5r+2XhHh1/3uxP256R14sGYB7m35r2sPr+aMzlnqOL573YZ77d9vxKjE6Ji2TwsVatWLUaNGsWLL75Y5vi0adP45ptvSntzblUyLCUqgqIoPLPxGbacyqQ4YRgOGhXrx3Yg0vfaK0xSClMYtX4U53PPA9YhhCfqP8F9VfrguPA+SNgL3lEwciO4+sHFXfDbCMhLBI0D9PgAmo0ss0+Uoijkr15NygeTMWdkAOA+8F7yuj7G4R2ZZCUVAqDWqLj3lSYERF77v4VDl3IY9OUOLAp8/1gzOsWUnSS955Nl7D3tgc5UyCMftMUlwPsKVxJCCNtV6LDUuXPn6Nu372XH+/Xrx+uvv27r5YS4I6w8t5I/L+1CnzYWgJHtql5XYgMQ4BKARqXB09GTx+s+zoPRD1qHCUrywMEVnLzg4V/B2Qf+/AQ2vQ+KGXyqwf3fQ3CDy66pP3WKxLHW5d/qqjXJvf9l9sSpKPglHgCdk4a67UJp0CUcV69r71dkMFl49dcjWBQY2Cj0ssSmOCOPwyfUoIP6NUyS2AghKpXNyU14eDgbN26kevXqZY5v2LCB8HDphhZ3n4ziDD7a+xGoLPRooOH8JXdGd6p+xfZ/Jf/FDyd/YEr7KThrnVGr1Hzc4WMCXAJwd3D/t6GThzWpyTwLTp6wcBCc+3tvt3oPwD3TwNG93Hs41aqF10MPovXzQ9/pATZ8Zp0n4+zhQIPOYdRtH4qjy5WXCP9/X245Q2xqPr6uDrx5T+3Ln+nT1Rh0/rgYs2g6RrZZEEJULpuTm5deeonnnnuOQ4cO0bq1tQ7Cjh07mDt3Lp9++qndAxTiVjd592Ry9bnU8o3hsz4PoFVpyy3YdyjtEJ8f/Jw9KXsAWBy7mGF1hgFQzavavw1Tj0NAbeswk0YHBSkwvx8UpILWGfp8DA0fLjMMVXTwIGkffkTIJ59Q7OhDZkIBVSdOBKxDVDWbBxJSw4volkFodbYV1ItNyWfm5jMAvN2vDj6uZefk5J5NJDbFAzTQtLUHWhfZZkEIUblsTm6efvppgoKC+OSTT1i8eDFgnYfz888/079/f7sHKMStbOPFjay7uA41Ot5p/c4VC6b9cPIHPtzzIQA6tY77a95Pn6p9Lm94egP8+AA0HQ7dP4A/P/57bygF/GtZh6EC/i2YZ87LI236dHIW/Uy+ayiHPtxMkjkErYOGoTVb4+SqQ6VS0e3xG6tbYrYovPLrYYxmhW61A7mnnL2xdnyxGbMmBC9TKnWGD76h+wghhD3d0FLwgQMHMnDgQHvHIsRtJVefy3u738NcHI46fRQXk72oVU7NvAJDATMPzQSgb9W+jGk0pvz9eVKOwS+PWefTFKbDggEQv9P6WeOh0PMjcLAu2VUUhfw1a0j+4AMyjD7E13uGLJ/a8PcmzUHVPNEXGXFyvf6hp/J8t/08hxNycXfS8t6Aupf1SKXsOcWFokBQQasBVa5awVwIIW6WG0puhBAwde9U0osyUdJfJLdQx8aTafSse3nS8tOpn8g35FPVsyrvtX2v/D1/8pKtPTaGfAioA+e2QUk2OLjBPTOg/r8bTxoSEkh55x1SDpznVM3HyfeIBKyjVNWbBNCoeyT+EeXPxbHFhYxCPlkfC8CEPrUI9Lh8uGnHd/tQVCEEkUTVe6RgnxDi1iDJjRA3YEfiDpafXY4ptyklhQG4OWp5uWf0Ze0URWFT/CYARtYbWX5ioy+wJjZ5ieDsDWnHrceD6sP9c8G3WpnmuUuWUrjtTxxc/SnwiECjU1OrdTANu0bg6e9sl+dTFIXXlhyhxGihTXVfHmh6+WKBcyt2kUIIKsVM25GXFw4UQojKIsmNEDYqNBYyadckFLMjqizrPLPnulQnwP3yng2VSsX83vNZf2E93aO6X34xi9lasybliHXrhOJs6/HmT0L3d0F7+TJt31FPYExKwnfUKNyyXQmp4YWLx5UL792In/Zc4q9zWTjrNEweWP+y4SiLxcLOFRdAG0iUSyqBzbrZ9f5CCPFfXFdyk5eXJwXvhPjbjP0zSC5MxiHvAbL0Oqr6ufJY6ypXbK9T6+hdtXf5H17cCXFrrd9bTNYl3/1nWisO/w+LXo/KwQGVSoXRoiHkw8kAXHnB+Y1Lzi1m8iprMc5xPaKJ8L28NP/x79eTqw1EY9bTdkznCohCCCFu3HXN/vP29iYtzbrPTefOncnJyanImIS4Ze1P3c+i2EVY9H7kpjUG4M17auOgvfw/pXM55zBajFe+mLEETq4A/i4SHtoUnvzzssRGsVhIeHYMSa++SvyhJOa/vpMjmxPs9Uhl76UoTFh6jHy9iUYRXjzWOuqyNqaiEvbuLAAgJjgPj6ohFRKLEELcqOvquXFzcyMzM5OAgAC2bNmC0XiVv7CFuEOVmEqYuNNaOybacTAHLNAp2v+yar0ABrOBUetHoVFpmNllJtW9/18fS8YZ+HW4dTgKoM3z0PlNa12b/yfr++8p/PNPFCcXDmjuwVBiJju50O7PB7DicBIbT6XhoFEz5d76aNSX1+vZ+/kqinXeOBjzafVCrwqJQwgh/ovrSm66du1Kp06dqFXLWl9j4MCBODiUP8a/adMm+0UnxC3kq8NfcTHvIgHOAcwdMITDF/WEeZe/m/Lys8tJLUolwDmAcI//Nxl361TY8gEoFnDxhYGzoUb5c1aKDx0ibfoMADIeepvc8yZcPBxoOaCqPR8NgMwCPZN+PwHAs52rUyPw8hVXRWnZHD2tAy00qAOOPjJcLYS49VxXcrNw4ULmzZvH2bNn2bp1K3Xq1MHFpfy/1IW4Ex3PPM684/MAmNByAh4OHrSrUX5bo8XIt0e/BWB43eE4av6eFGwoguXPwPGl1p9d/eHJbeBR/rCOOS+PxJfGgcmEuud9HL/kBii0faCGTVsnXK9Jv58gq9BATJA7T3WoVm6bXTPWYNQG4mrMpMnoAXaPQQgh7OG6khtnZ2eeeuopAPbt28dHH32El5dXRcYlxC3DaDby1o63MCtmGroOoY53q6u2X3VuFYkFifg4+XBvzXutB9NOws+PQuZp68+u/jB6D7j4lHsNRVFInvAmxsREtOHhHA/rh+VMPhG1faje5PJhsP9qw4lUVhxOQq2CKffVL3cOUXbsJeIyfEANzTv6onG0f4IlhBD2YHM50c2bN5cmNoqioCiKvWMS4pby7bFvicuOw10dyoEjjej88VZiU/LLbWu2mJlzdA4Aw+oMw1njBAfmw+yO/yY2zj4wassVExuAnEWLyF+3DnQ6Sp58n8Qz+Wh0ato/FF3uvlX/RV6JkTeWWTfWfKJ9VeqHeZXbbvvMrVjUOnzMKcQ83NGuMQghhD3dUK30+fPnU69ePZydnXF2dqZ+/fosWLDA3rEJUenOZJ9h9pHZAEQYx1BoMFPN35UaAW7ltl97YS0X8i7g6ejJ4KjesOQJWDEGzCXWBjoXeHQpeIZd9b4OUVFo/PwIGDsWvas/KrWKZn2i7Fak739NXnWK1Dw9VfxcebFrzXLbJG0/Rrw+CIDW99eUbRaEELc0m4v4TZs2jTfffJNnn32WNm3aALB9+3aeeuopMjIyePHFF+0epBCVwWwxM3HnREwWEw3c+7Hdupk3E/vVQV3OKiKAA2kHAHgkrCuu3/WCrHOAClBApYb7voeQhte8t2urVlT9fQUaLy98VSqq1PfDK9D+89x2ns3gpz3xAHw4qB5OV9gxfPuCQ6AKIUSdRGR3qWsjhLi12ZzcfP7553z11VcMHTq09Fi/fv2oU6cOb7/9tiQ34o6x8ORCjmQcwVXrRk5iN6CQQY1CaRzhfcVzJrR4g3tKLFTd8gWY9OARBr2nwNYp0PBhiO551XuaMjPR+lp339R6/3sf39Dye4r+i2KDmdd+sw5HPdIyghZVy9n1Ezj965+kq0JQWcy0fbKl3eMQQgh7s7lvOTk5mdatW192vHXr1iQnJ9slKCEqW3xePF8c/AKATt6vcCyxEBcHDa/2irnyScXZ8PMjNNzyCR4mPUT3hqf+hJg+MGI9tBh11XvmrljB2e49yFu1CrPJwsZ5J8hMLLDnY5XxybpY4rOKCPF04tWe5T+XxWTmr9WJAFT1SMO/UUXURBZCCPuyObmpXr06ixcvvuz4zz//TI0aV1gbK8RtxKJYeHvX25SYS2ji14aNB631XkZ3ql7uztgAZ2JXkDG7PZxaCWodtH8Z6t7776Rh7dX3ftKfP0/y25OwFBaiP3eeQxviObUrhRWfHcJsstj1+QAOxmfz3Y7zALw/sB7uTuWvfDoyZy15ugA0phLaPCf7Rwkhbg82D0tNmjSJwYMHs23bttI5Nzt27GDjxo3lJj1C3G5+O/0be1P24qx1ZnyL1/hFKWJTbBoj2pa/f5RyaR8Tt71KnKeaKbpIOvX8FFaNg8wzYCyCxkPLPe8fFr2exLEvoRQV4dK8Obr7h7HvvX0AtB5YDU05y7L/C73JzKu/HcGiwMBGoeVWWAYwFBSzf28J6JyoHV6Ie4T9l6ALIURFsDm5uffee9m9ezfTp09n2bJlANSqVYs9e/bQqFEje8cnxE2VUpjCJ/s+AWBMozFE+0Ux4R54uWc0jtpyJtumnuCvXwdzxMcFJ0VFvcGLYcWL1sTGMxxq9LjmPdOmTEV/8iQab2+Cp0xh7eIzmIwWQqO9qdkiyC7PlZpXwra4dLbGpbP9TAY5RUZ8XR14657aVzxn74w/KNH54GjMo4VssyCEuI3YnNwANGnShIULF/7nm0+ePJklS5Zw6tQpnJ2dad26NR999BHR0dFXPe+XX37hzTff5MKFC9SoUYOPPvqI3r2vsOuyENdJURTe/etdCo2F1Pevz0PRD5V+Vm5ik3UOFgxgtpu1Z+W+mvfjt/UTiN8Jjh7w8C/gHnjVe+atX0/2Dz8AEPLRh1xMgPjjWai1Kjo8VPOGa9roTWb2X8hm6+l0tsamc+r/1eXxctHxyQMN8HYtf7isMCmDYxecQQuNGmhx9LT/hGYhhKgoN5Tc2MvWrVsZPXo0zZo1w2Qy8frrr9O9e3dOnDiBq6truefs3LmThx56iMmTJ3PPPffw448/MmDAAA4cOEDdunVv8hOIO8kf5/9gW8I2dGod/YJfZcicvUzsW5s6IZ6XN85NhPn92WfKYb9zIDq1jscK9XB0Mai18MB8CKh11fsZk5NJfmMCAD6PP46uWWu2T/wLgCY9IvEOKv+/gSu5mFnI1rh0tsWls/NsJkUGc+lnKhXUD/OiQw0/OkT70yDMC63mysNdO2asw6QNws2YQcOnB9kUhxBCVDaVcguVGE5PTycgIICtW7fSvn37ctsMHjyYwsJCVq5cWXqsZcuWNGzYkFmzZl3WXq/Xo9frS3/Oy8sjPDyc3NxcPDxk0z9hlVmcyYDlA8jR5/BM/WdZuiWGuNQChreJYmLfOmUbF2bA970gI45RYZHs0ik84NuIN/ctt37e7/NrzrMBUAwG0qZNp+jgAaIWLODApiT+WnYOzwBnHnyzOdor1Jz5R5HBxK6zmaXDTRcyi8p87ufmSPuafnSo6U+7Gv74XKGX5rJ3cewCP38Wh6LW0qWjmpgHO17XeUIIUZHy8vLw9PS8rt/fldpz8//l5uYC4ONz5bL0u3btYuzYsWWO9ejRo3T+z/83efJkJk2aZLcYxZ1p8p7J5OhziPaOxtfck7jUY3g663ihy/+r2FuSCwsGQkYcR3zC2KVT0Kq0PO4UYf287djrSmwAVA4OBL72KhaDAZWDA426RaDVafANcys3sVEUhdjUfLbGprPtdDp7z2djMP+7kkqrVtEk0psO0f50qOlPrSCPKxYbvJrts7ajqEPwsyRT84GHrn2CEELcYm6Z5MZisfDCCy/Qpk2bqw4vpaSkEBhYdh5DYGAgKSkp5bYfP358mWTon54bIf6xMX4jay+sRaPS8GbLSYz+/iwAT3eshuf/7r5tKIIfB0PKEXDxI67taByOf0vvqr0JbfMuVO0GUe2ueT/9uXM4hIej0lmvrXaw9qioNWoadCn7ZzOnyMD2MxmlvTOpefoyn4d5O9OhpjWZaVXN94pLuq/XpU2HSDBZdylv/VAd2WZBCHFb+k/JzaVLlwDskiyMHj2aY8eOsX379v98rf/l6OiIo6OjXa8p7hy5+lze++s9AB6r8xiHz7qSkF1MgLsjw1pF/dvQpIefH4H4XeDoCY8u5T6PUNpXuwdF+/efr6odrnk/U3o6Fx8dikNEBGGffYrW35/ks7n4R1h7a8wWhSMJOaVzZw5dysHyPwPHTjo1Lav6liY0Vfxc7baRZs7pBLb/dAI0QYRpkwjvJNssCCFuTzYnNyaTiUmTJvHZZ59RUGCtnurm5saYMWOYOHEiOp3t/3J89tlnWblyJdu2bSMs7OobCgYFBZGamlrmWGpqKkFB9lkyK+4uH+/7mIziDKI8ohhWexTdPtkJwJguNXB2+HtoyGyC30bC2Y3WjS8f/gV8q8O8ewhQqeGhRdd1L8VsJvGVVzBnZmLx9UXt7k5+VgnLPz0IThpO1XJmy6UscoqMZc6rEeBmTWai/WkW5XPF/Z9uROreWOJWH+FivIlcbSBoglBZTLR9qq3d7iGEEDebzcnNmDFjWLJkCVOmTKFVq1aAdR7M22+/TWZmJl999dV1X0tRFMaMGcPSpUvZsmULVaqUXyTtf7Vq1YqNGzfywgsvlB5bv359aSxCXK+dSTtZdmYZKlS80+Ydlh9IJaNAT7iPM4Ob/t0babHA78/ByRWgcYAHfyTeI5D83x6lTuJ+cPaG4hxw9bvm/TK/+YaiXX+hcnYm7cU3+XnzeYo2phBkUEgwG1l2Kg9U4O6kpW1160Tg9jX9CfGy307gFouFxC1HOL3xJJdSNBTo/ABf698EigVvczqNOgXiWzfKbvcUQoibzebk5scff2TRokX06vVvUa/69esTHh7OQw89ZFNyM3r0aH788UeWL1+Ou7t76bwZT09PnJ2tf6EPHTqU0NBQJk+eDMDzzz9Phw4d+OSTT+jTpw+LFi1i3759fP3117Y+iriLFRmLmLTTOtH8oZiHaBTQiLq+Fhy0GnxcHXDQqkFRYO14OPQDqDTWHb2rdeKLn3uy2pDIc97ePDHgR/C79n5L5zftoPjTz1EBn9ftzx9rU6luUDOwyBEzCherODKmfgQdavrTMPzqy7RtZTaauLBqL2e2nyMhy4USnScQCDpQWcz4kUZUjBsxA5rhUTXEbvcVQojKYnNy4+joSFRU1GXHq1SpgoPD9S01/cc/iVDHjh3LHP/+++957LHHAIiPjy8zqbF169b8+OOPTJgwgddff50aNWqwbNkyqXEjbPLpgU9JKkwixDWE5xs/D4BOo2ZIi4h/G23+AHb/XV5gwJdQ6x7Ob5/KmuIEUKlo12IsRF6+iew/TqXk8dPuePYdvcirv72Lv2JhU1hj/ghpQpCLA32LdYCFup3Dee6Bmle8zo0wFBRzdsVfnN2dSFKhJ0atKxAMOlCbDQRqM6jawIeaA1viEnjl1YlCCHE7sjm5efbZZ3n33Xf5/vvvSyfq6vV63n//fZ599lmbrnU9JXa2bNly2bH777+f+++/36Z7CfGPg2kH+enUTwBMbDURi8UBg8li7a35x87PYdsU6/e9P4YGD8LpDcw5/BWKmysdncOIaflcudfXm8x8sekMX205i8mi8Mq+H/AvziXDKxDNS6+ysn4k2TtSObIpAQ8/J9oNqGaX5yrOyOP00p2cPZROqsEXs8YJCAEtaE1FhDjnULVZMDUGtMPBw7YCgUIIcTu5ruRm0KCyFUo3bNhAWFgYDRo0AODw4cMYDAa6dOli/wiFsCO9Wc9bO95CQWFA9QG0Dm3N2yuOs/FUKu/2r0vH6ADYPxfWWSsH0/lNaP4EmI1cWv0if3i4ADCq00flXn/fhSxe/e0IZ9MLAehaK5Dozi+g+3oqzd6ZRLvatUmPz2fb5gQA2j8Ujc7hxicI519M4dTSPVw4mUe6EoCidgBCQQOOxjxCPQup3jaSKr3boHWSVYNCiLvDdSU3np5ly8/fe++9ZX6WujHidvHVoa+4kHcBP2c/xjUdR0J2ET/svojRrKDTqOHor/D7C9bGbZ6Hdi9Zv9fo+LZeN8zx62kd3JJ6/vXLXLdQb2Lq2ljm7bqAolirA7/bvw696gUDoHRbXLpk29FVS0QdX3ROGiLr+Nr8DFnHL3Dq9/1cPKsnSx0AKre/v8DFmEV4gJEanWoQ3rkD6vL2xBJCiDvcdSU333//fUXHIUSFO5F5grnH5wIwoeUEPB09eXfFYYxmhTbVfWlj2Q9LnwQUaPo4dP23snVKYQrLEzYD8GSDp8tcd2tcOq8vOUpiTjEA9zcJY3zHCJwunQesyc3/1qLx8HWmz+j6mE0WrofFYiFt9yli1x4l/pJCni4A8Ia/8xYPYxoR4Sqie9YnoHlHKbwnhLjr3XARv/T0dGJjYwGIjo7G39/fbkEJYW9mi5m3d76NWTHTI6oHXSK6cCYtnyUHrMND4+oWw+KhYDFBvfuh9yegz4OfhkCb57jk4YOvky/h7uE0DmwMQHahgXf/OMGSA4mAtVrw5EH1aFvdj6SXXyF11SoC33gdn4cfBsBiUUq3Q1CpVFfdO8piMnNp00FObz7NpTQdRTofwB90gGLBx5JGZDVHou9pgm9dKbYnhBD/y+bkprCwkDFjxjB//nwsFuu/PDUaDUOHDuXzzz/HxcXF7kEK8V/9dvo3TmadxF3nzmvNXwPgk3VxWBToXtWRRpseAVMJRPeGAV+BPhcWDIKkA5B5hmbPH2LVoFVklWShKAqrjqYwccUxMgoMqFQwvHUVXupeE1dHLTm//UbeypWg0eBUq3ZpDOu/PY7OUUPrQdVxcru82KWpRM/5VXs5s/0iibmu6HUe/Ltk24S/Ko2oWh7EDGyOe6QUrRRCiCuxObkZO3YsW7du5ffff6dNmzYAbN++neeee46XXnrJpjo3QtwMOSU5fHbwMwBGNxqNn7MfRxJyWH0sBRXwUvoEMBZAlfbWWjb6fJjf37qHlLMPPPIr6JxxAFRmL0Yt2M/6E9Yq2TUC3Pjw3vo0ifQGQH/mDCnvWrdz8H/uOVwaNwLg4rFMzuxPQ6VWUa9TWGlyY8gr5PSyvzi3N5mkYi9MWhf+WbKtMZcQ6JBJtYb+1BjYCme/snPfhBBClM/m5Oa3337j119/LVObpnfv3jg7O/PAAw9IciNuOZ8f/JxcfS41vGswOHowABv+Tk4GOu4l2ngSQpvCgz+BocCa2KQeAxc/sh5ayI7CC/Q0x/Db/mTeX3WS/BITWrWKZzpVZ3Snajj+PWnXUlJC4otjUUpKcG3dGt8nRgJgNJjZ+pN1CLdB5zB8Q105MmctZ/ank2ryw6Jx4J8l2zpTISGuuVRrEUq1fp1wcLNfdWIhhLhb2JzcFBUVXbYrN0BAQABFRUV2CUoIezmReYJf4n4B4PXmr6NVW//Ij23hRuuDswktPA6Bdaz7RRmLYF4/SD8JboEwdAULEtYy5+gcPtz6K4lxDwDQIMyTj+6rT0yQR5l7pX4wGf3p02j8/AiZ8hGqvyf27vvjAvmZJbh5O9LsnipsefNnTmYGAiGgASdjLmE+RVRvW5Wonu3QOP63nb2FEOJuZ3Ny06pVKyZOnMj8+fNxcnICoLi4mEmTJsn+TuKWYlEsfLD7AxQUelfpTdOgptYPCjNgwQBaFseBX1V4dCm4+MCm96yJjXswDPudTBc/5h37AYDUpDo46dSM6x7N8DZV0KjL7sRdtH8/OYsXg0pF6JSP0PpZ95rKTCzg0Pp4ANo/WJPTi7f+ndhADfcUavWqTWhHWeEkhBD2ZHNyM2PGDHr27HlZET8nJyfWrl1r9wCFuFG/n/2dw+mHcdG68FJTa72akxeS8PtjOP4ZceARBkOXg/vfPZEdx4OxGJo+zimjP6MWfoDRsRhzSRDNAtvy4aAGRPqWX9nXuXFjgt5+G1NmBq6trVsyKBaFrT/GYrEoVGngh4shi7V/6kHjRHX3ZLpPffimvAchhLjb2Jzc1KtXj9OnT/PDDz9w6tQpAB566CEefvjh0s0uhahs+YZ8pu2fBsBTDZ4iwCUAS0khL363nouGUcxy09Jh6AzQOILZBBotqDXou7zDzE1n+HLbBpyqbkQF3F/tMd7u0qpMrZr/T6VS4f3g4DLH8jKLyUkrQueooVm3EFZN3oRZ54ePOYUu795XgU8vhBB3N5uSG6PRSExMDCtXruSJJ56oqJiE+M++PPQlWSVZRHlE8UitR8Ck5/dvJnLK0Al3imjw8Hug0cGcrtbNLwd8yf5Lebz62xHOpBXg4LsTlaaEcLdI3ur84BUTm9wVK3Dr2BGNh8dln3n6uzDk7ZakXcxl+7Q1FOhCcDTm0eeNjrIVghBCVCCbkhudTkdJSUlFxSKEXZzOPl26Meb45uPRocL46xNMS+oAwJPN/fDy8IDve0NeIpZLOj5Ztosv9+aiKODrrqAL+YtCEzzd8Ek06vKL7eVv2ULSK6+iCw2lyrKlaNzdL2vj5Krj0pItJFlCUFlMdL0vFI+qIRX38EIIIbB5FuPo0aP56KOPMJlMFRGPEP+JoihM3jMZs2Kma0RXWge3hN+fY/GxfC4qQfg5w+Mtg0oTmyKPagwoep2Ze6yJzb2Nw1gwqg7RPtUIcwujV5Ve5d7HmJpK8mvjAXDr1KlMYpMYl83pvakoisKZpTs4dMkLgKbRRUT1albh70AIIe52Ns+52bt3Lxs3bmTdunXUq1cPV9eyEyyXLFlit+CEsNXaC2vZm7IXR40jLzcdB2vHU3JwMZ+ZpgPwdlMTLgv7QFEmKY5VuCdtHBk4E+rlzAeD6tGhpnUbkXm95pFVklW6dPx/KWYzSeNexpyTg2PtWgS88nLpZyajmc0LT5GbVkzW2VQOb8gCrSsRDkk0fXHIzXkJQghxl7M5ufHy8rpsV3AhbgVFxiKm7psKwMh6IwnZOxd2z2K+uQ+p+PCA+zH6HJwOpmJOUI1Hcl8mW+XBY62ieLlHNK6OZf9z8HHyKfc+GV9+RdHevahdXAibNg21g0PpZ/vXXCQ3rRgXDx1xG05i1PrjYUyjx5SBstxbCCFuEpuTG9khXNyqvj7yNWlFaYS5hTG8wADbpgBQWL0fDqfVdKwbhfmgkW3mhjxrfI5gf1++ua8+TSKtSYzRbGTeiXncV+M+vJy8yr1H4e49ZPxdhTto0ts4REWVfpadUsiBtRcBcC1OJV3rj85URO8XW+LgUf4SciGEEPZ3w7uCp6WlldkVPCAgwG5BCWGrC7kXmHdiHgCv1hiM4zJrXRu6vMULbQfjsu0cr2w6zTeGNzmhqsaTnWsyunP10q0TAFacXcGnBz5l+ZnlrBiw4rIVUoqikDbtE7BY8Bw0CM++fct8tvXHWCwmBU9XE+mFPqBY6NjDE9+6URX+/EIIIf5lc3KTl5fH6NGjWbRoEWazGbDuCj548GBmzpyJp6ds7iduLkVR+HDPh5gsJtqFtKXDru/AYoKavchPPc+7sxaz+KIbAOawpiy7tz61gssu3TZZTMw5OgeA+2veX+7Sb5VKRfisWWTM/JKAsS+W+Sx2dwqJcTmoNZCbD6ihXkg2Ne+/v2IeWgghxBXZPAngiSeeYPfu3axcuZKcnBxycnJYuXIl+/bt48knn6yIGIW4qs2XNrMjaQc6tY5XtcGokg+jOHpwOj4B92MLeCb5Tdy0Fl7vHcOSp1tfltgArD6/moSCBHycfLg/+soJidbbm6AJb6B2cSk9ZtSb2fnbGQBUhmJQawlWJdH2TZmbJoQQlcHmnpuVK1eydu1a2rZtW3qsR48efPPNN/Ts2dOuwQlxLSWmEqbstc6teaxqfyI3zwQg3eBADeUo+Yoz33o+y8phnYnyK3/ei9li5usjXwMwtPZQnLVlK20XHzmC/vQZPAcNLLdHR+eooeuwGNZ+vgeDxhlXYya9PuorE4iFEKKS2Jzc+Pr6ljv05Onpibe3t12CEuJ6fX/sexILEgl0CeTx2F1gKkGvaAkggxTFmwkuE/n6xWFXTTTWX1zPhbwLeDh48GDMg2U+M+flkfjiWIyJiVj0JfgMKX85d+yC9RhUQWjMJfR8sh7OfjI8K4QQlcXmf1pOmDCBsWPHkpKSUnosJSWFl19+mTfffNOuwQlxNQn5CXx77FsAhjvUwy1+F4oCjioTcZZQBukn8dTg/ldNbCyKhdlHZgPwSO1HcNX927ujKArJb76FMTERXVhYmQnEAGaThfysEo7MWUtcbhAAbVrpCGoeY+9HFUIIYQObe26++uorzpw5Q0REBBEREQDEx8fj6OhIeno6s2fPLm174MAB+0UqxP8zde9U9GY9wZqa9D3wI6hApYJjunoMyR9D05iqNI0qv1bNP0pMJTTwb0BmcSYP1yq7S3fOzz+Tv3YtaLWETvvksu0VDq6LZ9+q81gMgBqivVKo97gU6hNCiMpmc3IzYMCACghDCNtsT9zOpkubQFEz7FwqHqoS4p2i0VTryKD9rTGg46XuNa95HRedC2+3fptXmr2Ci+7fScIlsbGkfjAZgICxY3GuX7/MebnpRexbdR6zSQG1Dj9LMp0mPWDfhxRCCHFDbE5uJk6cWBFxCHHd0vMLeWnjJDSKwn1prjxsOYpFrSPi8fk8viofA2n0bRBCnZDrn/fyv4mNpaiIxBfHohgMuLZvh89jw8q0VRSFrT/EWhMbwNGYS++J3dA46uzzgEIIIf4TWc4hbitrjiXT/fv3UMxJzEjJZkLRcQDUHV5B8Y+mU0wAIZ5OjO129V4bRVH47MBnxGbFXvZZwY4dGM6fRxsQQMiHH6L6f3N2zuxL49KpbABUFiM9hkThHiFFLIUQ4lahUhRFqewgbqa8vDw8PT3Jzc3Fw+Pyeifi1pSWX8LE5cdZcyqW8KiPmJmWRD2DwfqhZziMOQBa6x5PJrMFrebqeftfyX/xxLoncNI4semBTbg7lJ1PU7hrFyqtFpdmZXfx1hcZmf/an/xz65a1C2nyXNmJxkIIIezPlt/fN7z9ghA3g6Io/Lo/gXdXniCvxERM0GK+SUkg3GRCAVSo4IF5pYkNcM3EBmD2YevE90E1Bl2W2AC4tmpV7nmbvz1UmthEOSXR5LlHbH8oIYQQFUqGpcQt61JWEUO/28PLvx4hr8RE3+C/+ClvK+EmEwa1BhVAq9FYghvzzA/7WX4oEYvl2h2R+1P3sy91Hzq1juF1hwNgMRhIGv86hosXr3heSU4+l45YSyC4mrPo/v599nhMIYQQdiY9N+KWoygK3++4wNS1sRQbzThq1bzW3pOH/voCJ4uFRCc3QksKwDsKOr3BmuMprDqawtbYdNrV8MfH1eGq1/+nGvGA6gMIcrXWp0n/9FNyly6laN8+qq1ehUpb9j8Ni8XCujeXYdCEojUV0v/lFuhcnSrk+YUQQvw3Nic3Y8eOLfe4SqXCycmJ6tWr079/f3x8rl5fRIgr+W7HBd5deQKAFlV8+PDe+uxIW8pnXh600ZuoW1Robdjvc0waJz5etweAke2qXjOxOZp+lJ1JO9GoNDxe93EADJcukTV/AQCB48dfltgA7Pl4OZeMoagUM13u8cU7OtxejyuEEMLObE5uDh48yIEDBzCbzURHRwMQFxeHRqMhJiaGL7/8kpdeeont27dTu3Ztuwcs7mxZhQZmbIgD4KWu1Rndyp8sdTEzD82kwNODqoVq2hQWQONhUKU9S/Zd4lx6Id4uOka2q3LN6/9TjfieqvcQ5h4GQPqMT8FoxLVNG9w7d7rsnBO//sX+sx6gggaReVQf0M2OTyyEEMLebJ5z079/f7p27UpSUhL79+9n//79JCQk0K1bNx566CESExNp3749L7744jWvtW3bNvr27UtISAgqlYply5Zdtf2WLVtQqVSXff3vVhDi9vbZxtPkl5ioH+TM6JwpqOf14avdH1FgLKCOzouBaRfAPRi6vYPeZObTDacBeKZjddydrl5nRlEUmgU1I8A5gJH1RgJQfOw4eX/8AUDAS5f3SuacSWDLujxQqXCkhFavDbTvAwshhLA7m3tupk6dyvr168ssw/L09OTtt9+me/fuPP/887z11lt07979mtcqLCykQYMGPP744wwaNOi6Y4iNjS1z/4AAqTFyJziTVsCCvy7iQSELHD9FfWw3ilpD4sl0cHHm9YtxaAD6TANnL37ccZ7EnGKCPJx4tFXkNa+vUqkYVmcYD9d6GK1ai6IopH3yMQAeffvi9P96Gk1FJSz7aDeKxhsUhXtebCI7fQshxG3A5uQmNzeXtLS0y4ac0tPTycvLA8DLywvDP+tlr6JXr1706tXL1hAICAjAy8vrutrq9Xr0en3pz//EKG49H64+SaAlnV/cp+GZegHFwY33ImPYYUpjoElHfX0J1BkEMb0pMZqZufkMAM91qYGTTnPd99GqrX/sC3fupGjXX6h0Ovyff/6yduveXEKhxjrhuFZjT4JiAu3wlEIIISraDQ1LPf744yxdupSEhAQSEhJYunQpI0aMKN13as+ePdSsee19fW5Uw4YNCQ4Oplu3buzYseOqbSdPnoynp2fpV3i4TAS9Fe04k0Hiqb0scZxIqPECuAezrvNYFpvScFfreD7xPDh7Q68pADjpNHz2YCPuqR/M/U3Drnn9mYdmsvbCWiyKpfSYS5MmBLw8Dt8nn8QhLLRM+4Nf/sH5Qmti4+wE7Yc3st/DCiGEqFA2VyguKCjgxRdfZP78+ZhMJgC0Wi3Dhg1j+vTpuLq6cujQIcCahFx3ICoVS5cuverGnLGxsWzZsoWmTZui1+uZM2cOCxYsYPfu3TRu3Ljcc8rruQkPD5cKxbcQs0Xh1WmzmJg/CXdVMfjXIvf+7+izaRS5+lxey8rj4dwcGPg1NBhs8/Uv5F6g//L+WBQLv/b9lWif6Ku2T9x2hOULk1HU1jk8/Z5rSHhtWf0nhBCVqUIrFLu5ufHNN98wffp0zp07B0DVqlVxc3MrbWNLUmOL6Ojo0hVaAK1bt+bs2bNMnz6dBQsWlHuOo6Mjjo6OFRKPsI/f9ifwZ4YbhU4uOIU3RjfkRz479Dm5+lxqKFoG5+ZA9W5Q37rrtt5kxlF7/cNQc47OwaJY6BDWgWifaBSjEdRqVJrLr1GYlMHaeadRdN4AVG/iL4mNEELcZm54dqSbmxs+Pj74+PiUSWxutubNm3PmzJlKu7/4bwr1JqauiyUVH7a0nodu2FJOFCXxS9wvALyekojWwQ3umQ4qFWl5JbT5cDPT1sdhNFuucXVIyE9g5bmVAIyqPwqArAULOT9gAIU7d5Zpazaa+GPSGop13jgZs2nZO5S2D1Tc8KoQQoiKYXNyY7FYeOedd/D09CQyMpLIyEi8vLx49913sViu/cvG3g4dOkRwcPBNv6+wg71z2LhkDun5eqJ8XRjUuQ0WjY4Pdn+AgkLvIj1NS/TQ9W3wss6V+mLzGTIK9Gw/nY5WrbrmLb499i1mxUyr4FbU96+POTeXjNmz0Z8+gzE5uUzbzRN/I10VgtpsoOdjNWnSLxpXT+n1E0KI243Nw1JvvPEG3377LR9++CFt2rQBYPv27bz99tuUlJTw/vvvX/e1CgoKyvS6nD9/nkOHDuHj40NERATjx48nMTGR+fPnAzBjxgyqVKlCnTp1KCkpYc6cOWzatIl169bZ+hiisu2aCWtfp6eioYZqMi/16o+DVs3yM8s5nH4YF1S8lJEBEa2g6QjAutfUT3viAXi5Rwwq1dWTm5TCFJadWQbAkw2eBCDzm2+w5ObiWKM6nn/P7yopNLJh+p9czPIHoEUjC6Ht6lXAQwshhLgZbE5u5s2bx5w5c+jXr1/psfr16xMaGsozzzxjU3Kzb98+OnX6tyLsP1s7DBs2jLlz55KcnEx8fHzp5waDgZdeeonExERcXFyoX78+GzZsKHMNcRv48xPY+A4A35j74B1Zjx51Ask35DNt/zQAnsrKIgAt9Psc/q4tM319HEazQrsafrSq5nvN23x/7HtMFhNNApvQJLAJxqSk0m0W/MeOpaTYzKENFziyMR6T0XqOg9pIjQc7V8BDCyGEuFlsTm6ysrKIiYm57HhMTAxZWVk2Xatjx45cbbHW3Llzy/z8yiuv8Morr9h0D3ELURTYMhm2fgTANON9fGYeyO/31EGlUvHloS/JKskiymTmkdx86DIR/GoAEJeaz9JDiQC83OPqq53+0Ta0LUczjvJkfWuvTfpnn6MYDGiatuFodjhH39iFSW8ube9qzubhj3ujc5MNMYUQ4nZm85ybBg0a8MUXX1x2/IsvvqBBgwZ2CUrcgRQFNrxdmtj84PY4n5kHMahRGPXCPDmdfZqfTv0EwPiMTHRB9aH1mNLTP14bi6JAr7pB1A/zuq5btgtrxw+9f6BlcEtKYmPJXb4cAM1DT3FwXTwmvRkd1mKTDsZ8Br7WEp2bs/2eWQghRKWwuedmypQp9OnThw0bNtCqVSsAdu3axaVLl1i1apXdAxR3iJMrYMcM67cNXueN3XVx0qkZ1yMaRVGYvGcyZsVM18IiWpcYYegXoLHWmUnKKWZzbBpqFbzU3bbVSyqVisJcPWcWbcVBUXDv0YPQ3k2okxtH0YlTnM9wR2Ux0/XeEDyrhV77gkIIIW55Nic3HTp0IC4ujpkzZ3Lq1CkABg0axDPPPENISIjdAxR3iJi+0HgoxsAGPLWtGlDEqHZVCfFyZs35NexN2YujovByVja0eR6C/+0FDPFyZtNLHdlxJoPqAe7XvNXcY3PRm/X0D7qPuC2ZnPgzCbU2mkHTv8CjdnVUKhVhzpmsS3cBFTSuUUCV3rLTtxBC3ClsTm4AQkJCbJo4LO5SFjMoFmsPjFoNfT9j3vbzXMw8ib+7I092qEaRsYip+6YCMDInlxDPKtDh1csuFe7jwoPNI655yzxDHgv2LCL6YmvMGYdQzNYVVf4Rnmgb18Ih0IXsU/Fs+iMDRetGuC6J5i8Nse9zCyGEqFTXldwcOXLkui9Yv379Gw5G3EHMJlj6JFhMcO+3oNGSVWTk042nAXi5ezSujlpm7P+CtKI0woxGhufmwWOLQWed0KsoCmfSCqgReO3eGoDCXD0L522g/8mxaBQtChAU6kCzPlGENwpFpVJhLCzhj493YNQG4m5Mp+eUgbLTtxBC3GGuK7lp2LAhKpXqqiubwDq/wWw2X7WNuAuYDPDbCOs8G7UWkg5CeDM+23ia/BITtYI9uLdJGBdyLzDvxDwAXs3MwbHpExDZqvQy60+kMmrBfh5oGsaU+649WT2/uBDzSXc0iganSAs9+jWg5IWHKVmZR8nsWTg3bMi6N34lVxuC1lRE7xea4+DhWmGvQQghROW4ruTm/PnzFR2HuFOYjbB4KMStBo0DPDAfwptxJq2ABX9dBGBCn1qoVfDhng8xWUy0Kyqmg84Xuk789zIWhY/XxQLg715+leCc1CIuncyiXkfrruCrMpaxK/IAmgAjcx/7ktwffiL/0iU0fn441qjB3hkruFASAoqFjt088KtXpYJfhhBCiMpwXclNZGRkRcch7hTHl1oTG60TPPgDVO8KwIerT2K2KHStFUCb6n5sit/EjqQd6BSFVzOzUQ1eBI7/Dj+tOJxIXGoBHk5aRrWrVuYW2SmF7F99kbg9KSgKhNTwwiVQw7zj88gKzuL9tu9DUTEZX34JgP+zo0nYFcveE06ghnrBWUQPfuDmvRMhhBA31Q1NKBbiik7+bv3f1mNKE5sdZzLYcDINrVrF+N61KDGVMGWPtd7NY7l5RNa+D2r8u1rJYLIwfb11bs5THavh6WJdEp6VVMi+1Rc4sy+Vf0ZII+v5olKr+DXuV7JKsgh1C6VXlV5kfv4l5uxsHKKi0DRvz/r3d6PoPAkiibYTHrpJL0MIIURlkORG2I+xBM5stH4f0wewDi+998dJAB5pGUk1fze+OvQViYVJBJpMjNTroOfkMpf5ed8l4rOK8HNz5LHWURTm6tm++DRnDqTB30lNVH0/mvWJIiDSA4C2Lm05lXWKpoFNISObrLnWuTy+Y55j1eTNlOiCcTFm0WtyH9RazU14GUIIISqLJDfCfvKTwT8aClIhuCEAv+1P4GRyHh5OWp7vUoOE/AS+PfoNAC9nZuPS+0tw8Sm9RLHBzOcbT4MCz3WpjouDFqOiIjEuGxSo2sifpr2i8I8ou4KqimcV63AUkPzWRJTiYpwbNmTvtiwyNcFozHp6jayNS4D3zXkXQgghKo0kN8J+fKrAqM2gLwCVikK9ial/Twp+rksNvF0deHvTFPQWIy2KS+ge1gnqDCxziSMn0mmarSLI4MzgJuEA6Bw1dHokBndfZ/zC3K4aQumKPrWa/FrtiUsOBqBNSzVBrWrb+YGFEELcim44uTEYDKSlpWGxWMocj4i4dqE1cYdztCYgs7eeJT1fT6SvC4+2imR74nY2XdqMVlEYn29ENWQaqFSYjGbOHkjn+J+JJJ/JpQ7WwntpZ3MJr2Xt1anSwL/cW3195GvO5Z5jVL1RVPWqikqlIvidSViad+b35UWggZqeKdQbIYX6hBDibmFzcnP69Gkef/xxdu7cWea4oihS5+ZuVpgJGi04eQLW/aC+/vMcAON7xaBSmflw17sADMnLp1rnd8g3eXP419Oc2pWMvtAEgEqtIqqeL3XahRIaffUhpBVnV/D5wc8BaBHUgqpeVQEoSstm3dJ0zDoffM3JdH5HVkYJIcTdxObk5rHHHkOr1bJy5UqCg4NRqVQVEZe43fz1Jez41Lp1QoeX+XhtLCVGC82jfOhRJ4jvjn3HxcIkfE1mnvaoA42HUnAuj8MbLgHg4uWAroYH/QbWxMPH6Zq325m0k4k7rHVxhtcZzoDqA0ibPgP3Pn1YPW03RboQHI259HmrCxpHXYU+uhBCiFuLzcnNoUOH2L9/PzExMRURj7hdxa4CixG8IzmSkMOSg4kATLinFqlFqfy4axEtU/rRvqQQt3HPgUpFUFUP6nUMI6K2D/MvpPLdzgsc2qjw8f1Xr0YcmxXL2C1jMSkmekX14oUmL1CwcSOZs2eT/u1c0lt+gEptovuDEbhHBt2MpxdCCHELsTm5qV27NhkZGRURi7hdZZ2HtBOg0qBU78p7861Lvwc1CME5Rc/Cr7cwKG08AMU6MwaXCBywbtfR/sGaJOYUs/DneAD6N7z6zvLJBck8s+EZCo2FNAtqxntt30NltpA2bToA8SEdMGudaB5TQES3JhX3zEIIIW5ZNu8Y+NFHH/HKK6+wZcsWMjMzycvLK/Ml7kKxq6z/G9matef0xJ7NprNeR+39BaybcxzntAAULPh6nKbr4/XR6sr+sftsw2kMZgutqvrStrrfFW+jKAqv/vkqacVpVPeqzoxOM3DQOJAy+zsM585h0LpyMaI7VZyTaPZCv4p8YiGEELcwm3tuuna1Vp3t0qVLmeMyofguFrsaAFPNXkxefYp6Bg1N9Fr0GNE75HPMfyfVXTfy7MPzITCwzKln0wv4Zb913s3LPaOvOodLpVLxVsu3mLhrIp90+AQPBw9OfLsa46w5OAAXI7vTsKaB5uNkZZQQQtzNbE5uNm/eXBFxiNtU3rnTnDgWQZiuHtsLGnAxMxe1jyMhnt7kRJ7g65wpeChGpkfcD4GX15mZtj4OiwJdawXSOOLaBfaqe1dnYa+FGHIK+OONhXDqMFWN+ZQ4edPstcEEt7v27uFCCCHubDYnNx06dKiIOMRtxGK2cOFoJsf/TCT+eCZwH+lejXh/ZyEAT/eOpl1dZ/ouHo5FbeH5Emc8O75+2XWOJebyx5FkVCoY16PmFe/35aEvaRbUjGZBzQBI2HSIDT+exai40+rSBgAi33oFH0lshBBCcJ3JzZEjR6hbty5qtZojR45ctW39+vXtEpi49eRnlXBiexIndyRRmGv4+6iKsMAczvtHkJ9golawB/c2CWPimpEUYKaO3sDAXnNA63jZ9cwWhfphnlT1cyUmyKPcey48sZCvDn/Ft0e/ZUXf5Zz9dCfHEr1RdD44l2Ti0K0PznlJeA+QOTZCCCGsVEppvforU6vVpKSkEBAQgFqtRqVSUd5pt8Ocm7y8PDw9PcnNzcXDo/xfqKJ8iz/YS3p8PgDO7jpiWgVTu20IGVjoMWMbZovCDyNb4Oocx6MbngTgB5821O8764rXVBSFIoMZV8fL8+z1F9fz0paXUFB4MnQYvj+HkKWxLu0OUSfRfUJPXEP8Sud7CSGEuHPZ8vv7unpuzp8/j7+/f+n34u5Uu20IZ/anUqdtCFV129HUawkaHePm7cVsUegSE0DLqt489NPLAAzUQ/0en1z1miqVqtzE5kDqAV7b9hoKCl1MrdAsqkWW1hmNuYTm9UzU6t0S5xC/0msIIYQQ/7iu5CYyMrLc78WdLTE2m7zMYmo0C0Sr01CnXQh124fC4Z9h6SjYN5sd7Rey4WQaGrWK8b1r8dvuqZw05eFutvB8u8ng4HrZdbfFpXMwPocR7argVk5icy73HGM2jcFgMVAruwbVTj2ARavGy5RKt6E1Kf7uCy79OJkqv/2KLjT0ZrwKIYQQtxHZFVxc0d5V50mMzSEvo4QW/aybUpKfCqtfAcBSowfvrY4D4JEWEfi5FPDZqR9ADaPdauBb6/J5MBaLwuTVpziZnIfRbGFcj+gyn2cUZ/D0+qfJM+QRlBdG67hRaBSF2oHpNO1bk+SXx2FKSUHl4oL+3HlJboQQQlxGkhtRroyEAhJjc1CpVdRu+z9Vg1eNg5IcCKrPb06DOJl8EncnLc93rcnnq+8nVw01TBYG9/mm3OuuPJrMyeQ83By1jGhb5bLPXUwOBCZ7Uagz0iP2aTz0hXS+PwLP7AwuPT4cjEYcqlQh7PPPcKxevYKeXgghxO1MkhtRriObrIX1qjXyx/2fjSyPL4OTK0Ctpbj3Z0xZYN31+7nONUhOWM0vBWdBpeL1uk+idfW/7JpHE3J5Y8lRAJ5oVxVvV4cyn6fujWXdrIM01z1BfW0h0eo8Ok/qTu7n00hZvhwA9+7dCf7gfTRubhX05EIIIW53ktxUEIvFQtruU/g3rnHb7UpdnG8gbk8qAPU7h1sPFmZae20A2o7lq1hX0vOTiPBxYUizAB5b9BaKWkVvtTdNWzx32TXjUvMZ+t1u8vUmmlfx4ckOVUs/M5vNfPXlB6iPNEHRBaAzFdGhuQv1Hu9P+hczyV2+HDQaAsaOxefx4TKBWAghxFXZvLcUQE5ODnPmzGH8+PFkZWUBcODAARITE+0a3O0s7udt/DYvhV/HLK7sUGx2/M9EzCYLAZHuBFX9e7nduglQmA7+tUhuMJqvt50FYHyvGBaue5pTagteFgsv9/r6sutdyCjk4Tm7yS4y0iDci+8ea4aTTgNAfnwaz08ezWyPxayP+RlfSxKDX21Ivce7AeA7cgSu7dsR8d13+I54XBIbIYQQ12Rzz82RI0fo2rUrnp6eXLhwgSeeeAIfHx+WLFlCfHw88+fPr4g4bzspJ1MBf/Tm26tzzGyycHSLNUlt0CX832Si7YuQfQG6v8fUjRcoMVpoFuVNNefDvJ59EFQqXqsyCD+/mMuuqTdZUBSICXJn3vBmpSukTi3awldHN7K15g4AWmhCuf/zB8j/4w+UqsGoNBrUTk5EfH15wiSEEEJcic2/eceOHctjjz3GlClTcHd3Lz3eu3dvhgyRDQtL/Z0T+HkYKzcOGxXnG/ENc0OVWEC1xgH/fuBfE4av4khiLksOWJOR8T2q8daWHpjUKjqq3OndflK514wOcueXp1rh5qjFy8UBQ14hmyYtY5Mui80xKwAY4jWQcf1eJPHppyncuRNjYiL+Y56t8OcVQghx57E5udm7dy+zZ8++7HhoaCgpKSl2CepOYDZYANBqrSN/STuPE9Q8BrVWU5lhXZObtyP9nmuIvsiIRqu29tZ4RwGgAO/9cRKAgY1COXjsNY6rzbhbFN7s/TUq9b+jnFmFBs5nFNAk0geAKn7WejeJ246wYd4pznkZWV9zLorKwj0RvXne737O33svpqRkVM7OOERF3cSnFkIIcSexec6No6MjeXl5lx2Pi4srrWJ8vbZt20bfvn0JCQlBpVKxbNmya56zZcsWGjdujKOjI9WrV2fu3Lk23fNmMRr/Tm4cVPz57i8sm5fErinLKjcoGzi66ODsJviskXW+jaKw9ngqe85n4ahV83CdFL7M3AvAKxF9CAisW3puXomRod/tZsg3u/nzdDoAFpOZHe/9yvKFKSS5weqYWZg0BloFt+Kl9EbED3kYU1IyusgIohYtwrPvPZXy3EIIIW5/Nic3/fr145133sFotA63qFQq4uPjefXVV7n33nttulZhYSENGjRg5syZ19X+/Pnz9OnTh06dOnHo0CFeeOEFRo4cydq1a219jApnMlr33tI6aHByc0BRaThywY2Uv05WcmRXFrc3hcIcvfUHfT6seB4UC5gMGMwKk1dbY3+ibTjT976OQaWiLa707zS59BpFBhPDv9/LsURrLZtgT2eyYy+x+OlFHErwQVFrwfkkZicDdd1rMmGzN+lvTUIxGnHr0oUqv/6KU/SVdwgXQgghrsXmYalPPvmE++67j4CAAIqLi+nQoQMpKSm0atWK999/36Zr9erVi169el13+1mzZlGlShU++cS6X1GtWrXYvn0706dPp0ePHjbdu6KZTdb/1TpqaPJ8Xy48/SNp6hDWf3OYhxpWRet0+S7ZlSkvs5gN351ApVbx6Hutcds5CXLjwSsCurzF/F0XuJhZhL+7Iz7GTzmiNuJqUZjY86vS4agSo5kn5u9j/8VsPJy0zB/RnOLfd7B4lxGTNhi12UDT2nqaPP8mPTP6EZhcQt7bT4Fajf8LL+A7ckSZoS0hhBDiRtic3Hh6erJ+/Xp27NjB4cOHKSgooHHjxnTt2rUi4itj165dl92nR48evPDCC1c8R6/Xo9frS38ub0itIgREuGC5mIRvlXDUajXdxnXi548OkqcLYPsHy+j4zuCbEsf1OrYlEUWB0BpeuOXug71/Vxju9znZJgc+23gagGeb5/FFwi5QqxgX2o2g4EYAGM0WRv9wgB1nMnF10DD38eYU/byFPbHuoNXhbkyh+ajqxLRoDkCjgEYQAK7vv4/WzxfXVq0q5bmFEELceW54nXKbNm1o06YNYK17czOkpKQQGBhY5lhgYCB5eXkUFxfj7Ox82TmTJ09m0qTyV/FUpFavDirzs1eNUFo2P8r2A3Ai1YdqGw8S3qXRTY+rPEa9mRM7kgCo3y4AVvw936XxMKjakU9XHCevxERMoDObE9+iRK2iBU7c29Xag2a2KLzw8yE2nkrDUatmzrBmhGdm8usJR9BANfck9g04z/wLc/hieyciuw/AuV49AJlbI4QQwu5sHgP46KOP+Pnnn0t/fuCBB/D19SU0NJTDhw/bNTh7GD9+PLm5uaVfly5dqrRYGozqSYgmEUWlYeOPZzAUFFdaLP8r9q9k9EUmPPydiUr/ErLOgUcodH+Xs+kFLPzrIgC9w35kv8qAs0VhUteZpUNIZouCoijoNCpmPdKE5mEerJu5F4vGAV9zMkmP6ll59Gcen5eC8s2PJDz/PJaiosp8ZCGEEHcwm5ObWbNmER5uLcm/fv161q9fz+rVq+nVqxcvv/yy3QP8X0FBQaSmppY5lpqaioeHR7m9NmBd3eXh4VHm62YwFBRjMZkvO95tfE+cjdnExGjROjmUc+bNpVgUDm9KAKBB5zBU/jXA0RPumQFOnkxedQqTRaFvtXQWFvwFwIvBHQkNbV56DQetms8ebMTiJ1vRKSaAHR8uJUcbiMZUgmaYmWWrp/PR92YanVNQOTri/9xzqF1cKuNxhRBC3AVsHpZKSUkpTW5WrlzJAw88QPfu3YmKiqJFixZ2D/B/tWrVilWrVpU5tn79elrdgvM1fnj+D4p0PvTu60qVPv++F7cwf4bOvOeWmVAcfzKLnNQiHJw0xLQKBqfHoPYAcPZi55kMNpxMRaNWKNB+QbFaRVPFkcHdZqAoChtOptElJgC1WoVWo6ZRhDcJWw5zLNEb1ODe9Aw7ln3Du6vNOJhBFx5O2Gef4lSrVmU/thBCiDuYzT033t7epUM7a9asKZ3gqygKZvPlPRVXU1BQwKFDhzh06BBgXep96NAh4uPjAeuQ0tChQ0vbP/XUU5w7d45XXnmFU6dO8eWXX7J48WJefPFFWx+jwpn/zhu1zpcnMf+b2Oiz8tBn3ZxJzuXJSy9G66CmVutgHJz+znWdvTBblNKCfQ9V/419aj1OFoVJnWeg1miZufkMT8zfxyu/HUFRrMveDQXFbFwQi6LWoHE6QP62b3lypQkHM7h27ECVX3+RxEYIIUSFs7nnZtCgQQwZMoQaNWqQmZlZupT74MGDVK9e3aZr7du3j06dOpX+PHbsWACGDRvG3LlzSU5OLk10AKpUqcIff/zBiy++yKeffkpYWBhz5sy55ZaBA5hV1lfr4O50xTYXVu9l06/xBLoX02fGIzcrtDLqdQyjRvUSlF+fgLjxULM7AL8dSOBEch5VXS+yQbUXUPFcYBsiItry7fbzfLwuDrDuF/XP/lNb3llKgS4IB2M++3qfpO3XRhQV+Dw7msCnn5Fl3kIIIW4Km5Ob6dOnExUVxaVLl5gyZQpubm4AJCcn88wzz9h0rY4dO5b+q7885VUf7tixIwcPHrTpPpXBotYB4OBW/lwgALPBRLHWkwsl3pz8cTO1hnS6YtsKoyg4bX4Zcg7Azs+gRjcKDWY+XhsLWIgI/o79ahUNLTqGdP+cn/bE8+7KEwC82LUmI9tVBeDs8l2czg0AFNp2duehnp/yueuH9NB1JKB9xZcJEEIIIf5hc3Kj0+kYN27cZcdvxaGhymI2mkqTG637lSfOVuvfiho7fuR0XhDbN+YR3j4dtzDbtrC4USWFRnLSigjKW2PdZkHjCH0/BZWK2dvOkZavp2vQYnbr9DgoCpM6fszKY+m8vvQoAE+2r8pzXaw9dcUZuWxZnkhk0m4CjPHEzFqESqVifJf3bsqzCCGEEP/rhuvcnDhxgvj4eAwGQ5nj/fr1+89B3e4Muf8uc3Zwv3LPDUDHtwaS9PxKCnW+rJ+8hoEzH63o8AA4sT2JXUvPUsv9FJ1dgQ6vgG81knOL+XrbWfy1FznueQBQM9q3OaeLajN28QEUBR5pGcFrvWJKh6M2v/EjNU9twC/L2qNTuHs3bi1b3pTnEEIIIf4/m5Obc+fOMXDgQI4ePYpKpSodVvrnF52tk4rvRMaCwtLvdVfpuQHrsFWXIdX5fXEGSeZQDn+9hgajelZofGazhaNbrMu/g1UHIaA2tHkegKlrYykxmmha5TsOq9XUtWgZ2vNLNsRmo1ZB/8ahvNOvbun/38dfm0LYjsXoTIUYNDC3h5YxtQJwq9AnEEIIIa7M5hmezz//PFWqVCEtLQ0XFxeOHz/Otm3baNq0KVu2bKmAEG8/KrWGYFUSQSShvo5JtOFdGlE7MAuAv/aYyDmdWKHxnTuYTkG2Hmd1DjWcd0Dfz0Cj40hCDksOJNLCazGHnfToFIV323+EVudEz7rB/PpUa6bcWx+1WoUpO5sLTz6Detn36EyFnAuC14Zr6PjMe1T1rFqh8QshhBBXY3PPza5du9i0aRN+fn6o1WrUajVt27Zl8uTJPPfcc7fFZN+K5h4RwKCvbFv91Pb1AVwavRRnnQmNg6aCIrM6ssm6lL+O81q0LR6D8GYoinXpt58mgYQA63BUf4fOOPm0LT2vQbgXYF32f+nJpyg5cgSLSsVvrWFJGzVPNxlD/+r9KzR2IYQQ4lpsTm7MZjPu7u4A+Pn5kZSURHR0NJGRkcTGxto9wLuF1smRez/ojpO/13X19tyo1PN5pJzLQ61RUffhgVC7NQBrj6ey53wGrcK/4ZhGTWRRCEvS+rD24i5+frIV4T7/Dq+pVCpyolpSci6RqQPzOR1m4d4a9zKq/qgKi1sIIYS4XjYnN3Xr1uXw4cNUqVKFFi1aMGXKFBwcHPj666+pWlWGIwDrtgtqlc1JikugT5mf9bkFOHrad/bK4b97bWo0C8S1cW0ADCYLk1efpLnnUo656VGV+JKQOobcEhPV/N3wcXWgaO9eTBkZePTqReaxC2zND+OnUUaKHC20D2vPhJYTSufhCCGEEJXJ5uRmwoQJFBZaJ8y+88473HPPPbRr1w5fX98yG2rezeJ++ZONW0x4m9MYMmeIzecXJWey/v3VFJaoGfzVA2h0N7yorQyzwUjqyQuAMw06h5cen7/rAnk5ZzFX243F4Isq4RnyjCrqhHjw3ZAGFEz/hKx581A7O+MQU4t1n+5Epwmi7aW2JLdIZmr7qWjV9olRCCGE+K9s/o30v9WAq1evzqlTp8jKysLb21v+5f43Y5EBVFpu9HUUZeSRrPfBrHXir4+W0WbCfXaJS3NgDg+7vk6idxf8QxcBkF1o4LONp6kbPJvDFi+M8SMxGF2pHuDGty1dyXz4QQxnzwLg3rsX+7/fTpYmBI25hPH3j8arboQkNkIIIW4pNzy548yZM6xdu5bi4mJ8fHyufcJdxFhiBECjunL15avxq1eFJnVMAByO9yBp5/H/HlROPGx8F7XKQniPPqCxJiSfbjxNTYdfOeysoTh+JAajL1FejnxjOUj28KEYzp5F4+9H2FdfYu71AF877KdEW0iTOib86leVxEYIIcQtx+bkJjMzky5dulCzZk169+5NcnIyACNGjOCll16ye4C3I9PfyY1Wc2PJDUCT5+4hUElCUWvZ+O1RTEUlNx6QopD76yTMhhKIaA2NhwFwNr2A3/fsIS1wJ6DCR6MlzF3H7L2zKZkzG8xm3Hv1pOqKFTg1b8lrf37MgfANrK31GY3G9L7xeIQQQogKZHNy8+KLL6LT6YiPj8fF5d8VNIMHD2bNmjV2De52ZSyx9rpotDee3KjVarq90gmdqYg8XQB/frDshq+lHFvCqkNtWZA+m9RGU+Dvic6TV50iJnAW2Vo11VWF/DGmFwufbIN365aoPT0J+eRjwqZPR+vtzYSvxnEk+CAqRcXzzZ9Eq5EeGyGEELcmm5ObdevW8dFHHxEWFlbmeI0aNbh48aLdArudGQ3WKs1azX+bg+RZLZSWLa17VJ1M9yV+/X7bL1KcTcKS78kyRaJXe+EVHQPAzjMZZCV8yxGPIgKzLbwb8RSBPn5E+bniN2YMVVeswLNPHwDmLJnOKv9tADymDKJPM/vMARJCCCEqgs3JTWFhYZkem39kZWXh6Ohol6Bud2aDBQCt7r9PsK4/sgehmkQcTEUYCvS2X2DbxxzJshbiq9U6DEcXHWaLwscrt5EVuJVOezz5+Fs1Tl9uQjFZe5zUDg7oAgMA2HJqPZ/nzgWgfXJLxg5/+z8/kxBCCFGRbE5u2rVrx/z580t/VqlUWCwWpkyZQqdOnewa3O3KI9AVf0syPiH2qVHTfUJvHnq3DdUHtrb53Jzaz3NB3wyA+l0iAfjtQAIBxVN4Yik8vTETR6OJpCIL5ry8MufuTdnL2J2vYlFbiEmtz0fDP/zvDyOEEEJUMJsnTkyZMoUuXbqwb98+DAYDr7zyCsePHycrK4sdO3ZURIy3nWYv9KOZHa/nEuxb5meLyYxae31bNBzZmQOoiKzni1egCwV6E6e/Hcfju3JxLwGDWsu8Wj0ZPm082v+36q1k0yUcTa4E5QXzTvVRuAX6lnsPIYQQ4lZyQxWK4+Li+OKLL3B3d6egoIBBgwYxevRogoODKyJG8TeLxcLhWas5ur+A+yZ3xyXA+8qNkw+j96zNqZ3W1WwNOoVjLihkx/AnGXT0CABJ/l5MqjcSj1o1qRdZNrHJT0jn1EYN/V2fp55TIbVe6FBhzyWEEELYk03JjdFopGfPnsyaNYs33nijomISV2AqKObg/hKKdf5sfO8P+n52hc05007CN1246DIMo74P3sGuhNXyZt+5dLSphzCrYH0rFX/ETCI+y8j7zSNKT116eimuOlcKp6Rh0IUQVJhJt/dkArEQQojbh03JjU6n48iRIxUVyx3jlycXkmX2pnVrB+o93s1u13XwcKXjwFBW/15AvCGEEws3UfuRzmUbWSyw4jmwGKkZmoTHPXUwmlToTRZmLv+EpAHgbNBwb+NXOLPJiIuDhn4NQgD48eSPTN4zGY2iYZDTOPwKzHR5uAYOHq52ewYhhBCiotk8ofiRRx7h22+/rYhY7hhGswaT1hlVBWzuXfWeltT0SgNgx+Z88hPSyzbY9y0k7AEHN4qCh1Lw7CM4rl3IZ2t3k+CxkmRfFY0jwziYXR+Afg1CcHfSMefoHCbvmQxA3cRW+BQFUzs4i/DODe3/EEIIIUQFsnnOjclk4rvvvmPDhg00adIEV9ey/6qfNm2a3YK7XZkUa1ajc66YpfEd3hxI0vO/U6DzY/37axkwc4h1B/K8JNgwCYsZElO7U/D0S6AoZPyxmlPKUlL91QSZFcb1ncsH69Nx0ql5sFk4nx34jG+OfgNA20utqZNwH96mNNq9LsNRQgghbj82JzfHjh2jcePGAMTFxZX5TDbOtDJjXcmkc3WokOs7uDnT+ZGa/P5TOsnqEI58vYaGT/WGVS9TnFLCqWOt+KvaYIKrR9G0npH3vE3s9f8DgEn1nsbVLYj3BwbxSs9oZh2dzsKTCwG4N78b/gn3oDYb6PZ0EzSOugqJXwghhKhINic3mzdvrog47ij/JjdOFXaP8E4Nqb3lZ46n+pOdkIdydBkZv2wl44Qf52q2w6JxQNeqHcuaenDu4jBATT9tIK2bjS69xo7kDaWJzZigEZh31MKihkY1iwloUrPCYhdCCCEqks3JTW5uLmaz+bKdwLOystBqtXh4eNgtuNuVRWXt8XBwq7jkBqDd64OosuUwkT06Y9y/lqw4dwxad9KCmwMQ2KM2i7c/RLKXGn+zwmsD53EuvYAig5m6oZ70rNKTv5L/oqFPfYo+NpGr0+FvSab5Sw9VaNxCCCFERbJ5yuuDDz7IokWLLju+ePFiHnzwQbsEdbszq/9Jbpwr9D4aRx2RPZoCoGvSA6dXJpM+5B0saAis4sGcXd9w0NM64fjtOiNx9wjl802nuOfzLUxfH4dapeadNu/g/aOZXF0AWlMR3V/uYJ2/I4QQQtymbP4ttnv37nK3WejYsSO7d++2S1C3M4vFgg8ZeJlScfSyz/YLV7+hdZNOQ7GJbXGBxF60zvPJDla44PgzikpFb7Uf7Vu8QGpeHusyJ+MU+jPtalp73uI3HOBkmvX7ls21eNUIK/8+QgghxG3C5mEpvV6P6e8NFv+X0WikuLjYLkHdztRqNYO/fvjm3Mykh2+7Qa1+lMQ8ic7Bmqs6eTqwPet1Er1V+JoVXu//PYXGQh5bMwq1axwaxQF390z0uU5s+uksis6XUE0iDUY9enPiFkIIISqQzT03zZs35+uvv77s+KxZs2jSpIldghLXacuHkHwYds/Cw83Ifa82pePD0ZwI2s8BL+u2C29GPwrO3oxcN5KE4uMoZkceDH+XaJ9oNr+7nEKdL47GPLq90auSH0YIIYSwD5t7bt577z26du3K4cOH6dKlCwAbN25k7969rFu3zu4BiitI2A87Zli/v2c6uPigBY7rCjjp9g0WlYquKi8aNh7JiLUjiM2ORTG5YEoayeiHuhH3y5+cLbDuBdauuw+uIX6V9ihCCCGEPdncc9OmTRt27dpFWFgYixcv5vfff6d69eocOXKEdu3aVUSMt5WUPaeYPXIl80f8XHE3MZbAsqdAsUC9+6FWXwDS8ktY89dzXHJQ4W228HSXT3hszWPEZsfigCdF8aPoHd0MXU4e29ZkAlDVJYnowe0rLlYhhBDiJrO55wagYcOG/Pjjj/aO5Y5gyCvCpHXBaDRW3E22fAAZceAWCL2mlB6e+stsDnolAirGV3+QfK2G5MJkglyCKL40kky9G0OaR7D+/VXodSG4GLPo/Gb/iotTCCGEqAQ3lNycPXuW77//nnPnzjFjxgwCAgJYvXo1ERER1KlTx94x3laMhXoANFw+6douLu2BnZ9bv79nBrhYVzqtOnyOU6Y5mBxVdMSDXu3fBOCLLl8Q4R6Bv3MQO85k4LBhDwmmEFAsdH4gCkdv94qJUwghhKgkNg9Lbd26lXr16rF7925+++03CgoKADh8+DATJ060e4C3G2PRP8mNuWJukHkG1Dpo8BDE9AYgt8jIb1ue5YIjuJktDG3/dmnzlsEtCXELQadR00hr5K+/rElXLb+M0ho5QgghxJ3E5uTmtdde47333mP9+vU4OPy7d1Lnzp3566+/biiImTNnEhUVhZOTEy1atGDPnj1XbDt37lxUKlWZLyeniq0EbAtjkQEArdpSMTdoOASe+hN6Ti49NPXXORzwjAfApHXklb0fcCnvEgC5xUbMFgWLxcK6KZsxal3wMKbR/s2BFROfEEIIUclsHpY6evRoufNtAgICyMjIsDmAn3/+mbFjxzJr1ixatGjBjBkz6NGjB7GxsQQEBJR7joeHB7GxsaU/30obdhpLjIAWjUqpuJv4R5d+u/lkAkeKv8TkpEKjQAlGYtzC8HLyAmDyqpNsjUvnYQpRqUJQW4x0e6IBWqeK2bFcCCGEqGw299x4eXmRnJx82fGDBw8SGhpqcwDTpk3jiSeeYPjw4dSuXZtZs2bh4uLCd999d8VzVCoVQUFBpV+BgYE237eimEqsE4m1GjsmN4ZC+HEwJOwrc7hQb+KHdc9w3glQFMwqaBHUgtndZuPu4E5+iZEVh5NIzi0hKd66JUSDKgUEtaxlv9iEEEKIW8wN7S316quvkpKSgkqlwmKxsGPHDsaNG8fQoUNtupbBYGD//v107dr134DUarp27cquXbuueF5BQQGRkZGEh4fTv39/jh8/fsW2er2evLy8Ml8VycnTGU9TKh5eGvtddOM7ELeG7HkPc+hieunhaUsXsN/jnPUHlYr2Ye2Z2XUmLjoXAFYcTqLIYMbfZCZEccDXnEzLlwfYLy4hhBDiFmRzcvPBBx8QExNDeHg4BQUF1K5dm/bt29O6dWsmTJhg07UyMjIwm82X9bwEBgaSkpJS7jnR0dF89913LF++nIULF2KxWGjdujUJCQnltp88eTKenp6lX+Hh4TbFaKv6I3vwyJyH6DrZTjtrX9gOu2cB8HzhMBbuSQJg66lE/kj9HePfQ3LdI7szo+MMHDX/Djf9tMc6D6eu0QmtSU+PsW1Ra+2YdAkhhBC3IJuTGwcHB7755hvOnj3LypUrWbhwIadOnWLBggVoNBX/i7NVq1YMHTqUhg0b0qFDB5YsWYK/vz+zZ88ut/348ePJzc0t/bp06VKFx2g3+gJY9gwAv9KFbZYGDGoUSonRzOuLfyYl9WEUgx9t/drwUfuP0Gl0paceTcjlWGIeGgXqGDQ0b6TgXSuysp5ECCGEuGluqM4NQEREBBEREf/p5n5+fmg0GlJTU8scT01NJSgo6LquodPpaNSoEWfOnCn3c0dHRxwdb9PJsxvehpyLFLuE8HbWQwR7OtGyqi8fL/mJHLdYKK5C4YVn+TPRhV/8k3iwWXjp5OqFO6zDVTWNGqqSTMOnh1TigwghhBA3z3UlN2PHjr3uC06bNu262zo4ONCkSRM2btzIgAEDALBYLGzcuJFnn332uq5hNps5evQovXv3vu77VqR1L//IpSxn6tVW0fylATd+oXNbYe83AMz0eJGCLBceaRjKscRMtmR+iNpXoZnzBSzKW+yPz2H8kqOsPpbCh4Pq4eGkYdmBRFCpaFKkp9vr3VGrbe6kE0IIIW5L15XcHDx4sMzPBw4cwGQyER1tXZIcFxeHRqO5oV3Bx44dy7Bhw2jatCnNmzdnxowZFBYWMnz4cACGDh1KaGgokydb67q88847tGzZkurVq5OTk8PUqVO5ePEiI0eOtPneFaG42EKJzhOTIfu/XejoLwCUNBzOrD1hgEK/BsG8t2wIl1wV1IrC9L7vEBzSnO93nGfq2li2xaXTY/o2njbnc3+BF6e1Jh7u4IJ7RPlL6oUQQog70XUlN5s3by79ftq0abi7uzNv3jy8vb0ByM7OZvjw4Te0cebgwYNJT0/nrbfeIiUlhYYNG7JmzZrSScbx8fFleh2ys7N54oknSElJwdvbmyZNmrBz505q165t870rgunvwsRaxxse8bPq9zlEtua3vIaYLOepG+rBpgMbOe5yEVBR1ymYsLAWAIxsV5WO0QGM++Uw5xKzKcz2JlhR0a9qCbUe6vnf4hBCCCFuMypFUWwqyBIaGsq6desu20Pq2LFjdO/enaSkJLsGaG95eXl4enqSm5uLh4eH3a//86gfyFAH07pBCY2e/u9DZT/ujueLTacZ0DCEnRcf5KyztfLxot4/Uce/bpm2x37YxNLtJnwULbV8U+n8vp1WbAkhhBCVzJbf3zZPxMjLyyM9Pf2y4+np6eTn59t6uTuO2WKd0Ktz0l2jZTlKcmHdm1Dyby2eIS0i2PZyJy6enVya2NR0j7ossYlfv58/txjwUbRE6JLo+O7gG38IIYQQ4jZmc3IzcOBAhg8fzpIlS0hISCAhIYHffvuNESNGMGjQoIqI8bZiVqyvVOficI2W5Vj7Buz8DH5+uMzheZtWsdd1f+nPj9R7vMzn6QfPsObnJCwaB/wtyfSaer9MIBZCCHHXsnliyKxZsxg3bhxDhgzBaPx7qwGtlhEjRjB16lS7B3i7MWOt9aNztjG5Ob0eDi4AVNBxPBaLwrbT6UT5OLPu7JsUu1iTFTedGz2iepSelnchmd+/OIxR542HMY3+U/vKvlFCCCHuajYnNy4uLnz55ZdMnTqVs2fPAlCtWjVcXV3tHtztyE2rB2MmTr42THAuzoYVY6zft3waIluz91wmj32/F1dtPqrqZjSKgkWlom+1vqXbK+iz8ljx7laKdQE4G7Pp/0Z7HH3sP49ICCGEuJ3c8JIeV1dX6tevb89Y7gj3zX7E9pPWvA75yeBTDTq/CcCSA4kAWFxOolUpPO7amEYtRxLlEQWAqUTPivEryNWFoDMVcs8z9fCoGmKvxxBCCCFuW/9xvbL4z2JXw+EfARUM+AocXCgxmll59O9VZ14HiTaqGd3/a7Q6J8Ba6HDNq7+QpgpBbTbQ4/5gAprUrLxnEEIIIW4hktxUJkWBTe9Zv2/9LERY69ZsOJlKod6MSpuNo/N5JraeXJrYAGx9azEX9SGgWOjQwYHIHk0rI3ohhBDiliRLauxIn53PdyN+Y96IxRgKiq99gkoFjy6Dls9ApzdKD3+58SgAOs+DdHKKZMyhj/nu2HcA7J2+nBMZ1orDzaILqf1IZ7s/hxBCCHE7k+TGjgx5hRTrvCnQ+aF1us7VUm7+0HMy6JwBOJeWz4lUPQDVXA9S4hdGZkkmmcWZnFi4iT2nrBO3a/ul0Xxs/wp5DiGEEOJ2JsmNHRnySwBQmw2otZorNyzMhBPLy/3ojR++ADRonC7xcuth7EjaCUDr9Bi2bjWASk2EQxId3nnA3uELIYQQdwSZc2NHxr+HojSK8eoNV42D40ug7YvQ9e3SwxsO7uGS50JcXELppq7BcSUGBYWGrnU5vgQsWgcClCR6TZEifUIIIcSVyG9IOzKUJjemKzc6vsya2Kg0UKtf6eEig4l5u54lV6OmuiaZyY98wNLTSwEI2RuDUeuKhzGNfpP7SZE+IYQQ4iokubEjY6F1royGKyQ3Benwx1jr9+3GQmjj0o+m/vQaB11K0CgKE1tMZGfaXtKL03ExuBKa18papO/NDlKkTwghhLgGGZayI2PxP8mN+fIPFcWa2BRlQmBdaP9K6Ufbjx9ig3ENRfEvUcUll4iovszaMRqA6PRWOBn19B1dH4+o4JvyHEIIIcTtTJIbO9JoNbgYM3B1KKfn5vgSOLkC1FoY8CVoraup9CYzc7Y+TQbVsBgCSFMF4+agpvmmOuT7OlA7qSk9B4fg37jGTX4aIYQQ4vYkyY0d1bivHTXuK+cDfQH8Mc76ffuXIbhB6UfTf36T/a4FmJKsm2He0yCEXe/8hiGvAe1y69G5vZqIbk1uQvRCCCHEnUHm3NwMjm5w7xyI7g3tXio9fODMSdaULEex6FDyrQlPTOx5TmZai/Q1jymk1sNSpE8IIYSwhfTc3CzVu1i//mYyW5i5fiSZLmo8smpRYHEgUKficN4ZLlQ/Rr+SejR7cWwlBiyEEELcniS5saPdHy8l9qSRKpEq2r11P+SngtkAXuGXtf38t3fZ45KHSlHw416SgGp5cLzadpI8z1BQX4aihBBCiBshw1J2VJBZQr7Oj6J8g3V11O/PwZet4NQfZdqdiD/LivzFAHQ01eBYqrVuTZgmmyTPM6hVau6rWd7kHSGEEEJciyQ3dmQ0WgDQ6tRw+CeIWwNmPXhXKW1jsSjM+GM4GVo1wUaFZxtPomOBiVoGDem+mwBoH9qeINegSnkGIYQQ4nYnyY0dmU0KADq1CVa/Zj3YcTwE1i5tM2v5R+xyyQbgpdCn2D3zBE1M7gzMzyE26iQA90fff3MDF0IIIe4gktzYkenv8jbavDOgz4XQJtD6udLPzyVdYknmAgB6lgSSu9SbPF0AOlMBrg8VkG/KJ9g1mDYhbSojfCGEEOKOIMmNHZUmN4Y06zcdXgWNdc62oihMXTGUVJ2aQIOFNkceYbOjP6c0JXS6P5Q1JVsBuK/mfWjUV9lRXAghhBBXJaul7MhkUYEadIYM64GgeqWfzV31KdudrcefOjSCS+Zw/nQtoUANvUKC6EAHsvXZDKw+sDJCF0IIIe4YktzYkYPOgmNJHo7aQnD2BnfrXlAJ6cn8lDwHdCqe2d2JVEtDLmnNFKjB01lHl1pBOGpHMqLuCFQqVSU/hRBCCHF7k+TGjgbOfNT6jakX5CbA34nKR0uGkuykot/hOljM/bCoFOLcC1FpCmlYq4CUogQiPSIlsRFCCCHsQJKbiqB1BN9qAPy47iu2OibT9GwUWjpxIGQrx7xPU+ByCTddHgeKYcNFLSPqjajkoIUQQog7gyQ3FcRkMZGVm838izOJyPXhYJVE9qm/KP1cDajRUMs3Bi9Hr0qLUwghhLjTSHJjR4ue+AGLoQRD07VcbFCNnNhDmAs96H/iGWa3m4KT0QWDqQoqfRSvdOzBwDotcdY6V3bYQgghxB1Fkhs7ylb5YnF24M/iRBLiL6EY9Tx26HkMTgE8vudZHnutDwk6Z9wctUT4ulR2uEIIIcQdSZIbO7GYzFjUDgCkOpspMBXy5J4n2eURjKepkDee7IBHZCC1r3EdIYQQQvw3UsTPTox5haXfFzroqZ0Qw1rv6mxyMfKHl5rCMNkrSgghhLgZbonkZubMmURFReHk5ESLFi3Ys2fPVdv/8ssvxMTE4OTkRL169Vi1atVNivTKDPlFpd+XOJiIL+7JIUczKuDlnrWI8JFhKCGEEOJmqPTk5ueff2bs2LFMnDiRAwcO0KBBA3r06EFaWlq57Xfu3MlDDz3EiBEjOHjwIAMGDGDAgAEcO3bsJkdeliG/BAC12YCrwZMEcyCOKoVZjzbhifZVpYaNEEIIcZOoFEVRKjOAFi1a0KxZM774wrpM2mKxEB4ezpgxY3jttdcuaz948GAKCwtZuXJl6bGWLVvSsGFDZs2adc375eXl4enpSW5uLh4eHnZ7jpSdx/htfhpaYwFzYtZgyOzJwmc7UC/M0273EEIIIe5Wtvz+rtSeG4PBwP79++natWvpMbVaTdeuXdm1a1e55+zatatMe4AePXpcsb1erycvL6/MV0U4sPJXtIY81OZ81JlN+eOVLpLYCCGEEJWgUpObjIwMzGYzgYGBZY4HBgaSkpJS7jkpKSk2tZ88eTKenp6lX+Hh4fYJ/v8pCnZGAXZ4pbDm1QcI83GtkPsIIYQQ4uoqfc5NRRs/fjy5ubmlX5cuXaqQ+9w35lU6jgjktSc64O3tViH3EEIIIcS1VWqdGz8/PzQaDampqWWOp6amEhRU/tLpoKAgm9o7Ojri6Ohon4CvIaZNq5tyHyGEEEJcWaX23Dg4ONCkSRM2btxYesxisbBx40ZatSo/UWjVqlWZ9gDr16+/YnshhBBC3F0qvULx2LFjGTZsGE2bNqV58+bMmDGDwsJChg8fDsDQoUMJDQ1l8uTJADz//PN06NCBTz75hD59+rBo0SL27dvH119/XZmPIYQQQohbRKUnN4MHDyY9PZ233nqLlJQUGjZsyJo1a0onDcfHx6NW/9vB1Lp1a3788UcmTJjA66+/To0aNVi2bBl169atrEcQQgghxC2k0uvc3GwVVedGCCGEEBXntqlzI4QQQghhb5LcCCGEEOKOIsmNEEIIIe4oktwIIYQQ4o4iyY0QQggh7iiS3AghhBDijiLJjRBCCCHuKJLcCCGEEOKOIsmNEEIIIe4olb79ws32T0HmvLy8So5ECCGEENfrn9/b17Oxwl2X3OTn5wMQHh5eyZEIIYQQwlb5+fl4enpetc1dt7eUxWIhKSkJd3d3VCqVXa+dl5dHeHg4ly5dkn2rrkLe0/WTd3X95F1dH3lP10/e1fW7Ge9KURTy8/MJCQkps6F2ee66nhu1Wk1YWFiF3sPDw0P+Q7gO8p6un7yr6yfv6vrIe7p+8q6uX0W/q2v12PxDJhQLIYQQ4o4iyY0QQggh7iiS3NiRo6MjEydOxNHRsbJDuaXJe7p+8q6un7yr6yPv6frJu7p+t9q7uusmFAshhBDiziY9N0IIIYS4o0hyI4QQQog7iiQ3QgghhLijSHIjhBBCiDuKJDc2mjlzJlFRUTg5OdGiRQv27Nlz1fa//PILMTExODk5Ua9ePVatWnWTIq1ctryn48ePc++99xIVFYVKpWLGjBk3L9BbgC3v6ptvvqFdu3Z4e3vj7e1N165dr/ln8E5iy7tasmQJTZs2xcvLC1dXVxo2bMiCBQtuYrSVx9a/p/6xaNEiVCoVAwYMqNgAbyG2vKu5c+eiUqnKfDk5Od3EaCuXrX+ucnJyGD16NMHBwTg6OlKzZs2b9ztQEddt0aJFioODg/Ldd98px48fV5544gnFy8tLSU1NLbf9jh07FI1Go0yZMkU5ceKEMmHCBEWn0ylHjx69yZHfXLa+pz179ijjxo1TfvrpJyUoKEiZPn36zQ24Etn6roYMGaLMnDlTOXjwoHLy5EnlscceUzw9PZWEhISbHPnNZ+u72rx5s7JkyRLlxIkTypkzZ5QZM2YoGo1GWbNmzU2O/Oay9T394/z580poaKjSrl07pX///jcn2Epm67v6/vvvFQ8PDyU5Obn0KyUl5SZHXTlsfVd6vV5p2rSp0rt3b2X79u3K+fPnlS1btiiHDh26KfFKcmOD5s2bK6NHjy792Ww2KyEhIcrkyZPLbf/AAw8offr0KXOsRYsWypNPPlmhcVY2W9/T/4qMjLyrkpv/8q4URVFMJpPi7u6uzJs3r6JCvGX813elKIrSqFEjZcKECRUR3i3jRt6TyWRSWrdurcyZM0cZNmzYXZPc2Pquvv/+e8XT0/MmRXdrsfVdffXVV0rVqlUVg8Fws0IsQ4alrpPBYGD//v107dq19JharaZr167s2rWr3HN27dpVpj1Ajx49rtj+TnAj7+luZY93VVRUhNFoxMfHp6LCvCX813elKAobN24kNjaW9u3bV2SolepG39M777xDQEAAI0aMuBlh3hJu9F0VFBQQGRlJeHg4/fv35/jx4zcj3Ep1I+9qxYoVtGrVitGjRxMYGEjdunX54IMPMJvNNyVmSW6uU0ZGBmazmcDAwDLHAwMDSUlJKfeclJQUm9rfCW7kPd2t7PGuXn31VUJCQi5Lou80N/qucnNzcXNzw8HBgT59+vD555/TrVu3ig630tzIe9q+fTvffvst33zzzc0I8ZZxI+8qOjqa7777juXLl7Nw4UIsFgutW7cmISHhZoRcaW7kXZ07d45ff/0Vs9nMqlWrePPNN/nkk0947733bkbId9+u4ELcKT788EMWLVrEli1b7qpJjbZwd3fn0KFDFBQUsHHjRsaOHUvVqlXp2LFjZYd2S8jPz+fRRx/lm2++wc/Pr7LDueW1atWKVq1alf7cunVratWqxezZs3n33XcrMbJbj8ViISAggK+//hqNRkOTJk1ITExk6tSpTJw4scLvL8nNdfLz80Oj0ZCamlrmeGpqKkFBQeWeExQUZFP7O8GNvKe71X95Vx9//DEffvghGzZsoH79+hUZ5i3hRt+VWq2mevXqADRs2JCTJ08yefLkOza5sfU9nT17lgsXLtC3b9/SYxaLBQCtVktsbCzVqlWr2KAriT3+rtLpdDRq1IgzZ85URIi3jBt5V8HBweh0OjQaTemxWrVqkZKSgsFgwMHBoUJjlmGp6+Tg4ECTJk3YuHFj6TGLxcLGjRvLZPL/q1WrVmXaA6xfv/6K7e8EN/Ke7lY3+q6mTJnCu+++y5o1a2jatOnNCLXS2evPlcViQa/XV0SItwRb31NMTAxHjx7l0KFDpV/9+vWjU6dOHDp0iPDw8JsZ/k1ljz9TZrOZo0ePEhwcXFFh3hJu5F21adOGM2fOlCbLAHFxcQQHB1d4YgPIUnBbLFq0SHF0dFTmzp2rnDhxQhk1apTi5eVVuhTw0UcfVV577bXS9jt27FC0Wq3y8ccfKydPnlQmTpx41ywFt+U96fV65eDBg8rBgweV4OBgZdy4ccrBgweV06dPV9Yj/F97dx4T1dX+Afw7CLMwyCYuoMAEEcFdqyBgxYqKNpIuUau2BquxEUVKbdUak7K4tC4IVWtbMYG22liVahOlblQxolhRMcXiSBHU6iS2FFSKEYHv7w/jfR0ZWuZ9dehv+nwSEu655z7nOWfI8OTOuRmbsXatPvroI6rVau7evdvscdS7d++21xRsxtq1WrVqFQ8dOsSKigr+/PPPXLduHR0dHZmVldVeU7AJa9fpSf+mp6WsXavU1FQePHiQFRUVPHv2LKdOnUqtVsuLFy+21xRsxtq1unbtGjt27MiEhAQajUbu27ePXbp04YoVK2ySrxQ3Vtq4cSP9/PyoVqsZGhrKoqIi5VxUVBTj4uLM+u/cuZNBQUFUq9Xs27cv9+/fb+OM24c161RZWUkALX6ioqJsn3g7sGat/P39La5VcnKy7RNvB9as1bJlyxgYGEitVksPDw+Gh4dzx44d7ZC17Vn7PvW4f1NxQ1q3VklJSUrfrl278sUXX+S5c+faIev2Ye3f1cmTJxkWFkaNRsOAgACuXLmSjY2NNslVRZLP/v6QEEIIIYRtyJ4bIYQQQtgVKW6EEEIIYVekuBFCCCGEXZHiRgghhBB2RYobIYQQQtgVKW6EEEIIYVekuBFCCCGEXZHiRgghhBB2RYobIZ4iknjrrbfg6ekJlUqFkpISm45/7NgxqFQq1NbW2nRcSwoLC9G/f384OTnh5ZdffqqxDQYDMjMzn2rMf4ucnBy4u7s/k9gqlQp79+59JrGFsIYUN0I8RQcOHEBOTg727dsHk8mEfv36PbOxRo0ahaSkJLO2iIgImEwmuLm5PbNx22rhwoUYNGgQKisrkZOTY7GPFCl/71kWI0LYK8f2TkAIe1JRUQFvb29ERES02qehoeGZfSuuWq1Gt27dnklsa1VUVGDu3Lno0aNHe6fy/9aDBw/+0fGE+KeSOzdCPCUzZ87EggULcO3aNahUKhgMBgAP77AkJCQgKSkJXl5eiImJAQCsX78e/fv3h16vh6+vL+bNm4e6ujqzmIWFhRg1ahScnZ3h4eGBmJgY1NTUYObMmSgoKMDHH38MlUoFlUqFqqoqix9L5ebmom/fvtBoNDAYDEhPTzcbw2AwYNWqVZg1axY6duwIPz8/bNmy5S/nev/+fSQmJqJLly7QarUYMWIEzpw5AwCoqqqCSqVCdXU1Zs2aBZVKZfHOzahRo3D16lW88847yhzamvOTtm7dCnd3d+Tn5wMASktLMWHCBLi4uKBr166YMWMGfv/9d7OxExMTsXjxYnh6eqJbt25ISUlRzpNESkoK/Pz8oNFo4OPjg8TExFbHT0lJwaBBg/D555/D19cXzs7OmDJlCm7fvt0iz5CQEGi1WgQHB2Pz5s3KuUfr9s033yAqKgparRbbt2/Hm2++idu3bytr9ChPSx8Bubu7K2vdWrxH9u7di169ekGr1SImJgbXr183i/Xdd99hyJAh0Gq1CAgIQGpqKhobG5Xz5eXlGDlyJLRaLfr06YPDhw+3/gIJYWs2+XpOIf4FamtrmZaWxh49etBkMvHWrVskH35brouLCxctWsRLly7x0qVLJMmMjAz+8MMPrKysZH5+Pnv37s34+Hgl3vnz56nRaBgfH8+SkhKWlpZy48aN/O2331hbW8vw8HDOmTOHJpOJJpOJjY2NPHr0KAGwpqaGJFlcXEwHBwempaXRaDQyOzubOp2O2dnZyjj+/v709PTkJ598wvLycn744Yd0cHBQ8rQkMTGRPj4+zMvL48WLFxkXF0cPDw9WV1ezsbGRJpOJrq6uzMzMpMlkYn19fYsY1dXV7NGjB9PS0pQ5WJNzRkYGSXL16tXs1KkTT58+TZKsqalh586duXTpUpaVlfHcuXMcO3YsX3jhBeX6qKgourq6MiUlhZcvX+YXX3xBlUrFQ4cOkSR37dpFV1dX5uXl8erVqzx9+jS3bNnS6nokJydTr9dz9OjRPH/+PAsKChgYGMjp06crfbZt20Zvb2/m5ubyypUrzM3NpaenJ3NyckiSlZWVBECDwaD0qaqqYmZmJl1dXZU1unv3LkkSAPfs2WOWh5ubm7JOluLdvHmT2dnZdHJy4tChQ3ny5EkWFxczNDSUERERSpzjx4/T1dWVOTk5rKio4KFDh2gwGJiSkkKSbGpqYr9+/RgdHc2SkhIWFBRw8ODBFnMSoj1IcSPEU5SRkUF/f3+ztqioKA4ePPhvr921axc7deqkHE+bNo2RkZGt9o+KiuLbb79t1vZkcTN9+nSOHTvWrM+iRYvYp08f5djf359vvPGGctzc3MwuXbrw008/tThuXV0dnZycuH37dqWtoaGBPj4+XLNmjdL2+D/a1jxepDzS1pwzMjK4ePFient7s7S0VDm3fDdFunoAAAbzSURBVPlyjhs3zuz669evEwCNRiPJh2s3YsQIsz7Dhg3jkiVLSJLp6ekMCgpiQ0PDX+b/SHJyMjt06MBff/1Vafv+++/p4OCgFG09e/bk119/bXbd8uXLGR4eTvI/xUhmZqZZn+zsbLq5ubUYs63FjaV4AFhUVKS0lZWVEYBSIEZHR3PVqlVm13311Vf09vYmSR48eJCOjo68ceOG2XyluBH/FPKxlBA28Nxzz7VoO3LkCKKjo9G9e3d07NgRM2bMQHV1Nerr6wEAJSUliI6O/p/GLSsrQ2RkpFlbZGQkysvL0dTUpLQNGDBA+V2lUqFbt264deuWxZgVFRV48OCBWVwnJyeEhoairKzsf8rXmpzT09ORlZWFEydOoG/fvkr7hQsXcPToUbi4uCg/wcHBSu6PPD5nAPD29lbmPHnyZNy7dw8BAQGYM2cO9uzZY/aRjCV+fn7o3r27chweHo7m5mYYjUb8+eefqKiowOzZs83yWrFihVlOADB06NC2LFObWYrn6OiIYcOGKcfBwcFwd3dXXr8LFy4gLS3NLNc5c+bAZDKhvr4eZWVl8PX1hY+Pj9l8hfinkA3FQtiAXq83O66qqsLEiRMRHx+PlStXwtPTEydOnMDs2bPR0NAAZ2dn6HQ6m+Xn5ORkdqxSqdDc3Gyz8f8bzz//PPbv34+dO3fi/fffV9rr6uoQGxuL1atXt7jG29tb+f2v5uzr6wuj0YgjR47g8OHDmDdvHtauXYuCgoIW17XFo71UWVlZCAsLMzvXoUMHs+Mn/1Zao1KpQNKszdKG4bbGe1xdXR1SU1Px6quvtjin1WqtjieErUlxI0Q7OHv2LJqbm5Geng4Hh4c3UHfu3GnWZ8CAAcjPz0dqaqrFGGq12uxOhiUhISEoLCw0ayssLERQUFCLf6pt1bNnT6jVahQWFsLf3x/Aw3+qZ86cafFo+t+xNIe25hwaGoqEhASMHz8ejo6OeO+99wAAQ4YMQW5uLgwGAxwd//u3OJ1Oh9jYWMTGxmL+/PkIDg7GTz/9hCFDhljsf+3aNdy8eVO5m1FUVAQHBwf07t0bXbt2hY+PD65cuYLXX3/dqjxae507d+4Mk8mkHJeXlyt3/f5OY2MjiouLERoaCgAwGo2ora1FSEgIgIdraDQaERgYaPH6kJAQXL9+HSaTSSkYi4qKrJqXEM+SFDdCtIPAwEA8ePAAGzduRGxsLAoLC/HZZ5+Z9Vm6dCn69++PefPmYe7cuVCr1Th69CgmT54MLy8vGAwGnD59GlVVVXBxcYGnp2eLcd59910MGzYMy5cvx2uvvYZTp05h06ZNZk/pWEuv1yM+Ph6LFi2Cp6cn/Pz8sGbNGtTX12P27NlWxTIYDDh+/DimTp0KjUYDLy8vq3KOiIhAXl4eJkyYAEdHRyQlJWH+/PnIysrCtGnTlKehfvnlF+zYsQNbt25tU1GXk5ODpqYmhIWFwdnZGdu2bYNOp1OKOUu0Wi3i4uKwbt063LlzB4mJiZgyZYryaH5qaioSExPh5uaG8ePH4/79+yguLkZNTQ0WLlz4l2tUV1eH/Px8DBw4EM7OznB2dsbo0aOxadMmhIeHo6mpCUuWLGnzXSUnJycsWLAAGzZsgKOjIxISEjB8+HCl2Pnggw8wceJE+Pn5YdKkSXBwcMCFCxdQWlqKFStWYMyYMQgKCkJcXBzWrl2LO3fuYNmyZW0aWwibaO9NP0LYk9Y2FD+58Zck169fT29vb+p0OsbExPDLL7802wxMkseOHWNERAQ1Gg3d3d0ZExOjnDcajRw+fDh1Oh0BsLKyssWGYpLcvXs3+/TpQycnJ/r5+XHt2rVmeVja1Dtw4EAmJye3Os979+5xwYIF9PLyokajYWRkJH/88UezPm3ZUHzq1CkOGDCAGo2Gj78dWZtzQUEB9Xo9N2zYQJK8fPkyX3nlFbq7u1On0zE4OJhJSUlsbm4mafk1eemllxgXF0eS3LNnD8PCwujq6kq9Xs/hw4fzyJEjrc4jOTmZAwcO5ObNm+nj40OtVstJkybxjz/+MOu3fft2Dho0iGq1mh4eHhw5ciS//fZbkv/ZAHz+/PkW8efOnctOnToRgPK63Lhxg+PGjaNer2evXr2Yl5dncUPxk/EebVDOzc1lQEAANRoNx4wZw6tXr5r1O3DgACMiIqjT6ejq6srQ0FCzJ8aMRiNHjBhBtVrNoKAgHjhwQDYUi38MFfnEh7ZCCCGskpKSgr1799r86zaEEJbJ01JCCCGEsCtS3AghhBDCrsjHUkIIIYSwK3LnRgghhBB2RYobIYQQQtgVKW6EEEIIYVekuBFCCCGEXZHiRgghhBB2RYobIYQQQtgVKW6EEEIIYVekuBFCCCGEXfk/odmn+G8iKu8AAAAASUVORK5CYII=", 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", 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" ] diff --git a/icx360/algorithms/mexgen/clime.py b/icx360/algorithms/mexgen/clime.py index f32b9d4..a0620c9 100644 --- a/icx360/algorithms/mexgen/clime.py +++ b/icx360/algorithms/mexgen/clime.py @@ -32,7 +32,7 @@ class CLIME(MExGenExplainer): based on the model's inputs or outputs. """ def explain_instance(self, input_orig, unit_types="p", output_orig=None, - ind_segment=True, segment_type="s", max_phrase_length=10, + ind_segment=True, segment_type="s", max_phrase_length=10, segment_type_output=None, model_params={}, scalarize_params={}, oversampling_factor=10, max_units_replace=2, empty_subset=True, replacement_str="", num_nonzeros=None, debias=True): @@ -51,7 +51,8 @@ def explain_instance(self, input_orig, unit_types="p", output_orig=None, "n" for not to be perturbed/attributed to. If str, applies to all units in input_orig, otherwise unit-specific. output_orig (str or List[str] or icx360.utils.model_wrappers.GeneratedOutput or None): - [output] Output for original input if provided, otherwise None. + [output] Output for original input. + Can be a single unit (str), segmented into units (List[str]), a GeneratedOutput object, or None. ind_segment (bool or List[bool]): [segmentation] Whether to segment input text. If bool, applies to all units; if List[bool], applies to each unit individually. @@ -59,6 +60,9 @@ def explain_instance(self, input_orig, unit_types="p", output_orig=None, [segmentation] Type of units to segment into: "s" for sentences, "w" for words, "ph" for phrases. max_phrase_length (int): [segmentation] Maximum phrase length in terms of spaCy tokens (default 10). + segment_type_output (str or None): + [segmentation] Type of units to segment output text into: + "s" for sentences, "ph" for phrases, None for no segmentation. model_params (dict): Additional keyword arguments for model generation (for the self.model.generate() method). scalarize_params (dict): @@ -101,6 +105,8 @@ def explain_instance(self, input_orig, unit_types="p", output_orig=None, # 2) Generate output for original input or wrap provided output output_orig = self.generate_or_wrap_output(input_orig, output_orig, model_params) + # Segment output text if needed + output_orig = self.segment_output(output_orig, segment_type_output, max_phrase_length) # 3) Enumerate subsets of units that will be perturbed/replaced idx_replace = (np.array(unit_types) != "n").nonzero()[0] @@ -130,7 +136,7 @@ def explain_instance(self, input_orig, unit_types="p", output_orig=None, coef[key], intercept[key], num_nonzeros_out[key] = fit_linear_model(features, target[key].cpu().numpy(), subset_weights, num_nonzeros, debias) else: - # Single target vector + # Single target array (could contain multiple columns) coef, intercept, num_nonzeros_out = fit_linear_model(features, target.cpu().numpy(), subset_weights, num_nonzeros, debias) # 8) Construct output dictionary @@ -186,8 +192,8 @@ def fit_linear_model(features, target, sample_weights, num_nonzeros, debias): Args: features ((num_perturb, num_units) np.ndarray): Feature values. - target ((num_perturb,) np.ndarray): - Target values to predict. + target ((num_perturb,) or (num_perturb, num_output_units) np.ndarray): + Target values to predict (one column for each output unit). sample_weights ((num_perturb,) np.ndarray): Sample weights. num_nonzeros (int or None): @@ -196,22 +202,25 @@ def fit_linear_model(features, target, sample_weights, num_nonzeros, debias): Refit linear model with no penalty after selecting features. Returns: - coef ((num_units,) np.ndarray): - Coefficients of linear model. - intercept (float): - Intercept of linear model. - num_nonzeros (int): - Actual number of non-zero coefficients. + coef ((num_units,) or (num_units, num_output_units) np.ndarray): + Coefficients of linear model(s) (one per output unit). + intercept (float or (num_output_units,) np.ndarray): + Intercept(s) of linear model(s) (one per output unit). + num_nonzeros (List[int]): + Actual numbers of non-zero coefficients. """ num_units = features.shape[1] + # Promote target array to 2D if needed + target = target[:, None] if target.ndim == 1 else target + num_output_units = target.shape[1] if num_nonzeros is None: # Fit dense linear model over the units that were perturbed (`active`) active = features.any(axis=0).nonzero()[0] - coef = np.zeros(num_units) + coef = np.zeros((num_units, num_output_units)) lr = LinearRegression() lr.fit(features[:, active], target, sample_weight=sample_weights) - coef[active] = lr.coef_ + coef[active, :] = lr.coef_.T intercept = lr.intercept_ else: @@ -219,28 +228,48 @@ def fit_linear_model(features, target, sample_weights, num_nonzeros, debias): # Center feature and target values features_mean = features.mean(axis=0) - target_mean = target.mean() + target_mean = target.mean(axis=0) features_centered = features - features_mean target_centered = target - target_mean - # Call lars_path to obtain sparse linear model with num_nonzeros coefficients - # NOTE: may return fewer than num_nonzeros if coefficients leave the active set - alphas, active, coef = lars_path(np.sqrt(sample_weights)[:, None] * features_centered, np.sqrt(sample_weights) * target_centered, max_iter=num_nonzeros, method="lasso", return_path=False) - - if debias: - coef = np.zeros(num_units) - if len(active): - # Refit linear model on selected features with no penalty - lr = LinearRegression() - lr.fit(features[:, active], target, sample_weight=sample_weights) - coef[active] = lr.coef_ - intercept = lr.intercept_ + # Initialize outputs + coef = np.zeros((num_units, num_output_units)) + intercept = np.zeros(num_output_units) + active = [None] * num_output_units + + # Iterate over output units + for u in range(num_output_units): + # Call lars_path to obtain sparse linear model with num_nonzeros coefficients + # NOTE: may return fewer than num_nonzeros if coefficients leave the active set + alphas, active[u], coef[:, u] = lars_path(np.sqrt(sample_weights)[:, None] * features_centered, + np.sqrt(sample_weights) * target_centered[:, u], + max_iter=num_nonzeros, + method="lasso", + return_path=False) + + if debias: + coef[:, u] = np.zeros(num_units) + if len(active[u]): + # Refit linear model on selected features with no penalty + lr = LinearRegression() + lr.fit(features[:, active[u]], target[:, u], sample_weight=sample_weights) + coef[active[u], u] = lr.coef_ + intercept[u] = lr.intercept_ + else: + # No active set, coefficients all zero + intercept[u] = target_mean[u] else: - # No active set, coefficients all zero - intercept = target_mean - else: - # Compute intercept to account for centering - intercept = target_mean - coef @ features_mean - + # Compute intercept to account for centering + intercept[u] = target_mean[u] - coef[:, u] @ features_mean + + if num_output_units == 1: + coef, intercept = coef.squeeze(axis=1), intercept.squeeze() + # Actual number(s) of non-zero coefficients + if type(active[0]) is int: + # Single active set (single list of indices) so number of non-zeros is same for all output units + num_nonzeros = [len(active)] * num_output_units + else: + # Multiple active sets, one for each output unit + num_nonzeros = map(len, active) # Negate coefficients so that important units have positive coefficients - return -coef, intercept, len(active) + return -coef, intercept, num_nonzeros diff --git a/icx360/algorithms/mexgen/lshap.py b/icx360/algorithms/mexgen/lshap.py index fb01326..866005e 100644 --- a/icx360/algorithms/mexgen/lshap.py +++ b/icx360/algorithms/mexgen/lshap.py @@ -32,7 +32,7 @@ class LSHAP(MExGenExplainer): based on the model's inputs or outputs. """ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, output_orig=None, - ind_segment=True, segment_type="s", max_phrase_length=10, + ind_segment=True, segment_type="s", max_phrase_length=10, segment_type_output=None, model_params={}, scalarize_params={}, num_neighbors=2, max_units_replace=2, replacement_str=""): """ @@ -53,7 +53,8 @@ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, output [input] Indicator of units to attribute to ("of interest"). Default None means np.array(unit_types) != "n". output_orig (str or List[str] or icx360.utils.model_wrappers.GeneratedOutput or None): - [output] Output for original input if provided, otherwise None. + [output] Output for original input. + Can be a single unit (str), segmented into units (List[str]), a GeneratedOutput object, or None. ind_segment (bool or List[bool]): [segmentation] Whether to segment input text. If bool, applies to all units; if List[bool], applies to each unit individually. @@ -61,6 +62,9 @@ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, output [segmentation] Type of units to segment into: "s" for sentences, "w" for words, "ph" for phrases. max_phrase_length (int): [segmentation] Maximum phrase length in terms of spaCy tokens (default 10). + segment_type_output (str or None): + [segmentation] Type of units to segment output text into: + "s" for sentences, "ph" for phrases, None for no segmentation. model_params (dict): Additional keyword arguments for model generation (for the self.model.generate() method). scalarize_params (dict): @@ -106,6 +110,9 @@ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, output # 2) Generate output for original input or wrap provided output output_orig = self.generate_or_wrap_output(input_orig, output_orig, model_params) + # Segment output text if needed + output_orig = self.segment_output(output_orig, segment_type_output, max_phrase_length) + num_output_units = 1 if type(output_orig.output_text[0]) is str else len(output_orig.output_text[0]) # 3) Initialize quantities # Initialize importance scores @@ -115,7 +122,7 @@ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, output for key in self.scalarized_model.sim_scores: importance_scores[key] = np.zeros(num_units) else: - importance_scores = np.zeros(num_units) + importance_scores = np.zeros((num_units, num_output_units)) # Initialize quantities associated with units of interest idx_replace_i = [None] * len(idx_interest) @@ -187,21 +194,23 @@ def explain_instance(self, input_orig, unit_types="p", ind_interest=None, output importance_scores[key][idx_interest[i]] = np.inner(scalar_outputs_excl_interest - scalar_outputs_incl_interest, 1 / normalization) else: - # Extract scalarized output corresponding to original input/empty subset - scalar_output_orig = scalar_outputs[0].item() + # Extract scalarized output(s) corresponding to original input/empty subset + scalar_output_orig = scalar_outputs[[0]].cpu().numpy() # Extract scalarized outputs for this unit of interest scalar_outputs_excl_interest = scalar_outputs[idx_excl_interest].cpu().numpy() scalar_outputs_incl_interest = scalar_outputs[idx_incl_interest].cpu().numpy() # Prepend output corresponding to empty subset - scalar_outputs_excl_interest = np.append(scalar_output_orig, scalar_outputs_excl_interest) + scalar_outputs_excl_interest = np.append(scalar_output_orig, scalar_outputs_excl_interest, axis=0) # 9) Compute Shapley values normalization = get_normalization_constants(len(idx_replace_i[i]), max_units_replace) * (max_units_replace + 1) - importance_scores[idx_interest[i]] = np.inner(scalar_outputs_excl_interest - scalar_outputs_incl_interest, 1 / normalization) + importance_scores[idx_interest[i]] = np.dot(1 / normalization, scalar_outputs_excl_interest - scalar_outputs_incl_interest) # 10) Construct output dictionary if type(importance_scores) is not dict: # Convert importance_scores to dictionary + if num_output_units == 1: + importance_scores = importance_scores.squeeze(axis=1) if isinstance(self.scalarized_model, ProbScalarizedModel): # Label scores with type of scalarizer importance_scores = {"prob": importance_scores} diff --git a/icx360/algorithms/mexgen/mexgen.py b/icx360/algorithms/mexgen/mexgen.py index c656389..928bab1 100644 --- a/icx360/algorithms/mexgen/mexgen.py +++ b/icx360/algorithms/mexgen/mexgen.py @@ -12,7 +12,7 @@ from icx360.algorithms.lbbe import LocalBBExplainer from icx360.utils.model_wrappers import GeneratedOutput, HFModel from icx360.utils.scalarizers import ProbScalarizedModel, TextScalarizedModel -from icx360.utils.segmenters import SpaCySegmenter, exclude_non_alphanumeric +from icx360.utils.segmenters import SpaCySegmenter, exclude_non_alphanumeric, merge_non_alphanumeric class MExGenExplainer(LocalBBExplainer): @@ -115,7 +115,8 @@ def generate_or_wrap_output(self, input_orig, output_orig=None, model_params={}) input_orig (List[str]): Original input segmented into units. output_orig (str or List[str] or icx360.utils.model_wrappers.GeneratedOutput or None): - Output for original input if provided, otherwise None. + Output for original input. + Can be a single unit (str), segmented into units (List[str]), a GeneratedOutput object, or None. model_params (dict): Additional keyword arguments for model generation (for the self.model.generate() method). @@ -130,11 +131,8 @@ def generate_or_wrap_output(self, input_orig, output_orig=None, model_params={}) # Generate output for original input output_orig = self.model.generate([input_orig], text_only=False, **model_params) elif type(output_orig) in (str, list): - if type(output_orig) is str: - output_orig = [output_orig] - # Wrap output text in a GeneratedOutput object - output_orig = GeneratedOutput(output_text=output_orig) + output_orig = GeneratedOutput(output_text=[output_orig]) if isinstance(self.model, HFModel): # Also include output token IDs for HFModel @@ -145,3 +143,31 @@ def generate_or_wrap_output(self, input_orig, output_orig=None, model_params={}) raise TypeError("output_orig must be a str, List[str], GeneratedOutput, or None.") return output_orig + + def segment_output(self, output_orig, segment_type_output=None, max_phrase_length=10): + """ + Segment output text (if needed). + + Args: + output_orig (icx360.utils.model_wrappers.GeneratedOutput): + Object containing output for original input, in particular output text (output_orig.output_text). + segment_type_output (str or None): + Type of units to segment into: "s" for sentences, "ph" for phrases, None for no segmentation. + max_phrase_length (int): + Maximum phrase length in terms of spaCy tokens (default 10). + + Returns: + output_orig (icx360.utils.model_wrappers.GeneratedOutput): + Output object with possibly segmented text. + """ + if type(output_orig.output_text[0]) is str and segment_type_output is not None: + # Output text not already segmented and segmentation requested, call segmenter + output_orig.output_text[0], _, _ = self.segmenter.segment_units(output_orig.output_text[0], + unit_types="p", + segment_type=segment_type_output, + max_phrase_length=max_phrase_length) + + # Merge non-alphanumeric units into adjacent units + output_orig.output_text[0] = merge_non_alphanumeric(output_orig.output_text[0]) + + return output_orig diff --git a/icx360/metrics/perturb_curve.py b/icx360/metrics/perturb_curve.py index 377d573..1e3c5db 100644 --- a/icx360/metrics/perturb_curve.py +++ b/icx360/metrics/perturb_curve.py @@ -59,9 +59,9 @@ def __init__(self, model, scalarizer="prob", **kwargs): def eval_perturb_curve(self, explainer_dict, score_label, token_frac=False, max_frac_perturb=0.5, replacement_str="", model_params={}, scalarize_params={}): """ - Evaluate perturbation curve for given input attributions. + Evaluate perturbation curve(s) for given input attributions. - This method evaluates the perturbation curve for a set of attribution scores + This method evaluates the perturbation curve(s) for a set of attribution scores by perturbing units in decreasing order of their attribution scores. Args: @@ -84,8 +84,10 @@ def eval_perturb_curve(self, explainer_dict, score_label, token_frac=False, max_ (for the self.scalarized_model.scalarize_output() method). Returns: - output_perturbed (dict): - Dictionary with the following items: + output_perturbed (dict or List[dict]): + Single dictionary if explainer_dict["attributions"][score_label] has a single column, i.e., + explainer_dict["output_orig"] has a single output unit, otherwise a list of dictionaries if multiple. + Each dictionary has the following items: "frac" (torch.Tensor): Fractions of units or tokens perturbed. `score_label` (torch.Tensor): @@ -103,70 +105,87 @@ def eval_perturb_curve(self, explainer_dict, score_label, token_frac=False, max_ num_units = len(input_orig) # Avoid choosing units of type "n" for perturbation scores[unit_types == "n"] = -np.inf + # Promote scores array to 2D if needed + if scores.ndim == 1: + scores = scores[:, None] + num_score_cols = scores.shape[1] + + # Iterate over score columns corresponding to different output units + output_perturbed = [None] * num_score_cols + for col in range(num_score_cols): + + if token_frac: + if self.model._tokenizer is None: + raise ValueError("If token_frac==True, need to include model's tokenizer in model wrapper to count numbers of tokens") + + # 1) Count number of tokens in each unit + # First encode segmented input + input_encoding = self.model._tokenizer(input_orig, is_split_into_words=True, return_tensors="pt") + # Iterate over units + num_tokens = np.zeros(num_units, dtype=int) + for u in range(num_units): + # Token span and token count of unit + token_span = input_encoding.word_to_tokens(u) + if token_span is not None: + num_tokens[u] = token_span.end - token_span.start + # Total number of tokens in units that can be perturbed + tot_tokens_can_perturb = num_tokens[unit_types != "n"].sum() + + # 2) Sort units in increasing order of attribution scores divided by token counts + scores_argsort = np.argsort(scores[:, col] / num_tokens) + + # 3) Determine subsets of units to perturb + # Initialize fractions to perturb and subsets to perturb + k = 0 + frac_perturbed = [0.0] + subsets_replace = [[]] + # Increase fractions and subsets until fraction exceeds maximum specified + while frac_perturbed[-1] < max_frac_perturb: + k += 1 + frac_perturbed.append(num_tokens[scores_argsort[-k:]].sum() / tot_tokens_can_perturb) + subsets_replace.append(np.sort(scores_argsort[-k:])) + frac_perturbed = torch.tensor(frac_perturbed) - if token_frac: - if self.model._tokenizer is None: - raise ValueError("If token_frac==True, need to include model's tokenizer in model wrapper to count numbers of tokens") - - # 1) Count number of tokens in each unit - # First encode segmented input - input_encoding = self.model._tokenizer(input_orig, is_split_into_words=True, return_tensors="pt") - # Iterate over units - num_tokens = np.zeros(num_units, dtype=int) - for u in range(num_units): - # Token span and token count of unit - token_span = input_encoding.word_to_tokens(u) - if token_span is not None: - num_tokens[u] = token_span.end - token_span.start - # Total number of tokens in units that can be perturbed - tot_tokens_can_perturb = num_tokens[unit_types != "n"].sum() - - # 2) Sort units in increasing order of attribution scores divided by token counts - scores_argsort = np.argsort(scores / num_tokens) - - # 3) Determine subsets of units to perturb - # Initialize fractions to perturb and subsets to perturb - k = 0 - frac_perturbed = [0.0] - subsets_replace = [[]] - # Increase fractions and subsets until fraction exceeds maximum specified - while frac_perturbed[-1] < max_frac_perturb: - k += 1 - frac_perturbed.append(num_tokens[scores_argsort[-k:]].sum() / tot_tokens_can_perturb) - subsets_replace.append(np.sort(scores_argsort[-k:])) - - else: - # 1) Number of units that can be perturbed - num_units_can_perturb = (unit_types != "n").sum() - - # 2) Sort units in increasing order of attribution scores - scores_argsort = np.argsort(scores) - - # 3) Determine subsets of units to perturb - # Maximum number of units to perturb - max_num_perturb = ceil(max_frac_perturb * num_units_can_perturb) - # Fractions to perturb - frac_perturbed = torch.arange(max_num_perturb + 1) / num_units_can_perturb - # Subsets to perturb - subsets_replace = [np.sort(scores_argsort[num_units - k:]) for k in range(max_num_perturb + 1)] - - # 4) Replace subsets of units with replacement string - input_perturbed = mask_subsets(input_orig, subsets_replace, replacement_str) - - # 5) Compute model's scalarized outputs for perturbed inputs - output_perturbed = self.scalarized_model.scalarize_output(inputs=input_perturbed, ref_output=explainer_dict["output_orig"], **model_params, **scalarize_params) - - # Move scalarized outputs to CPU - if type(output_perturbed) is dict: - for key in output_perturbed: - output_perturbed[key] = output_perturbed[key].cpu() - else: - # Convert to dictionary - if isinstance(self.scalarized_model, ProbScalarizedModel): - output_perturbed = {"prob": output_perturbed.cpu()} else: - output_perturbed = {"score": output_perturbed.cpu()} - # Add fractions to dictionary - output_perturbed["frac"] = torch.tensor(frac_perturbed) + # 1) Number of units that can be perturbed + num_units_can_perturb = (unit_types != "n").sum() + + # 2) Sort units in increasing order of attribution scores + scores_argsort = np.argsort(scores[:, col]) + + # 3) Determine subsets of units to perturb + # Maximum number of units to perturb + max_num_perturb = ceil(max_frac_perturb * num_units_can_perturb) + # Fractions to perturb + frac_perturbed = torch.arange(max_num_perturb + 1) / num_units_can_perturb + # Subsets to perturb + subsets_replace = [np.sort(scores_argsort[num_units - k:]) for k in range(max_num_perturb + 1)] + + # 4) Replace subsets of units with replacement string + input_perturbed = mask_subsets(input_orig, subsets_replace, replacement_str) + + # 5) Compute model's scalarized outputs for perturbed inputs + output_perturbed[col] = self.scalarized_model.scalarize_output( + inputs=input_perturbed, + ref_output=explainer_dict["output_orig"], + **model_params, + **scalarize_params, + ) + + # Move scalarized outputs to CPU + if type(output_perturbed[col]) is dict: + for key in output_perturbed[col]: + output_perturbed[col][key] = output_perturbed[col][key].cpu() + else: + # Extract scalarized outputs for the current output unit/score column and convert to dictionary + if isinstance(self.scalarized_model, ProbScalarizedModel): + output_perturbed[col] = {"prob": output_perturbed[col][:, col].cpu()} + else: + output_perturbed[col] = {"score": output_perturbed[col][:, col].cpu()} + # Add fractions to dictionary + output_perturbed[col]["frac"] = frac_perturbed + + if num_score_cols == 1: + output_perturbed = output_perturbed[0] return output_perturbed diff --git a/icx360/utils/scalarizers/prob.py b/icx360/utils/scalarizers/prob.py index a1616d7..5956d37 100644 --- a/icx360/utils/scalarizers/prob.py +++ b/icx360/utils/scalarizers/prob.py @@ -12,6 +12,7 @@ from icx360.utils.model_wrappers import HFModel, VLLMModel from icx360.utils.scalarizers import Scalarizer +from icx360.utils.segmenters import find_unit_boundaries from icx360.utils.toma import toma_get_probs @@ -41,7 +42,7 @@ def __init__(self, model): def scalarize_output(self, inputs=None, outputs=None, ref_input=None, ref_output=None, chat_template=False, system_prompt=None, tokenizer_kwargs={}, transformation="log_prob_mean", **kwargs): """ - Compute probability of reference output conditioned on inputs. + Compute probability of generating reference output (or each unit thereof) conditioned on inputs. Args: inputs (str or List[str] or List[List[str]]): @@ -69,8 +70,8 @@ def scalarize_output(self, inputs=None, outputs=None, ref_input=None, ref_output Additional keyword arguments for model. Returns: - probs_transformed ((num_inputs,) torch.Tensor): - Transformed probability of generating the reference output conditioned on each input. + probs_transformed ((num_inputs, num_output_units) torch.Tensor): + Transformed probability of generating each unit of the reference output conditioned on each input. """ # Check for and convert inputs if inputs is None: @@ -82,20 +83,27 @@ def scalarize_output(self, inputs=None, outputs=None, ref_input=None, ref_output raise ValueError("ref_output must be provided for ProbScalarizedModel.scalarize_output()") # Compute log probabilities of reference output tokens conditioned on inputs + # Also find token boundaries of units of the reference output if isinstance(self.model, HFModel): - log_probs = self._compute_log_probs_hf(inputs, ref_output, **kwargs) + log_probs, boundaries = self._compute_log_probs_hf(inputs, ref_output, **kwargs) elif isinstance(self.model, VLLMModel): - log_probs = self._compute_log_probs_vllm(inputs, ref_output, **kwargs) - - # Transform probabilities - if transformation in ("log_prob_mean", "prob_geo_mean"): - # Mean of log probabilities - probs_transformed = log_probs.mean(dim=1) - elif transformation in ("log_prob_sum", "prob_prod"): - # Sum of log probabilities - probs_transformed = log_probs.sum(dim=1) - else: - raise ValueError("Transformation not recognized") + log_probs, boundaries = self._compute_log_probs_vllm(inputs, ref_output, **kwargs) + + # Initialize transformed probabilities + num_output_units = len(boundaries) - 1 + probs_transformed = torch.zeros(log_probs.shape[0], num_output_units) + # Iterate over reference output units + for u in range(num_output_units): + # Transform probabilities + if transformation in ("log_prob_mean", "prob_geo_mean"): + if boundaries[u + 1] > boundaries[u]: + # Mean of log probabilities (only if this unit has a non-zero number of tokens) + probs_transformed[:, u] = log_probs[:, boundaries[u] : boundaries[u + 1]].mean(dim=1) + elif transformation in ("log_prob_sum", "prob_prod"): + # Sum of log probabilities + probs_transformed[:, u] = log_probs[:, boundaries[u] : boundaries[u + 1]].sum(dim=1) + else: + raise ValueError("Transformation not recognized") if transformation.startswith("prob"): # Convert log probabilities to probabilities probs_transformed = probs_transformed.exp() @@ -117,26 +125,28 @@ def _compute_log_probs_hf(self, inputs, ref_output, **kwargs): Returns: log_probs ((num_inputs, gen_length) torch.Tensor): Log probabilities of reference output tokens. + boundaries (List[int]): + Token boundaries of units of the reference output. """ num_inputs = inputs["input_ids"].shape[0] # Get token IDs of reference output - ref_output = ref_output.output_ids + output_ids = ref_output.output_ids # Number of generated tokens in output # encoder-decoder output always begins with a fixed special token e.g. , # while decoder-only output has been truncated to only the generated response - gen_length = ref_output.shape[1] - self.model._model.config.is_encoder_decoder + gen_length = output_ids.shape[1] - self.model._model.config.is_encoder_decoder if num_inputs == 1 or not torch.cuda.is_available(): # Call underlying HuggingFace model on given input and output sequences to obtain logits - ref_output_expanded = ref_output.expand(num_inputs, -1) + output_ids_expanded = output_ids.expand(num_inputs, -1) with torch.no_grad(): if self.model._model.config.is_encoder_decoder: # Encoder-decoder model: pass inputs and reference output as separate arguments - output_dict = self.model._model(**inputs, decoder_input_ids=ref_output_expanded) + output_dict = self.model._model(**inputs, decoder_input_ids=output_ids_expanded) else: # Decoder-only model: concatenate inputs with reference output - combined_input_output = torch.cat([inputs["input_ids"], ref_output_expanded], dim=1) + combined_input_output = torch.cat([inputs["input_ids"], output_ids_expanded], dim=1) output_dict = self.model._model(combined_input_output) # Position where generated output starts (in concatenated input-output for decoder-only) @@ -146,9 +156,9 @@ def _compute_log_probs_hf(self, inputs, ref_output, **kwargs): scores = tuple(output_dict.logits[:, pos, :] for pos in range(gen_start - 1, gen_start + gen_length - 1)) # Compute probabilities of tokens in reference output - # NOTE: although ref_output_expanded and scores have different token lengths, + # NOTE: although output_ids_expanded and scores have different token lengths, # compute_transition_scores() seems to align their last positions - log_probs = self.model._model.compute_transition_scores(ref_output_expanded, scores, normalize_logits=True) + log_probs = self.model._model.compute_transition_scores(output_ids_expanded, scores, normalize_logits=True) else: # Call using toma @@ -157,11 +167,18 @@ def _compute_log_probs_hf(self, inputs, ref_output, **kwargs): # Call using toma batch_size_init = 2 ** ceil(log2(num_inputs)) - toma_get_probs(0, num_inputs, self.model._model, inputs, ref_output, log_probs, toma_initial_step=batch_size_init) + toma_get_probs(0, num_inputs, self.model._model, inputs, output_ids, log_probs, toma_initial_step=batch_size_init) + + # Get list of reference output tokens + tokens = [] + for id in output_ids[0]: + tokens.append("" if id in self.model._tokenizer.all_special_ids else self.model._tokenizer.decode(id)) + # Find token boundaries of units of the reference output + boundaries = find_unit_boundaries(ref_output.output_text[0], tokens) - return log_probs + return log_probs, boundaries - def _compute_log_probs_vllm(self, inputs, ref_output, **kwargs): + def _compute_log_probs_vllm(self, inputs, ref_output, max_inputs_per_call=200, **kwargs): """ Compute log probabilities of reference output tokens conditioned on inputs for a VLLMModel. @@ -176,25 +193,46 @@ def _compute_log_probs_vllm(self, inputs, ref_output, **kwargs): Returns: log_probs ((num_inputs, gen_length) torch.Tensor): Log probabilities of reference output tokens. + boundaries (List[int]): + Token boundaries of units of the reference output. """ # VLLM parameters for computing log probs of a given input + output without generating kwargs["logprobs"] = 0 kwargs["max_tokens"] = 0 kwargs["echo"] = True + # Number of batch inference calls + num_calls = ceil(len(inputs) / max_inputs_per_call) + # Call underlying VLLM model on inputs only to get their token lengths - completion = self.model._model.completions.create(model=self.model._model_name, prompt=inputs, **kwargs) input_lengths = [] - for result in completion.choices: - input_lengths.append(len(result.logprobs.tokens)) + for call in range(num_calls): + if num_calls > 1: + print(f"Call {call + 1} of {num_calls}") + completion = self.model._model.completions.create( + model=self.model._model_name, + prompt=inputs[call * max_inputs_per_call : (call + 1) * max_inputs_per_call], + **kwargs + ) + for result in completion.choices: + input_lengths.append(len(result.logprobs.tokens)) # Combined inputs + output - combined_input_output = [inp + ref_output.output_text[0] for inp in inputs] + combined_input_output = [inp + "".join(ref_output.output_text[0]) for inp in inputs] # Call VLLM model on combined inputs + output to get log probs - completion = self.model._model.completions.create(model=self.model._model_name, prompt=combined_input_output, **kwargs) log_probs = [] - for i, result in enumerate(completion.choices): - log_probs.append(result.logprobs.token_logprobs[input_lengths[i]:]) - - return torch.tensor(log_probs) + for call in range(num_calls): + completion = self.model._model.completions.create( + model=self.model._model_name, + prompt=combined_input_output[call * max_inputs_per_call : (call + 1) * max_inputs_per_call], + **kwargs + ) + for i, result in enumerate(completion.choices): + log_probs.append(result.logprobs.token_logprobs[input_lengths[call * max_inputs_per_call + i]:]) + + # Find token boundaries of units of the reference output + boundaries = find_unit_boundaries(ref_output.output_text[0], + result.logprobs.tokens[input_lengths[call * max_inputs_per_call + i]:]) + + return torch.tensor(log_probs), boundaries diff --git a/icx360/utils/segmenters/__init__.py b/icx360/utils/segmenters/__init__.py index 332762b..59de919 100644 --- a/icx360/utils/segmenters/__init__.py +++ b/icx360/utils/segmenters/__init__.py @@ -5,4 +5,4 @@ """ from .spacy import SpaCySegmenter -from .utils import exclude_non_alphanumeric +from .utils import exclude_non_alphanumeric, find_unit_boundaries, merge_non_alphanumeric diff --git a/icx360/utils/segmenters/utils.py b/icx360/utils/segmenters/utils.py index 3a369d0..4f64840 100644 --- a/icx360/utils/segmenters/utils.py +++ b/icx360/utils/segmenters/utils.py @@ -1,5 +1,5 @@ """ -Other utilities for segmenting input text into units. +Other utilities related to segmenting text into units. """ # Assisted by watsonx Code Assistant in formatting and augmenting docstrings. @@ -21,7 +21,79 @@ def exclude_non_alphanumeric(unit_types, units): """ # Check whether units that can be replaced have alphanumeric characters for u, unit in enumerate(units): - if unit_types[u] != "n" and not any(c.isalnum() for c in unit): + if unit_types[u] != "n" and not isalnum_string(unit): unit_types[u] = "n" return unit_types + +def isalnum_string(string): + # Check whether string contains any alphanumeric characters + return any(c.isalnum() for c in string) + +def merge_non_alphanumeric(units): + """ + Merge non-alphanumeric units into adjacent units. + + Args: + units (List[str]): + List of units. + + Returns: + units_merged (List[str]): + List of units with non-alphanumeric units merged. + """ + units_merged = [] + for unit in units: + if units_merged and (not isalnum_string(unit) or not isalnum_string(units_merged[-1])): + # Current unit or previous unit is not alphanumeric, merge with previous unit + units_merged[-1] += unit + else: + units_merged.append(unit) + + return units_merged + +def find_unit_boundaries(units, tokens): + """ + Find boundaries of units in terms of tokens (starting token index of unit to starting index of next unit). + + Args: + units (str or List[str]): + List of units (or single unit if string). + tokens (List[str]): + List of tokens. + + Returns: + boundaries (List[int]): + A list of (num_units + 1) token indices, where boundaries[i]:boundaries[i+1] are the boundaries of unit i. + """ + boundaries = [0] + + if type(units) is list and len(units) > 1: + # More than one unit, find the ending index of each unit except the last + idx_token = 0 + for unit in units[:-1]: + # Look for the current token in the current unit + token = tokens[idx_token].strip() + idx_char = unit.find(token) + # Stay in current unit if token found there, + # or if not found and current unit is still long enough + # The latter can happen with the second half of a token that is split between units + # or special tokens that cannot be found anywhere in the text + stay_in_unit = idx_char > -1 or len(unit) >= len(token) + while stay_in_unit and idx_token < len(tokens) - 1: + # Token found or skipped, advance to next token + idx_token += 1 + if idx_char > -1: + # Token found, advance in the unit as well + unit = unit[idx_char + len(token):] + # Look for the next token in the current unit + token = tokens[idx_token].strip() + idx_char = unit.find(token) + stay_in_unit = idx_char > -1 or len(unit) >= len(token) + # Token not found, record ending index of unit + boundaries.append(idx_token) + + # Ending index of last unit + boundaries.append(len(tokens)) + + return boundaries diff --git a/tests/unit/test_prob_scalarized_model.py b/tests/unit/test_prob_scalarized_model.py index 1fcec41..b8d344f 100644 --- a/tests/unit/test_prob_scalarized_model.py +++ b/tests/unit/test_prob_scalarized_model.py @@ -15,7 +15,7 @@ def test_scalarize_output(wrapped_model, tiny_model_prompts): # Reference output for scalarization if isinstance(wrapped_model, HFModel): - ref_output = GeneratedOutput(output_ids=torch.LongTensor([[1, 2, 3]])) + ref_output = GeneratedOutput(output_ids=torch.LongTensor([[1, 2, 3]]), output_text=[""]) elif isinstance(wrapped_model, VLLMModel): ref_output = GeneratedOutput(output_text=["Hello! How can I help you?"])