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InternVL-main/clip_benchmark/benchmark/README.md

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+# Benchmark
+
+the benchmark results are available in [benchmark.csv](benchmark.csv).
+You can visualize the results in the [notebook](results.ipynb)
+
+# How to reproduce the CLIP benchmark results
+
+## Webdataset evaluation: VTAB+ and retrieval datasets (MSCOCO, Flickr8k, Flickr30k)
+
+```bash
+clip_benchmark eval --pretrained_model openai openclip_base \
+    --dataset "webdatasets.txt" \
+    --dataset_root "https://huggingface.co/datasets/clip-benchmark/wds_{dataset_cleaned}/tree/main" \
+    --output "benchmark_{dataset}_{pretrained}_{model}_{language}_{task}.json"
+```
+
+Once the evaluation finishes, you can construct a CSV with all the results:
+
+```bash
+clip_benchmark build benchmark_*.json --output benchmark.csv
+```
+
+*Notes:* Pascal VOC 2007 multilabel is not yet included in the webdataset test suite. Multilingual support with webdataset is in progress.
+
+## Alternative: Local download
+
+```bash
+clip_benchmark eval --pretrained_model  openai openclip_base  --dataset vtab+ retrieval \
+--dataset_root "clip_benchmark_datasets/{dataset}" \
+--output "benchmark_{dataset}_{pretrained}_{model}_{language}_{task}.json"
+```
+
+(Change `--dataset_root` accordingly)
+
+## Multilingual ImageNet benchmark
+
+To run the multilingual ImageNet benchmark, use:
+
+```bash
+clip_benchmark eval --pretrained_model openclip_multilingual openclip_base openai  --dataset imagenet1k --language cn it jp en ar\
+--dataset_root "clip_benchmark_datasets/{dataset}" \
+--output "multilingual_{dataset}_{pretrained}_{model}_{language}_{task}.json"
+```
+
+(Change `--dataset_root` accordingly)
+
+## Multilingual MS-COCO benchmark
+
+To run the multilingual MS-COCO benchmark, use:
+
+```bash
+clip_benchmark eval --pretrained_model openclip_multilingual openclip_base openai --dataset multilingual_mscoco_captions --language es it ko pl ru tr zh en \
+--dataset_root "clip_benchmark_datasets/{dataset}" \
+--output "multilingual_{dataset}_{pretrained}_{model}_{language}_{task}.json"
+```
+
+(Change `--dataset_root` accordingly)

InternVL-main/clip_benchmark/benchmark/benchmark.csv

@@ -0,0 +1,508 @@
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+0.6769,0.9012,0.6781,imagenetv2,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
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+0.2380126552613077,1.0,0.233320813717688,vtab/diabetic_retinopathy,ViT-H-14,laion2b_s32b_b79k,zeroshot_classification,,,,ViT-H-14 laion2b_s32b_b79k
+0.2307582938388625,0.4708056872037914,0.2308530805687204,country211,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
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+,,,voc2007_multilabel,ViT-B-32-quickgelu,laion400m_e32,zeroshot_classification,0.7621888518333435,,,ViT-B-32-quickgelu laion400m_e32
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+,,,flickr8k,ViT-g-14,laion2b_s12b_b42k,zeroshot_retrieval,,0.9175999760627747,0.9739999771118164,ViT-g-14 laion2b_s12b_b42k
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+0.8732624693376942,0.9926410466067048,0.8695864391489154,vtab/pets,ViT-B-32,openai,zeroshot_classification,,,,ViT-B-32 openai
+0.1486,0.9095333333333332,0.1443124682607226,vtab/clevr_closest_object_distance,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
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+0.596238872840889,0.8439741397944546,0.5964254901960785,imagenet_sketch,ViT-L-14,openai,zeroshot_classification,,,,ViT-L-14 openai
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+,,,voc2007_multilabel,ViT-B-16,openai,zeroshot_classification,0.788827121257782,,,ViT-B-16 openai
+,,,flickr30k,ViT-L-14,openai,zeroshot_retrieval,,0.8715999722480774,0.9739999771118164,ViT-L-14 openai
+0.2505250525052505,0.6012601260126013,0.2483244206773618,fgvc_aircraft,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
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+,,,flickr30k,ViT-B-16,openai,zeroshot_retrieval,,0.855400025844574,0.9629999995231628,ViT-B-16 openai
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+,,,flickr30k,ViT-H-14,laion2b_s32b_b79k,zeroshot_retrieval,,0.9409999847412108,0.9929999709129332,ViT-H-14 laion2b_s32b_b79k
+,,,voc2007_multilabel,ViT-L-14,openai,zeroshot_classification,0.7903817892074585,,,ViT-L-14 openai
+0.4267205349679576,0.9361939258846476,0.3989364402674789,fer2013,ViT-B-32-quickgelu,laion400m_e32,zeroshot_classification,,,,ViT-B-32-quickgelu laion400m_e32
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+,,,voc2007_multilabel,ViT-L-14-336,openai,zeroshot_classification,0.8035513162612915,,,ViT-L-14-336 openai
+0.5922666666666667,0.8565333333333334,0.5810468077571583,imagenet-a,ViT-H-14,laion2b_s32b_b79k,zeroshot_classification,,,,ViT-H-14 laion2b_s32b_b79k
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+,,,flickr30k,ViT-B-16-plus-240,laion400m_e32,zeroshot_retrieval,,0.8894000053405762,0.9710000157356262,ViT-B-16-plus-240 laion400m_e32
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+0.5388,0.8212,0.5362716338708938,imagenet-a,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+,,,mscoco_captions,ViT-B-16,openai,zeroshot_retrieval,,0.5836865305900574,0.7681999802589417,ViT-B-16 openai
+,,,flickr8k,ViT-B-32,laion2b_s34b_b79k,zeroshot_retrieval,,0.8629999756813049,0.9409999847412109,ViT-B-32 laion2b_s34b_b79k
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+0.5612707437000615,0.8849492931776275,0.5565312569182044,vtab/svhn,ViT-H-14,laion2b_s32b_b79k,zeroshot_classification,,,,ViT-H-14 laion2b_s32b_b79k
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+0.805221688034188,0.9551282051282052,0.8491537874687232,voc2007,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+,,,flickr8k,ViT-L-14-336,openai,zeroshot_retrieval,,0.8795999884605408,0.9390000104904175,ViT-L-14-336 openai
+0.1716,0.9095333333333332,0.1619443104834767,vtab/clevr_closest_object_distance,ViT-B-32,openai,zeroshot_classification,,,,ViT-B-32 openai
+0.1573333333333333,0.6964666666666667,0.1495026928557915,vtab/clevr_count_all,ViT-B-32,laion2b_s34b_b79k,zeroshot_classification,,,,ViT-B-32 laion2b_s34b_b79k
+,,,mscoco_captions,ViT-B-16-plus-240,laion400m_e32,zeroshot_retrieval,,0.6620951890945435,0.8101999759674072,ViT-B-16-plus-240 laion400m_e32
+0.8336,0.9666,0.8325000000000001,vtab/cifar100,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
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+,,,voc2007_multilabel,ViT-g-14,laion2b_s12b_b42k,zeroshot_classification,0.8066232800483704,,,ViT-g-14 laion2b_s12b_b42k
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+0.5526123046875,,0.5526478181769814,vtab/pcam,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+,,,mscoco_captions,ViT-L-14,openai,zeroshot_retrieval,,0.6108356714248657,0.7918000221252441,ViT-L-14 openai
+,,,mscoco_captions,ViT-B-32-quickgelu,laion400m_e32,zeroshot_retrieval,,0.6084766387939453,0.7675999999046326,ViT-B-32-quickgelu laion400m_e32
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+0.1659634317862166,,0.3247233185334074,vtab/kitti_closest_vehicle_distance,ViT-B-32,laion2b_e16,zeroshot_classification,,,,ViT-B-32 laion2b_e16
+0.0193684895833333,0.1180826822916666,0.0197744129948227,vtab/dsprites_label_orientation,ViT-B-32-quickgelu,laion400m_e32,zeroshot_classification,,,,ViT-B-32-quickgelu laion400m_e32
+0.9037884982284,0.9970019078768056,0.9039815014388496,vtab/pets,ViT-B-16-plus-240,laion400m_e32,zeroshot_classification,,,,ViT-B-16-plus-240 laion400m_e32
+0.3742,0.7294,0.3706020613065869,mnist,ViT-B-32-quickgelu,laion400m_e32,zeroshot_classification,,,,ViT-B-32-quickgelu laion400m_e32
+0.6746527403897922,0.8668030580381177,0.6650572756513145,objectnet,ViT-g-14,laion2b_s12b_b42k,zeroshot_classification,,,,ViT-g-14 laion2b_s12b_b42k
+0.765625,0.959869123931624,0.8071517771314477,voc2007,ViT-B-32,openai,zeroshot_classification,,,,ViT-B-32 openai
+0.546031746031746,0.902063492063492,0.5542849348347576,vtab/resisc45,ViT-B-32-quickgelu,laion400m_e32,zeroshot_classification,,,,ViT-B-32-quickgelu laion400m_e32
+0.0427924262152777,0.1601019965277778,0.0430371102717507,vtab/dsprites_label_x_position,ViT-B-16-plus-240,laion400m_e32,zeroshot_classification,,,,ViT-B-16-plus-240 laion400m_e32
+0.6957142857142857,0.9571428571428572,0.706242238089474,vtab/resisc45,ViT-H-14,laion2b_s32b_b79k,zeroshot_classification,,,,ViT-H-14 laion2b_s32b_b79k
+0.6514814814814814,0.9551851851851852,0.6638062361650154,vtab/eurosat,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+0.8475333333333334,0.9550666666666666,0.8331685531673508,imagenet-r,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
+0.1902,0.9085333333333332,0.1387289271305892,vtab/clevr_closest_object_distance,ViT-B-32,laion2b_s34b_b79k,zeroshot_classification,,,,ViT-B-32 laion2b_s34b_b79k
+0.3852181929932391,0.7822295636140135,0.379296565517112,vtab/svhn,ViT-B-32,laion2b_e16,zeroshot_classification,,,,ViT-B-32 laion2b_e16
+0.3182464454976303,0.5937914691943128,0.3175829383886256,country211,ViT-L-14,openai,zeroshot_classification,,,,ViT-L-14 openai
+0.6659,0.9224,0.6665327286676481,mnist,ViT-B-16,laion400m_e32,zeroshot_classification,,,,ViT-B-16 laion400m_e32
+0.6327693607655879,0.8639588123170037,0.6325447058823529,imagenet_sketch,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+0.2279620853080568,0.486303317535545,0.2282938388625592,country211,ViT-B-16,openai,zeroshot_classification,,,,ViT-B-16 openai
+0.764,0.9231,0.7589335620721703,mnist,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
+0.0268283420138888,0.1092258029513889,0.025387675404758,vtab/dsprites_label_orientation,ViT-B-32,laion2b_e16,zeroshot_classification,,,,ViT-B-32 laion2b_e16
+0.3693369336933693,0.744974497449745,0.3649286987522281,fgvc_aircraft,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+0.4102258758451137,0.8022818070067609,0.4216815643098484,vtab/svhn,ViT-B-32,laion2b_s34b_b79k,zeroshot_classification,,,,ViT-B-32 laion2b_s34b_b79k
+0.743319785938908,0.9583279695459478,0.7348385903018446,sun397,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+,,,flickr8k,ViT-H-14,laion2b_s32b_b79k,zeroshot_retrieval,,0.9277999997138977,0.9729999899864197,ViT-H-14 laion2b_s32b_b79k
+0.5487,0.8411,0.5430718178404617,mnist,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+0.7171428571428572,0.958095238095238,0.7258469461953507,vtab/resisc45,ViT-g-14,laion2b_s12b_b42k,zeroshot_classification,,,,ViT-g-14 laion2b_s12b_b42k
+0.3454976303317535,0.6221800947867299,0.3445971563981042,country211,ViT-L-14-336,openai,zeroshot_classification,,,,ViT-L-14-336 openai
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+0.4774310392867094,0.9402340484814712,0.4649079545536754,fer2013,ViT-B-32,laion2b_e16,zeroshot_classification,,,,ViT-B-32 laion2b_e16
+0.5230010414824422,0.7913694511584036,0.5233670588235293,imagenet_sketch,ViT-B-16,laion400m_e32,zeroshot_classification,,,,ViT-B-16 laion400m_e32
+0.7341666666666666,0.9035,0.7214778957504825,imagenet-r,ViT-B-32-quickgelu,laion400m_e32,zeroshot_classification,,,,ViT-B-32-quickgelu laion400m_e32
+0.8353057199211046,0.9546351084812624,0.9005042720791782,vtab/caltech101,ViT-B-16,laion400m_e32,zeroshot_classification,,,,ViT-B-16 laion400m_e32
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+0.6985172981878089,,0.6986603265589717,renderedsst2,ViT-L-14,openai,zeroshot_classification,,,,ViT-L-14 openai
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+0.9351,0.998,0.936,vtab/cifar10,ViT-B-32,laion2b_s34b_b79k,zeroshot_classification,,,,ViT-B-32 laion2b_s34b_b79k
+,,,flickr8k,ViT-L-14,openai,zeroshot_retrieval,,0.8633999824523926,0.9409999847412109,ViT-L-14 openai
+0.65528,0.894,0.6563199999999999,imagenet1k,ViT-B-32,laion2b_e16,zeroshot_classification,,,,ViT-B-32 laion2b_e16
+0.5930807248764415,,0.5925413265010712,renderedsst2,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+0.1650710900473933,0.3788625592417061,0.164218009478673,country211,ViT-B-32,laion2b_e16,zeroshot_classification,,,,ViT-B-32 laion2b_e16
+0.168016801680168,0.4119411941194119,0.1658110516934046,fgvc_aircraft,ViT-B-32-quickgelu,laion400m_e32,zeroshot_classification,,,,ViT-B-32-quickgelu laion400m_e32
+0.0303819444444444,0.1311170789930555,0.0332405586865836,vtab/dsprites_label_orientation,ViT-B-16,laion400m_e32,zeroshot_classification,,,,ViT-B-16 laion400m_e32
+0.3171317131713171,0.7827782778277828,0.3170053475935828,fgvc_aircraft,ViT-L-14,openai,zeroshot_classification,,,,ViT-L-14 openai
+0.6691,0.8925,0.6685000000000001,vtab/cifar100,ViT-B-16,openai,zeroshot_classification,,,,ViT-B-16 openai
+0.0516872427983539,0.2725925925925926,0.0526600212836742,vtab/smallnorb_label_azimuth,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
+0.6559,0.8854,0.6541,imagenetv2,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
+0.5569148936170213,0.8558510638297873,0.5542553191489361,vtab/dtd,ViT-B-16-plus-240,laion400m_e32,zeroshot_classification,,,,ViT-B-16-plus-240 laion400m_e32
+0.1663981042654028,0.3825118483412322,0.1670142180094786,country211,ViT-B-32,laion2b_s34b_b79k,zeroshot_classification,,,,ViT-B-32 laion2b_s34b_b79k
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+0.3137333333333333,0.6410666666666667,0.3236449813371542,imagenet-a,ViT-B-32,openai,zeroshot_classification,,,,ViT-B-32 openai
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+0.8741,0.9655666666666668,0.8599962924086103,imagenet-r,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+0.4275427542754275,0.8361836183618362,0.4260962566844919,fgvc_aircraft,ViT-H-14,laion2b_s32b_b79k,zeroshot_classification,,,,ViT-H-14 laion2b_s32b_b79k
+,,,mscoco_captions,ViT-L-14,laion2b_s32b_b82k,zeroshot_retrieval,,0.7107957005500793,0.8399999737739563,ViT-L-14 laion2b_s32b_b82k
+0.5958,0.8547,0.5955999999999999,imagenetv2,ViT-B-16,laion400m_e32,zeroshot_classification,,,,ViT-B-16 laion400m_e32
+0.7540228405391985,0.9617025580668296,0.7523924485563404,sun397,ViT-g-14,laion2b_s12b_b42k,zeroshot_classification,,,,ViT-g-14 laion2b_s12b_b42k
+0.2418241824182418,0.6054605460546054,0.2405525846702317,fgvc_aircraft,ViT-B-16,openai,zeroshot_classification,,,,ViT-B-16 openai
+0.0590123456790123,0.279835390946502,0.0603763349553147,vtab/smallnorb_label_azimuth,ViT-B-16,laion400m_e32,zeroshot_classification,,,,ViT-B-16 laion400m_e32
+0.7613,0.9289,0.7611,vtab/cifar100,ViT-L-14,openai,zeroshot_classification,,,,ViT-L-14 openai
+0.9277453053102848,0.9983832856609874,0.9288577913034778,cars,ViT-g-14,laion2b_s12b_b42k,zeroshot_classification,,,,ViT-g-14 laion2b_s12b_b42k
+0.0257297092013888,0.1208224826388889,0.0259727501505128,vtab/dsprites_label_orientation,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
+0.8254437869822485,0.954963839579224,0.908884143116176,vtab/caltech101,ViT-B-16,openai,zeroshot_classification,,,,ViT-B-16 openai
+0.980125,0.999875,0.980875,stl10,ViT-L-14,laion400m_e32,zeroshot_classification,,,,ViT-L-14 laion400m_e32
+0.2463246324632463,0.5724572457245725,0.2460249554367201,fgvc_aircraft,ViT-B-32,laion2b_s34b_b79k,zeroshot_classification,,,,ViT-B-32 laion2b_s34b_b79k
+0.8102964743589743,0.9655448717948718,0.8579252085748035,voc2007,ViT-g-14,laion2b_s12b_b42k,zeroshot_classification,,,,ViT-g-14 laion2b_s12b_b42k
+0.2044,0.7866666666666666,0.2151124081246672,vtab/clevr_count_all,ViT-B-16,openai,zeroshot_classification,,,,ViT-B-16 openai
+0.2872511848341232,0.542085308056872,0.2880094786729857,country211,ViT-g-14,laion2b_s12b_b42k,zeroshot_classification,,,,ViT-g-14 laion2b_s12b_b42k
+0.06,0.288312757201646,0.0630119225348046,vtab/smallnorb_label_azimuth,ViT-B-32,openai,zeroshot_classification,,,,ViT-B-32 openai
+0.9664,0.9987,0.9665,vtab/cifar10,ViT-L-14,laion2b_s32b_b82k,zeroshot_classification,,,,ViT-L-14 laion2b_s32b_b82k
+0.1895333333333333,0.7248666666666667,0.1870254885776544,vtab/clevr_count_all,ViT-L-14,openai,zeroshot_classification,,,,ViT-L-14 openai
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+0.1582,0.8814666666666666,0.1812267071156741,vtab/clevr_closest_object_distance,ViT-L-14-336,openai,zeroshot_classification,,,,ViT-L-14-336 openai
+0.0475720164609053,0.2691358024691358,0.0457953163680852,vtab/smallnorb_label_azimuth,ViT-L-14-336,openai,zeroshot_classification,,,,ViT-L-14-336 openai
+0.3651623119556611,0.7007917656373713,0.3512103003164681,gtsrb,ViT-B-32,laion2b_e16,zeroshot_classification,,,,ViT-B-32 laion2b_e16
+0.1592258632065097,0.8009676709918627,0.1713416703583154,vtab/dmlab,ViT-L-14-336,openai,zeroshot_classification,,,,ViT-L-14-336 openai
+0.4948535233570863,0.7578780680918448,0.4319824074083427,gtsrb,ViT-B-16-plus-240,laion400m_e32,zeroshot_classification,,,,ViT-B-16-plus-240 laion400m_e32
+0.3294329432943294,0.7836783678367837,0.3317290552584671,fgvc_aircraft,ViT-L-14-336,openai,zeroshot_classification,,,,ViT-L-14-336 openai
+0.684701252367729,0.9328576420177648,0.6783238400960281,sun397,ViT-B-32,laion2b_e16,zeroshot_classification,,,,ViT-B-32 laion2b_e16
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+0.96975,0.999875,0.96975,stl10,ViT-B-16,laion400m_e32,zeroshot_classification,,,,ViT-B-16 laion400m_e32

InternVL-main/clip_benchmark/benchmark/dataset_type.csv

@@ -0,0 +1,42 @@
+dataset,type
+imagenet1k,natural
+imagenetv2,natural
+imagenet-r,natural
+imagenet_sketch,specialized
+objectnet,natural
+imagenet-a,natural
+imagenet-o,natural
+vtab/cifar10,natural
+vtab/cifar100,natural
+mnist,specialized
+vtab/flowers,natural
+cars,natural
+vtab/svhn,natural
+fer2013,natural
+renderedsst2,specialized
+vtab/pets,natural
+vtab/caltech101,natural
+voc2007_multilabel,natural
+voc2007,natural
+sun397,natural
+fgvc_aircraft,natural
+country211,natural
+vtab/dtd,natural
+gtsrb,natural
+stl10,natural
+vtab/diabetic_retinopathy,specialized
+vtab/eurosat,specialized
+vtab/resisc45,specialized
+vtab/pcam,specialized
+vtab/clevr_count_all,structured
+vtab/clevr_closest_object_distance,structured
+vtab/dsprites_label_orientation,structured
+vtab/dsprites_label_x_position,structured
+vtab/dsprites_label_y_position,structured
+vtab/smallnorb_label_elevation,structured
+vtab/smallnorb_label_azimuth,structured
+vtab/dmlab,structured
+vtab/kitti_closest_vehicle_distance,structured
+mscoco_captions,retrieval
+flickr8k,retrieval
+flickr30k,retrieval

InternVL-main/clip_benchmark/benchmark/datasets.txt

@@ -0,0 +1,39 @@
+mscoco_captions
+flickr8k
+flickr30k
+imagenet1k
+imagenetv2
+imagenet_sketch
+imagenet-a
+imagenet-r
+objectnet
+fer2013
+voc2007
+voc2007_multilabel
+sun397
+cars
+fgvc_aircraft
+mnist
+stl10
+gtsrb
+country211
+renderedsst2
+vtab/caltech101
+vtab/cifar10
+vtab/cifar100
+vtab/clevr_count_all
+vtab/clevr_closest_object_distance
+vtab/diabetic_retinopathy
+vtab/dmlab
+vtab/dsprites_label_orientation
+vtab/dsprites_label_x_position
+vtab/dtd
+vtab/eurosat
+vtab/kitti_closest_vehicle_distance
+vtab/flowers
+vtab/pets
+vtab/pcam
+vtab/resisc45
+vtab/smallnorb_label_azimuth
+vtab/smallnorb_label_elevation
+vtab/svhn

InternVL-main/clip_benchmark/benchmark/datasets_multilingual.txt

@@ -0,0 +1,13 @@
+multilingual_mscoco_captions,es
+multilingual_mscoco_captions,it
+multilingual_mscoco_captions,ko
+multilingual_mscoco_captions,pl
+multilingual_mscoco_captions,ru
+multilingual_mscoco_captions,tr
+multilingual_mscoco_captions,zh
+multilingual_mscoco_captions,en
+imagenet1k,zh
+imagenet1k,it
+imagenet1k,jp
+imagenet1k,en
+imagenet1k,ar

InternVL-main/clip_benchmark/benchmark/models.txt

@@ -0,0 +1,13 @@
+ViT-B-32,openai
+ViT-B-16,openai
+ViT-L-14,openai
+ViT-L-14-336,openai
+ViT-B-32-quickgelu,laion400m_e32
+ViT-B-32,laion2b_e16
+ViT-B-32,laion2b_s34b_b79k
+ViT-B-16,laion400m_e32
+ViT-B-16-plus-240,laion400m_e32
+ViT-L-14,laion400m_e32
+ViT-L-14,laion2b_s32b_b82k
+ViT-H-14,laion2b_s32b_b79k
+ViT-g-14,laion2b_s12b_b42k

InternVL-main/clip_benchmark/benchmark/webdatasets.txt

@@ -0,0 +1,41 @@
+wds/mscoco_captions
+wds/flickr8k
+wds/flickr30k
+wds/imagenet1k
+wds/imagenetv2
+wds/imagenet_sketch
+wds/imagenet-a
+wds/imagenet-r
+wds/imagenet-o
+wds/objectnet
+wds/fer2013
+wds/voc2007
+wds/voc2007_multilabel
+wds/sun397
+wds/cars
+wds/fgvc_aircraft
+wds/mnist
+wds/stl10
+wds/gtsrb
+wds/country211
+wds/renderedsst2
+wds/vtab/caltech101
+wds/vtab/cifar10
+wds/vtab/cifar100
+wds/vtab/clevr_count_all
+wds/vtab/clevr_closest_object_distance
+wds/vtab/diabetic_retinopathy
+wds/vtab/dmlab
+wds/vtab/dsprites_label_orientation
+wds/vtab/dsprites_label_x_position
+wds/vtab/dsprites_label_y_position
+wds/vtab/dtd
+wds/vtab/eurosat
+wds/vtab/kitti_closest_vehicle_distance
+wds/vtab/flowers
+wds/vtab/pets
+wds/vtab/pcam
+wds/vtab/resisc45
+wds/vtab/smallnorb_label_azimuth
+wds/vtab/smallnorb_label_elevation
+wds/vtab/svhn

InternVL-main/clip_benchmark/clip_benchmark/datasets/ar_classnames.json

@@ -0,0 +1,1004 @@
+{
+  "imagenet1k": [
+    "\u0633\u0645\u0643 \u0627\u0644\u062a\u0646\u0634",
+    "\u0627\u0644\u0633\u0645\u0643\u0629 \u0627\u0644\u0630\u0647\u0628\u064a\u0629",
+    "\u0627\u0644\u0642\u0631\u0634 \u0627\u0644\u0623\u0628\u064a\u0636 \u0627\u0644\u0643\u0628\u064a\u0631",
+    "\u0627\u0644\u0642\u0631\u0634 \u0627\u0644\u0628\u0628\u0631\u064a",
+    "\u0627\u0644\u0642\u0631\u0634 \u0627\u0644\u0645\u0637\u0631\u0642\u0629",
+    "\u0633\u0645\u0643 \u0627\u0644\u0631\u0639\u0627\u062f",
+    "\u0633\u0645\u0643 \u0627\u0644\u0631\u0642\u064a\u0637\u0629",
+    "\u062f\u064a\u0643",
+    "\u062f\u062c\u0627\u062c\u0629",
+    "\u0646\u0639\u0627\u0645\u0629",
+    "\u0627\u0644\u0634\u0631\u0634\u0648\u0631 \u0627\u0644\u062c\u0628\u0644\u064a",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u062d\u0633\u0648\u0646",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u062a\u0641\u0627\u062d\u064a \u0627\u0644\u0627\u0648\u0631\u0648\u0628\u064a",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u062c\u0646\u0643 \u062f\u0627\u0643\u0646 \u0627\u0644\u0639\u064a\u0648\u0646",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u062f\u0631\u0633\u0629 \u0627\u0644\u0633\u0645\u0627\u0648\u064a",
+    "\u0637\u0627\u0626\u0631 \u0627\u0628\u0648 \u0627\u0644\u062d\u0646\u0627\u0621",
+    "\u0628\u0644\u0628\u0644",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0642\u064a\u0642",
+    "\u0639\u0642\u0639\u0642 \u0637\u0627\u0626\u0631 \u0627\u0644\u0630\u064a\u0644 \u0627\u0644\u0637\u0648\u064a\u0644",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0642\u0631\u0642\u0641",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u063a\u0637\u0627\u0633",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u062d\u062f\u0623\u0629",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0639\u0642\u0627\u0628 \u0627\u0644\u0631\u062e\u0645\u0629",
+    "\u0646\u0633\u0631",
+    "\u0627\u0644\u0628\u0648\u0645\u0629 \u0627\u0644\u0631\u0645\u0627\u062f\u064a\u0629",
+    "\u0627\u0644\u0633\u0645\u0646\u062f\u0631 \u0627\u0644\u0646\u0627\u0631\u064a",
+    "\u0644\u064a\u0633\u0648\u062a\u0631\u064a\u062a\u0648\u0646 \u0641\u0648\u0644\u062c\u0627\u0631\u064a\u0633",
+    "\u0627\u0644\u0633\u0645\u0646\u062f\u0631 \u0627\u0644\u0645\u0627\u0626\u064a",
+    "\u0627\u0644\u0633\u0645\u0646\u062f\u0631 \u0627\u0644\u0645\u0631\u0642\u0637",
+    "\u0627\u0644\u0633\u0645\u0646\u062f\u0631 \u0627\u0644\u0645\u0643\u0633\u064a\u0643\u064a",
+    "\u0636\u0641\u062f\u0639 \u0627\u0644\u062b\u0648\u0631 \u0627\u0644\u0627\u0645\u0631\u064a\u0643\u064a",
+    "\u0636\u0641\u062f\u0639 \u0627\u0644\u0634\u062c\u0631",
+    "\u0627\u0644\u0636\u0641\u0627\u062f\u0639 \u0630\u0627\u062a \u0627\u0644\u0630\u064a\u0644",
+    "\u0627\u0644\u0633\u0644\u062d\u0641\u0627\u0629 \u0627\u0644\u0628\u062d\u0631\u064a\u0629 \u0636\u062e\u0645\u0629 \u0627\u0644\u0631\u0623\u0633",
+    "\u0633\u0644\u062d\u0641\u0627\u0629 \u0627\u0644\u0645\u062d\u064a\u0637 \u062c\u0644\u062f\u064a\u0629 \u0627\u0644\u0638\u0647\u0631",
+    "\u0633\u0644\u062d\u0641\u0627\u0629 \u0627\u0644\u0637\u064a\u0646",
+    "\u0627\u0644\u0633\u0644\u062d\u0641\u0627\u0629 \u0630\u0627\u062a \u0638\u0647\u0631 \u0627\u0644\u0645\u0639\u064a\u0646",
+    "\u0633\u0644\u062d\u0641\u0627\u0629 \u0635\u0646\u062f\u0648\u0642\u064a\u0629",
+    "\u0627\u0644\u0648\u0632\u063a",
+    "\u0627\u0644\u0625\u063a\u0648\u0627\u0646\u0629",
+    "\u0627\u0644\u062d\u0631\u0628\u0627\u0621 \u0627\u0644\u062e\u0636\u0631\u0627\u0621",
+    "\u0627\u0644\u0633\u062d\u0627\u0644\u064a \u0627\u0644\u0635\u062d\u0631\u0627\u0648\u064a\u0629",
+    "\u0627\u0644\u0639\u064e\u0636\u0652\u0631\u064e\u0641\u064f\u0648\u0637",
+    " \u0633\u062d\u0644\u064a\u0629 \u0647\u062f\u0628 \u0627\u0644\u0639\u0646\u0642",
+    "\u0633\u062d\u0644\u064a\u0629 \u0627\u0644\u062a\u0645\u0633\u0627\u062d",
+    "\u0648\u062d\u0634 \u062c\u064a\u0644\u0627",
+    "\u0633\u062d\u0644\u064a\u0629 \u062e\u0636\u0631\u0627\u0621",
+    "\u062d\u0631\u0628\u0627\u0621 \u0627\u0641\u0631\u064a\u0642\u064a\u0629",
+    "\u062a\u0646\u064a\u0646 \u0643\u0648\u0645\u0648\u062f\u0648",
+    "\u0627\u0644\u062a\u0645\u0633\u0627\u062d \u0627\u0644\u0623\u0641\u0631\u064a\u0642\u064a",
+    "\u062a\u0645\u0633\u0627\u062d \u0627\u0644\u0642\u0627\u0637\u0648\u0631 \u0627\u0644\u0623\u0645\u0631\u064a\u0643\u064a",
+    "\u0627\u0644\u062f\u064a\u0646\u0627\u0635\u0648\u0631 \u062b\u064f\u0644\u0627\u062b\u064a\u064f\u0651 \u0627\u0644\u0642\u064f\u0631\u0648\u0646",
+    "\u0627\u0644\u062b\u0639\u0627\u0628\u064a\u0646 \u0627\u0644\u062f\u0648\u062f\u064a\u0629",
+    "\u0623\u0641\u0639\u0649 \u0627\u0644\u0637\u0648\u0642",
+    "\u0627\u0644\u0623\u0641\u0639\u0649 \u0627\u0644\u0646\u0641\u0627\u062b\u0629 ",
+    "\u0627\u0644\u0623\u0641\u0639\u0649 \u0627\u0644\u062e\u0636\u0631\u0627\u0621",
+    "\u0627\u0644\u062b\u0639\u0628\u0627\u0646 \u0627\u0644\u0645\u0644\u0643",
+    "\u0623\u0641\u0639\u0649 \u0627\u0644\u0631\u0628\u0627\u0637",
+    "\u0627\u0641\u0639\u0649 \u0627\u0644\u0645\u0627\u0621",
+    "\u062b\u0639\u0628\u0627\u0646 \u0646\u0628\u0627\u062a \u0643\u0631\u0645\u0629",
+    "\u0627\u0644\u062b\u0639\u0628\u0627\u0646 \u0627\u0644\u0644\u064a\u0644\u064a ",
+    "\u062b\u0639\u0628\u0627\u0646 \u0627\u0644\u0623\u0635\u0644\u0629 \u0627\u0644\u0639\u0627\u0635\u0631\u0629",
+    "\u0627\u0644\u062b\u0639\u0628\u0627\u0646 \u0627\u0644\u0635\u062e\u0631\u064a",
+    "\u0643\u0648\u0628\u0631\u0627 \u0647\u0646\u062f\u064a\u0629",
+    "\u0645\u0627\u0645\u0628\u0627 \u062e\u0636\u0631\u0627\u0621",
+    "\u0627\u0644\u063a\u064a\u062f\u0642\u0627\u0648\u0627\u062a",
+    "\u0627\u0644\u0623\u0641\u0639\u0649 \u0627\u0644\u0645\u0642\u0631\u0646\u0629",
+    "\u0627\u0644\u0623\u0641\u0639\u0649 \u0627\u0644\u062c\u0631\u0633\u064a\u0629 \u0630\u0627\u062a \u0627\u0644\u0635\u062f\u0631 \u0627\u0644\u0645\u0627\u0633\u064a",
+    "\u0627\u0641\u0639\u0649 \u0644\u0627\u0641\u0651\u0629 \u0627\u0644\u062c\u0646\u0628",
+    "\u0645\u0641\u0635\u0644\u064a\u0627\u062a \u062b\u0644\u0627\u062b\u064a\u0629 \u0627\u0644\u0641\u0635\u0648\u0635",
+    "\u062d\u0634\u0631\u0629 \u0627\u0644\u0639\u0646\u0643\u0628\u0648\u062a \u0630\u0627\u062a \u0627\u0644\u0642\u0648\u0627\u0626\u0645 \u0627\u0644\u0637\u0648\u064a\u0644\u0629",
+    "\u0639\u0642\u0631\u0628",
+    "\u0639\u0646\u0643\u0628\u0648\u062a \u0627\u0644\u062d\u062f\u064a\u0642\u0629 \u0630\u0627 \u0627\u0644\u0644\u0648\u0646 \u0627\u0644\u0623\u0633\u0648\u062f \u0648\u0627\u0644\u0623\u0635\u0641\u0631 ",
+    "\u0639\u0646\u0643\u0628\u0648\u062a \u0627\u0644\u062d\u0638\u064a\u0631\u0629",
+    "\u0639\u0646\u0643\u0628\u0648\u062a \u0627\u0644\u062d\u062f\u064a\u0642\u0629 \u0627\u0644\u0623\u0648\u0631\u0628\u064a",
+    "\u0639\u0646\u0643\u0628\u0648\u062a \u0627\u0644\u0623\u0631\u0645\u0644\u0629 \u0627\u0644\u0633\u0648\u062f\u0627\u0621",
+    "\u0639\u0646\u0643\u0628\u0648\u062a \u0631\u062a\u064a\u0644\u0627\u0621 \u0630\u0627\u062a \u0623\u0631\u062c\u0644 \u062d\u0645\u0631\u0627\u0621",
+    "\u0627\u0644\u0639\u0646\u0643\u0628\u0648\u062a \u0627\u0644\u0630\u0626\u0628\u064a",
+    "\u0639\u0646\u0643\u0628\u0648\u062a \u0627\u0644\u0642\u064f\u0631\u064e\u0627\u062f",
+    "\u062d\u0634\u0631\u0629 \u0623\u0645 \u0623\u0631\u0628\u0639\u0629 \u0648\u0623\u0631\u0628\u0639\u064a\u0646",
+    "\u062f\u062c\u0627\u062c\u0629 \u0627\u0644\u0637\u0647\u064a\u0648\u062c \u0627\u0644\u0623\u0633\u0648\u062f",
+    "\u062f\u062c\u0627\u062c\u0629 \u062a\u0631\u0645\u062c\u0627\u0646",
+    "\u062f\u062c\u0627\u062c\u0629 \u0637\u064a\u0647\u0648\u062c \u0645\u0637\u0648\u0642",
+    "\u062f\u062c\u0627\u062c\u0629 \u0627\u0644\u0637\u0647\u0628\u0648\u062c",
+    "\u0627\u0644\u0637\u0627\u0648\u0648\u0633",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0633\u0645\u0627\u0646",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u062d\u062c\u0644",
+    "\u0627\u0644\u0628\u0628\u063a\u0627\u0621 \u0627\u0644\u0631\u0645\u0627\u062f\u064a",
+    "\u0628\u0628\u063a\u0627\u0621 \u0645\u0643\u0627\u0648",
+    "\u0628\u0628\u063a\u0627\u0621 \u0643\u0648\u0643\u0627\u062a\u0648 \u0643\u0628\u0631\u064a\u062a\u064a \u0627\u0644\u0639\u0631\u0641",
+    "\u0628\u0628\u063a\u0627\u0621 \u0642\u0648\u0633 \u0642\u0632\u062d",
+    "\u0637\u064a\u0631 \u0627\u0644\u0648\u0642\u0648\u0627\u0642 \u0643\u0648\u0643\u0627\u0644",
+    "\u0637\u064a\u0631 \u0648\u0631\u0648\u0627\u0631 ",
+    "\u0637\u064a\u0631 \u0623\u0628\u0648 \u0642\u0631\u0646",
+    "\u0627\u0644\u0637\u0627\u0626\u0631 \u0627\u0644\u0637\u0646\u0627\u0646",
+    "\u0637\u064a\u0648\u0631 \u0627\u0644\u064a\u0642\u0645\u064e\u0631",
+    "\u0637\u0627\u0626\u0631 \u0645\u0637\u0648\u0642",
+    "\u0630\u0643\u0631 \u0627\u0644\u0628\u0637",
+    "\u0627\u0644\u0628\u0637\u0629 \u0627\u0644\u063a\u0648\u0627\u0635\u0629 \u062d\u0645\u0631\u0627\u0621 \u0627\u0644\u0635\u062f\u0631",
+    "\u0625\u0648\u0632\u0629",
+    "\u0627\u0644\u0625\u0648\u0632\u0629 \u0633\u0648\u062f\u0627\u0621",
+    "\u0627\u0644\u0641\u064a\u0644",
+    " \u0622\u0643\u0644 \u0627\u0644\u0646\u0645\u0644 \u0627\u0644\u0634\u0648\u0643\u064a",
+    "\u062e\u0644\u062f \u0627\u0644\u0645\u0627\u0621",
+    "\u0648\u0644\u0628",
+    "\u0643\u0648\u0627\u0644\u0627",
+    "\u0627\u0644\u0648\u0645\u0628\u062a\u064a\u0627\u062a",
+    "\u0642\u0646\u062f\u064a\u0644 \u0628\u062d\u0631",
+    "\u0634\u0642\u0627\u0626\u0642 \u0646\u0639\u0645\u0627\u0646 \u0627\u0644\u0628\u062d\u0631",
+    "\u0645\u0631\u062c\u0627\u0646 \u0627\u0644\u062f\u0645\u0627\u063a",
+    "\u062f\u064a\u062f\u0627\u0646 \u0645\u0633\u0637\u062d\u0629",
+    "\u062f\u064a\u062f\u0627\u0646 \u0623\u0633\u0637\u0648\u0627\u0646\u064a\u0629",
+    "\u0645\u062d\u0627\u0631\u0629",
+    "\u062d\u0644\u0632\u0648\u0646",
+    "\u0628\u0632\u0627\u0642",
+    "\u062d\u0644\u0632\u0648\u0646 \u0645\u0627\u0626\u064a",
+    "\u0631\u062e\u0648\u064a\u0627\u062a \u0628\u062d\u0631\u064a\u0647 \u062a\u0633\u0645\u0649 \u0627\u0644\u062e\u064a\u062a\u0648\u0646",
+    "\u0646\u0648\u062a\u064a\u0644\u0648\u0633 \u0627\u0644\u062d\u062c\u0631\u064a",
+    "\u0633\u0631\u0637\u0627\u0646 \u0627\u0644\u0628\u062d\u0631",
+    "\u0633\u0631\u0637\u0627\u0646 \u0627\u0644\u0628\u062d\u0631 \u0627\u0644\u0623\u0637\u0644\u0633\u064a",
+    "\u0627\u0644\u0633\u0631\u0637\u0627\u0646 \u0627\u0644\u0639\u0627\u0632\u0641",
+    "\u0645\u0644\u0643 \u0627\u0644\u0633\u0644\u0637\u0639\u0648\u0646",
+    "\u062c\u0631\u0627\u062f \u0627\u0644\u0628\u062d\u0631 \u0627\u0644\u0623\u0645\u0631\u064a\u0643\u064a",
+    "\u0643\u0631\u0643\u0646\u062f \u0634\u0627\u0626\u0643",
+    "\u062c\u0631\u0627\u062f \u0627\u0644\u0645\u064a\u0627\u0647 \u0627\u0644\u0639\u0630\u0628\u0629",
+    "\u0627\u0644\u0633\u0631\u0637\u0627\u0646 \u0627\u0644\u0646\u0627\u0633\u0643",
+    "\u0645\u062a\u0633\u0627\u0648\u064a\u0627\u062a \u0627\u0644\u0623\u0642\u062f\u0627\u0645",
+    "\u0644\u0642\u0644\u0642 \u0623\u0628\u064a\u0636",
+    "\u0644\u0642\u0644\u0642 \u0623\u0633\u0648\u062f",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0628\u062c\u0639 \u0627\u0644\u0645\u0633\u0645\u0649 \u0623\u0628\u0648 \u0645\u0644\u0639\u0642\u0629",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0641\u0644\u0627\u0645\u064a\u0646\u063a\u0648",
+    "\u0637\u0627\u0626\u0631 \u0628\u0644\u0634\u0648\u0646 \u0627\u0644\u0623\u0632\u0631\u0642 \u0627\u0644\u0635\u063a\u064a\u0631",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0628\u0644\u0634\u0648\u0646 \u0627\u0644\u0623\u0628\u064a\u0636 \u0627\u0644\u0643\u0628\u064a\u0631",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0639\u062c\u0627\u062c",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0643\u0631\u0643\u064a\u0629",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0631\u0637\u0627\u0633",
+    "\u062f\u062c\u0627\u062c\u0629 \u0627\u0644\u0645\u0627\u0621 \u0627\u0644\u0623\u0631\u062c\u0648\u0627\u0646\u064a\u0629",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u063a\u0631 \u0627\u0644\u0623\u0645\u0631\u064a\u0643\u064a",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u062d\u0628\u0627\u0631",
+    "\u0637\u0627\u0626\u0631 \u0642\u0646\u0628\u0631\u0629 \u0627\u0644\u0645\u0627\u0621 \u0627\u0644\u0645\u062a\u0648\u0631\u062f",
+    "\u0637\u0627\u0626\u0631 \u062f\u0631\u064a\u062c\u0629 \u0623\u0644\u0628\u064a\u0629",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0637\u064a\u0637\u0648\u064a \u0623\u062d\u0645\u0631 \u0627\u0644\u0633\u0627\u0642",
+    "\u0639\u064e\u062f\u0651\u0627\u0621 \u0627\u0644\u0645\u0633\u062a\u0646\u0642\u0639\u0627\u062a \u0623\u0648 \u0627\u0644\u062f\u0651\u062a\u0634\u0631",
+    "\u0635\u0627\u0626\u062f \u0627\u0644\u0645\u062d\u0627\u0631",
+    "\u0628\u062c\u0639\u0629",
+    "\u0627\u0644\u0628\u0637\u0631\u064a\u0642 \u0627\u0644\u0645\u0644\u0643",
+    "\u0637\u0627\u0626\u0631 \u0627\u0644\u0642\u0637\u0631\u0633",
+    "\u0627\u0644\u062d\u0648\u062a \u0627\u0644\u0631\u0645\u0627\u062f\u064a \u0627\u0644\u0635\u0644\u0628",
+    "\u0627\u0644\u062d\u064f\u0648\u062a\u064f \u0627\u0644\u0642\u0627\u062a\u0650\u0644\u064f",
+    "\u0628\u0642\u0631\u0629 \u0627\u0644\u0628\u062d\u0631",
+    "\u0623\u0633\u062f \u0627\u0644\u0628\u062d\u0631",
+    "\u0643\u0644\u0628 \u0634\u064a\u0648\u0627\u0648\u0627",
+    "\u0643\u0644\u0628 \u0627\u0644\u0630\u0642\u0646 \u0627\u0644\u064a\u0627\u0628\u0627\u0646\u064a",
+    "\u0643\u0644\u0628 \u0645\u0627\u0644\u0637\u064a",
+    "\u0643\u0644\u0628 \u0628\u0643\u064a\u0646\u064a",
+    "\u0643\u0644\u0628 \u062a\u0634\u064a\u0647 \u062a\u0632\u0648 \u0627\u0644\u0635\u064a\u0646\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0645\u0644\u0643 \u062a\u0634\u0627\u0631\u0644\u0632",
+    "\u0643\u0644\u0628 \u0628\u0627\u0628\u064a\u0644\u0648\u0646",
+    "\u0643\u0644\u0628 \u0627\u0644\u062a\u0631\u064a\u0631 \u0627\u0644\u0639\u0631\u0636",
+    "\u0643\u0644\u0627\u0628 \u0631\u064a\u062f\u062c \u0628\u0627\u0643",
+    "\u0643\u0644\u0628 \u0627\u0644\u0635\u064a\u062f \u0627\u0644\u0623\u0641\u063a\u0627\u0646\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0628\u0627\u0633\u0637",
+    "\u0643\u0644\u0628 \u0628\u064a\u063a\u0644",
+    "\u0643\u0644\u0628 \u0627\u0644\u062f\u0645\u0648\u0645",
+    "\u0643\u0644\u0628 \u0628\u0644\u0648\u064a\u062a\u064a\u0643 \u0643\u0648\u0646\u0647\u0648\u0646\u062f",
+    "\u0643\u0644\u0628 \u0643\u0648\u0646\u0647\u0648\u0646\u062f \u0627\u0644\u0628\u0646\u064a \u0648\u0627\u0644\u0623\u0633\u0648\u062f",
+    "\u0643\u0644\u0628 \u0627\u0644\u0648\u0648\u0643\u0631",
+    "\u0643\u0644\u0628 \u0635\u064a\u062f \u0627\u0644\u062b\u0639\u0627\u0644\u0628 \u0627\u0644\u0625\u0646\u062c\u0644\u064a\u0632\u064a",
+    "\u0643\u0644\u0628 \u0631\u064a\u062f\u0628\u0648\u0646 \u0643\u0648\u0646\u0647\u0648\u0646\u062f",
+    "\u0643\u0644\u0628 \u0628\u0648\u0631\u0632\u0648\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0630\u0626\u0628 \u0627\u0644\u0627\u064a\u0631\u0644\u0646\u062f\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0633\u0644\u0648\u0642\u064a \u0627\u0644\u0627\u064a\u0637\u0627\u0644\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0648\u064a\u0628\u062a",
+    "\u0643\u0644\u0628 \u0627\u064a\u0628\u064a\u0632\u0627\u0646 \u0647\u0648\u0646\u062f",
+    "\u0627\u0644\u0643\u0644\u0628 \u0627\u0644\u0646\u0631\u0648\u064a\u062c\u064a",
+    "\u0643\u0644\u0628 \u0623\u0648\u062a\u064a\u0631 \u0647\u0627\u0648\u0646\u062f",
+    "\u0643\u0644\u0628 \u0633\u0644\u0648\u0642\u064a",
+    "\u0643\u0644\u0628 \u062f\u064a\u0631 \u0647\u0627\u0648\u0646\u062f \u0627\u0644\u0627\u0633\u0643\u062a\u0644\u0646\u062f\u064a",
+    "\u0643\u0644\u0628 \u0648\u0627\u064a\u0645\u0631\u064a",
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+    "\u0643\u0644\u0628 \u0633\u062a\u0627\u0641\u0648\u0631\u062f\u0634\u0627\u064a\u0631 \u0627\u0644\u0623\u0645\u0631\u064a\u0643\u064a",
+    "\u0643\u0644\u0628 \u0628\u064a\u062f\u0644\u064a\u0646\u062c\u062a\u0648\u0646 \u062a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u0628\u0648\u0631\u062f\u0631 \u062a\u064a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u0643\u064a\u0631\u064a \u0628\u0644\u0648 \u062a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u062a\u0631\u064a\u0631 \u0627\u0644\u0625\u064a\u0631\u0644\u0646\u062f\u064a",
+    "\u0643\u0644\u0628 \u0646\u0648\u0631\u0641\u0648\u0644\u0643 \u062a\u064a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u0646\u0648\u0631\u064a\u062a\u0634 \u062a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u064a\u0648\u0631\u0643 \u0634\u0627\u064a\u0631",
+    "\u0643\u0644\u0628 \u0648\u064a\u0631 \u0641\u0648\u0643\u0633 \u062a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u0644\u064a\u0643\u0644\u0627\u0646\u062f \u062a\u064a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u0633\u064a\u0627\u0644\u064a\u0647\u0627\u0645 \u062a\u064a\u0631\u064a\u0631",
+    "\u0643\u0644\u0627\u0628 \u0627\u0644\u0623\u0631\u062f\u064a\u0644",
+    "\u0643\u0644\u0628 \u0643\u064a\u0631\u0646 \u062a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u062a\u0631\u064a\u0631 \u0627\u0633\u062a\u0631\u0627\u0644\u064a",
+    "\u0643\u0644\u0628 \u062f\u0627\u0646\u062f\u064a \u062f\u064a\u0646\u0645\u0648\u0646\u062a",
+    "\u0643\u0644\u0628 \u0628\u0648\u0633\u0637\u0646 \u062a\u064a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u0634\u0646\u0627\u0648\u062a\u0633\u0631 \u0645\u0646\u0645\u0646\u0645",
+    "\u0643\u0644\u0628 \u0634\u0646\u0627\u0648\u062a\u0633\u0631 \u0627\u0644\u0639\u0645\u0644\u0627\u0642",
+    "\u0643\u0644\u0628 \u0634\u0646\u0627\u0648\u062a\u0633\u0631 \u0627\u0644\u0639\u0627\u062f\u064a",
+    "\u0627\u0644\u0643\u0644\u0628 \u0627\u0644\u0627\u0633\u0643\u062a\u0644\u0646\u062f\u064a",
+    "\u0643\u0644\u0628 \u062a\u0631\u064a\u0631 \u0627\u0644\u062a\u0628\u062a",
+    "\u0643\u0644\u0628 \u0633\u064a\u0644\u0643\u064a \u062a\u064a\u0631\u064a\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u062a\u064a\u0631\u064a\u0631 \u0627\u0644\u0642\u0645\u062d\u064a \u0627\u0644\u0646\u0627\u0639\u0645",
+    "\u0643\u0644\u0628 \u0648\u064a\u0633\u062a\u064a",
+    "\u0643\u0644\u0628 \u0644\u0627\u0633\u0627 \u0623\u0628\u0633\u0648",
+    "\u0643\u0644\u0628 \u0627\u0644\u0645\u0633\u062a\u0631\u062f \u0627\u0644\u0630\u0647\u0628\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0645\u0633\u062a\u0631\u062f \u0645\u062c\u0639\u062f \u0627\u0644\u0634\u0639\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u0645\u0633\u062a\u0631\u062f \u0627\u0644\u0630\u0647\u0628\u064a",
+    "\u0643\u0644\u0628 \u0644\u0627\u0628\u0631\u0627\u062f\u0648\u0631 \u0631\u064a\u062a\u0631\u064a\u0641\u0631",
+    "\u0643\u0644\u0628 \u0634\u064a\u0633\u0628\u064a\u0643\u0627",
+    "\u0643\u0644\u0628 \u0628\u0648\u064a\u0646\u062a\u0631 \u0627\u0644\u0623\u0644\u0645\u0627\u0646\u064a \u0642\u0635\u064a\u0631 \u0627\u0644\u0634\u0639\u0631",
+    "\u0643\u0644\u0628 \u0641\u064a\u0632\u0644\u0627",
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+    "\u0643\u0644\u0628 \u0633\u064a\u062a\u0631 \u0627\u0644\u0625\u064a\u0631\u0644\u0646\u062f\u064a",
+    "\u0643\u0644\u0628 \u0633\u064a\u062a\u0631 \u0627\u0644\u062c\u0648\u0631\u062f\u0648\u0646\u064a",
+    "\u0643\u0644\u0628 \u0628\u0631\u064a\u062a\u0627\u0646\u064a \u0633\u0628\u0627\u0646\u064a\u0644",
+    "\u0643\u0644\u0628 \u0642\u0644\u0645\u0628\u0631",
+    "\u0643\u0644\u0628 \u0633\u0628\u0631\u064a\u0646\u063a\u0631 \u0633\u0628\u0627\u0646\u064a\u0644 \u0627\u0644\u0625\u0646\u062c\u0644\u064a\u0632\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0633\u0628\u0631\u064a\u0646\u063a\u0631 \u0627\u0644\u0648\u064a\u0644\u0632\u064a",
+    "\u0643\u0644\u0628 \u062f\u0644\u0644 \u0627\u0644\u0630\u0644\u064a\u0644",
+    "\u0643\u0644\u0628 \u0627\u0644\u0633\u0627\u0643\u0633 \u0627\u0644\u0625\u0633\u0628\u0627\u0646\u064a",
+    "\u0643\u0644\u0628 \u0633\u0628\u0627\u064a\u0646\u0644 \u0627\u0644\u0645\u0627\u0621 \u0627\u0644\u0625\u064a\u0631\u0644\u0646\u062f\u064a",
+    "\u0643\u0644\u0628 \u0643\u0648\u0641\u0627\u0632",
+    "\u0643\u0644\u0628 \u0634\u064a\u0628\u0631\u0643",
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+    "\u0643\u0644\u0628 \u0645\u0627\u0644\u064a\u0646\u0648",
+    "\u0643\u0644\u0628 \u0628\u0631\u064a\u0627\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u0643\u0644\u064a\u0628\u064a \u0627\u0644\u0625\u0633\u062a\u0631\u0627\u0644\u064a",
+    "\u0643\u0644\u0628 \u0643\u0648\u0645\u0648\u0646\u062f\u0648\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u0631\u0627\u0639\u064a \u0627\u0644\u0625\u0646\u062c\u0644\u064a\u0632\u064a \u0627\u0644\u0642\u062f\u064a\u0645",
+    "\u0643\u0644\u0628 \u0627\u0644\u0631\u0627\u0639\u064a \u0627\u0644\u0634\u062a\u0644\u0646\u062f\u0649",
+    "\u0643\u0644\u0628 \u0627\u0644\u0643\u0648\u0644\u064a",
+    "\u0643\u0644\u0628 \u0628\u0648\u0631\u062f\u0631 \u0643\u0648\u0644\u064a",
+    "\u0643\u0644\u0628 \u0628\u0648\u0641\u064a \u062f\u064a \u0641\u0644\u0627\u0646\u062f\u0631",
+    "\u0643\u0644\u0628 \u0631\u0648\u062a \u0648\u0627\u064a\u0644\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u0631\u0627\u0639\u064a \u0627\u0644\u0623\u0644\u0645\u0627\u0646\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u062f\u0648\u0628\u064a\u0631\u0645\u0627\u0646",
+    "\u0643\u0644\u0628 \u0628\u064a\u0646\u0634\u0631 \u0627\u0644\u0645\u0635\u063a\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u062c\u0628\u0644 \u0627\u0644\u0633\u0648\u064a\u0633\u0631\u064a",
+    "\u0643\u0644\u0628 \u062c\u0628\u0644 \u0627\u0644\u0628\u0631\u0646\u064a\u0632",
+    "\u0643\u0644\u0628 \u0627\u067e\u064a\u0646\u0632\u064a\u0644\u064a\u0631 \u0633\u064a\u0646\u064a\u0646\u0647\u0648\u0646\u062f",
+    "\u0643\u0644\u0628 \u0627\u0646\u062a\u0644\u0628\u062a\u0634\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u0628\u0648\u0643\u0633\u0631",
+    "\u0643\u0644\u0628 \u0628\u0648\u0644 \u0645\u0627\u0633\u062a\u064a\u0641",
+    "\u0643\u0644\u0628 \u0627\u0644\u0645\u0627\u0633\u062a\u064a\u0641 \u0627\u0644\u062a\u064a\u0628\u062a\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0628\u0648\u0644\u062f\u0648\u063a \u0627\u0644\u0641\u0631\u0646\u0633\u064a",
+    "\u0627\u0644\u0643\u0644\u0628 \u0627\u0644\u062f\u0627\u0646\u0645\u0627\u0631\u0643\u064a \u0627\u0644\u0636\u062e\u0645",
+    "\u0643\u0644\u0628 \u0633\u0627\u0646\u062a \u0628\u0631\u0646\u0627\u0631\u062f",
+    "\u0643\u0644\u0628 \u0627\u0644\u0647\u0627\u0633\u0643\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0645\u0644\u0645\u0648\u062a \u0627\u0644\u0623\u0644\u0627\u0633\u0643\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0647\u0633\u0643\u064a \u0627\u0644\u0633\u064a\u0628\u064a\u0631\u064a",
+    "\u0643\u0644\u0628 \u062f\u0644\u0645\u0627\u0633\u064a \u0627\u0644\u0645\u0631\u0642\u0637",
+    "\u0643\u0644\u0628 \u0623\u0641\u064a\u0646\u0628\u064a\u0646\u0634\u0631",
+    "\u0643\u0644\u0628 \u0628\u0627\u0633\u0646\u062c\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u0628\u062c",
+    "\u0643\u0644\u0628 \u0644\u064a\u0648\u0646 \u0628\u064a\u0631\u062c\u0631",
+    "\u0643\u0644\u0628 \u0646\u064a\u0648\u0641\u0627\u0648\u0646\u062f\u0644\u0627\u0646\u062f",
+    "\u0643\u0644\u0640\u0628 \u062c\u0628\u0627\u0644 \u0627\u0644\u0628\u0631\u0627\u0646\u0633",
+    "\u0643\u0644\u0628 \u0633\u0627\u0645\u0648\u062f\u064a",
+    "\u0643\u0644\u0628 \u0628\u0648\u0645\u064a\u0631\u0627\u0646\u064a\u0627\u0646",
+    "\u0643\u0644\u0628 \u0627\u0644\u062a\u0634\u0627\u0648 \u062a\u0634\u0627\u0648",
+    "\u0643\u0644\u0628 \u0627\u0644\u0643\u064a\u0634\u0648\u0646\u062f",
+    "\u0643\u0644\u0628 \u0627\u0644\u062c\u0631\u064a\u0641\u0648\u0646",
+    "\u0643\u0644\u0628\u0628 \u0628\u064a\u0645\u0628\u0631\u0648\u0643 \u0648\u064a\u0644\u0634 \u0643\u0648\u0631\u062c\u0649",
+    "\u0643\u0644\u0628 \u0627\u0644\u0643\u0627\u0631\u062f\u064a\u062c\u0627\u0646",
+    "\u0643\u0644\u0628 \u0627\u0644\u062a\u0648\u064a \u0627\u0644\u0628\u0648\u062f\u0644",
+    "\u0643\u0644\u0628 \u0627\u0644\u0628\u0648\u062f\u0644 \u0627\u0644\u0635\u063a\u064a\u0631",
+    "\u0643\u0644\u0628 \u0627\u0644\u0628\u0648\u062f\u0644 \u0627\u0644\u0642\u064a\u0627\u0633\u064a",
+    "\u0627\u0644\u0643\u0644\u0628 \u0627\u0644\u0645\u0643\u0633\u064a\u0643\u064a \u0628\u0644\u0627 \u0634\u0639\u0631",
+    "\u0627\u0644\u0630\u0626\u0628 \u0627\u0644\u0631\u0645\u0627\u062f\u064a",
+    "\u0627\u0644\u0630\u0626\u0628 \u0627\u0644\u0623\u0628\u064a\u0636",
+    "\u0627\u0644\u0630\u0626\u0628 \u0627\u0644\u0623\u062d\u0645\u0631",
+    "\u0627\u0644\u0642\u064a\u0648\u0637",
+    "\u0627\u0644\u0643\u0644\u0628 \u0627\u0644\u0625\u0633\u062a\u0631\u0627\u0644\u064a",
+    "\u0643\u0644\u0628 \u0627\u0644\u062f\u0648\u0644",
+    "\u0627\u0644\u0643\u0644\u0628 \u0627\u0644\u0628\u0631\u064a \u0627\u0644\u0625\u0641\u0631\u064a\u0642\u064a",
+    "\u0627\u0644\u0636\u0628\u0639",
+    "\u0627\u0644\u062b\u0639\u0644\u0628 \u0627\u0644\u0623\u062d\u0645\u0631",
+    "\u0627\u0644\u062b\u0639\u0644\u0628 \u0627\u0644\u0642\u0632\u0645",
+    "\u0627\u0644\u062b\u0639\u0644\u0628 \u0627\u0644\u0642\u0637\u0628\u064a",
+    "\u0627\u0644\u062b\u0639\u0644\u0628 \u0627\u0644\u0631\u0645\u0627\u062f\u064a",
+    "\u0627\u0644\u0642\u0637\u0637 \u0627\u0644\u0646\u0645\u0631\u064a\u0629",
+    "\u0627\u0644\u0633\u0646\u0648\u0631 \u0627\u0644\u0645\u064f\u0631\u0642\u0637 \u0627\u0644\u0635\u063a\u064a\u0631",
+    "\u0642\u0637 \u0634\u064a\u0631\u0627\u0632\u064a",
+    "\u0627\u0644\u0642\u0637 \u0627\u0644\u0633\u064a\u0627\u0645\u064a",
+    "\u0642\u0637 \u0644\u064a\u0628\u064a",
+    "\u0623\u0633\u062f \u0627\u0644\u062c\u0628\u0627\u0644",
+    "\u0627\u0644\u0648\u064e\u0634\u064e\u0642",
+    "\u0646\u0645\u0631 ",
+    "\u0646\u0645\u0631 \u0627\u0644\u062b\u0644\u0648\u062c",
+    "\u064a\u063a\u0648\u0631",
+    "\u0623\u0633\u062f",
+    "\u0627\u0644\u0628\u0628\u0631",
+    "\u0627\u0644\u0641\u0647\u062f",
+    "\u062f\u0628 \u0628\u0646\u064a",
+    "\u062f\u0628 \u0623\u0633\u0648\u062f \u0623\u0645\u0631\u064a\u0643\u064a",
+    "\u062f\u0628 \u0642\u0637\u0628\u064a",
+    "\u0627\u0644\u062f\u0628 \u0627\u0644\u0643\u0633\u0644\u0627\u0646 ",
+    "\u0627\u0644\u0633\u0645\u0648\u0631\u064a\u0627\u062a",
+    "\u0633\u0631\u0642\u0627\u0637",
+    "\u062e\u0646\u0627\u0641\u0633 \u0646\u0645\u0631\u064a\u0629",
+    "\u062f\u0639\u0633\u0648\u0642\u0629",
+    "\u062e\u0646\u0641\u0633\u0627\u0621 \u0623\u0631\u0636\u064a\u0629",
+    "\u062e\u0646\u0627\u0641\u0633 \u0637\u0648\u064a\u0644\u0629 \u0627\u0644\u0642\u0631\u0648\u0646 ",
+    "\u062e\u0646\u0641\u0633\u0629 \u0627\u0644\u0623\u0648\u0631\u0627\u0642",
+    "\u062e\u0646\u0627\u0641\u0633 \u0627\u0644\u0631\u0648\u062b",
+    "\u062e\u0646\u0641\u0633\u0627\u0621 \u0648\u062d\u064a\u062f \u0627\u0644\u0642\u0631\u0646",
+    "\u0627\u0644\u0633\u064f\u0648\u0633\u064a\u0646\u0627\u062a",
+    "\u062d\u0634\u0631\u0629 \u0630\u0648\u0627\u062a \u0627\u0644\u062c\u0646\u0627\u062d\u064a\u0646",
+    "\u0646\u062d\u0644",
+    "\u0627\u0644\u0646\u0645\u0644",
+    "\u062c\u0646\u062f\u0628",
+    "\u062d\u0634\u0631\u0629 \u0627\u0644\u0643\u0631\u064a\u0643\u064a\u062a",
+    "\u0627\u0644\u062d\u0634\u0631\u0629 \u0627\u0644\u0639\u0635\u0648\u064a\u0629",
+    "\u0635\u0631\u0635\u0648\u0631",
+    "\u0641\u0631\u0633 \u0627\u0644\u0646\u0628\u064a",
+    "\u062d\u0634\u0631\u0629 \u0632\u064a\u0632\u064a\u0627\u062a",
+    "\u0642\u0627\u0641\u0632\u0627\u062a \u0627\u0644\u0623\u0648\u0631\u0627\u0642",
+    "\u0639\u0631\u0642\u064a\u0627\u062a \u0627\u0644\u0623\u062c\u0646\u062d\u0629",
+    "\u0627\u0644\u064a\u0639\u0633\u0648\u0628",
+    "\u0645\u0642\u062a\u0631\u0646\u0627\u062a \u0627\u0644\u0623\u062c\u0646\u062d\u0629",
+    "\u0641\u0631\u0627\u0634\u0629 \u0628\u0634\u0648\u0631\u0629 \u0627\u0644\u0635\u064a\u0641",
+    "\u0641\u0631\u0627\u0634\u0629 \u062d\u0644\u064a\u0642\u0629",
+    "\u0641\u064e\u0631\u064e\u0627\u0634\u0629 \u0627\u0644\u0645\u064e\u0644\u0643",
+    "\u0641\u0631\u0627\u0634\u0629 \u0627\u0644\u0628\u064a\u0636\u0627\u0621 \u0627\u0644\u0635\u063a\u064a\u0631\u0629",
+    "\u0641\u0631\u0627\u0634\u0629 \u0627\u0644\u0643\u0628\u0631\u064a\u062a",
+    "\u0627\u0644\u0641\u0631\u0627\u0634\u0629 \u0627\u0644\u0646\u062d\u0627\u0633\u064a\u0629",
+    "\u0646\u062c\u0645 \u0627\u0644\u0628\u062d\u0631",
+    "\u0642\u0646\u0641\u0630 \u0627\u0644\u0628\u062d\u0631",
+    "\u062e\u064a\u0627\u0631 \u0627\u0644\u0628\u062d\u0631",
+    "\u0623\u0631\u0627\u0646\u0628 \u0642\u0637\u0646\u064a\u0629 \u0627\u0644\u0630\u064a\u0644",
+    "\u0623\u0631\u0646\u0628 \u0628\u0631\u064a",
+    "\u0627\u0644\u0623\u0646\u062c\u0648\u0631\u0627",
+    "\u0623\u0642\u062f\u0627\u062f",
+    "\u0627\u0644\u0646\u064a\u0635",
+    "\u0633\u0646\u062c\u0627\u0628 \u062b\u0639\u0644\u0628\u064a",
+    "\u0627\u0644\u0645\u0631\u0645\u0648\u0637",
+    "\u0627\u0644\u0642\u0646\u062f\u0633",
+    "\u0643\u0627\u0628\u064a\u0627\u0621 \u062e\u0646\u0632\u064a\u0631\u064a\u0629",
+    "\u0627\u0644\u062d\u0635\u0627\u0646 \u0627\u0644\u062d\u0645\u064a\u0636",
+    "\u0627\u0644\u062d\u0645\u0627\u0631 \u0627\u0644\u0645\u062e\u0637\u0637",
+    "\u0627\u0644\u062e\u0646\u0632\u064a\u0631 \u0627\u0644\u0623\u0644\u064a\u0641 \u0623\u0648 \u0627\u0644\u062e\u0646\u0632\u064a\u0631 \u0627\u0644\u0645\u0633\u062a\u0623\u0646\u0633",
+    "\u0627\u0644\u062e\u0646\u0632\u064a\u0631 \u0627\u0644\u0628\u0631\u064a ",
+    "\u062e\u0646\u0627\u0632\u064a\u0631 \u062a\u0624\u0644\u0648\u0644\u064a\u0629",
+    "\u0641\u0631\u0633 \u0627\u0644\u0646\u0647\u0631",
+    "\u0627\u0644\u062b\u0648\u0631",
+    "\u062c\u0627\u0645\u0648\u0633 \u0627\u0644\u0645\u0627\u0621",
+    "\u0627\u0644\u0628\u064a\u0633\u0648\u0646",
+    "\u0630\u0643\u0631 \u0627\u0644\u062e\u0631\u0648\u0641",
+    "\u0643\u0628\u0634 \u0627\u0644\u062c\u0628\u0627\u0644 \u0627\u0644\u0635\u062e\u0631\u064a\u0629",
+    "\u0627\u0644\u0648\u0639\u0644",
+    "\u062b\u064a\u062a\u0644 \u0627\u0644\u0647\u0631\u062a\u0628\u064a\u0633",
+    "\u0625\u0645\u0628\u0627\u0644\u0629",
+    "\u063a\u0632\u0627\u0644",
+    "\u0627\u0644\u062c\u0645\u0644 \u0627\u0644\u0639\u0631\u0628\u064a",
+    "\u0627\u0644\u0644\u0627\u0651\u0645\u0629",
+    "\u0627\u0628\u0646 \u0639\u0631\u0633",
+    "\u0627\u0644\u0645\u0646\u0643",
+    "\u0627\u0644\u0633\u0641\u0634\u0629",
+    "\u0627\u0628\u0646 \u0645\u0642\u0631\u0636 \u0623\u0633\u0648\u062f \u0627\u0644\u0623\u0642\u062f\u0627\u0645",
+    "\u0642\u0636\u0627\u0639\u0629",
+    "\u0638\u0631\u0628\u0627\u0646",
+    "\u0627\u0644\u063a\u0631\u064a\u0631",
+    "\u0627\u0644\u0645\u064f\u062f\u064e\u0631\u064e\u0651\u0639 \u0623\u0648 \u0627\u0644\u0623\u0631\u0645\u0627\u062f\u064a\u0644\u0644\u0648",
+    "\u0643\u0633\u0644\u0627\u0646 \u062b\u0644\u0627\u062b\u064a \u0627\u0644\u0623\u0635\u0627\u0628\u0639",
+    "\u0642\u0631\u062f \u0627\u0644\u0627\u0648\u0631\u0627\u0646\u063a\u0648\u062a\u0627\u0646",
+    "\u0627\u0644\u063a\u0648\u0631\u064a\u0644\u0627 \u0623\u0648 \u0627\u0644\u0642\u064f\u0631\u062f\u0648\u062d",
+    "\u0627\u0644\u0634\u0645\u0628\u0627\u0646\u0632\u064a \u0627\u0644\u0634\u0627\u0626\u0639 \u0623\u0648 \u0627\u0644\u0628\u064e\u0639\u0627\u0645",
+    "\u0642\u0631\u062f \u0627\u0644\u062c\u0628\u0648\u0646",
+    "\u0642\u0631\u062f \u0627\u0644\u0633\u064a\u0627\u0645\u0646\u062c",
+    "\u0633\u0639\u062f\u0627\u0646 \u0627\u0644\u063a\u064a\u0646\u0648\u0646",
+    "\u0633\u0639\u062f\u0627\u0646 \u0627\u0644\u0628\u0627\u062a\u0627\u0633",
+    "\u0627\u0644\u0631\u064f\u0628\u064e\u0651\u0627\u062d",
+    "\u0642\u0631\u062f \u0627\u0644\u0645\u0643\u0627\u0643",
+    "\u0642\u0631\u062f \u0627\u0644\u0643\u0648\u0644\u0628\u0633\u0627\u0648\u0627\u062a",
+    "\u0642\u0631\u062f \u0627\u0644\u0643\u0648\u0644\u0628\u0633",
+    "\u0642\u0631\u062f \u0627\u0644\u0645\u0644\u0645\u0644\u0629",
+    "\u0642\u0631\u0648\u062f \u0627\u0644\u0642\u0634\u0629",
+    "\u0642\u0631\u062f \u0627\u0644\u0643\u0628\u0648\u0634\u0629 \u0623\u0628\u064a\u0636 \u0627\u0644\u0648\u062c\u0647",
+    "\u0633\u0639\u062f\u0627\u0646 \u0627\u0644\u0639\u0648\u0627\u0621",
+    "\u0642\u0631\u062f \u0633\u0639\u062f\u0627\u0646 \u0627\u0644\u062a\u064a\u062a\u064a",
+    "\u0627\u0644\u0633\u064e\u0651\u0639\u062f\u0627\u0646 \u0627\u0644\u0639\u0646\u0643\u0628\u0648\u062a\u064a",
+    "\u0627\u0644\u0633\u0639\u062f\u0627\u0646 \u0627\u0644\u0633\u0646\u062c\u0627\u0628\u064a",
+    "\u0644\u064a\u0645\u0648\u0631 \u062d\u0644\u0642\u064a \u0627\u0644\u0630\u064a\u0644",
+    "\u062d\u064a\u0648\u0627\u0646 \u0627\u0644\u0627\u0646\u062f\u0631\u064a",
+    "\u0641\u064a\u0644 \u0647\u0646\u062f\u064a",
+    "\u0641\u064a\u0644 \u0623\u0641\u0631\u064a\u0642\u064a",
+    "\u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u0623\u062d\u0645\u0631",
+    "\u0627\u0644\u0628\u0627\u0646\u062f\u0627 \u0627\u0644\u0639\u0645\u0644\u0627\u0642\u0629",
+    "\u062b\u064a\u0631\u0633\u064a\u062a\u064a\u0627\u062a",
+    "\u0633\u0645\u0643 \u0627\u0644\u0627\u0646\u0642\u0644\u064a\u0633",
+    "\u0633\u0645\u0643 \u0627\u0644\u0643\u0648\u0647\u0648 \u0627\u0644\u0633\u064a\u0644\u0645\u0648\u0646",
+    "\u0633\u0645\u0643 \u0627\u0644\u062c\u0645\u0627\u0644 \u0627\u0644\u0635\u062e\u0631\u064a",
+    "\u0633\u0645\u0643\u0629 \u0627\u0644\u0645\u0647\u0631\u062c",
+    "\u0633\u0645\u0643\u0629 \u0627\u0644\u062d\u0641\u0634\u064a\u0629",
+    "\u0633\u0645\u0643 \u0627\u0644\u0631\u0645\u062d",
+    "\u0633\u0645\u0643\u0629 \u0627\u0644\u062a\u0646\u064a\u0646",
+    "\u0633\u0645\u0643\u0629 \u0627\u0644\u064a\u0646\u0641\u0648\u062e\u064a\u0629",
+    "\u0627\u0644\u0645\u0650\u0639\u0652\u062f\u064e\u0627\u062f",
+    "\u0627\u0644\u0639\u0628\u0627\u0621\u0629",
+    "\u0644\u0628\u0627\u0633 \u062a\u062e\u0631\u062c",
+    "\u0623\u0643\u0648\u0631\u062f\u064a\u0648\u0646",
+    "\u0627\u0644\u0642\u064a\u062b\u0627\u0631\u0629 \u0627\u0644\u0635\u0648\u062a\u064a\u0629",
+    "\u062d\u0627\u0645\u0644\u0629 \u0637\u0627\u0626\u0631\u0627\u062a",
+    "\u0637\u0627\u0626\u0631\u0629 \u0631\u062d\u0644\u0627\u062a",
+    "\u0633\u0641\u064a\u0646\u0629 \u0647\u0648\u0627\u0626\u064a\u0629",
+    "\u0645\u0630\u0628\u062d",
+    "\u0633\u064a\u0627\u0631\u0629 \u0625\u0633\u0639\u0627\u0641",
+    "\u0627\u0644\u0645\u0631\u0643\u0628\u0629 \u0627\u0644\u0628\u0631\u0645\u0627\u0626\u064a\u0629",
+    "\u0627\u0644\u0633\u0627\u0639\u0629 \u0627\u0644\u0645\u062a\u0646\u0627\u0638\u0631\u0629",
+    "\u0627\u0644\u0645\u0646\u062d\u0644 \u0623\u0648 \u0627\u0644\u0645\u064e\u0646\u062d\u064e\u0644\u064e\u0629",
+    "\u0645\u0626\u0632\u0631",
+    "\u062d\u0627\u0648\u064a\u0629 \u0627\u0644\u0646\u0641\u0627\u064a\u0627\u062a",
+    "\u0628\u0646\u062f\u0642\u064a\u0629 \u0627\u0642\u062a\u062d\u0627\u0645",
+    "\u062d\u0642\u064a\u0628\u0629 \u0638\u0647\u0631",
+    "\u0627\u0644\u0645\u062e\u0628\u0632",
+    "\u0639\u0627\u0631\u0636\u0629 \u0627\u0644\u062a\u0648\u0627\u0632\u0646",
+    "\u0627\u0644\u0628\u0627\u0644\u0648\u0646",
+    "\u0642\u0644\u0645 \u062d\u0628\u0631 \u062c\u0627\u0641",
+    "\u0636\u0645\u0627\u062f\u0629 \u0637\u0628\u064a\u0629 \u0644\u0627\u0635\u0642\u0629",
+    "\u0627\u0644\u0628\u0627\u0646\u062c\u0648",
+    "\u062f\u0631\u0627\u0628\u0632\u064a\u0646",
+    "\u062d\u062f\u064a\u062f\u0629 (\u0631\u0641\u0639 \u0623\u062b\u0642\u0627\u0644)",
+    "\u0643\u0631\u0633\u064a \u0627\u0644\u062d\u0644\u0627\u0642\u0629",
+    "\u0645\u062d\u0644 \u0635\u0627\u0644\u0648\u0646 \u0627\u0644\u062d\u0644\u0627\u0642\u0629",
+    "\u062d\u0638\u064a\u0631\u0629",
+    "\u0627\u0644\u0628\u0627\u0631\u0648\u0645\u062a\u0631",
+    "\u0627\u0644\u0628\u0631\u0645\u064a\u0644",
+    "\u0639\u062c\u0644\u0629 \u0627\u0644\u064a\u062f",
+    "\u0643\u0631\u0629 \u0627\u0644\u0642\u0627\u0639\u062f\u0629 \u0623\u0648 \u0627\u0644\u0628\u064a\u0633\u0628\u0648\u0644",
+    "\u0643\u0631\u0629 \u0633\u0644\u0629",
+    "\u0633\u0631\u064a\u0631 \u0627\u0644\u0623\u0637\u0641\u0627\u0644",
+    "\u0645\u0632\u0645\u0627\u0631",
+    "\u0642\u0628\u0639\u0629 \u0633\u0628\u0627\u062d\u0629",
+    "\u0645\u0646\u0634\u0641\u0629",
+    "\u062d\u0648\u0636 \u0627\u0644\u0627\u0633\u062a\u062d\u0645\u0627\u0645",
+    "\u0633\u064a\u0627\u0629 \u0648\u0627\u063a\u0646",
+    "\u0627\u0644\u0645\u0646\u0627\u0631\u0629 \u0623\u0648 \u0627\u0644\u0641\u0646\u0627\u0631",
+    "\u0643\u0648\u0628 \u0632\u062c\u0627\u062c\u064a",
+    "\u0642\u0628\u0639\u0629 \u0627\u0644\u062f\u0628",
+    "\u0632\u062c\u0627\u062c\u0629 \u0627\u0644\u0628\u064a\u0631\u0629",
+    "\u0643\u0623\u0633 \u062c\u0639\u0629",
+    "\u0628\u0631\u062c \u0627\u0644\u0646\u0627\u0642\u0648\u0633",
+    "\u0645\u0631\u0648\u0644\u0629",
+    "\u0627\u0644\u062f\u0631\u0627\u062c\u0629 \u0627\u0644\u062a\u0631\u0627\u062f\u0641\u064a\u0629",
+    "\u0628\u0643\u064a\u0646\u064a",
+    "\u0627\u0644\u0645\u062c\u0644\u062f\u0627\u062a \u0627\u0644\u062d\u0644\u0642\u064a\u0629",
+    "\u0627\u0644\u0645\u0650\u0646\u0652\u0638\u0627\u0631",
+    "\u0635\u0646\u062f\u0648\u0642 \u0627\u0644\u0639\u0634",
+    "\u0627\u0644\u0645\u0631\u0641\u0623",
+    "\u0627\u0644\u0632\u0644\u0627\u062c\u0629 \u0627\u0644\u062c\u0645\u0627\u0639\u064a\u0629",
+    "\u0631\u0628\u0637\u0629 \u0639\u0646\u0642 \u0628\u0648\u0644\u0648",
+    "\u0627\u0644\u0643\u0632\u0629",
+    "\u0631\u0641 \u0627\u0644\u0643\u062a\u0628",
+    "\u0645\u0643\u062a\u0628\u0629",
+    "\u063a\u0637\u0627\u0621 \u0642\u0627\u0631\u0648\u0631\u0629",
+    "\u0627\u0644\u0642\u0648\u0633",
+    "\u0623\u0631\u0628\u0629 \u0641\u0631\u0627\u0634\u064a\u0629",
+    "\u0644\u0627\u0641\u062a\u0629 \u062a\u0627\u0631\u064a\u062e\u064a\u0629 \u0645\u0646 \u0627\u0644\u0646\u062d\u0627\u0633",
+    "\u062d\u0645\u0627\u0644\u0629 \u0627\u0644\u0635\u062f\u0631",
+    "\u062d\u0627\u062c\u0632 \u0627\u0644\u0623\u0645\u0648\u0627\u062c",
+    "\u062f\u0631\u0639 \u0627\u0644\u0635\u062f\u0631",
+    "\u0627\u0644\u0645\u0643\u0646\u0633\u0629",
+    "\u0627\u0644\u062f\u0644\u0648",
+    "\u0645\u0631\u0628\u0637 \u0627\u0644\u062d\u0632\u0627\u0645",
+    "\u0627\u0644\u0633\u062a\u0631\u0629 \u0627\u0644\u0648\u0627\u0642\u064a\u0629 \u0645\u0646 \u0627\u0644\u0631\u0635\u0627\u0635",
+    "\u0642\u0637\u0627\u0631 \u0627\u0644\u0637\u0644\u0642\u0629",
+    "\u0627\u0644\u0645\u062c\u0632\u0631\u0629",
+    "\u0633\u064a\u0627\u0631\u0629 \u0623\u062c\u0631\u0629",
+    "\u062d\u0644\u0629 (\u0622\u0646\u064a\u0629)",
+    "\u0634\u0645\u0639\u0629",
+    "\u0645\u062f\u0641\u0639",
+    "\u0642\u0627\u0631\u0628 \u0627\u0644\u0643\u0627\u0646\u0648",
+    "\u0641\u0627\u062a\u062d\u0629 \u0639\u0644\u0628",
+    "\u0633\u062a\u0631\u0629 \u0645\u062d\u0628\u0648\u0643\u0629",
+    "\u0627\u0644\u0645\u0631\u0622\u0629 \u0627\u0644\u062c\u0627\u0646\u0628\u064a\u0629",
+    "\u062f\u0648\u0627\u0645\u0629 \u0627\u0644\u062e\u064a\u0644",
+    " \u0623\u062f\u0648\u0627\u062a \u0627\u0644\u0635\u064a\u0627\u0646\u0629",
+    "\u0635\u0646\u062f\u0648\u0642 \u0643\u0631\u062a\u0648\u0646",
+    "\u0627\u0644\u0625\u0637\u0627\u0631 \u0627\u0644\u0645\u0637\u0627\u0637",
+    "\u0627\u0644\u0635\u0631\u0627\u0641 \u0627\u0644\u0622\u0644\u064a",
+    "\u0627\u0644\u0634\u0631\u064a\u0637 \u0627\u0644\u0645\u062f\u0645\u062c",
+    "\u0627\u0644\u0645\u0633\u062c\u0644",
+    "\u0627\u0644\u0642\u064e\u0644\u0652\u0639\u064e\u0629",
+    "\u0627\u0644\u0642\u0637\u0645\u0631\u0627\u0646",
+    "\u062c\u0647\u0627\u0632 \u0627\u0644\u0642\u0631\u0635 \u0627\u0644\u0645\u0636\u063a\u0648\u0637",
+    "\u062a\u0634\u064a\u0644\u0648",
+    "\u0647\u0627\u062a\u0641 \u0645\u062d\u0645\u0648\u0644",
+    "\u0633\u0644\u0633\u0644\u0629",
+    "\u0633\u064a\u0627\u062c \u0645\u0634\u0628\u0643",
+    "\u0627\u0644\u0632\u0631\u062f",
+    "\u0645\u0646\u0634\u0627\u0631 \u062c\u0646\u0632\u064a\u0631\u064a",
+    " \u0635\u0646\u062f\u0648\u0642 \u0627\u0644\u062a\u062e\u0632\u064a\u0646",
+    "\u062e\u0632\u0627\u0646\u0629 \u0627\u0644\u0623\u062b\u0627\u062b",
+    "\u0627\u0644\u0622\u0644\u0629 \u0627\u0644\u0625\u064a\u0642\u0627\u0639\u064a\u0629 ",
+    "\u0627\u0644\u062e\u0632\u0627\u0646\u0629 \u0627\u0644\u0635\u064a\u0646\u064a\u0629",
+    "\u062c\u0648\u0631\u0628 \u0639\u064a\u062f \u0627\u0644\u0645\u064a\u0644\u0627\u062f",
+    "\u0643\u0646\u064a\u0633\u0629",
+    "\u0645\u0633\u0631\u062d \u0623\u0641\u0644\u0627\u0645",
+    "\u0627\u0644\u0633\u0627\u0637\u0648\u0631",
+    "\u0645\u0633\u0627\u0643\u0646 \u0627\u0644\u062c\u0631\u0641",
+    "\u0627\u0644\u0645\u0639\u0637\u0641 \u0627\u0644\u0641\u0636\u0641\u0627\u0636",
+    "\u0627\u0644\u0642\u0628\u0642\u0627\u0628",
+    "\u062e\u0627\u0644\u0637 \u0627\u0644\u0645\u0634\u0631\u0648\u0628\u0627\u062a \u0627\u0644\u0643\u062d\u0648\u0644\u064a\u0629",
+    "\u0643\u0648\u0632 (\u0622\u0646\u064a\u0629)",
+    "\u0622\u0644\u0629 \u062a\u062d\u0636\u064a\u0631 \u0627\u0644\u0642\u0647\u0648\u0629",
+    "\u0627\u0644\u0634\u0643\u0644 \u0627\u0644\u062d\u0644\u0632\u0648\u0646\u064a",
+    "\u0627\u0644\u0642\u0641\u0644 \u0627\u0644\u0631\u0645\u0632\u064a",
+    "\u0644\u0648\u062d\u0629 \u0627\u0644\u0645\u0641\u0627\u062a\u064a\u062d",
+    "\u0645\u062a\u062c\u0631 \u0627\u0644\u062d\u0644\u0648\u064a\u0627\u062a",
+    "\u0633\u0641\u064a\u0646\u0629 \u062d\u0627\u0648\u064a\u0627\u062a",
+    "\u0633\u064a\u0627\u0631\u0629 \u0645\u0643\u0634\u0648\u0641\u0629",
+    "\u0628\u0631\u0627\u0645\u0629",
+    "\u0627\u0644\u0634\u064a\u0627\u0639",
+    "\u062c\u0632\u0645\u0629 \u0631\u0627\u0639\u064a \u0627\u0644\u0628\u0642\u0631",
+    "\u0642\u0628\u0639\u0629 \u0631\u0627\u0639\u064a \u0627\u0644\u0628\u0642\u0631",
+    "\u0627\u0644\u0645\u0647\u062f",
+    "\u0631\u0627\u0641\u0639\u0629",
+    "\u0627\u0644\u062e\u0648\u0630\u0629",
+    "\u062d\u0627\u0648\u064a\u0629 \u0634\u062d\u0646 \u0643\u0628\u064a\u0631\u0629",
+    "\u0633\u0631\u064a\u0631 \u0627\u0644\u0631\u0636\u064a\u0639",
+    "\u0642\u062f\u0631 \u0627\u0644\u0637\u0628\u062e \u0627\u0644\u0643\u0647\u0631\u0628\u0627\u0626\u064a",
+    "\u0643\u0631\u0648\u0643\u064a\u062a",
+    "\u0627\u0644\u0639\u0643\u0627\u0632",
+    "\u0643\u0648\u064a\u0631\u0633",
+    "\u0627\u0644\u0633\u062f",
+    "\u0627\u0644\u0645\u0643\u062a\u0628",
+    "\u062d\u0627\u0633\u0648\u0628 \u0645\u0643\u062a\u0628\u064a",
+    "\u0627\u0644\u0647\u0627\u062a\u0641 \u0627\u0644\u062f\u0648\u0627\u0631",
+    "\u0627\u0644\u062d\u0641\u0627\u0638\u0629",
+    "\u0627\u0644\u0633\u0627\u0639\u0629 \u0627\u0644\u0631\u0642\u0645\u064a\u0629",
+    "\u0633\u0627\u0639\u0627\u062a \u0627\u0644\u064a\u062f \u0627\u0644\u0631\u0642\u0645\u064a\u0629",
+    "\u0627\u0644\u0645\u0646\u0636\u062f\u0629",
+    "\u0642\u0645\u0627\u0634 \u0627\u0644\u0623\u0637\u0628\u0627\u0642",
+    "\u063a\u0633\u0627\u0644\u0629 \u0635\u062d\u0648\u0646",
+    "\u0645\u0643\u0628\u062d \u0642\u0631\u0635\u064a",
+    "\u0645\u064a\u0646\u0627\u0621",
+    "\u0627\u0644\u0632\u0644\u0627\u062c\u0629 \u0627\u0644\u062a\u064a \u062a\u062c\u0631\u0647\u0627 \u0627\u0644\u0643\u0644\u0627\u0628",
+    "\u0642\u0628\u0629",
+    "\u0627\u0644\u062d\u0635\u064a\u0631\u0629",
+    "\u0645\u0646\u0635\u0629 \u062d\u0641\u0631",
+    "\u0627\u0644\u0637\u0628\u0644",
+    "\u0639\u0635\u0627 \u0627\u0644\u0637\u0628\u0644",
+    "\u062b\u0642\u0627\u0644\u0627\u062a \u062d\u062f\u064a\u062f",
+    "\u0641\u0631\u0646 \u0647\u0648\u0644\u0646\u062f\u064a",
+    "\u0645\u0631\u0648\u062d\u0629",
+    "\u0627\u0644\u062c\u064a\u062a\u0627\u0631 \u0627\u0644\u0643\u0647\u0631\u0628\u0627\u0626\u064a",
+    "\u0627\u0644\u0642\u0627\u0637\u0631\u0629 \u0627\u0644\u0643\u0647\u0631\u0628\u0627\u0626\u064a\u0629",
+    "\u0645\u0631\u0643\u0632 \u0627\u0644\u062a\u0631\u0641\u064a\u0647",
+    "\u0638\u0631\u0641 \u0628\u0631\u064a\u062f\u064a",
+    "\u0622\u0644\u0629 \u0627\u0644\u0625\u0633\u0628\u0631\u064a\u0633\u0648",
+    "\u0628\u0648\u062f\u0631\u0629 \u0627\u0644\u0648\u062c\u0647",
+    "\u0623\u0635\u0644\u0629 \u0631\u064a\u0634\u064a\u0629",
+    "\u062e\u0632\u0627\u0646\u0629 \u0627\u0644\u0645\u0644\u0641\u0627\u062a",
+    "\u0632\u0648\u0631\u0642 \u0627\u0644\u0625\u0637\u0641\u0627\u0621",
+    "\u0633\u064a\u0627\u0631\u0629 \u0627\u0644\u0625\u0637\u0641\u0627\u0621",
+    "\u0648\u0627\u0642\u064a \u0627\u0644\u0646\u0627\u0631",
+    "\u0633\u0627\u0631\u064a\u0629 \u0627\u0644\u0639\u0644\u0645",
+    "\u0627\u0644\u0641\u0644\u0648\u062a",
+    "\u0643\u0631\u0633\u064a \u0642\u0627\u0628\u0644 \u0644\u0644\u0637\u064a",
+    "\u062e\u0648\u0630\u0629 \u0643\u0631\u0629 \u0627\u0644\u0642\u062f\u0645",
+    "\u0631\u0627\u0641\u0639\u0629 \u0627\u0644\u062d\u0645\u0648\u0644\u0629",
+    "\u0627\u0644\u0646\u0627\u0641\u0648\u0631\u0629",
+    "\u0642\u0644\u0645 \u062d\u0628\u0631 \u0633\u0627\u0626\u0644",
+    "\u0627\u0644\u0633\u0631\u064a\u0631 \u0628\u0623\u0631\u0628\u0639\u0629 \u0623\u0639\u0645\u062f\u0629",
+    "\u0633\u064a\u0627\u0631\u0629 \u0634\u062d\u0646",
+    "\u0627\u0644\u0628\u0648\u0642 \u0627\u0644\u0641\u0631\u0646\u0633\u064a",
+    "\u0645\u0642\u0644\u0627\u0629",
+    "\u0627\u0644\u0645\u0644\u0627\u0628\u0633 \u0627\u0644\u0645\u0635\u0646\u0648\u0639\u0629 \u0645\u0646 \u0627\u0644\u0641\u0631\u0648\u0629",
+    "\u0634\u0627\u062d\u0646\u0629 \u0642\u0645\u0627\u0645\u0629",
+    "\u0642\u0646\u0627\u0639 \u0627\u0644\u063a\u0627\u0632",
+    "\u0645\u0636\u062e\u0629 \u0627\u0644\u0648\u0642\u0648\u062f",
+    "\u0643\u0623\u0633 \u0627\u0644\u0646\u0628\u064a\u0630",
+    "\u0633\u064a\u0627\u0631\u0629 \u062c\u0648 \u0643\u0627\u0631\u062a",
+    "\u0643\u0631\u0629 \u0627\u0644\u062c\u0648\u0644\u0641",
+    "\u0639\u0631\u0628\u0629 \u0627\u0644\u062c\u0648\u0644\u0641",
+    "\u0627\u0644\u0642\u0627\u0631\u0628 \u0627\u0644\u0637\u0648\u064a\u0644 \u0623\u0648 \u0627\u0644\u063a\u0646\u062f\u0648\u0644",
+    "\u0627\u0644\u0635\u0646\u062c\u0629",
+    "\u0627\u0644\u062b\u0648\u0628 \u0627\u0644\u0646\u0633\u0627\u0626\u064a \u0623\u0648 \u0627\u0644\u0641\u0633\u062a\u0627\u0646",
+    "\u0627\u0644\u0628\u064a\u0627\u0646\u0648 \u0627\u0644\u0643\u0628\u064a\u0631",
+    "\u0627\u0644\u062f\u0641\u064a\u0626\u0629 \u0627\u0644\u0632\u0631\u0627\u0639\u064a\u0629",
+    "\u0634\u0628\u0643 \u0627\u0644\u0633\u064a\u0627\u0631\u0629",
+    "\u0627\u0644\u0628\u0642\u0627\u0644\u0629",
+    "\u0627\u0644\u0645\u0642\u0635\u0644\u0629",
+    "\u0645\u0634\u0628\u0643 \u0644\u0644\u0634\u0639\u0631",
+    "\u0628\u062e\u0627\u062e \u0645\u062b\u0628\u062a \u0627\u0644\u0634\u0639\u0631",
+    "\u0627\u0644\u0639\u0631\u0628\u0629 \u0646\u0635\u0641 \u0627\u0644\u0645\u062c\u0646\u0632\u0631\u0629",
+    "\u0645\u0637\u0631\u0642\u0629",
+    "\u0627\u0644\u0633\u0644\u0629",
+    "\u0645\u062c\u0641\u0641 \u0627\u0644\u0634\u0639\u0631",
+    "\u062c\u0647\u0627\u0632 \u0645\u062d\u0645\u0648\u0644 \u0628\u0627\u0644\u064a\u062f",
+    "\u0627\u0644\u0645\u0646\u062f\u064a\u0644 \u0623\u0648 \u0627\u0644\u0645\u062d\u0631\u0645\u0629",
+    "\u0642\u0631\u0635 \u0635\u0644\u0628",
+    "\u0627\u0644\u0634\u064e\u0651\u0641\u064e\u0648\u0650\u064a\u064e\u0651\u0629",
+    "\u0627\u0644\u0642\u064a\u062b\u0627\u0631\u0629",
+    "\u0627\u0644\u062d\u0635\u0651\u0627\u062f\u0629",
+    "\u062e\u0635\u064a\u0646",
+    "\u062d\u0627\u0641\u0638\u0629 \u0627\u0644\u0645\u0633\u062f\u0633",
+    "\u0627\u0644\u0645\u0633\u0631\u062d \u0627\u0644\u0645\u0646\u0632\u0644\u064a",
+    "\u0642\u0631\u0635 \u0639\u0633\u0644",
+    "\u0627\u0644\u062e\u0637\u0627\u0641",
+    "\u0627\u0644\u062a\u0646\u0648\u0631\u0629 \u0627\u0644\u0645\u064f\u0637\u064e\u0648\u064e\u0651\u0642\u0629",
+    "\u0639\u0642\u0644\u0629 (\u062c\u0645\u0628\u0627\u0632)",
+    "\u0639\u0631\u0628\u0629 \u0627\u0644\u062e\u064a\u0648\u0644",
+    "\u0633\u0627\u0639\u0629 \u0631\u0645\u0644\u064a\u0629",
+    "\u0622\u064a \u0628\u0648\u062f",
+    "\u0627\u0644\u0645\u0643\u0648\u0627\u0629",
+    "\u0627\u0644\u0642\u0631\u0639\u0629 \u0627\u0644\u0645\u0636\u064a\u0626\u0629",
+    "\u0627\u0644\u062c\u064a\u0646\u0632",
+    "\u062c\u064a\u0628 (\u0633\u064a\u0627\u0631\u0629)",
+    "\u0642\u0645\u064a\u0635 \u0642\u0635\u064a\u0631 \u0627\u0644\u0643\u0645\u064a\u0646",
+    "\u0623\u062d\u062c\u064a\u0629 \u0627\u0644\u0635\u0648\u0631 \u0627\u0644\u0645\u0642\u0637\u0648\u0639\u0629",
+    "\u0631\u064a\u0643\u0634\u0627",
+    "\u0630\u0631\u0627\u0639 \u0627\u0644\u062a\u0648\u062c\u064a\u0647",
+    "\u0627\u0644\u0643\u064a\u0645\u0648\u0646\u0648",
+    "\u0648\u0633\u0627\u062f\u0627\u062a \u0627\u0644\u0631\u0643\u0628\u0629",
+    "\u0627\u0644\u0639\u0642\u062f\u0629",
+    "\u0645\u0639\u0637\u0641 \u0627\u0644\u0645\u062e\u062a\u0628\u0631",
+    "\u0627\u0644\u0645\u063a\u0631\u0641\u0629",
+    "\u0639\u0627\u0643\u0633 \u0627\u0644\u0636\u0648\u0621",
+    "\u062d\u0627\u0633\u0648\u0628 \u0645\u062d\u0645\u0648\u0644",
+    "\u062c\u0632\u0627\u0632\u0629 \u0627\u0644\u0639\u0634\u0628",
+    "\u063a\u0637\u0627\u0621 \u0627\u0644\u0639\u062f\u0633\u0629",
+    "\u0641\u062a\u0627\u062d\u0629 \u0627\u0644\u0631\u0633\u0627\u0626\u0644",
+    "\u0645\u0643\u062a\u0628\u0629",
+    "\u0642\u0627\u0631\u0628 \u0627\u0644\u0646\u062c\u0627\u0629",
+    "\u0627\u0644\u0642\u064e\u062f\u064e\u0651\u0627\u062d\u064e\u0629",
+    "\u0627\u0644\u0644\u064a\u0645\u0648\u0632\u064a\u0646",
+    "\u0639\u0627\u0628\u0631\u0629 \u0645\u062d\u064a\u0637 \u0645\u0646\u062a\u0638\u0645\u0629",
+    "\u0623\u062d\u0645\u0631 \u0634\u0641\u0627\u0647",
+    "\u0627\u0644\u062d\u0630\u0627\u0621 \u0633\u0647\u0644 \u0627\u0644\u0627\u0631\u062a\u062f\u0627\u0621",
+    "\u063a\u0633\u0648\u0644",
+    "\u0645\u0643\u0628\u0631 \u0627\u0644\u0635\u0648\u062a",
+    "\u0639\u062f\u0633\u0629",
+    "\u0627\u0644\u0645\u0646\u0634\u0631\u0629",
+    "\u0627\u0644\u0628\u0648\u0635\u0644\u0629",
+    "\u062d\u0642\u064a\u0628\u0629 \u0633\u0627\u0639\u064a \u0627\u0644\u0628\u0631\u064a\u062f",
+    "\u0635\u0646\u062f\u0648\u0642 \u0628\u0631\u064a\u062f",
+    "\u0627\u0644\u062c\u0648\u0627\u0631\u0628 \u0627\u0644\u0637\u0648\u064a\u0644\u0629",
+    "\u0627\u0644\u0645\u0627\u064a\u0648\u0647",
+    "\u063a\u0637\u0627\u0621 \u0627\u0644\u0645\u0637\u0628\u0642",
+    "\u0622\u0644\u0629 \u0645\u0627\u0631\u0627\u0643\u0633",
+    "\u0627\u0644\u0645\u0627\u0631\u064a\u0645\u0628\u0627",
+    "\u0642\u0646\u0627\u0639",
+    "\u0623\u0639\u0648\u0627\u062f \u0627\u0644\u062b\u0642\u0627\u0628",
+    "\u0633\u0627\u0631\u064a\u0629 \u0645\u0627\u064a\u0648",
+    "\u0627\u0644\u0645\u062a\u0627\u0647\u0629",
+    "\u0643\u0648\u0628 \u0627\u0644\u0642\u064a\u0627\u0633",
+    "\u062e\u0632\u0627\u0646\u0629 \u0627\u0644\u0623\u062f\u0648\u064a\u0629",
+    "\u0627\u0644\u0622\u062b\u0627\u0631 \u0627\u0644\u0635\u062e\u0631\u064a\u0629",
+    "\u0627\u0644\u0644\u0627\u0642\u0637 \u0627\u0644\u0635\u0648\u062a\u064a",
+    "\u0641\u0631\u0646 \u0627\u0644\u0645\u064a\u0643\u0631\u0648\u064a\u0641",
+    "\u0627\u0644\u0632\u064a \u0627\u0644\u0639\u0633\u0643\u0631\u064a",
+    "\u0645\u062f\u0644\u062c\u0629",
+    "\u0627\u0644\u062d\u0627\u0641\u0644\u0629 \u0627\u0644\u0635\u063a\u064a\u0631\u0629",
+    "\u0627\u0644\u062a\u0646\u0648\u0631\u0629 \u0627\u0644\u0642\u0635\u064a\u0631\u0629",
+    "\u0633\u064a\u0627\u0631\u0629 \u0627\u0644\u0645\u064a\u0646\u064a \u0641\u0627\u0646 \u0627\u0644\u0639\u0627\u0626\u0644\u064a\u0629",
+    "\u0627\u0644\u0642\u0630\u064a\u0641\u0629 \u0627\u0644\u0645\u0648\u062c\u0647\u0629",
+    "\u0627\u0644\u0642\u0641\u0627\u0632 \u0645\u0644\u062a\u0635\u0642 \u0627\u0644\u0623\u0635\u0627\u0628\u0639",
+    "\u0637\u0628\u0642 \u062e\u0644\u0637",
+    "\u0627\u0644\u0645\u0646\u0632\u0644 \u0627\u0644\u0645\u062a\u0646\u0642\u0644",
+    "\u0641\u0648\u0631\u062f \u0645\u0648\u062f\u064a\u0644 \u062a\u064a",
+    "\u0627\u0644\u0645\u0648\u062f\u0645",
+    "\u0627\u0644\u062f\u064a\u0631",
+    "\u0634\u0627\u0634\u0629 \u062d\u0627\u0633\u0648\u0628",
+    "\u0627\u0644\u062f\u0631\u0627\u062c\u0629 \u0627\u0644\u0646\u0627\u0631\u064a\u0629 \u0627\u0644\u0635\u063a\u064a\u0631\u0629",
+    "\u0627\u0644\u0647\u0627\u0648\u0646 \u0648\u0627\u0644\u0645\u062f\u0642\u0629",
+    "\u0627\u0644\u0642\u0628\u0639\u0629 \u0627\u0644\u062c\u0627\u0645\u0639\u064a\u0629 \u0627\u0644\u0645\u0631\u0628\u0639\u0629",
+    "\u0645\u0633\u062c\u062f",
+    "\u0627\u0644\u0646\u0627\u0645\u0648\u0633\u064a\u0651\u0629",
+    "\u0627\u0644\u062f\u0639\u0631\u0648\u0645\u0629",
+    "\u0627\u0644\u062f\u0631\u0627\u062c\u0629 \u0627\u0644\u0647\u0648\u0627\u0626\u064a\u0629 \u0627\u0644\u062c\u0628\u0644\u064a\u0629",
+    "\u062e\u064a\u0645\u0629",
+    "\u0627\u0644\u0641\u0623\u0631\u0629",
+    "\u0645\u0635\u064a\u062f\u0629 \u0627\u0644\u0641\u0626\u0631\u0627\u0646",
+    "\u0633\u064a\u0627\u0631\u0627\u062a \u0634\u0631\u0643\u0629 \u0627\u0644\u0646\u0642\u0644",
+    "\u0643\u0645\u0627\u0645 \u0627\u0644\u0641\u0645",
+    "\u0645\u0633\u0645\u0627\u0631",
+    "\u0637\u0648\u0642 \u0627\u0644\u0639\u0646\u0642",
+    "\u0627\u0644\u0642\u0644\u0627\u062f\u0629",
+    "\u0627\u0644\u0631\u0636\u0627\u0639\u0629",
+    "\u062d\u0627\u0633\u0628 \u0627\u0644\u0645\u0641\u0643\u0631\u0629",
+    "\u0627\u0644\u0645\u0633\u0644\u0629",
+    "\u0622\u0644\u0629 \u0627\u0644\u0623\u0648\u0628\u0648\u0627",
+    "\u0623\u0643\u0631\u064a\u0646\u0629",
+    "\u0639\u062f\u0627\u062f \u0627\u0644\u0645\u0633\u0627\u0641\u0627\u062a",
+    "\u0641\u0644\u062a\u0631 \u0627\u0644\u0632\u064a\u062a",
+    "\u0627\u0644\u0623\u0631\u063a\u0646 \u0630\u0648 \u0627\u0644\u0623\u0646\u0627\u0628\u064a\u0628",
+    "\u062c\u0647\u0627\u0632 \u0631\u0627\u0633\u0645 \u0627\u0644\u0625\u0634\u0627\u0631\u0629",
+    "\u0627\u0644\u062a\u0646\u0648\u0631\u0629 \u0627\u0644\u062e\u0627\u0631\u062c\u064a\u0629",
+    "\u0639\u0631\u0628\u0629 \u064a\u062c\u0631\u0647\u0627 \u0627\u0644\u062b\u0648\u0631",
+    "\u0642\u0646\u0627\u0639 \u0623\u0643\u0633\u062c\u064a\u0646",
+    "\u0627\u0644\u062a\u063a\u0644\u064a\u0641",
+    "\u0627\u0644\u0645\u0650\u063a\u0652\u062f\u0627\u0641",
+    "\u0639\u064e\u062c\u064e\u0644\u0629 \u0627\u0644\u062a\u064e\u063a\u062f\u064a\u0641",
+    "\u0642\u0641\u0644 \u062d\u0644\u0642\u064a",
+    "\u0641\u0631\u0634\u0627\u0629 \u0627\u0644\u0631\u0633\u0645",
+    "\u0627\u0644\u0645\u0650\u0646\u064e\u0627\u0645\u064e\u0629\u064f",
+    "\u0642\u0635\u0631",
+    "\u0627\u0644\u0645\u0650\u0635\u0641\u0627\u0631",
+    "\u0645\u0646\u0634\u0641\u0629 \u0648\u0631\u0642\u064a\u0629",
+    "\u0645\u0650\u0638\u064e\u0644\u064e\u0651\u0629 \u0627\u0644\u0647\u0628\u064f\u0648\u0637",
+    "\u062c\u0647\u0627\u0632 \u0627\u0644\u0639\u0642\u0644\u0629",
+    "\u0645\u0642\u0639\u062f \u0639\u0627\u0645",
+    "\u0639\u062f\u0627\u062f \u0627\u0646\u062a\u0638\u0627\u0631 \u0627\u0644\u0633\u064a\u0627\u0631\u0627\u062a",
+    "\u0639\u0631\u0628\u0629 \u0627\u0644\u0642\u0637\u0627\u0631",
+    "\u0627\u0644\u0641\u0646\u0627\u0621",
+    "\u0627\u0644\u0647\u0627\u062a\u0641 \u0627\u0644\u0639\u0645\u0648\u0645\u064a",
+    "\u0627\u0644\u0631\u0643\u064a\u0632\u0629",
+    "\u0627\u0644\u0645\u064e\u0642\u0652\u0644\u064e\u0645\u064e\u0629",
+    "\u0627\u0644\u0645\u0628\u0631\u0627\u0629",
+    "\u0627\u0644\u0639\u0637\u0631",
+    "\u0637\u0628\u0642 \u0628\u062a\u0631\u064a",
+    "\u0627\u0644\u0622\u0644\u0629 \u0627\u0644\u0646\u0627\u0633\u062e\u0629",
+    "\u0627\u0644\u0631\u064a\u0634\u0629 \u0627\u0644\u0645\u0648\u0633\u064a\u0642\u064a\u0629",
+    "\u062e\u0648\u0630\u0629 \u0628\u064a\u0643\u0644\u0647\u0627\u0648\u0628\u0647",
+    "\u0627\u0644\u0633\u064a\u0627\u062c \u0627\u0644\u0648\u062a\u062f\u064a",
+    "\u0627\u0644\u0633\u064a\u0627\u0631\u0629 \u0627\u0644\u0646\u0635\u0641-\u0646\u0642\u0644",
+    "\u0627\u0644\u0631\u0635\u064a\u0641 \u0627\u0644\u0628\u062d\u0631\u064a",
+    "\u062d\u0635\u0627\u0644\u0629",
+    "\u0627\u0644\u062a\u063a\u0644\u064a\u0641 \u0627\u0644\u0635\u064a\u062f\u0644\u0627\u0646\u064a",
+    "\u0648\u0633\u0627\u062f\u0629",
+    "\u0643\u0631\u0629 \u0627\u0644\u0637\u0627\u0648\u0644\u0629",
+    "\u0644\u0639\u0628\u0629 \u0637\u0627\u062d\u0648\u0646\u0629 \u0647\u0648\u0627\u0621",
+    "\u0633\u0641\u064a\u0646\u0629 \u0627\u0644\u0642\u0631\u0627\u0635\u0646\u0629",
+    "\u0627\u0644\u0625\u0628\u0631\u064a\u0642",
+    "\u0627\u0644\u0645\u0650\u0633\u0652\u062d\u064e\u062c",
+    "\u0627\u0644\u0642\u0628\u0629 \u0627\u0644\u0641\u0644\u0643\u064a\u0629",
+    "\u0643\u064a\u0633 \u0646\u0627\u064a\u0644\u0648\u0646",
+    "\u0631\u0641 \u062a\u0646\u0634\u064a\u0641 \u0627\u0644\u0623\u0637\u0628\u0627\u0642",
+    "\u0627\u0644\u0645\u062d\u0627\u0631\u064a\u062b \u0627\u0644\u062d\u0641\u0627\u0631\u0629",
+    "\u0627\u0644\u0645\u0643\u0628\u0633 \u0627\u0644\u063a\u0637\u064e\u0651\u0627\u0633",
+    "\u0627\u0644\u0643\u0627\u0645\u064a\u0631\u0627 \u0627\u0644\u0641\u0648\u0631\u064a\u0629",
+    "\u0627\u0644\u0639\u0645\u0648\u062f",
+    "\u0639\u0631\u0628\u0629 \u0627\u0644\u0634\u0631\u0637\u0629",
+    "\u0644\u0628\u0627\u0633 \u0627\u0644\u0628\u0646\u0634",
+    "\u0637\u0627\u0648\u0644\u0629 \u0627\u0644\u0628\u0644\u064a\u0627\u0631\u062f\u0648",
+    "\u0639\u0628\u0648\u0629 \u0627\u0644\u0645\u0634\u0631\u0648\u0628\u0627\u062a",
+    "\u0627\u0644\u0623\u0635\u064a\u0635",
+    "\u0639\u062c\u0644\u0629 \u0641\u062e\u0627\u0631",
+    "\u0627\u0644\u0645\u0650\u062b\u0652\u0642\u064e\u0628",
+    "\u0633\u062c\u0627\u062f\u0629 \u0627\u0644\u0635\u0644\u0627\u0629",
+    "\u0627\u0644\u0637\u0627\u0628\u0639\u0629 \u0627\u0644\u062d\u0627\u0633\u0648\u0628\u064a\u0629",
+    "\u0627\u0644\u0633\u062c\u0646",
+    "\u0627\u0644\u0642\u0630\u064a\u0641\u0629",
+    "\u0627\u0644\u0628\u0631\u0648\u062c\u0643\u062a\u0631",
+    "\u0642\u0631\u0635 \u0627\u0644\u0647\u0648\u0643\u064a",
+    "\u0627\u0644\u0645\u0644\u0643\u0645\u0629",
+    "\u062d\u0642\u064a\u0628\u0629 \u064a\u062f",
+    "\u0627\u0644\u0631\u064a\u0634\u0629 ",
+    "\u0627\u0644\u0644\u0650\u062d\u064e\u0627\u0641",
+    "\u0633\u064a\u0627\u0631\u0629 \u0627\u0644\u0633\u0628\u0627\u0642",
+    "\u0645\u0636\u0631\u0628 \u0627\u0644\u062a\u0646\u0633",
+    "\u0645\u0634\u0639\u0627\u0639",
+    "\u0627\u0644\u0645\u0630\u064a\u0627\u0639",
+    "\u0627\u0644\u0645\u0642\u0631\u0627\u0628 \u0627\u0644\u0627\u0630\u0627\u0639\u064a",
+    "\u062e\u0632\u0627\u0646 \u0645\u064a\u0627\u0647 \u0627\u0644\u0623\u0645\u0637\u0627\u0631",
+    "\u0627\u0644\u0645\u0631\u0643\u0628\u0627\u062a \u0627\u0644\u062a\u0631\u0641\u064a\u0647\u064a\u0629",
+    "\u0628\u0643\u0631\u0629 \u0635\u064a\u062f",
+    "\u0627\u0644\u0643\u0627\u0645\u064a\u0631\u0627 \u0627\u0644\u0627\u0646\u0639\u0643\u0627\u0633\u064a\u0629",
+    "\u0627\u0644\u062b\u0644\u0627\u064e\u0651\u062c\u0629",
+    "\u062c\u0647\u0627\u0632 \u062a\u062d\u0643\u0645 \u0639\u0646 \u0628\u0639\u062f",
+    "\u0645\u0637\u0639\u0645",
+    "\u0627\u0644\u0645\u0633\u062f\u0633 \u0627\u0644\u062f\u0648\u0627\u0631",
+    "\u0628\u0646\u062f\u0642\u064a\u0629",
+    "\u0627\u0644\u0643\u0631\u0633\u064a \u0627\u0644\u0647\u0632\u0627\u0632",
+    "\u0627\u0644\u0645\u0634\u0648\u0627\u0629",
+    "\u0627\u0644\u0645\u0645\u062d\u0627\u0629",
+    "\u0643\u0631\u0629 \u0627\u0644\u0631\u063a\u0628\u064a",
+    "\u0627\u0644\u0645\u0633\u0637\u0631\u0629",
+    "\u0627\u0644\u062d\u0630\u0627\u0621 \u0627\u0644\u0631\u064a\u0627\u0636\u064a",
+    "\u0627\u0644\u062e\u0632\u0627\u0646\u0629",
+    "\u062f\u0628\u0648\u0633 \u0645\u0634\u0628\u0643",
+    "\u0639\u0644\u0628 \u0627\u0644\u0645\u0644\u062d \u0648\u0627\u0644\u0641\u0644\u0641\u0644",
+    "\u0627\u0644\u0635\u0646\u062f\u0644",
+    "\u0627\u0644\u0633\u0627\u0631\u0648\u0646\u062c",
+    "\u0633\u0627\u0643\u0633\u0641\u0648\u0646 \u0627\u0644\u0629 \u0646\u0641\u062e \u0645\u0648\u0633\u064a\u0642\u064a\u0629",
+    "\u0627\u0644\u063a\u0650\u0645\u0652\u062f \u0623\u0648 \u063a\u0650\u0645\u0652\u062f \u0627\u0644\u0633\u064a\u0641 \u0623\u0648 \u063a\u0650\u0645\u0652\u062f \u0627\u0644\u062e\u0646\u062c\u0631",
+    "\u0627\u0644\u0645\u064a\u0632\u0627\u0646",
+    "\u062d\u0627\u0641\u0644\u0629 \u0645\u062f\u0631\u0633\u064a\u0629",
+    "\u0627\u0644\u0645\u0631\u0643\u0628 \u0627\u0644\u0634\u0631\u0627\u0639\u064a ",
+    "\u0644\u0648\u062d\u0629 \u0627\u0644\u0646\u062a\u0627\u0626\u062c",
+    "\u0634\u0627\u0634\u0629 \u0627\u0644\u0633\u064a \u0623\u0631 \u062a\u064a",
+    "\u0628\u0631\u063a\u064a",
+    "\u0645\u0641\u0643 \u0627\u0644\u0628\u0631\u0627\u063a\u064a",
+    "\u062d\u0632\u0627\u0645 \u0627\u0644\u0623\u0645\u0627\u0646",
+    "\u0622\u0644\u0629 \u0627\u0644\u062e\u064a\u0627\u0637\u0629",
+    "\u0627\u0644\u062f\u0631\u0639",
+    "\u0645\u062a\u062c\u0631 \u0627\u0644\u0623\u062d\u0630\u064a\u0629",
+    "\u0645\u0642\u0633\u0645 \u0627\u0644\u063a\u0631\u0641\u0629",
+    "\u0633\u0644\u0629 \u0627\u0644\u062a\u0633\u0648\u0642",
+    "\u0639\u0631\u0628\u0629 \u0627\u0644\u062a\u0633\u0648\u0642",
+    "\u0627\u0644\u0645\u0650\u062c\u0652\u0631\u064e\u0641\u064e\u0629",
+    "\u0642\u0628\u0639\u0629 \u0627\u0644\u0627\u0633\u062a\u062d\u0645\u0627\u0645",
+    "\u0633\u062a\u0627\u0626\u0631 \u0627\u0644\u0627\u0633\u062a\u062d\u0645\u0627\u0645",
+    "\u062a\u0632\u062d\u0644\u0642 \u0639\u0644\u0649 \u0627\u0644\u062b\u0644\u062c",
+    "\u0642\u0646\u0627\u0639 \u0627\u0644\u062a\u0632\u0644\u062c",
+    "\u0643\u064a\u0633 \u0627\u0644\u0646\u0648\u0645",
+    "\u0627\u0644\u0645\u0633\u0637\u0631\u0629 \u0627\u0644\u062d\u0627\u0633\u0628\u0629",
+    "\u0627\u0644\u0628\u0627\u0628 \u0627\u0644\u0645\u0646\u0632\u0644\u0642",
+    "\u0645\u0627\u0643\u064a\u0646\u0629 \u0627\u0644\u062d\u0638",
+    "\u0627\u0644\u063a\u0637\u0633 \u062a\u062d\u062a \u0627\u0644\u0645\u0627\u0621",
+    "\u0639\u0631\u0628\u0629 \u0627\u0644\u062c\u0644\u064a\u062f \u0627\u0644\u0622\u0644\u064a\u0629",
+    "\u0643\u0627\u0633\u062d\u0629 \u062b\u0644\u0648\u062c",
+    "\u0645\u0648\u0632\u0639 \u0627\u0644\u0635\u0627\u0628\u0648\u0646",
+    "\u0643\u0631\u0629 (\u0643\u0631\u0629 \u0627\u0644\u0642\u062f\u0645)",
+    "\u062c\u0648\u0631\u0628",
+    "\u0645\u062c\u0645\u0639 \u0627\u0644\u0637\u0627\u0642\u0629 \u0627\u0644\u0634\u0645\u0633\u064a\u0629 \u0627\u0644\u062d\u0631\u0627\u0631\u064a\u0629",
+    "\u0627\u0644\u0635\u064e\u0645\u0652\u0628\u0631\u0650\u064a\u0631\u0629",
+    "\u0648\u0639\u0627\u0621 \u0627\u0644\u0634\u0648\u0631\u0628\u0629",
+    "\u0645\u0641\u062a\u0627\u062d \u0627\u0644\u0645\u0633\u0627\u0641\u0629",
+    "\u0627\u0644\u0645\u062f\u0641\u0623\u0629",
+    "\u0645\u0643\u0648\u0643 \u0627\u0644\u0641\u0636\u0627\u0621",
+    "\u0627\u0644\u0645\u0650\u0644\u0648\u064e\u0642",
+    "\u0627\u0644\u0642\u0627\u0631\u0628 \u0627\u0644\u0633\u0631\u064a\u0639",
+    "\u0634\u0628\u0643\u0629 \u0627\u0644\u0639\u0646\u0643\u0628\u0648\u062a",
+    "\u062e\u0634\u0628\u0629 \u0627\u0644\u0645\u063a\u0632\u0644",
+    "\u0627\u0644\u0633\u064a\u0627\u0631\u0629 \u0627\u0644\u0631\u064a\u0627\u0636\u064a\u0629",
+    "\u0628\u0642\u0639\u0629 \u0636\u0648\u0621",
+    "\u0633\u0637\u062d \u0627\u0644\u0645\u0633\u0631\u062d",
+    "\u0627\u0644\u0642\u0627\u0637\u0631\u0629 \u0627\u0644\u0628\u062e\u0627\u0631\u064a\u0629",
+    "\u062c\u0633\u0631 \u0645\u0642\u0648\u0633 \u0646\u0641\u0642\u064a",
+    "\u0627\u0644\u0637\u0628\u0644 \u0627\u0644\u0646\u062d\u0627\u0633\u064a",
+    "\u0627\u0644\u0633\u0645\u0627\u0639\u0629 \u0627\u0644\u0637\u0628\u064a\u0629",
+    "\u0627\u0644\u0644\u0650\u0641\u0627\u0639",
+    "\u0627\u0644\u062c\u062f\u0627\u0631 \u0627\u0644\u062c\u0627\u0641",
+    "\u0645\u0624\u0642\u062a",
+    "\u0627\u0644\u0645\u0648\u0642\u062f",
+    "\u0627\u0644\u063a\u0631\u0628\u0627\u0644",
+    "\u0627\u0644\u062a\u0631\u0627\u0645",
+    "\u0627\u0644\u0646\u0642\u0627\u0644\u0629",
+    "\u0627\u0644\u0623\u0631\u064a\u0643\u0629",
+    "\u0645\u0628\u0646\u0649 \u0633\u062a\u0648\u064a\u0627",
+    "\u0627\u0644\u063a\u0648\u0627\u0635\u0629",
+    "\u0627\u0644\u0628\u0630\u0644\u0629",
+    "\u0627\u0644\u0645\u0632\u0648\u0644\u0629",
+    "\u0627\u0644\u0646\u0638\u0627\u0631\u0629 \u0627\u0644\u0634\u0645\u0633\u064a\u0629",
+    "\u0627\u0644\u0646\u0638\u0627\u0631\u0629 \u0627\u0644\u0634\u0645\u0633\u064a\u0629",
+    "\u0627\u0644\u0648\u0627\u0642\u064a \u0627\u0644\u0634\u0645\u0633\u064a",
+    "\u0627\u0644\u062c\u0633\u0631 \u0627\u0644\u0645\u0639\u0644\u0642",
+    "\u0627\u0644\u0645\u0645\u0633\u062d\u0629",
+    "\u0627\u0644\u0642\u0645\u064a\u0635 \u0627\u0644\u062b\u0642\u064a\u0644",
+    "\u0627\u0644\u062a\u064f\u0628\u0651\u0627\u0646 \u0623\u0648 \u0627\u0644\u0628\u0646\u0637\u0627\u0644 \u0627\u0644\u0642\u0635\u064a\u0631\\",
+    "\u0627\u0644\u0623\u0631\u062c\u0648\u062d\u0629",
+    "\u0627\u0644\u0645\u0641\u062a\u0627\u062d \u0627\u0644\u0643\u0647\u0631\u0628\u0627\u0626\u064a",
+    "\u0645\u062d\u0642\u0646\u0629 \u0623\u0648 \u0627\u0644\u0625\u0628\u0631\u0629",
+    "\u0627\u0644\u0623\u0628\u0627\u062c\u0648\u0631\u0629",
+    "\u0627\u0644\u062f\u0628\u0627\u0628\u0629",
+    "\u0645\u0633\u062c\u0644 \u0627\u0644\u0634\u0631\u064a\u0637 \u0627\u0644\u0635\u0648\u062a\u064a",
+    "\u0625\u0628\u0631\u064a\u0642 \u0627\u0644\u0634\u0627\u064a",
+    "\u0627\u0644\u062f\u0628\u062f\u0648\u0628",
+    "\u0627\u0644\u0631\u0627\u0626\u064a",
+    "\u0643\u0631\u0629 \u062a\u0646\u0633",
+    "\u0627\u0644\u062a\u0633\u0642\u064a\u0641 \u0628\u0627\u0644\u0642\u0634",
+    "\u0627\u0644\u0633\u062a\u0627\u0631\u0629 \u0627\u0644\u0645\u0633\u0631\u062d\u064a\u0629",
+    "\u0627\u0644\u0643\u064f\u0634\u0652\u062a\u0650\u0628\u064e\u0627\u0646",
+    "\u0627\u0644\u062f\u0631\u064e\u0651\u0627\u0633\u0629",
+    "\u0639\u0631\u0634",
+    "\u0628\u0644\u0627\u0637 \u0627\u0644\u0633\u0642\u0641",
+    "\u0622\u0644\u0629 \u062a\u062d\u0645\u064a\u0635 \u0627\u0644\u062e\u0628\u0632",
+    "\u0645\u062d\u0644\u0627\u062a \u0628\u064a\u0639 \u0644\u0648\u0627\u0632\u0645 \u0627\u0644\u062a\u062f\u062e\u064a\u0646",
+    "\u0645\u0642\u0639\u062f \u0627\u0644\u0645\u0631\u062d\u0627\u0636",
+    "\u0645\u0634\u0639\u0644\u0629",
+    "\u0627\u0644\u0623\u064e\u0639\u0652\u0645\u0650\u062f\u064e\u0629\u064f \u0627\u0644\u0637\u064e\u0651\u0648\u0652\u0637\u064e\u0645\u0650\u064a\u064e\u0651\u0629\u0650",
+    "\u0634\u0627\u062d\u0646\u0629 \u0627\u0644\u0642\u0637\u0631",
+    "\u0645\u062a\u062c\u0631 \u0627\u0644\u0623\u0644\u0639\u0627\u0628",
+    "\u0633\u064a\u0627\u0631\u0629 \u0627\u0644\u062c\u0631\u0627\u0631",
+    "\u0634\u0627\u062d\u0646\u0629 \u0646\u0635\u0641 \u0645\u0642\u0637\u0648\u0631\u0629",
+    "\u0627\u0644\u0635\u064a\u0646\u064a\u0629",
+    "\u0645\u0639\u0637\u0641 \u0627\u0644\u062e\u0646\u062f\u0642",
+    "\u0627\u0644\u062f\u0631\u0627\u062c\u0629 \u062b\u0644\u0627\u062b\u064a\u0629 \u0627\u0644\u0639\u062c\u0644\u0627\u062a",
+    "\u0642\u0627\u0631\u0628 \u0627\u0644\u062f\u0639\u0627\u0645\u0629 \u0627\u0644\u0645\u0632\u062f\u0648\u062c\u0629",
+    "\u062d\u0627\u0645\u0644 \u062b\u0644\u0627\u062b\u064a",
+    "\u0642\u0648\u0633 \u0627\u0644\u0646\u0635\u0631",
+    "\u0627\u0644\u062d\u0627\u0641\u0644\u0629 \u0633\u0637\u062d\u064a\u0629 \u0627\u0644\u062a\u0645\u062f\u064a\u062f \u0627\u0644\u0643\u0647\u0631\u0628\u0627\u0626\u064a",
+    "\u0627\u0644\u062a\u0631\u0648\u0645\u0628\u0648\u0646",
+    "\u062d\u0648\u0636 \u0627\u0644\u0627\u0633\u062a\u062d\u0645\u0627\u0645",
+    "\u0627\u0644\u0628\u0648\u0627\u0628\u0629 \u0627\u0644\u062f\u0648\u0627\u0631\u0629",
+    "\u0627\u0644\u0622\u0644\u0629 \u0627\u0644\u0643\u0627\u062a\u0628\u0629",
+    "\u0627\u0644\u0645\u0638\u0644\u0629",
+    "\u0627\u0644\u062f\u0631\u0627\u062c\u0629 \u0627\u0644\u0623\u062d\u0627\u062f\u064a\u0629",
+    "\u0627\u0644\u0628\u064a\u0627\u0646\u0648 \u0627\u0644\u0642\u0627\u0626\u0645",
+    "\u0627\u0644\u0645\u0643\u0646\u0633\u0629 \u0627\u0644\u0643\u0647\u0631\u0628\u0627\u0626\u064a\u0629",
+    "\u0627\u0644\u0645\u0632\u0647\u0631\u064a\u0629",
+    "\u0627\u0644\u0642\u0646\u0637\u0631\u0629",
+    "\u0627\u0644\u0642\u0645\u0627\u0634 \u0627\u0644\u0645\u062e\u0645\u0644\u064a",
+    "\u0622\u0644\u0629 \u0627\u0644\u0628\u064a\u0639 \u0627\u0644\u0630\u0627\u062a\u064a",
+    "\u0627\u0644\u0635\u062f\u0627\u0631\u064a",
+    "\u0642\u0646\u0637\u0631\u0629 \u0645\u062a\u0639\u062f\u062f\u0629 \u0627\u0644\u0631\u0643\u0627\u0626\u0632",
+    "\u0627\u0644\u0643\u0645\u0627\u0646",
+    "\u0627\u0644\u0643\u0631\u0629 \u0627\u0644\u0637\u0627\u0626\u0631\u0629",
+    "\u0635\u0627\u0646\u0639\u0629 \u0627\u0644\u0648\u0627\u0641\u0644",
+    "\u0633\u0627\u0639\u0629 \u0627\u0644\u062d\u0627\u0626\u0637",
+    "\u0645\u062d\u0641\u0638\u0629",
+    "\u062e\u0632\u0627\u0646\u0629 \u0627\u0644\u0635\u0648\u0627\u0646",
+    "\u0637\u0627\u0626\u0631\u0629 \u0639\u0633\u0643\u0631\u064a\u0629",
+    "\u0627\u0644\u0645\u062c\u0644\u0649",
+    "\u0627\u0644\u063a\u0633\u0627\u0644\u0629",
+    "\u0642\u0627\u0631\u0648\u0631\u0629 \u0645\u0627\u0621",
+    "\u0625\u0628\u0631\u064a\u0642 \u0627\u0644\u0645\u0627\u0621",
+    "\u0628\u0631\u062c \u0627\u0644\u0645\u064a\u0627\u0647",
+    "\u0625\u0628\u0631\u064a\u0642 \u0627\u0644\u0643\u062d\u0648\u0644\u064a\u0627\u062a",
+    "\u0627\u0644\u0635\u0627\u0641\u0631\u0629",
+    "\u0634\u0639\u0631 \u0645\u0633\u062a\u0639\u0627\u0631",
+    "\u0627\u0644\u0646\u0627\u0641\u0630\u0629 \u0627\u0644\u0648\u0627\u0642\u064a\u0629",
+    "\u0627\u0644\u0633\u062a\u0627\u0631 \u0627\u0644\u0644\u0641\u064e\u0651\u0627\u0641",
+    "\u0631\u0628\u0637\u0629 \u0639\u0646\u0642 \u0648\u0646\u062f\u0633\u0648\u0631 ",
+    "\u0632\u062c\u0627\u062c\u0629 \u0627\u0644\u0646\u0628\u064a\u0630",
+    "\u062c\u0646\u0627\u062d \u0627\u0644\u0637\u0627\u0626\u0631\u0629",
+    "\u0645\u0642\u0644\u0627\u0629 \u0635\u064a\u0646\u064a\u0629",
+    "\u0627\u0644\u0645\u0644\u0639\u0642\u0629 \u0627\u0644\u062e\u0634\u0628\u064a\u0629",
+    "\u0627\u0644\u0635\u0648\u0641",
+    "\u0627\u0644\u0633\u064a\u0627\u062c \u0627\u0644\u0645\u0646\u0642\u0633\u0645",
+    "\u062d\u0637\u0627\u0645 \u0627\u0644\u0633\u0641\u064a\u0646\u0629",
+    "\u0627\u0644\u0632\u0648\u0631\u0642 \u0627\u0644\u0634\u0631\u0627\u0639\u064a",
+    "\u0645\u0646\u0632\u0644 \u0627\u0644\u064a\u0648\u0631\u062a",
+    "\u0645\u0648\u0627\u0642\u0639 \u0627\u0644\u0648\u064a\u0628",
+    "\u0643\u062a\u0627\u0628 \u0631\u0633\u0648\u0645 \u0647\u0632\u0644\u064a\u0629",
+    "\u0627\u0644\u0643\u0644\u0645\u0627\u062a \u0627\u0644\u0645\u062a\u0642\u0627\u0637\u0639\u0629",
+    "\u0644\u0627\u0641\u062a\u0629 \u0645\u0631\u0648\u0631\u064a\u0629",
+    "\u0625\u0634\u0627\u0631\u0629 \u0627\u0644\u0645\u0631\u0648\u0631 \u0627\u0644\u0636\u0648\u0626\u064a\u0629",
+    "\u063a\u0644\u0627\u0641 \u0627\u0644\u062d\u0645\u0627\u064a\u0629 \u0644\u0644\u0643\u062a\u0627\u0628",
+    "\u0642\u0627\u0626\u0645\u0629 \u0637\u0639\u0627\u0645",
+    "\u0635\u062d\u0646",
+    "\u0642\u0646\u0628\u064a\u0637 \u0623\u062e\u0636\u0631",
+    "\u062d\u0633\u0627\u0621 \u0627\u0644\u0643\u0648\u0646\u0633\u0648\u0645\u064a\u0629",
+    "\u0627\u0644\u0648\u0639\u0627\u0621 \u0627\u0644\u0633\u0627\u062e\u0646",
+    "\u062a\u0631\u0627\u064a\u0641\u0644",
+    "\u0627\u0644\u0645\u062b\u0644\u062c\u0627\u062a",
+    "\u0627\u0644\u0645\u0635\u0627\u0635\u0629",
+    "\u0627\u0644\u062e\u0628\u0632 \u0627\u0644\u0641\u0631\u0646\u0633\u064a",
+    "\u062e\u0628\u0632 \u0627\u0644\u0628\u064a\u063a\u0644",
+    "\u0627\u0644\u0645\u062e\u0628\u0648\u0632\u0627\u062a \u0627\u0644\u0639\u064f\u0642\u0652\u062f\u0650\u064a\u064e\u0651\u0629",
+    "\u062a\u0634\u064a\u0632 \u0628\u0631\u062c\u0631",
+    "\u0627\u0644\u0646\u0642\u0627\u0646\u0642",
+    "\u0627\u0644\u0628\u0637\u0627\u0637\u0627 \u0627\u0644\u0645\u0647\u0631\u0648\u0633\u0629",
+    "\u0645\u0644\u0641\u0648\u0641",
+    "\u0627\u0644\u0642\u0631\u0646\u0628\u064a\u0637 \u0627\u0644\u0623\u062e\u0636\u0631",
+    "\u0627\u0644\u0642\u0631\u0646\u0628\u064a\u0637",
+    "\u0627\u0644\u0643\u0648\u0633\u0627",
+    "\u0645\u0639\u0643\u0631\u0648\u0646\u0629 \u0627\u0644\u0627\u0633\u0643\u0648\u0627\u0634",
+    "\u0642\u0631\u0639 \u0627\u0644\u0628\u0644\u0648\u0637",
+    "\u0642\u0631\u0639 \u0627\u0644\u062c\u0648\u0632",
+    "\u062e\u064a\u0627\u0631",
+    "\u0627\u0644\u062e\u0631\u0634\u0648\u0641 \u0627\u0644\u0634\u0648\u0643\u064a",
+    "\u0641\u0644\u0641\u0644 \u062d\u0644\u0648",
+    "\u0627\u0644\u062e\u0631\u0634\u0648\u0641 \u0627\u0644\u0633\u0643\u0648\u0644\u064a\u0645\u064a",
+    "\u0639\u064a\u0634 \u0627\u0644\u063a\u0631\u0627\u0628",
+    "\u062a\u0641\u0627\u062d \u0623\u062e\u0636\u0631",
+    "\u0627\u0644\u0641\u0631\u0627\u0648\u0644\u0629",
+    "\u0627\u0644\u0628\u0631\u062a\u0642\u0627\u0644",
+    "\u0627\u0644\u0644\u064a\u0645\u0648\u0646",
+    "\u0627\u0644\u062a\u064a\u0646",
+    "\u0627\u0644\u0623\u0646\u0627\u0646\u0627\u0633",
+    "\u0627\u0644\u0645\u0648\u0632",
+    "\u062c\u0627\u0643 \u0641\u0631\u0648\u062a",
+    "\u0642\u0634\u0637\u0629 \u0634\u0631\u064a\u0645\u0648\u0644\u064a\u0627",
+    "\u0627\u0644\u0631\u0645\u0627\u0646",
+    "\u0627\u0644\u062f\u0631\u064a\u0633",
+    "\u0643\u0631\u0628\u0646\u0627\u0631\u0629",
+    "\u0635\u0644\u0635\u0629 \u0627\u0644\u0634\u0648\u0643\u0648\u0644\u0627",
+    "\u0639\u062c\u064a\u0646\u0629 \u0627\u0644\u062e\u0628\u0632",
+    "\u0631\u063a\u064a\u0641 \u0627\u0644\u0644\u062d\u0645",
+    "\u0628\u064a\u062a\u0632\u0627",
+    "\u0641\u0637\u064a\u0631\u0629 \u0627\u0644\u0642\u062f\u0631",
+    "\u0628\u0648\u0631\u064a\u062a\u0648",
+    "\u0627\u0644\u0646\u0628\u064a\u0630 \u0627\u0644\u0623\u062d\u0645\u0631",
+    "\u0642\u0647\u0648\u0629 \u0625\u0633\u0628\u0631\u064a\u0633\u0648",
+    "\u0641\u0646\u062c\u0627\u0646 \u0627\u0644\u0634\u0627\u064a",
+    "\u062d\u0644\u064a\u0628 \u0627\u0644\u0628\u064a\u0636",
+    "\u0627\u0644\u062c\u0628\u0644",
+    "\u0641\u0642\u0627\u0639\u0629",
+    "\u0627\u0644\u062c\u0631\u0641",
+    "\u0627\u0644\u0634\u0639\u0627\u0628 \u0627\u0644\u0645\u0631\u062c\u0627\u0646\u064a\u0629",
+    "\u0627\u0644\u0641\u0648\u0627\u0631\u0629 \u0627\u0644\u062d\u0627\u0631\u0629",
+    "\u0627\u0644\u0636\u0641\u0629",
+    "\u0627\u0644\u0634\u0646\u062e\u0629",
+    "\u0627\u0644\u0645\u064a\u0627\u0647 \u0627\u0644\u0636\u062d\u0644\u0629",
+    "\u0627\u0644\u0634\u0627\u0637\u0626",
+    "\u0627\u0644\u0648\u0627\u062f\u064a",
+    "\u0628\u0631\u0643\u0627\u0646",
+    "\u0644\u0627\u0639\u0628 \u0643\u0631\u0629 \u0627\u0644\u0642\u0627\u0639\u062f\u0629",
+    "\u0627\u0644\u0639\u0631\u064a\u0633",
+    "\u0627\u0644\u063a\u0648\u0635 \u0628\u062c\u0647\u0627\u0632 \u0627\u0644\u062a\u0646\u0641\u0633 ",
+    "\u0627\u0644\u0633\u0644\u062c\u0645",
+    "\u0632\u0647\u0631\u0629 \u0627\u0644\u0644\u0624\u0644\u0624 ",
+    "\u062e\u0641 \u0627\u0644\u0633\u064a\u062f\u0629 \u0627\u0644\u0623\u0635\u0641\u0631",
+    "\u0627\u0644\u0630\u0631\u0629",
+    "\u0634\u062c\u0631\u0629 \u062b\u0645\u0631\u0629 \u0627\u0644\u0628\u0644\u0648\u0637",
+    "\u062b\u0645\u0631 \u0627\u0644\u0648\u0631\u062f \u0627\u0644\u0628\u0631\u064a",
+    "\u0628\u0630\u0648\u0631 \u0643\u0633\u062a\u0646\u0627\u0621 \u0627\u0644\u062d\u0635\u0627\u0646",
+    "\u0627\u0644\u0641\u0637\u0631\u064a\u0627\u062a \u0627\u0644\u0645\u0631\u062c\u0627\u0646\u064a\u0629",
+    "\u0641\u0637\u0631 \u063a\u0627\u0631\u064a\u0642\u0648\u0646",
+    "\u0641\u0637\u0631 \u062c\u0627\u0631\u0648\u0645\u064a\u062a\u0631\u0627 \u0627\u064a\u0633\u0643\u0644\u0646\u062a\u0627",
+    "\u0627\u0644\u0642\u0631\u0646 \u0627\u0644\u0646\u062a\u0646",
+    "\u0641\u0637\u0631 \u0646\u062c\u0645 \u0627\u0644\u0623\u0631\u0636",
+    "\u0641\u0637\u0631 \u0631\u0641 \u0627\u0644\u0643\u0628\u0631\u064a\u062a",
+    "\u0641\u0637\u0631 \u0627\u0644\u0628\u0648\u0644\u064a\u0637",
+    "\u0627\u0644\u0639\u0631\u0646\u0627\u0633",
+    "\u0648\u0631\u0642 \u0627\u0644\u0645\u0631\u062d\u0627\u0636"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/ar_zeroshot_classification_templates.json

@@ -0,0 +1,59 @@
+{
+  "imagenet1k": [
+    "{c}",
+    "\u0635\u0648\u0631\u0629 \u0633\u064a\u0626\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0633\u064a\u0626\u0629 \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 {c}",
+    "\u0646\u062d\u062a \u0644\u0634\u0643\u0644 {c}",
+    "\u0646\u062d\u062a \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0630\u0627\u062a \u062c\u0648\u0648\u062f\u0629 \u0645\u0646\u062e\u0641\u0636\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0630\u0627\u062a \u062c\u0648\u0648\u062f\u0629 \u0645\u0646\u062e\u0641\u0636\u0629 \u062a\u062d\u062a\u0648\u064a {c}",
+    "\u0631\u0633\u0648\u0645\u0627\u062a \u062c\u062f\u0627\u0631\u064a\u0629 \u062a\u062d\u062a\u0648\u064a {c}",
+    "\u0631\u0633\u0648\u0645\u0627\u062a \u062c\u062f\u0627\u0631\u064a\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0645\u0642\u062a\u0637\u0639\u0629 \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 {c}",
+    "\u0635\u0648\u0631\u0629 \u0645\u0642\u062a\u0637\u0639\u0629 \u0644\u0640 {c}",
+    "\u062a\u0637\u0631\u064a\u0632 {c} ",
+    " \u0635\u0648\u0631\u0629 \u064a\u0635\u0639\u0628 \u0641\u064a\u0647\u0627 \u0631\u0624\u064a\u0629 {c} ",
+    "\u0635\u0648\u0631\u0629 \u0633\u0627\u0637\u0639\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0648\u0627\u0636\u062d\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0645\u062a\u0633\u062e\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0645\u0638\u0644\u0645\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0623\u0628\u064a\u0636 \u0648\u0623\u0633\u0648\u062f {c}",
+    "{c} \u0641\u064a \u0644\u0642\u0637\u0629 \u0642\u0631\u064a\u0628\u0629",
+    "\u0635\u0648\u0631\u0629 \u0631\u0627\u0626\u0639\u0629 \u0644\u0640 {c}",
+    "\u0644\u0642\u0637\u0629 \u0642\u0631\u064a\u0628\u0629 \u0644\u0640 {c}",
+    "\u0631\u0633\u0645 \u062d\u0627\u0633\u0648\u0628\u064a \u064a\u062d\u062a\u0648\u064a {c}",
+    "\u0635\u0648\u0631\u0629 \u0645\u0631\u0633\u0648\u0645\u0629 \u062a\u062d\u062a\u0648\u064a {c}",
+    "\u0631\u0633\u0645\u0629 \u0644\u0640 {c}",
+    "\u0631\u0633\u0645\u0629 {c}",
+    "\u0631\u0633\u0645 \u064a\u062d\u062a\u0648\u064a {c} ",
+    "\u0635\u0648\u0631\u0629 \u0628\u0646\u0645\u0637 \u0627\u0644\u0628\u0643\u0633\u0644 \u0644\u0640 {c}",
+    " \u0635\u0648\u0631\u0629 \u0633\u0627\u0637\u0639\u0629 {c}",
+    "\u0648\u0634\u0645 {c}",
+    "{c} \u0641\u064a \u0627\u0644\u0635\u0648\u0631\u0629",
+    "\u0635\u0648\u0631\u0629 \u0645\u062a\u0633\u062e\u0629 \u062a\u062d\u062a\u0648\u064a {c}",
+    "\u0635\u0648\u0631\u0629 \u062a\u0627\u0644\u0641\u0629 {c}",
+    "\u0635\u0648\u0631\u0629 \u0636\u0628\u0627\u0628\u064a\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 {c}",
+    "\u0635\u0648\u0631\u0629 \u062c\u064a\u062f\u0629 \u0644\u0640 {c}",
+    "\u0635\u0648\u0631\u0629 \u0644\u0640 {c}",
+    "\u062a\u0635\u064a\u064a\u0631 \u0644\u0640 {c}",
+    "{c} \u0639\u0644\u0649 \u0634\u0643\u0644 \u0631\u0633\u0645 \u062d\u0627\u0633\u0648\u0628\u064a \u062b\u0646\u0627\u0626\u064a \u0623\u0648 \u062b\u0644\u0627\u062b\u064a \u0627\u0644\u0623\u0628\u0639\u0627\u062f",
+    "\u064a\u0648\u062c\u062f {c} \u0648\u0627\u062d\u062f \u0641\u064a \u0627\u0644\u0635\u0648\u0631\u0629",
+    "\u0631\u0633\u0645 \u062d\u0627\u0633\u0648\u0628\u064a \u0644\u0640 {c}",
+    "\u0627\u0648\u0631\u064a\u063a\u0627\u0645\u064a \u0644\u0640 {c}",
+    "{c} \u0645\u0635\u0646\u0648\u0639 \u0639\u0646 \u0637\u0631\u064a\u0642 \u0641\u0646 \u0637\u064a \u0627\u0644\u0648\u0631\u0642",
+    "{c} \u0641\u064a \u0644\u0639\u0628\u0629 \u0641\u064a\u062f\u064a\u0648",
+    "{c} \u0645\u0648\u062c\u0648\u062f \u0641\u064a \u0644\u0639\u0628\u0629 \u0627\u0644\u0641\u064a\u062f\u064a\u0648",
+    "\u0631\u0633\u0645 \u062a\u0642\u0631\u064a\u0628\u064a \u0644\u0640 {c}",
+    "{c} \u0645\u0631\u0633\u0648\u0645 \u0628\u0627\u0644\u062e\u0631\u0627\u0628\u064a\u0634",
+    "\u0635\u0648\u0631\u0629 \u0628\u0641\u0646 \u0627\u0644\u062e\u0631\u0627\u0628\u064a\u0634 \u0644\u0640 {c}",
+    "\u0644\u0639\u0628\u0629 {c}",
+    "\u0635\u0648\u0631\u0629 \u064a\u0648\u062c\u062f \u0641\u064a\u0647\u0627 {c}",
+    "\u0631\u0633\u0648\u0645 \u0645\u062a\u062d\u0631\u0643\u0629 \u0644\u0640 {c} ",
+    "\u0635\u0648\u0631\u0629 \u0644\u0639\u062f\u062f \u0645\u0646 {c}",
+    "\u0635\u0648\u0631\u0629 \u064a\u0638\u0647\u0631 \u0641\u064a\u0647\u0627 {c}",
+    "\u0635\u0648\u0631\u0629 {c} \u0635\u063a\u064a\u0631 ",
+    "\u0635\u0648\u0631\u0629 {c} \u0643\u0628\u064a\u0631",
+    "{c} \u064a\u0638\u0647\u0631 \u0641\u064a \u0627\u0644\u0635\u0648\u0631\u0629"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/birdsnap.py

@@ -0,0 +1,235 @@
+import concurrent.futures
+import csv
+import hashlib
+import os
+from pathlib import Path
+
+import torch
+from PIL import Image as Image
+from torchvision.datasets.utils import download_and_extract_archive
+
+Image.LOAD_TRUNCATED_IMAGES = True
+
+
+class Birdsnap(torch.utils.data.Dataset):
+    """This is the BirdSnap dataset presented in
+    - Berg et al., "Birdsnap: Large-scale Fine-grained Visual Categorization of Birds"
+    It contains a lot of classes of birds and can be used as a replacement for ImageNet validation images
+    with similar image fidelity but less of the baggage, given that all subjects are in fact birds.
+
+    This is too small to train on though and hence not even partitioned into train/test.
+    Several images are missing from flickr (in 2021), these will be discarded automatically.
+    """
+
+    METADATA_URL = 'http://thomasberg.org/datasets/birdsnap/1.1/birdsnap.tgz'
+    METADATA_ARCHIVE = 'birdsnap.tgz'
+    META_MD5 = '1788158175f6ae794aebf27bcd7a3f5d'
+    BASE_FOLDER = 'birdsnap'
+
+    def __init__(self, root, split='train', transform=None, target_transform=None, download=True, crop_to_bbx=False):
+        """Init with split, transform, target_transform."""
+        self.root = os.path.expanduser(root)
+        self.transform = transform
+        self.target_transform = target_transform
+
+        self.crop_to_bbx = crop_to_bbx  # Crop to dataset default bounding boxes
+
+        if download:
+            self.download()
+        if not self.check_integrity():
+            raise ValueError('Dataset Birdsnap not downloaded completely or possibly corrupted.')
+
+        # self._purge_missing_data()
+
+    def _check_integrity_of_metadata(self, chunk_size=8192):
+        """This only checks if all files are there."""
+        try:
+            with open(os.path.join(self.root, self.METADATA_ARCHIVE), 'rb') as f:
+                archive_hash = hashlib.md5()
+                while chunk := f.read(chunk_size):
+                    archive_hash.update(chunk)
+            return self.META_MD5 == archive_hash.hexdigest()
+        except FileNotFoundError:
+            return False
+
+    def check_integrity(self):
+        """Full integrity check."""
+        if not self._check_integrity_of_metadata():
+            return False
+        else:
+            self._parse_metadata()
+            missing_images = 0
+            for idx, file in enumerate(self.meta):
+                if not self._verify_image(idx):
+                    missing_images += 1
+            if missing_images > 0:
+                print(f'{missing_images} images could not be downloaded.')
+            return True
+
+    def download(self):
+        # Metadata:
+        if self._check_integrity_of_metadata():
+            print('Metadata already downloaded and verified')
+        else:
+            download_and_extract_archive(self.METADATA_URL, self.root, filename=self.METADATA_ARCHIVE)
+        # Actual files:
+        self._parse_metadata()
+
+        missing_ids = []
+        for idx, file in enumerate(self.meta):
+            if not self._verify_image(idx):
+                missing_ids.append(idx)
+        if len(missing_ids) > 0:
+            print(f'Downloading {len(missing_ids)} missing files now...')
+            self.scrape_images(missing_ids)
+
+    def __len__(self):
+        """Return length via metainfo."""
+        return len(self.meta)
+
+    def __getitem__(self, index):
+        """Return image, label."""
+        img = Image.open(self.paths[index])
+        if self.crop_to_bbx:
+            img = img.crop(
+                (
+                    self.meta[index]['bb_x1'],
+                    self.meta[index]['bb_y1'],
+                    self.meta[index]['bb_x2'],
+                    self.meta[index]['bb_y2'],
+                )
+            )
+        img = img.convert('RGB')
+        label = self.labels[index]
+
+        img = self.transform(img) if self.transform else img
+        label = self.target_transform(label) if self.target_transform else label
+        return img, label
+
+    def _parse_metadata(self):
+        """Metadata keys are
+        dict_keys(['url', 'md5', 'path', 'species_id', 'bb_x1', 'bb_y1', 'bb_x2', 'bb_y2', 'back_x', 'back_y', 'beak_x',
+        'beak_y', 'belly_x', 'belly_y', 'breast_x', 'breast_y', 'crown_x', 'crown_y', 'forehead_x', 'forehead_y',
+        'left_cheek_x', 'left_cheek_y', 'left_eye_x', 'left_eye_y', 'left_leg_x', 'left_leg_y', 'left_wing_x',
+        'left_wing_y', 'nape_x', 'nape_y', 'right_cheek_x', 'right_cheek_y', 'right_eye_x', 'right_eye_y',
+        'right_leg_x', 'right_leg_y', 'right_wing_x', 'right_wing_y', 'tail_x', 'tail_y', 'throat_x', 'throat_y']
+        """
+        with open(os.path.join(self.root, self.BASE_FOLDER, 'images.txt'), 'r') as f:
+            reader = csv.DictReader(f, delimiter='\t')
+            self.meta = list(reader)  # List of dictionaries.
+        self.labels = [int(entry['species_id']) for entry in self.meta]
+        self.paths = [os.path.join(self.root, self.BASE_FOLDER, entry['path']) for entry in self.meta]
+        with open(os.path.join(self.root, self.BASE_FOLDER, 'species.txt'), 'r') as f:
+            reader = csv.DictReader(f, delimiter='\t')
+            self.classes_metadata = list(reader)
+        self.classes = [str(entry['common']) for entry in self.classes_metadata]
+
+    def _verify_image(self, idx):
+        try:
+            # Do this if you want to check in detail:
+            with open(os.path.join(self.root, self.BASE_FOLDER, self.meta[idx]['path']), 'rb') as fin:
+                return (hashlib.md5(fin.read()).hexdigest() == self.meta[idx]['md5'])
+            # In the mean time, just check if everything is there:
+            # return os.path.exists(os.path.join(self.root, self.BASE_FOLDER, self.meta[idx]["path"]))
+        except FileNotFoundError:
+            return False
+
+    def scrape_images(self, missing_ids, chunk_size=8196):
+        """Scrape images using the python default ThreadPool example."""
+        import requests
+
+        def _load_url_and_save_image(idx, timeout):
+            full_path = os.path.join(self.root, self.BASE_FOLDER, self.meta[idx]['path'])
+            os.makedirs(os.path.split(full_path)[0], exist_ok=True)
+            r = requests.get(self.meta[idx]['url'], stream=True)
+            with open(full_path, 'wb') as write_file:
+                for chunk in r.iter_content(chunk_size=chunk_size):
+                    write_file.write(chunk)
+            return True
+
+        # We can use a with statement to ensure threads are cleaned up promptly
+        with concurrent.futures.ThreadPoolExecutor(max_workers=None) as executor:  # Choose max_workers dynamically
+            # Start the load operations and mark each future with its URL
+            future_to_url = {
+                executor.submit(_load_url_and_save_image, idx, 600): self.meta[idx]['url'] for idx in missing_ids
+            }
+            for future in concurrent.futures.as_completed(future_to_url):
+                url = future_to_url[future]
+                try:
+                    data = future.result()
+                except Exception as exc:
+                    print(f'{url} generated exception: {exc}')
+                else:
+                    print(f'{url} downloaded successfully.')
+
+    def _purge_missing_data(self):
+        """Iterate over all data and throw out missing images."""
+        JPG = b'\xff\xd8\xff'
+
+        clean_meta = []
+        invalid_files = 0
+        for entry in self.meta:
+            full_path = os.path.join(self.root, self.BASE_FOLDER, entry['path'])
+            with open(full_path, 'rb') as file_handle:
+                if file_handle.read(3) == JPG:
+                    clean_meta.append(entry)
+                else:
+                    invalid_files += 1
+        print(f'Discarded {invalid_files} invalid files.')
+        self.meta = clean_meta
+
+        self.labels = [int(entry['species_id']) for entry in self.meta]
+        self.paths = [os.path.join(self.root, self.BASE_FOLDER, entry['path']) for entry in self.meta]
+
+
+class BirdsnapV2(torch.utils.data.Dataset):
+    def __init__(self, root, split='test', transform=None, target_transform=None):
+        assert split == 'test', print('Only test split is supported for now.')
+        self.root_dir = Path(root)
+        self.image_dir = self.root_dir
+        self.test_elements = self.root_dir / 'test_images_valid.txt'
+        self.classes_file = self.root_dir / 'species.txt'
+        self.target_transform = target_transform
+        self.transform = transform
+        with open(self.test_elements, 'r') as f:
+            self.imgs = [line.rstrip('\n') for line in f][1:]
+
+        with open(self.classes_file, 'r') as f:
+            self.classes = [line.rstrip('\n').split('\t')[1] for line in f][1:]
+        with open(self.classes_file, 'r') as f:
+            self.dirs = [line.rstrip('\n').split('\t')[-1] for line in f][1:]
+            dir2id = {dir: idx for idx, dir in enumerate(self.dirs)}
+        self.labels = []
+        for image in self.imgs:
+            dir = image.split('/')[0].lower()
+            label = dir2id[dir]
+            self.labels.append(label)
+        self.imgs = [os.path.join(self.root_dir, item) for item in self.imgs]
+
+    def __len__(self):
+        """Return length via metainfo."""
+        return len(self.imgs)
+
+    def __getitem__(self, index):
+        """Return image, label."""
+        img = Image.open(self.imgs[index])
+        img = img.convert('RGB')
+        label = self.labels[index]
+
+        img = self.transform(img) if self.transform else img
+        label = self.target_transform(label) if self.target_transform else label
+        return img, label
+
+
+if __name__ == '__main__':
+    from tqdm import tqdm
+
+    dataset = BirdsnapV2(root='/mnt/petrelfs/wangwenhai/workspace/InternVL2/benchmark/imagenet/birdsnap', split='test')
+    print(len(dataset.imgs))
+    for i in tqdm(range(len(dataset))):
+        try:
+            dataset.__getitem__(i)
+            print(dataset.imgs[i])
+        except:
+            print(dataset.imgs[i])
+    print(dataset.classes)

InternVL-main/clip_benchmark/clip_benchmark/datasets/builder.py

@@ -0,0 +1,1012 @@
+import json
+import os
+import sys
+import warnings
+from subprocess import call
+
+import torch
+from torch.utils.data import default_collate
+from torchvision.datasets import (CIFAR10, CIFAR100, DTD, GTSRB, MNIST, PCAM,
+                                  STL10, SUN397, CocoCaptions, Country211,
+                                  EuroSAT, FGVCAircraft, Flowers102, Food101,
+                                  ImageFolder, ImageNet, OxfordIIITPet,
+                                  RenderedSST2, StanfordCars)
+
+from . import caltech101, flickr, imagenetv2, objectnet, voc2007
+from .birdsnap import BirdsnapV2
+from .tools import pre_caption
+
+
+def _load_classnames_and_classification_templates(dataset_name, current_folder, language):
+    with open(os.path.join(current_folder, language + '_classnames.json'), 'r') as f:
+        classnames = json.load(f)
+
+    # Zero-shot classification templates, collected from a bunch of sources
+    # - CLIP paper (https://github.com/openai/CLIP/blob/main/data/prompts.md)
+    # - Lit Paper (https://arxiv.org/pdf/2111.07991.pdf)
+    # - SLIP paper (https://github.com/facebookresearch/SLIP/blob/main/templates.json)
+    # Some are fixed mnaually
+
+    with open(os.path.join(current_folder, language + '_zeroshot_classification_templates.json'), 'r') as f:
+        zeroshot_classification_templates = json.load(f)
+    # default template to use when the dataset name does not belong to `zeroshot_classification_templates`
+    DEFAULT_ZEROSHOT_CLASSIFICATION_TEMPLATES = zeroshot_classification_templates['imagenet1k']
+
+    if dataset_name.startswith('tfds/') or dataset_name.startswith('vtab/') or dataset_name.startswith('wds/'):
+        name = dataset_name.split('/')[-1]
+    else:
+        name = dataset_name
+    templates = zeroshot_classification_templates.get(name, DEFAULT_ZEROSHOT_CLASSIFICATION_TEMPLATES)
+    return classnames, templates
+
+
+def build_dataset(dataset_name, root='root', transform=None, split='test', download=True, annotation_file=None,
+                  language='en', task='zeroshot_classification', cupl=False, wds_cache_dir=None, **kwargs):
+    """
+    Main function to use in order to build a dataset instance,
+
+    dataset_name: str
+        name of the dataset
+
+    root: str
+        root folder where the dataset is downloaded and stored. can be shared among datasets.
+
+    transform: torchvision transform applied to images
+
+    split: str
+        split to use, depending on the dataset can have different options.
+        In general, `train` and `test` are available.
+        For specific splits, please look at the corresponding dataset.
+
+    annotation_file: str or None
+        only for datasets with captions (used for retrieval) such as COCO
+        and Flickr.
+    """
+    current_folder = os.path.dirname(__file__)
+    if task in ('zeroshot_classification', 'linear_probe'):  # Only load templates and classnames if we have to
+        classnames, templates = _load_classnames_and_classification_templates(dataset_name, current_folder, language)
+    else:
+        classnames, templates = None, None
+
+    with open(os.path.join(current_folder, 'cupl_prompts.json'), 'r') as f:
+        cupl_prompts = json.load(f)
+    templates_cupl = None
+
+    train = (split == 'train')
+    if dataset_name == 'cifar10':
+        ds = CIFAR10(root=root, train=train, transform=transform, download=download, **kwargs)
+    elif dataset_name == 'cifar100':
+        ds = CIFAR100(root=root, train=train, transform=transform, download=download, **kwargs)
+    elif dataset_name == 'imagenet1k':
+        if not os.path.exists(root):
+            os.makedirs(root, exist_ok=True)
+            call(
+                f'wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_devkit_t12.tar.gz --output-document={root}/ILSVRC2012_devkit_t12.tar.gz',
+                shell=True)
+            call(
+                f'wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar --output-document={root}/ILSVRC2012_img_val.tar',
+                shell=True)
+
+        ds = ImageNet(root=root, split='train' if train else 'val', transform=transform, **kwargs)
+        # use classnames from OpenAI
+        ds.classes = classnames['imagenet1k']
+        templates_cupl = cupl_prompts['imagenet1k']
+    elif dataset_name == 'imagenet1k-unverified':
+        split = 'train' if train else 'val'
+        ds = ImageFolder(root=os.path.join(root, split), transform=transform, **kwargs)
+        # use classnames from OpenAI
+        ds.classes = classnames['imagenet1k']
+        templates_cupl = cupl_prompts['imagenet1k']
+    elif dataset_name == 'imagenetv2':
+        assert split == 'test', f'Only test split available for {dataset_name}'
+        os.makedirs(root, exist_ok=True)
+        ds = imagenetv2.ImageNetV2Dataset(variant='matched-frequency', transform=transform, location=root)
+        ds.classes = classnames['imagenet1k']
+        templates_cupl = cupl_prompts['imagenet1k']
+    elif dataset_name == 'imagenet_sketch':
+        assert split == 'test', f'Only test split available for {dataset_name}'
+        # Downloadable from https://drive.google.com/open?id=1Mj0i5HBthqH1p_yeXzsg22gZduvgoNeA
+        if not os.path.exists(root):
+            # Automatic download
+            print('Downloading imagenet_sketch...')
+            if not has_gdown():
+                print('GDown is needed to download the dataset. Please install it via `pip install gdown`')
+                sys.exit(1)
+            # Download ImageNet-Sketch.zip
+            call('gdown --id 1Mj0i5HBthqH1p_yeXzsg22gZduvgoNeA', shell=True)
+            assert os.path.exists('ImageNet-Sketch.zip')
+            # Unzip and move to `root`
+            call('unzip ImageNet-Sketch.zip', shell=True)
+            call(f'mv sketch {root}', shell=True)
+        ds = ImageFolder(root=root, transform=transform, **kwargs)
+        ds.classes = classnames['imagenet1k']
+        templates_cupl = cupl_prompts['imagenet1k']
+    elif dataset_name == 'imagenet-a':
+        assert split == 'test', f'Only test split available for {dataset_name}'
+        # Downloadable from https://people.eecs.berkeley.edu/~hendrycks/imagenet-a.tar
+        if not os.path.exists(root):
+            print('Downloading imagenet-a...')
+            call('wget https://people.eecs.berkeley.edu/~hendrycks/imagenet-a.tar', shell=True)
+            # Untar and move to `root`
+            call('tar xvf imagenet-a.tar', shell=True)
+            call(f'mv imagenet-a {root}', shell=True)
+        ds = ImageFolder(root=root, transform=transform, **kwargs)
+        ds.classes = classnames['imagenet1k']
+        imagenet_a_wnids = ['n01498041', 'n01531178', 'n01534433', 'n01558993', 'n01580077', 'n01614925', 'n01616318',
+                            'n01631663', 'n01641577', 'n01669191', 'n01677366', 'n01687978', 'n01694178', 'n01698640',
+                            'n01735189', 'n01770081', 'n01770393', 'n01774750', 'n01784675', 'n01819313', 'n01820546',
+                            'n01833805', 'n01843383', 'n01847000', 'n01855672', 'n01882714', 'n01910747', 'n01914609',
+                            'n01924916', 'n01944390', 'n01985128', 'n01986214', 'n02007558', 'n02009912', 'n02037110',
+                            'n02051845', 'n02077923', 'n02085620', 'n02099601', 'n02106550', 'n02106662', 'n02110958',
+                            'n02119022', 'n02123394', 'n02127052', 'n02129165', 'n02133161', 'n02137549', 'n02165456',
+                            'n02174001', 'n02177972', 'n02190166', 'n02206856', 'n02219486', 'n02226429', 'n02231487',
+                            'n02233338', 'n02236044', 'n02259212', 'n02268443', 'n02279972', 'n02280649', 'n02281787',
+                            'n02317335', 'n02325366', 'n02346627', 'n02356798', 'n02361337', 'n02410509', 'n02445715',
+                            'n02454379', 'n02486410', 'n02492035', 'n02504458', 'n02655020', 'n02669723', 'n02672831',
+                            'n02676566', 'n02690373', 'n02701002', 'n02730930', 'n02777292', 'n02782093', 'n02787622',
+                            'n02793495', 'n02797295', 'n02802426', 'n02814860', 'n02815834', 'n02837789', 'n02879718',
+                            'n02883205', 'n02895154', 'n02906734', 'n02948072', 'n02951358', 'n02980441', 'n02992211',
+                            'n02999410', 'n03014705', 'n03026506', 'n03124043', 'n03125729', 'n03187595', 'n03196217',
+                            'n03223299', 'n03250847', 'n03255030', 'n03291819', 'n03325584', 'n03355925', 'n03384352',
+                            'n03388043', 'n03417042', 'n03443371', 'n03444034', 'n03445924', 'n03452741', 'n03483316',
+                            'n03584829', 'n03590841', 'n03594945', 'n03617480', 'n03666591', 'n03670208', 'n03717622',
+                            'n03720891', 'n03721384', 'n03724870', 'n03775071', 'n03788195', 'n03804744', 'n03837869',
+                            'n03840681', 'n03854065', 'n03888257', 'n03891332', 'n03935335', 'n03982430', 'n04019541',
+                            'n04033901', 'n04039381', 'n04067472', 'n04086273', 'n04099969', 'n04118538', 'n04131690',
+                            'n04133789', 'n04141076', 'n04146614', 'n04147183', 'n04179913', 'n04208210', 'n04235860',
+                            'n04252077', 'n04252225', 'n04254120', 'n04270147', 'n04275548', 'n04310018', 'n04317175',
+                            'n04344873', 'n04347754', 'n04355338', 'n04366367', 'n04376876', 'n04389033', 'n04399382',
+                            'n04442312', 'n04456115', 'n04482393', 'n04507155', 'n04509417', 'n04532670', 'n04540053',
+                            'n04554684', 'n04562935', 'n04591713', 'n04606251', 'n07583066', 'n07695742', 'n07697313',
+                            'n07697537', 'n07714990', 'n07718472', 'n07720875', 'n07734744', 'n07749582', 'n07753592',
+                            'n07760859', 'n07768694', 'n07831146', 'n09229709', 'n09246464', 'n09472597', 'n09835506',
+                            'n11879895', 'n12057211', 'n12144580', 'n12267677']
+        imagenet_a_mask = [wnid in set(imagenet_a_wnids) for wnid in all_imagenet_wordnet_ids]
+        ds.classes = [cl for cl, mask in zip(ds.classes, imagenet_a_mask) if mask]
+    elif dataset_name == 'imagenet-r':
+        assert split == 'test', f'Only test split available for {dataset_name}'
+        # downloadable from https://people.eecs.berkeley.edu/~hendrycks/imagenet-r.tar
+        if not os.path.exists(root):
+            print('Downloading imagenet-r...')
+            call('wget https://people.eecs.berkeley.edu/~hendrycks/imagenet-r.tar', shell=True)
+            # Untar and move to `root`
+            call('tar xvf imagenet-r.tar', shell=True)
+            call(f'mv imagenet-r {root}', shell=True)
+        imagenet_r_wnids = {'n01443537', 'n01484850', 'n01494475', 'n01498041', 'n01514859', 'n01518878', 'n01531178',
+                            'n01534433', 'n01614925', 'n01616318', 'n01630670', 'n01632777', 'n01644373', 'n01677366',
+                            'n01694178', 'n01748264', 'n01770393', 'n01774750', 'n01784675', 'n01806143', 'n01820546',
+                            'n01833805', 'n01843383', 'n01847000', 'n01855672', 'n01860187', 'n01882714', 'n01910747',
+                            'n01944390', 'n01983481', 'n01986214', 'n02007558', 'n02009912', 'n02051845', 'n02056570',
+                            'n02066245', 'n02071294', 'n02077923', 'n02085620', 'n02086240', 'n02088094', 'n02088238',
+                            'n02088364', 'n02088466', 'n02091032', 'n02091134', 'n02092339', 'n02094433', 'n02096585',
+                            'n02097298', 'n02098286', 'n02099601', 'n02099712', 'n02102318', 'n02106030', 'n02106166',
+                            'n02106550', 'n02106662', 'n02108089', 'n02108915', 'n02109525', 'n02110185', 'n02110341',
+                            'n02110958', 'n02112018', 'n02112137', 'n02113023', 'n02113624', 'n02113799', 'n02114367',
+                            'n02117135', 'n02119022', 'n02123045', 'n02128385', 'n02128757', 'n02129165', 'n02129604',
+                            'n02130308', 'n02134084', 'n02138441', 'n02165456', 'n02190166', 'n02206856', 'n02219486',
+                            'n02226429', 'n02233338', 'n02236044', 'n02268443', 'n02279972', 'n02317335', 'n02325366',
+                            'n02346627', 'n02356798', 'n02363005', 'n02364673', 'n02391049', 'n02395406', 'n02398521',
+                            'n02410509', 'n02423022', 'n02437616', 'n02445715', 'n02447366', 'n02480495', 'n02480855',
+                            'n02481823', 'n02483362', 'n02486410', 'n02510455', 'n02526121', 'n02607072', 'n02655020',
+                            'n02672831', 'n02701002', 'n02749479', 'n02769748', 'n02793495', 'n02797295', 'n02802426',
+                            'n02808440', 'n02814860', 'n02823750', 'n02841315', 'n02843684', 'n02883205', 'n02906734',
+                            'n02909870', 'n02939185', 'n02948072', 'n02950826', 'n02951358', 'n02966193', 'n02980441',
+                            'n02992529', 'n03124170', 'n03272010', 'n03345487', 'n03372029', 'n03424325', 'n03452741',
+                            'n03467068', 'n03481172', 'n03494278', 'n03495258', 'n03498962', 'n03594945', 'n03602883',
+                            'n03630383', 'n03649909', 'n03676483', 'n03710193', 'n03773504', 'n03775071', 'n03888257',
+                            'n03930630', 'n03947888', 'n04086273', 'n04118538', 'n04133789', 'n04141076', 'n04146614',
+                            'n04147183', 'n04192698', 'n04254680', 'n04266014', 'n04275548', 'n04310018', 'n04325704',
+                            'n04347754', 'n04389033', 'n04409515', 'n04465501', 'n04487394', 'n04522168', 'n04536866',
+                            'n04552348', 'n04591713', 'n07614500', 'n07693725', 'n07695742', 'n07697313', 'n07697537',
+                            'n07714571', 'n07714990', 'n07718472', 'n07720875', 'n07734744', 'n07742313', 'n07745940',
+                            'n07749582', 'n07753275', 'n07753592', 'n07768694', 'n07873807', 'n07880968', 'n07920052',
+                            'n09472597', 'n09835506', 'n10565667', 'n12267677'}
+        imagenet_r_mask = [wnid in imagenet_r_wnids for wnid in all_imagenet_wordnet_ids]
+        ds = ImageFolder(root=root, transform=transform, **kwargs)
+        ds.classes = classnames['imagenet1k']
+        ds.classes = [cl for cl, mask in zip(ds.classes, imagenet_r_mask) if mask]
+    elif dataset_name == 'imagenet-o':
+        assert split == 'test', f'Only test split available for {dataset_name}'
+        # downloadable from https://people.eecs.berkeley.edu/~hendrycks/imagenet-o.tar
+        if not os.path.exists(root):
+            print('Downloading imagenet-o...')
+            call('wget https://people.eecs.berkeley.edu/~hendrycks/imagenet-o.tar', shell=True)
+            # Untar and move to `root`
+            call('tar xvf imagenet-o.tar', shell=True)
+            call(f'mv imagenet-o {root}', shell=True)
+        ds = ImageFolder(root=root, transform=transform, **kwargs)
+        ds.classes = classnames['imagenet1k']
+        imagenet_o_wnids = ['n01443537', 'n01704323', 'n01770081', 'n01784675', 'n01819313', 'n01820546', 'n01910747',
+                            'n01917289', 'n01968897', 'n02074367', 'n02317335', 'n02319095', 'n02395406', 'n02454379',
+                            'n02606052', 'n02655020', 'n02666196', 'n02672831', 'n02730930', 'n02777292', 'n02783161',
+                            'n02786058', 'n02787622', 'n02791270', 'n02808304', 'n02817516', 'n02841315', 'n02865351',
+                            'n02877765', 'n02892767', 'n02906734', 'n02910353', 'n02916936', 'n02948072', 'n02965783',
+                            'n03000134', 'n03000684', 'n03017168', 'n03026506', 'n03032252', 'n03075370', 'n03109150',
+                            'n03126707', 'n03134739', 'n03160309', 'n03196217', 'n03207743', 'n03218198', 'n03223299',
+                            'n03240683', 'n03271574', 'n03291819', 'n03297495', 'n03314780', 'n03325584', 'n03344393',
+                            'n03347037', 'n03372029', 'n03376595', 'n03388043', 'n03388183', 'n03400231', 'n03445777',
+                            'n03457902', 'n03467068', 'n03482405', 'n03483316', 'n03494278', 'n03530642', 'n03544143',
+                            'n03584829', 'n03590841', 'n03598930', 'n03602883', 'n03649909', 'n03661043', 'n03666591',
+                            'n03676483', 'n03692522', 'n03706229', 'n03717622', 'n03720891', 'n03721384', 'n03724870',
+                            'n03729826', 'n03733131', 'n03733281', 'n03742115', 'n03786901', 'n03788365', 'n03794056',
+                            'n03804744', 'n03814639', 'n03814906', 'n03825788', 'n03840681', 'n03843555', 'n03854065',
+                            'n03857828', 'n03868863', 'n03874293', 'n03884397', 'n03891251', 'n03908714', 'n03920288',
+                            'n03929660', 'n03930313', 'n03937543', 'n03942813', 'n03944341', 'n03961711', 'n03970156',
+                            'n03982430', 'n03991062', 'n03995372', 'n03998194', 'n04005630', 'n04023962', 'n04033901',
+                            'n04040759', 'n04067472', 'n04074963', 'n04116512', 'n04118776', 'n04125021', 'n04127249',
+                            'n04131690', 'n04141975', 'n04153751', 'n04154565', 'n04201297', 'n04204347', 'n04209133',
+                            'n04209239', 'n04228054', 'n04235860', 'n04243546', 'n04252077', 'n04254120', 'n04258138',
+                            'n04265275', 'n04270147', 'n04275548', 'n04330267', 'n04332243', 'n04336792', 'n04347754',
+                            'n04371430', 'n04371774', 'n04372370', 'n04376876', 'n04409515', 'n04417672', 'n04418357',
+                            'n04423845', 'n04429376', 'n04435653', 'n04442312', 'n04482393', 'n04501370', 'n04507155',
+                            'n04525305', 'n04542943', 'n04554684', 'n04557648', 'n04562935', 'n04579432', 'n04591157',
+                            'n04597913', 'n04599235', 'n06785654', 'n06874185', 'n07615774', 'n07693725', 'n07695742',
+                            'n07697537', 'n07711569', 'n07714990', 'n07715103', 'n07716358', 'n07717410', 'n07718472',
+                            'n07720875', 'n07742313', 'n07745940', 'n07747607', 'n07749582', 'n07753275', 'n07753592',
+                            'n07754684', 'n07768694', 'n07836838', 'n07871810', 'n07873807', 'n07880968', 'n09229709',
+                            'n09472597', 'n12144580', 'n12267677', 'n13052670']
+        imagenet_o_mask = [wnid in set(imagenet_o_wnids) for wnid in all_imagenet_wordnet_ids]
+        ds.classes = [cl for cl, mask in zip(ds.classes, imagenet_o_mask) if mask]
+    elif dataset_name == 'objectnet':
+        assert split == 'test', f'Only test split available for {dataset_name}'
+        # downloadable from https://objectnet.dev/downloads/objectnet-1.0.zip or https://www.dropbox.com/s/raw/cxeztdtm16nzvuw/objectnet-1.0.zip
+        if not os.path.exists(root):
+            print('Downloading objectnet...')
+            call('wget https://objectnet.dev/downloads/objectnet-1.0.zip', shell=True)
+            # Untar and move to `root`
+            call('UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE unzip -P objectnetisatestset objectnet-1.0.zip', shell=True)
+            os.makedirs(root)
+            call(f'mv objectnet-1.0 {root}', shell=True)
+            call(f'cp {root}/objectnet-1.0/mappings/* {root}', shell=True)
+        ds = objectnet.ObjectNetDataset(root=root, transform=transform)
+    elif dataset_name == 'voc2007':
+        ds = voc2007.PASCALVoc2007Cropped(root=root, set='train' if train else 'test', transform=transform,
+                                          download=download, **kwargs)
+    elif dataset_name == 'voc2007_multilabel':
+        ds = voc2007.PASCALVoc2007(root=root, set='train' if train else 'test', transform=transform, download=download,
+                                   **kwargs)
+    elif dataset_name == 'mscoco_captions':
+        # https://github.com/mehdidc/retrieval_annotations/releases/tag/1.0.0(annotations)
+        if split == 'train':
+            archive_name = 'train2014.zip'
+        elif split in ('val', 'test'):
+            archive_name = 'val2014.zip'
+        else:
+            raise ValueError(f'split should be train or val or test for `{dataset_name}`')
+        root_split = os.path.join(root, archive_name.replace('.zip', ''))
+        if not os.path.exists(root_split):
+            print(f'Downloading mscoco_captions {archive_name}...')
+            if not os.path.exists(os.path.join(root, archive_name)):
+                call(f'wget http://images.cocodataset.org/zips/{archive_name} --output-document={root}/{archive_name}',
+                     shell=True)
+            call(f'unzip {root}/{archive_name} -d {root}', shell=True)
+        if not annotation_file:
+            annotation_file = f'{root}/coco_{split}_karpathy.json'
+        if language == 'cn':
+            annotation_file = f'{root}/coco-cn_{split}.json'
+            root_split = root
+            print(annotation_file)
+        if not os.path.exists(annotation_file):
+            call(
+                f'wget https://github.com/mehdidc/retrieval_annotations/releases/download/1.0.0/coco_{split}_karpathy.json --output-document={annotation_file}',
+                shell=True)
+        ds = CocoCaptions(root=root_split, annFile=annotation_file, transform=transform,
+                          target_transform=pre_caption, **kwargs)
+    elif dataset_name == 'multilingual_mscoco_captions':
+        from clip_benchmark.datasets import multilingual_mscoco
+        if (language not in multilingual_mscoco.SUPPORTED_LANGUAGES):
+            raise ValueError('Unsupported language for multilingual_ms_coco:', language)
+
+        def get_archive_name(target_split):
+            if target_split == 'train':
+                return 'train2014.zip'
+            elif target_split in ('val', 'test'):
+                return 'val2014.zip'
+            else:
+                raise ValueError(f'split should be train or val or test for `{dataset_name}`')
+
+        def download_mscoco_split(target_split):
+            archive_name = get_archive_name(target_split)
+            root_split = os.path.join(root, archive_name.replace('.zip', ''))
+            if not os.path.exists(root_split):
+                print(f'Downloading mscoco_captions {archive_name}...')
+                if not os.path.exists(os.path.join(root, archive_name)):
+                    call(
+                        f'wget http://images.cocodataset.org/zips/{archive_name} --output-document={root}/{archive_name}',
+                        shell=True)
+                call(f'unzip {root}/{archive_name} -d {root}', shell=True)
+
+                # The multilingual MS-COCO uses images from various splits
+
+        for target_split in ['train', 'val', 'test']:
+            download_mscoco_split(target_split)
+
+        annotation_file = os.path.join(root, multilingual_mscoco.CAPTIONS_FILE_NAME.format(language))
+        # if (os.path.exists(annotation_file) == False):
+        multilingual_mscoco.create_annotation_file(root, language)
+
+        ds = multilingual_mscoco.Multilingual_MSCOCO(root=root, ann_file=annotation_file, transform=transform, **kwargs)
+    elif dataset_name == 'flickr30k':
+        # downloadable from https://www.kaggle.com/datasets/adityajn105/flickr30k
+        # https://github.com/mehdidc/retrieval_annotations/releases/tag/1.0.0(annotations)
+        # `kaggle datasets download -d adityajn105/flickr30k`
+        if not os.path.exists(root):
+            # Automatic download
+            print('Downloading flickr30k...')
+            if not has_kaggle():
+                print('Kaggle is needed to download the dataset. Please install it via `pip install kaggle`')
+                sys.exit(1)
+            call('kaggle datasets download -d adityajn105/flickr30k', shell=True)
+            call(f'unzip flickr30k.zip', shell=True)
+            call(f'mv Images {root}', shell=True)
+            call(f'mv captions.txt {root}', shell=True)
+        if not annotation_file:
+            annotation_file = f'{root}/flickr30k_{split}_karpathy.txt'
+        if not os.path.exists(annotation_file):
+            # Download Flickr30K Karpathy test set
+            annotation_file = f'{root}/flickr30k_{split}_karpathy.txt'
+            call(
+                f'wget https://github.com/mehdidc/retrieval_annotations/releases/download/1.0.0/flickr30k_{split}_karpathy.txt --output-document={annotation_file}',
+                shell=True)
+        if language == 'cn':
+            annotation_file = f'{root}/flickr30k_cn_{split}.txt'
+            print(annotation_file)
+        ds = flickr.Flickr(root=f'{root}/Images', ann_file=annotation_file, transform=transform,
+                           target_transform=pre_caption, **kwargs)
+    elif dataset_name == 'flickr8k':
+        # downloadable from https://www.kaggle.com/datasets/adityajn105/flickr8k
+        # `kaggle datasets download -d adityajn105/flickr8k`
+        # https://github.com/mehdidc/retrieval_annotations/releases/tag/1.0.0(annotations)
+        if not os.path.exists(root):
+            # Automatic download
+            print('Downloading flickr8k...')
+            if not has_kaggle():
+                print('Kaggle is needed to download the dataset. Please install it via `pip install kaggle`')
+                sys.exit(1)
+            call('kaggle datasets download -d adityajn105/flickr8k', shell=True)
+            call(f'unzip flickr8k.zip', shell=True)
+            call(f'mv Images {root}', shell=True)
+            call(f'mv captions.txt {root}', shell=True)
+        if not annotation_file:
+            annotation_file = f'{root}/flickr8k_{split}_karpathy.txt'
+        if not os.path.exists(annotation_file):
+            # Download Flickr8K Karpathy test set
+            annotation_file = f'{root}/flickr8k_{split}_karpathy.txt'
+            call(
+                f'wget https://github.com/mehdidc/retrieval_annotations/releases/download/1.0.0/flickr8k_{split}_karpathy.txt --output-document={annotation_file}',
+                shell=True)
+        ds = flickr.Flickr(root=root, ann_file=annotation_file, transform=transform, **kwargs)
+    elif dataset_name == 'food101':
+        ds = Food101(root=root, split='train' if train else 'test', transform=transform, download=download, **kwargs)
+        # we use the default class names, we just  replace "_" by spaces
+        # to delimit words
+        ds.classes = [cl.replace('_', ' ') for cl in ds.classes]
+    elif dataset_name == 'sun397':
+        warnings.warn(
+            f'split argument ignored for `{dataset_name}`, there are no pre-defined train/test splits for this dataset')
+        # we use the default class names, we just  replace "_" and "/" by spaces
+        # to delimit words
+        ds = SUN397(root=root, transform=transform, download=download, **kwargs)
+        ds.classes = [cl.replace('_', ' ').replace('/', ' ') for cl in ds.classes]
+    elif dataset_name == 'cars':
+        ds = StanfordCars(root=root, split='train' if train else 'test', transform=transform, download=download,
+                          **kwargs)
+    elif dataset_name == 'fgvc_aircraft':
+        ds = FGVCAircraft(root=root, annotation_level='variant', split='train' if train else 'test',
+                          transform=transform, download=download, **kwargs)
+    elif dataset_name == 'dtd':
+        ds = DTD(root=root, split='train' if train else 'test', transform=transform, download=download, **kwargs)
+    elif dataset_name == 'pets':
+        ds = OxfordIIITPet(root=root, split='train' if train else 'test', target_types='category', transform=transform,
+                           download=download, **kwargs)
+    elif dataset_name == 'caltech101':
+        warnings.warn(
+            f'split argument ignored for `{dataset_name}`, there are no pre-defined train/test splits for this dataset')
+        # broken download link (can't download google drive), fixed by this PR https://github.com/pytorch/vision/pull/5645
+        # also available in "vtab/caltech101" using VTAB splits, we advice to use VTAB version rather than this one
+        # since in this one (torchvision) there are no pre-defined test splits
+        ds = caltech101.Caltech101(root=root, target_type='category', transform=transform, download=download, **kwargs)
+        ds.classes = classnames['caltech101']
+    elif dataset_name == 'flowers':
+        ds = Flowers102(root=root, split='train' if train else 'test', transform=transform, download=download, **kwargs)
+        # class indices started by 1 until it was fixed in  a  PR (#TODO link of the PR)
+        # if older torchvision version, fix it using a target transform that decrements label index
+        # TODO figure out minimal torchvision version needed instead of decrementing
+        if ds[0][1] == 1:
+            ds.target_transform = lambda y: y - 1
+        ds.classes = classnames['flowers']
+    elif dataset_name == 'birdsnap':
+        ds = BirdsnapV2(root=root, split='train' if train else 'test', transform=transform, **kwargs)
+        # ds.classes = ds.classes
+    elif dataset_name == 'mnist':
+        ds = MNIST(root=root, train=train, transform=transform, download=download, **kwargs)
+        ds.classes = classnames['mnist']
+    elif dataset_name == 'stl10':
+        ds = STL10(root=root, split='train' if train else 'test', transform=transform, download=download, **kwargs)
+    elif dataset_name == 'eurosat':
+        warnings.warn(
+            f'split argument ignored for `{dataset_name}`, there are no pre-defined train/test splits for this dataset')
+        ds = EuroSAT(root=root, transform=transform, download=download, **kwargs)
+        ds.classes = classnames['eurosat']
+    elif dataset_name == 'gtsrb':
+        ds = GTSRB(root=root, split='train' if train else 'test', transform=transform, download=download, **kwargs)
+        ds.classes = classnames['gtsrb']
+    elif dataset_name == 'country211':
+        ds = Country211(root=root, split='train' if train else 'test', transform=transform, download=download, **kwargs)
+        ds.classes = classnames['country211']
+    elif dataset_name == 'pcam':
+        # Dead link. Fixed by this PR on torchvision https://github.com/pytorch/vision/pull/5645
+        # TODO figure out minimal torchvision version needed
+        ds = PCAM(root=root, split='train' if train else 'test', transform=transform, download=download, **kwargs)
+        ds.classes = classnames['pcam']
+    elif dataset_name == 'renderedsst2':
+        ds = RenderedSST2(root=root, split='train' if train else 'test', transform=transform, download=download,
+                          **kwargs)
+    elif dataset_name == 'fer2013':
+        # Downloadable from  https://www.kaggle.com/datasets/msambare/fer2013
+        # `kaggle datasets download -d msambare/fer2013`
+        if not os.path.exists(root):
+            # Automatic download
+            print('Downloading fer2013...')
+            if not has_kaggle():
+                print('Kaggle is needed to download the dataset. Please install it via `pip install kaggle`')
+                sys.exit(1)
+            call('kaggle datasets download -d msambare/fer2013', shell=True)
+            call(f'unzip fer2013.zip -d {root}', shell=True)
+        root = os.path.join(root, 'train' if train else 'test')
+        ds = ImageFolder(root=root, transform=transform)
+        ds.classes = classnames['fer2013']
+    elif dataset_name.startswith('tfds/'):
+        # TFDS datasets support using `timm` and `tensorflow_datasets`
+        prefix, *name_list = dataset_name.split('/')
+        name = '/'.join(name_list)
+        ds = build_tfds_dataset(name, download=download, split=split, data_dir=root, transform=transform)
+    elif dataset_name.startswith('vtab/'):
+        # VTAB datasets support using `tensorflow_datasets` and `task_adaptation`
+        prefix, *name_list = dataset_name.split('/')
+        name = '/'.join(name_list)
+        ds = build_vtab_dataset(name, download=download, split=split, data_dir=root, transform=transform,
+                                classnames=classnames)
+    elif dataset_name.startswith('wds/'):
+        # WebDataset support using `webdataset` library
+        name = dataset_name.split('/', 1)[1]
+        ds = build_wds_dataset(name, transform=transform, split=split, data_dir=root, cache_dir=wds_cache_dir)
+        return ds
+    elif dataset_name == 'dummy':
+        ds = Dummy()
+    else:
+        raise ValueError(f'Unsupported dataset: {dataset_name}.')
+
+    if cupl:
+        ds.templates = templates_cupl
+    else:
+        ds.templates = templates
+
+    return ds
+
+
+class Dummy():
+
+    def __init__(self):
+        self.classes = ['blank image', 'noisy image']
+
+    def __getitem__(self, i):
+        return torch.zeros(3, 224, 224), 0
+
+    def __len__(self):
+        return 1
+
+
+def get_dataset_default_task(dataset):
+    if dataset in ('flickr30k', 'flickr8k', 'mscoco_captions', 'multilingual_mscoco_captions'):
+        return 'zeroshot_retrieval'
+    else:
+        return 'zeroshot_classification'
+
+
+def get_dataset_collate_fn(dataset_name):
+    if dataset_name in ('mscoco_captions', 'multilingual_mscoco_captions', 'flickr30k', 'flickr8k'):
+        return image_captions_collate_fn
+    else:
+        return default_collate
+
+
+def has_gdown():
+    return call('which gdown', shell=True) == 0
+
+
+def has_kaggle():
+    return call('which kaggle', shell=True) == 0
+
+
+def build_vtab_dataset(dataset_name, transform, download=True, split='test', data_dir='root', classnames=[]):
+    # Using VTAB splits instead of default TFDS splits
+    from .tfds import (VTABIterableDataset, disable_gpus_on_tensorflow,
+                       download_tfds_dataset)
+
+    # avoid Tensorflow owning GPUs to not clash with PyTorch
+    disable_gpus_on_tensorflow()
+
+    # by default we take classes from TFDS (default behavior if `classes` stays None),
+    # except for the datasets that will override `classes` (e.g., clevr_*)
+    classes = None
+    if dataset_name == 'caltech101':
+        from task_adaptation.data.caltech import Caltech101
+        tfds_dataset = Caltech101(data_dir=data_dir)
+        classes = classnames['caltech101_vtab']
+    elif dataset_name == 'cars':
+        from task_adaptation.data.cars import CarsData
+        tfds_dataset = CarsData(data_dir=data_dir)
+    elif dataset_name in ('cifar10', 'cifar100'):
+        from task_adaptation.data.cifar import CifarData
+        tfds_dataset = CifarData(data_dir=data_dir, num_classes=10 if dataset_name == 'cifar10' else 100)
+    elif dataset_name.startswith('clevr_'):
+        from task_adaptation.data.clevr import CLEVRData
+        task = _extract_task(dataset_name)
+        assert task in ('count_all', 'closest_object_distance')
+        tfds_dataset = CLEVRData(task=task, data_dir=data_dir)
+        if task == 'count_all':
+            classes = classnames['clevr_count_all']
+        elif task == 'closest_object_distance':
+            classes = classnames['clevr_closest_object_distance']
+        else:
+            raise ValueError(f'non supported: {task}')
+    elif dataset_name == 'cub':
+        from task_adaptation.data.cub import CUB2011Data
+        tfds_dataset = CUB2011Data(data_dir=data_dir)
+    elif dataset_name == 'diabetic_retinopathy':
+        # Needs manual download from Kaggle
+        # 1) `kaggle competitions download -c diabetic-retinopathy-detection` on $ROOT/downloads/manual
+        # 2) extract archives  on $ROOT/downloads/manual
+        if not os.path.exists(data_dir):
+            # Automatic download
+            print('Downloading diabetic_retinopathy...')
+            if not has_kaggle():
+                print('Kaggle is needed to download the dataset. Please install it via `pip install kaggle`')
+                sys.exit(1)
+            os.makedirs(os.path.join(data_dir, 'downloads', 'manual'))
+            call(f'kaggle competitions download -c diabetic-retinopathy-detection -p {data_dir}/downloads/manual',
+                 shell=True)
+            call(
+                f'cd {data_dir}/downloads/manual;unzip diabetic-retinopathy-detection.zip;cat train.zip*>train.zip;cat test.zip*>test.zip;unzip train.zip; unzip test.zip;unzip sample.zip;unzip trainLabels.csv.zip',
+                shell=True)
+        from task_adaptation.data.diabetic_retinopathy import RetinopathyData
+        tfds_dataset = RetinopathyData(config='btgraham-300', data_dir=data_dir)
+        classes = classnames['diabetic_retinopathy']
+    elif dataset_name == 'dmlab':
+        from task_adaptation.data.dmlab import DmlabData
+        download_tfds_dataset('dmlab',
+                              data_dir=data_dir)  # it's not called in the original VTAB code, so we do it explictly
+        tfds_dataset = DmlabData(data_dir=data_dir)
+        classes = classnames['dmlab']
+    elif dataset_name.startswith('dsprites_'):
+        from task_adaptation.data.dsprites import DSpritesData
+        task = _extract_task(dataset_name)
+        assert task in ('label_shape', 'label_scale', 'label_orientation', 'label_x_position', 'label_y_position')
+        tfds_dataset = DSpritesData(task, data_dir=data_dir)
+        classes = tfds_dataset._dataset_builder.info.features[task].names
+    elif dataset_name == 'dtd':
+        from task_adaptation.data.dtd import DTDData
+        tfds_dataset = DTDData(data_dir=data_dir)
+    elif dataset_name == 'eurosat':
+        from task_adaptation.data.eurosat import EurosatData
+        tfds_dataset = EurosatData(subset='rgb', data_key='image', data_dir=data_dir)
+        classes = classnames['eurosat']
+    elif dataset_name == 'food101':
+        from task_adaptation.data.food101 import Food101Data
+        tfds_dataset = Food101Data(data_dir=data_dir)
+    elif dataset_name == 'inaturalist':
+        from task_adaptation.data.inaturalist import INaturalistData
+        tfds_dataset = INaturalistData(data_dir=data_dir, year=2017)
+    elif dataset_name.startswith('kitti_'):
+        from .kitti import KittiData
+        task = _extract_task(dataset_name)
+        assert task in (
+            'count_all', 'count_left', 'count_far', 'count_near',
+            'closest_object_distance', 'closest_object_x_location',
+            'count_vehicles', 'closest_vehicle_distance',
+        )
+        tfds_dataset = KittiData(task=task, data_dir=data_dir)
+        if task == 'closest_vehicle_distance':
+            classes = classnames['kitti_closest_vehicle_distance']
+        else:
+            raise ValueError(f'Unsupported task: {task}')
+    elif dataset_name == 'flowers':
+        from task_adaptation.data.oxford_flowers102 import OxfordFlowers102Data
+        tfds_dataset = OxfordFlowers102Data(data_dir=data_dir)
+    elif dataset_name == 'pets':
+        from task_adaptation.data.oxford_iiit_pet import OxfordIIITPetData
+        tfds_dataset = OxfordIIITPetData(data_dir=data_dir)
+        classes = classnames['pets']
+    elif dataset_name == 'pcam':
+        from task_adaptation.data.patch_camelyon import PatchCamelyonData
+        tfds_dataset = PatchCamelyonData(data_dir=data_dir)
+        classes = classnames['pcam']
+    elif dataset_name == 'resisc45':
+        # Needs download from OneDrive: https://1drv.ms/u/s!AmgKYzARBl5ca3HNaHIlzp_IXjs
+        # The archive needs to to be put at <DATASET_ROOT>/downloads/manual then extracted
+        # if not os.path.exists(data_dir):
+        #     os.makedirs(os.path.join(data_dir, "downloads", "manual"))
+        #     call(f"wget 'https://onedrive.live.com/download?resid=5C5E061130630A68!107&authkey=!AHHNaHIlzp_IXjs' --output-document={data_dir}/downloads/manual/resisc45.rar", shell=True)
+        #     call(f"cd {data_dir}/downloads/manual;unrar x resisc45.rar", shell=True)
+        from task_adaptation.data.resisc45 import Resisc45Data
+        tfds_dataset = Resisc45Data(data_dir=data_dir)
+    elif dataset_name.startswith('smallnorb_'):
+        from task_adaptation.data.smallnorb import SmallNORBData
+        task = _extract_task(dataset_name)
+        assert task in ('label_category', 'label_elevation', 'label_azimuth', 'label_lighting')
+        tfds_dataset = SmallNORBData(predicted_attribute=task, data_dir=data_dir)
+        classes = tfds_dataset._dataset_builder.info.features[task].names
+    elif dataset_name == 'sun397':
+        from task_adaptation.data.sun397 import Sun397Data
+
+        # FIXME There is a problem in `sun397`, when TFDS tries download it
+        # there is an image that cannot be decoded. For the time being
+        # we will use torchvision's SUN397 instead.
+        tfds_dataset = Sun397Data(config='tfds', data_dir=data_dir)
+    elif dataset_name == 'svhn':
+        from task_adaptation.data.svhn import SvhnData
+        tfds_dataset = SvhnData(data_dir=data_dir)
+        classes = classnames['svhn']
+    else:
+        raise ValueError(f'Unsupported dataset: {dataset_name}')
+    ds = VTABIterableDataset(
+        tfds_dataset,
+        input_name='image', label_name='label',
+        transform=transform,
+        target_transform=int,
+        split=split,
+        classes=classes,
+    )
+    return ds
+
+
+def build_tfds_dataset(name, transform, download=True, split='test', data_dir='root', classes=None):
+    from .tfds import disable_gpus_on_tensorflow
+    disable_gpus_on_tensorflow()
+    import tensorflow_datasets as tfds
+    import timm
+    builder = tfds.builder(name, data_dir=data_dir)
+    if download:
+        builder.download_and_prepare()
+    splits = list(builder.info.splits.keys())
+    assert split in splits, (split, splits)
+    ds = timm.data.create_dataset(f'tfds/{name}', data_dir, split=split, transform=transform, target_transform=int)
+    ds.classes = builder.info.features['label'].names if classes is None else classes
+    return ds
+
+
+def build_wds_dataset(dataset_name, transform, split='test', data_dir='root', cache_dir=None):
+    """
+    Load a dataset in WebDataset format. Either local paths or HTTP URLs can be specified.
+    Expected file structure is:
+    ```
+    data_dir/
+        train/
+            nshards.txt
+            0.tar
+            1.tar
+            ...
+        test/
+            nshards.txt
+            0.tar
+            1.tar
+            ...
+        classnames.txt
+        zeroshot_classification_templates.txt
+        dataset_type.txt
+    ```
+    Classnames and templates are required for zeroshot classification, while dataset type
+    (equal to "retrieval") is required for zeroshot retrieval datasets.
+
+    You can use the `clip_benchmark_export_wds` or corresponding API
+    (`clip_benchmark.webdataset_builder.convert_dataset`) to convert datasets to this format.
+
+    Set `cache_dir` to a path to cache the dataset, otherwise, no caching will occur.
+    """
+    import webdataset as wds
+
+    def read_txt(fname):
+        if '://' in fname:
+            stream = os.popen("curl -L -s --fail '%s'" % fname, 'r')
+            value = stream.read()
+            if stream.close():
+                raise FileNotFoundError('Failed to retreive data')
+        else:
+            with open(fname, 'r') as file:
+                value = file.read()
+        return value
+
+    # Special handling for Huggingface datasets
+    # Git LFS files have a different file path to access the raw data than other files
+    if data_dir.startswith('https://huggingface.co/datasets'):
+        # Format: https://huggingface.co/datasets/<USERNAME>/<REPO>/tree/<BRANCH>
+        *split_url_head, _, url_path = data_dir.split('/', 7)
+        url_head = '/'.join(split_url_head)
+        metadata_dir = '/'.join([url_head, 'raw', url_path])
+        tardata_dir = '/'.join([url_head, 'resolve', url_path])
+    else:
+        metadata_dir = tardata_dir = data_dir
+    # Get number of shards
+    nshards_fname = os.path.join(metadata_dir, split, 'nshards.txt')
+    nshards = int(read_txt(nshards_fname))  # Do not catch FileNotFound, nshards.txt should be mandatory
+    # Get dataset type (classification or retrieval)
+    type_fname = os.path.join(metadata_dir, 'dataset_type.txt')
+    try:
+        dataset_type = read_txt(type_fname).strip().lower()
+    except FileNotFoundError:
+        # print("WARNING: dataset_type.txt not found, assuming type=classification")
+        dataset_type = 'classification'
+    #
+    filepattern = os.path.join(tardata_dir, split, '{0..%d}.tar' % (nshards - 1))
+    # Load webdataset (support WEBP, PNG, and JPG for now)
+    if not cache_dir or not isinstance(cache_dir, str):
+        cache_dir = None
+    dataset = wds.WebDataset(filepattern, cache_dir=cache_dir).decode(
+        wds.autodecode.ImageHandler('pil', extensions=['webp', 'png', 'jpg', 'jpeg']))
+
+    # Load based on classification or retrieval task
+    if dataset_type == 'retrieval':
+        dataset = (dataset
+                   .to_tuple(['webp', 'png', 'jpg', 'jpeg'], 'txt')
+                   .map_tuple(transform, str.splitlines)
+                   )
+        dataset.classes = dataset.templates = None
+    else:
+        label_type = 'npy' if dataset_type == 'multilabel' else 'cls'  # Special case for multilabel
+        dataset = (dataset
+                   .to_tuple(['webp', 'png', 'jpg', 'jpeg'], label_type)
+                   .map_tuple(transform, None)
+                   )
+        # Get class names if present
+        classnames_fname = os.path.join(metadata_dir, 'classnames.txt')
+        try:
+            dataset.classes = [line.strip() for line in read_txt(classnames_fname).splitlines() if line.strip()]
+        except FileNotFoundError:
+            print('WARNING: classnames.txt not found')
+            dataset.classes = None
+        # Get zeroshot classification templates if present
+        templates_fname = os.path.join(metadata_dir, 'zeroshot_classification_templates.txt')
+        try:
+            dataset.templates = [line.strip() for line in read_txt(templates_fname).splitlines() if line.strip()]
+        except FileNotFoundError:
+            print('WARNING: zeroshot_classification_templates.txt not found')
+            dataset.templates = None
+
+    return dataset
+
+
+def _extract_task(dataset_name):
+    prefix, *task_name_list = dataset_name.split('_')
+    task = '_'.join(task_name_list)
+    return task
+
+
+def image_captions_collate_fn(batch):
+    transposed = list(zip(*batch))
+    imgs = default_collate(transposed[0])
+    texts = transposed[1]
+    return imgs, texts
+
+
+def get_dataset_collection_from_file(path):
+    return [l.strip() for l in open(path).readlines()]
+
+
+dataset_collection = {
+    'vtab': [
+        'vtab/caltech101',
+        'vtab/cifar100',
+        'vtab/clevr_count_all',
+        'vtab/clevr_closest_object_distance',
+        'vtab/diabetic_retinopathy',
+        'vtab/dmlab',
+        'vtab/dsprites_label_orientation',
+        'vtab/dsprites_label_x_position',
+        'vtab/dtd',
+        'vtab/eurosat',
+        'vtab/kitti_closest_vehicle_distance',
+        'vtab/flowers',
+        'vtab/pets',
+        'vtab/pcam',
+        'vtab/resisc45',
+        'vtab/smallnorb_label_azimuth',
+        'vtab/smallnorb_label_elevation',
+        'sun397',
+        'vtab/svhn',
+    ],
+    'vtab+': [
+        'imagenet1k',
+        'imagenetv2',
+        'imagenet_sketch',
+        'imagenet-a',
+        'imagenet-r',
+        'objectnet',
+        'fer2013',
+        'voc2007',
+        'voc2007_multilabel',
+        'sun397',
+        'cars',
+        'fgvc_aircraft',
+        'mnist',
+        'stl10',
+        'gtsrb',
+        'country211',
+        'renderedsst2',
+        'vtab/caltech101',
+        'vtab/cifar10',
+        'vtab/cifar100',
+        'vtab/clevr_count_all',
+        'vtab/clevr_closest_object_distance',
+        'vtab/diabetic_retinopathy',
+        'vtab/dmlab',
+        'vtab/dsprites_label_orientation',
+        'vtab/dsprites_label_x_position',
+        'vtab/dtd',
+        'vtab/eurosat',
+        'vtab/kitti_closest_vehicle_distance',
+        'vtab/flowers',
+        'vtab/pets',
+        'vtab/pcam',
+        'vtab/resisc45',
+        'vtab/smallnorb_label_azimuth',
+        'vtab/smallnorb_label_elevation',
+        'vtab/svhn',
+    ],
+    'retrieval': [
+        'mscoco_captions',
+        'flickr8k',
+        'flickr30k',
+    ],
+    'imagenet_robustness': [
+        'imagenetv2',
+        'imagenet_sketch',
+        'imagenet-a',
+        'imagenet-r',
+        'objectnet',
+    ],
+}
+# use by imagenet robustness datasets
+all_imagenet_wordnet_ids = ['n01440764', 'n01443537', 'n01484850', 'n01491361', 'n01494475', 'n01496331', 'n01498041',
+                            'n01514668', 'n01514859', 'n01518878', 'n01530575', 'n01531178', 'n01532829', 'n01534433',
+                            'n01537544', 'n01558993', 'n01560419', 'n01580077', 'n01582220', 'n01592084', 'n01601694',
+                            'n01608432', 'n01614925', 'n01616318', 'n01622779', 'n01629819', 'n01630670', 'n01631663',
+                            'n01632458', 'n01632777', 'n01641577', 'n01644373', 'n01644900', 'n01664065', 'n01665541',
+                            'n01667114', 'n01667778', 'n01669191', 'n01675722', 'n01677366', 'n01682714', 'n01685808',
+                            'n01687978', 'n01688243', 'n01689811', 'n01692333', 'n01693334', 'n01694178', 'n01695060',
+                            'n01697457', 'n01698640', 'n01704323', 'n01728572', 'n01728920', 'n01729322', 'n01729977',
+                            'n01734418', 'n01735189', 'n01737021', 'n01739381', 'n01740131', 'n01742172', 'n01744401',
+                            'n01748264', 'n01749939', 'n01751748', 'n01753488', 'n01755581', 'n01756291', 'n01768244',
+                            'n01770081', 'n01770393', 'n01773157', 'n01773549', 'n01773797', 'n01774384', 'n01774750',
+                            'n01775062', 'n01776313', 'n01784675', 'n01795545', 'n01796340', 'n01797886', 'n01798484',
+                            'n01806143', 'n01806567', 'n01807496', 'n01817953', 'n01818515', 'n01819313', 'n01820546',
+                            'n01824575', 'n01828970', 'n01829413', 'n01833805', 'n01843065', 'n01843383', 'n01847000',
+                            'n01855032', 'n01855672', 'n01860187', 'n01871265', 'n01872401', 'n01873310', 'n01877812',
+                            'n01882714', 'n01883070', 'n01910747', 'n01914609', 'n01917289', 'n01924916', 'n01930112',
+                            'n01943899', 'n01944390', 'n01945685', 'n01950731', 'n01955084', 'n01968897', 'n01978287',
+                            'n01978455', 'n01980166', 'n01981276', 'n01983481', 'n01984695', 'n01985128', 'n01986214',
+                            'n01990800', 'n02002556', 'n02002724', 'n02006656', 'n02007558', 'n02009229', 'n02009912',
+                            'n02011460', 'n02012849', 'n02013706', 'n02017213', 'n02018207', 'n02018795', 'n02025239',
+                            'n02027492', 'n02028035', 'n02033041', 'n02037110', 'n02051845', 'n02056570', 'n02058221',
+                            'n02066245', 'n02071294', 'n02074367', 'n02077923', 'n02085620', 'n02085782', 'n02085936',
+                            'n02086079', 'n02086240', 'n02086646', 'n02086910', 'n02087046', 'n02087394', 'n02088094',
+                            'n02088238', 'n02088364', 'n02088466', 'n02088632', 'n02089078', 'n02089867', 'n02089973',
+                            'n02090379', 'n02090622', 'n02090721', 'n02091032', 'n02091134', 'n02091244', 'n02091467',
+                            'n02091635', 'n02091831', 'n02092002', 'n02092339', 'n02093256', 'n02093428', 'n02093647',
+                            'n02093754', 'n02093859', 'n02093991', 'n02094114', 'n02094258', 'n02094433', 'n02095314',
+                            'n02095570', 'n02095889', 'n02096051', 'n02096177', 'n02096294', 'n02096437', 'n02096585',
+                            'n02097047', 'n02097130', 'n02097209', 'n02097298', 'n02097474', 'n02097658', 'n02098105',
+                            'n02098286', 'n02098413', 'n02099267', 'n02099429', 'n02099601', 'n02099712', 'n02099849',
+                            'n02100236', 'n02100583', 'n02100735', 'n02100877', 'n02101006', 'n02101388', 'n02101556',
+                            'n02102040', 'n02102177', 'n02102318', 'n02102480', 'n02102973', 'n02104029', 'n02104365',
+                            'n02105056', 'n02105162', 'n02105251', 'n02105412', 'n02105505', 'n02105641', 'n02105855',
+                            'n02106030', 'n02106166', 'n02106382', 'n02106550', 'n02106662', 'n02107142', 'n02107312',
+                            'n02107574', 'n02107683', 'n02107908', 'n02108000', 'n02108089', 'n02108422', 'n02108551',
+                            'n02108915', 'n02109047', 'n02109525', 'n02109961', 'n02110063', 'n02110185', 'n02110341',
+                            'n02110627', 'n02110806', 'n02110958', 'n02111129', 'n02111277', 'n02111500', 'n02111889',
+                            'n02112018', 'n02112137', 'n02112350', 'n02112706', 'n02113023', 'n02113186', 'n02113624',
+                            'n02113712', 'n02113799', 'n02113978', 'n02114367', 'n02114548', 'n02114712', 'n02114855',
+                            'n02115641', 'n02115913', 'n02116738', 'n02117135', 'n02119022', 'n02119789', 'n02120079',
+                            'n02120505', 'n02123045', 'n02123159', 'n02123394', 'n02123597', 'n02124075', 'n02125311',
+                            'n02127052', 'n02128385', 'n02128757', 'n02128925', 'n02129165', 'n02129604', 'n02130308',
+                            'n02132136', 'n02133161', 'n02134084', 'n02134418', 'n02137549', 'n02138441', 'n02165105',
+                            'n02165456', 'n02167151', 'n02168699', 'n02169497', 'n02172182', 'n02174001', 'n02177972',
+                            'n02190166', 'n02206856', 'n02219486', 'n02226429', 'n02229544', 'n02231487', 'n02233338',
+                            'n02236044', 'n02256656', 'n02259212', 'n02264363', 'n02268443', 'n02268853', 'n02276258',
+                            'n02277742', 'n02279972', 'n02280649', 'n02281406', 'n02281787', 'n02317335', 'n02319095',
+                            'n02321529', 'n02325366', 'n02326432', 'n02328150', 'n02342885', 'n02346627', 'n02356798',
+                            'n02361337', 'n02363005', 'n02364673', 'n02389026', 'n02391049', 'n02395406', 'n02396427',
+                            'n02397096', 'n02398521', 'n02403003', 'n02408429', 'n02410509', 'n02412080', 'n02415577',
+                            'n02417914', 'n02422106', 'n02422699', 'n02423022', 'n02437312', 'n02437616', 'n02441942',
+                            'n02442845', 'n02443114', 'n02443484', 'n02444819', 'n02445715', 'n02447366', 'n02454379',
+                            'n02457408', 'n02480495', 'n02480855', 'n02481823', 'n02483362', 'n02483708', 'n02484975',
+                            'n02486261', 'n02486410', 'n02487347', 'n02488291', 'n02488702', 'n02489166', 'n02490219',
+                            'n02492035', 'n02492660', 'n02493509', 'n02493793', 'n02494079', 'n02497673', 'n02500267',
+                            'n02504013', 'n02504458', 'n02509815', 'n02510455', 'n02514041', 'n02526121', 'n02536864',
+                            'n02606052', 'n02607072', 'n02640242', 'n02641379', 'n02643566', 'n02655020', 'n02666196',
+                            'n02667093', 'n02669723', 'n02672831', 'n02676566', 'n02687172', 'n02690373', 'n02692877',
+                            'n02699494', 'n02701002', 'n02704792', 'n02708093', 'n02727426', 'n02730930', 'n02747177',
+                            'n02749479', 'n02769748', 'n02776631', 'n02777292', 'n02782093', 'n02783161', 'n02786058',
+                            'n02787622', 'n02788148', 'n02790996', 'n02791124', 'n02791270', 'n02793495', 'n02794156',
+                            'n02795169', 'n02797295', 'n02799071', 'n02802426', 'n02804414', 'n02804610', 'n02807133',
+                            'n02808304', 'n02808440', 'n02814533', 'n02814860', 'n02815834', 'n02817516', 'n02823428',
+                            'n02823750', 'n02825657', 'n02834397', 'n02835271', 'n02837789', 'n02840245', 'n02841315',
+                            'n02843684', 'n02859443', 'n02860847', 'n02865351', 'n02869837', 'n02870880', 'n02871525',
+                            'n02877765', 'n02879718', 'n02883205', 'n02892201', 'n02892767', 'n02894605', 'n02895154',
+                            'n02906734', 'n02909870', 'n02910353', 'n02916936', 'n02917067', 'n02927161', 'n02930766',
+                            'n02939185', 'n02948072', 'n02950826', 'n02951358', 'n02951585', 'n02963159', 'n02965783',
+                            'n02966193', 'n02966687', 'n02971356', 'n02974003', 'n02977058', 'n02978881', 'n02979186',
+                            'n02980441', 'n02981792', 'n02988304', 'n02992211', 'n02992529', 'n02999410', 'n03000134',
+                            'n03000247', 'n03000684', 'n03014705', 'n03016953', 'n03017168', 'n03018349', 'n03026506',
+                            'n03028079', 'n03032252', 'n03041632', 'n03042490', 'n03045698', 'n03047690', 'n03062245',
+                            'n03063599', 'n03063689', 'n03065424', 'n03075370', 'n03085013', 'n03089624', 'n03095699',
+                            'n03100240', 'n03109150', 'n03110669', 'n03124043', 'n03124170', 'n03125729', 'n03126707',
+                            'n03127747', 'n03127925', 'n03131574', 'n03133878', 'n03134739', 'n03141823', 'n03146219',
+                            'n03160309', 'n03179701', 'n03180011', 'n03187595', 'n03188531', 'n03196217', 'n03197337',
+                            'n03201208', 'n03207743', 'n03207941', 'n03208938', 'n03216828', 'n03218198', 'n03220513',
+                            'n03223299', 'n03240683', 'n03249569', 'n03250847', 'n03255030', 'n03259280', 'n03271574',
+                            'n03272010', 'n03272562', 'n03290653', 'n03291819', 'n03297495', 'n03314780', 'n03325584',
+                            'n03337140', 'n03344393', 'n03345487', 'n03347037', 'n03355925', 'n03372029', 'n03376595',
+                            'n03379051', 'n03384352', 'n03388043', 'n03388183', 'n03388549', 'n03393912', 'n03394916',
+                            'n03400231', 'n03404251', 'n03417042', 'n03424325', 'n03425413', 'n03443371', 'n03444034',
+                            'n03445777', 'n03445924', 'n03447447', 'n03447721', 'n03450230', 'n03452741', 'n03457902',
+                            'n03459775', 'n03461385', 'n03467068', 'n03476684', 'n03476991', 'n03478589', 'n03481172',
+                            'n03482405', 'n03483316', 'n03485407', 'n03485794', 'n03492542', 'n03494278', 'n03495258',
+                            'n03496892', 'n03498962', 'n03527444', 'n03529860', 'n03530642', 'n03532672', 'n03534580',
+                            'n03535780', 'n03538406', 'n03544143', 'n03584254', 'n03584829', 'n03590841', 'n03594734',
+                            'n03594945', 'n03595614', 'n03598930', 'n03599486', 'n03602883', 'n03617480', 'n03623198',
+                            'n03627232', 'n03630383', 'n03633091', 'n03637318', 'n03642806', 'n03649909', 'n03657121',
+                            'n03658185', 'n03661043', 'n03662601', 'n03666591', 'n03670208', 'n03673027', 'n03676483',
+                            'n03680355', 'n03690938', 'n03691459', 'n03692522', 'n03697007', 'n03706229', 'n03709823',
+                            'n03710193', 'n03710637', 'n03710721', 'n03717622', 'n03720891', 'n03721384', 'n03724870',
+                            'n03729826', 'n03733131', 'n03733281', 'n03733805', 'n03742115', 'n03743016', 'n03759954',
+                            'n03761084', 'n03763968', 'n03764736', 'n03769881', 'n03770439', 'n03770679', 'n03773504',
+                            'n03775071', 'n03775546', 'n03776460', 'n03777568', 'n03777754', 'n03781244', 'n03782006',
+                            'n03785016', 'n03786901', 'n03787032', 'n03788195', 'n03788365', 'n03791053', 'n03792782',
+                            'n03792972', 'n03793489', 'n03794056', 'n03796401', 'n03803284', 'n03804744', 'n03814639',
+                            'n03814906', 'n03825788', 'n03832673', 'n03837869', 'n03838899', 'n03840681', 'n03841143',
+                            'n03843555', 'n03854065', 'n03857828', 'n03866082', 'n03868242', 'n03868863', 'n03871628',
+                            'n03873416', 'n03874293', 'n03874599', 'n03876231', 'n03877472', 'n03877845', 'n03884397',
+                            'n03887697', 'n03888257', 'n03888605', 'n03891251', 'n03891332', 'n03895866', 'n03899768',
+                            'n03902125', 'n03903868', 'n03908618', 'n03908714', 'n03916031', 'n03920288', 'n03924679',
+                            'n03929660', 'n03929855', 'n03930313', 'n03930630', 'n03933933', 'n03935335', 'n03937543',
+                            'n03938244', 'n03942813', 'n03944341', 'n03947888', 'n03950228', 'n03954731', 'n03956157',
+                            'n03958227', 'n03961711', 'n03967562', 'n03970156', 'n03976467', 'n03976657', 'n03977966',
+                            'n03980874', 'n03982430', 'n03983396', 'n03991062', 'n03992509', 'n03995372', 'n03998194',
+                            'n04004767', 'n04005630', 'n04008634', 'n04009552', 'n04019541', 'n04023962', 'n04026417',
+                            'n04033901', 'n04033995', 'n04037443', 'n04039381', 'n04040759', 'n04041544', 'n04044716',
+                            'n04049303', 'n04065272', 'n04067472', 'n04069434', 'n04070727', 'n04074963', 'n04081281',
+                            'n04086273', 'n04090263', 'n04099969', 'n04111531', 'n04116512', 'n04118538', 'n04118776',
+                            'n04120489', 'n04125021', 'n04127249', 'n04131690', 'n04133789', 'n04136333', 'n04141076',
+                            'n04141327', 'n04141975', 'n04146614', 'n04147183', 'n04149813', 'n04152593', 'n04153751',
+                            'n04154565', 'n04162706', 'n04179913', 'n04192698', 'n04200800', 'n04201297', 'n04204238',
+                            'n04204347', 'n04208210', 'n04209133', 'n04209239', 'n04228054', 'n04229816', 'n04235860',
+                            'n04238763', 'n04239074', 'n04243546', 'n04251144', 'n04252077', 'n04252225', 'n04254120',
+                            'n04254680', 'n04254777', 'n04258138', 'n04259630', 'n04263257', 'n04264628', 'n04265275',
+                            'n04266014', 'n04270147', 'n04273569', 'n04275548', 'n04277352', 'n04285008', 'n04286575',
+                            'n04296562', 'n04310018', 'n04311004', 'n04311174', 'n04317175', 'n04325704', 'n04326547',
+                            'n04328186', 'n04330267', 'n04332243', 'n04335435', 'n04336792', 'n04344873', 'n04346328',
+                            'n04347754', 'n04350905', 'n04355338', 'n04355933', 'n04356056', 'n04357314', 'n04366367',
+                            'n04367480', 'n04370456', 'n04371430', 'n04371774', 'n04372370', 'n04376876', 'n04380533',
+                            'n04389033', 'n04392985', 'n04398044', 'n04399382', 'n04404412', 'n04409515', 'n04417672',
+                            'n04418357', 'n04423845', 'n04428191', 'n04429376', 'n04435653', 'n04442312', 'n04443257',
+                            'n04447861', 'n04456115', 'n04458633', 'n04461696', 'n04462240', 'n04465501', 'n04467665',
+                            'n04476259', 'n04479046', 'n04482393', 'n04483307', 'n04485082', 'n04486054', 'n04487081',
+                            'n04487394', 'n04493381', 'n04501370', 'n04505470', 'n04507155', 'n04509417', 'n04515003',
+                            'n04517823', 'n04522168', 'n04523525', 'n04525038', 'n04525305', 'n04532106', 'n04532670',
+                            'n04536866', 'n04540053', 'n04542943', 'n04548280', 'n04548362', 'n04550184', 'n04552348',
+                            'n04553703', 'n04554684', 'n04557648', 'n04560804', 'n04562935', 'n04579145', 'n04579432',
+                            'n04584207', 'n04589890', 'n04590129', 'n04591157', 'n04591713', 'n04592741', 'n04596742',
+                            'n04597913', 'n04599235', 'n04604644', 'n04606251', 'n04612504', 'n04613696', 'n06359193',
+                            'n06596364', 'n06785654', 'n06794110', 'n06874185', 'n07248320', 'n07565083', 'n07579787',
+                            'n07583066', 'n07584110', 'n07590611', 'n07613480', 'n07614500', 'n07615774', 'n07684084',
+                            'n07693725', 'n07695742', 'n07697313', 'n07697537', 'n07711569', 'n07714571', 'n07714990',
+                            'n07715103', 'n07716358', 'n07716906', 'n07717410', 'n07717556', 'n07718472', 'n07718747',
+                            'n07720875', 'n07730033', 'n07734744', 'n07742313', 'n07745940', 'n07747607', 'n07749582',
+                            'n07753113', 'n07753275', 'n07753592', 'n07754684', 'n07760859', 'n07768694', 'n07802026',
+                            'n07831146', 'n07836838', 'n07860988', 'n07871810', 'n07873807', 'n07875152', 'n07880968',
+                            'n07892512', 'n07920052', 'n07930864', 'n07932039', 'n09193705', 'n09229709', 'n09246464',
+                            'n09256479', 'n09288635', 'n09332890', 'n09399592', 'n09421951', 'n09428293', 'n09468604',
+                            'n09472597', 'n09835506', 'n10148035', 'n10565667', 'n11879895', 'n11939491', 'n12057211',
+                            'n12144580', 'n12267677', 'n12620546', 'n12768682', 'n12985857', 'n12998815', 'n13037406',
+                            'n13040303', 'n13044778', 'n13052670', 'n13054560', 'n13133613', 'n15075141']

InternVL-main/clip_benchmark/clip_benchmark/datasets/caltech101.py

@@ -0,0 +1,243 @@
+"""
+Code adapted from https://github.com/pytorch/vision/blob/main/torchvision/datasets/caltech.py
+Modification of caltech101 from torchvision where the background class is not removed
+Thanks to the authors of torchvision
+"""
+import os
+import os.path
+from glob import glob
+from typing import Any, Callable, List, Optional, Tuple, Union
+
+from PIL import Image
+from torchvision.datasets.utils import (download_and_extract_archive,
+                                        verify_str_arg)
+from torchvision.datasets.vision import VisionDataset
+
+
+class Caltech101(VisionDataset):
+    """`Caltech 101 <http://www.vision.caltech.edu/Image_Datasets/Caltech101/>`_ Dataset.
+
+    .. warning::
+
+        This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
+
+    Args:
+        root (string): Root directory of dataset where directory
+            ``caltech101`` exists or will be saved to if download is set to True.
+        target_type (string or list, optional): Type of target to use, ``category`` or
+            ``annotation``. Can also be a list to output a tuple with all specified
+            target types.  ``category`` represents the target class, and
+            ``annotation`` is a list of points from a hand-generated outline.
+            Defaults to ``category``.
+        transform (callable, optional): A function/transform that takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+    """
+
+    def __init__(
+            self,
+            root: str,
+            target_type: Union[List[str], str] = 'category',
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+            download: bool = False,
+    ) -> None:
+        super().__init__(os.path.join(root, 'caltech101'), transform=transform, target_transform=target_transform)
+        os.makedirs(self.root, exist_ok=True)
+        if isinstance(target_type, str):
+            target_type = [target_type]
+        self.target_type = [verify_str_arg(t, 'target_type', ('category', 'annotation')) for t in target_type]
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError('Dataset not found or corrupted. You can use download=True to download it')
+
+        self.categories = sorted(os.listdir(os.path.join(self.root, '101_ObjectCategories')))
+        # self.categories.remove("BACKGROUND_Google")  # this is not a real class
+
+        # For some reason, the category names in "101_ObjectCategories" and
+        # "Annotations" do not always match. This is a manual map between the
+        # two. Defaults to using same name, since most names are fine.
+        name_map = {
+            'Faces': 'Faces_2',
+            'Faces_easy': 'Faces_3',
+            'Motorbikes': 'Motorbikes_16',
+            'airplanes': 'Airplanes_Side_2',
+        }
+        self.annotation_categories = list(map(lambda x: name_map[x] if x in name_map else x, self.categories))
+
+        self.index: List[int] = []
+        self.y = []
+        for (i, c) in enumerate(self.categories):
+            n = len(glob(os.path.join(self.root, '101_ObjectCategories', c, '*.jpg')))
+            self.index.extend(range(1, n + 1))
+            self.y.extend(n * [i])
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where the type of target specified by target_type.
+        """
+        import scipy.io
+
+        img = Image.open(
+            os.path.join(
+                self.root,
+                '101_ObjectCategories',
+                self.categories[self.y[index]],
+                f'image_{self.index[index]:04d}.jpg',
+            )
+        )
+
+        target: Any = []
+        for t in self.target_type:
+            if t == 'category':
+                target.append(self.y[index])
+            elif t == 'annotation':
+                data = scipy.io.loadmat(
+                    os.path.join(
+                        self.root,
+                        'Annotations',
+                        self.annotation_categories[self.y[index]],
+                        f'annotation_{self.index[index]:04d}.mat',
+                    )
+                )
+                target.append(data['obj_contour'])
+        target = tuple(target) if len(target) > 1 else target[0]
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def _check_integrity(self) -> bool:
+        # can be more robust and check hash of files
+        return os.path.exists(os.path.join(self.root, '101_ObjectCategories'))
+
+    def __len__(self) -> int:
+        return len(self.index)
+
+    def download(self) -> None:
+        if self._check_integrity():
+            print('Files already downloaded and verified')
+            return
+
+        download_and_extract_archive(
+            'https://drive.google.com/file/d/137RyRjvTBkBiIfeYBNZBtViDHQ6_Ewsp',
+            self.root,
+            filename='101_ObjectCategories.tar.gz',
+            md5='b224c7392d521a49829488ab0f1120d9',
+        )
+        download_and_extract_archive(
+            'https://drive.google.com/file/d/175kQy3UsZ0wUEHZjqkUDdNVssr7bgh_m',
+            self.root,
+            filename='Annotations.tar',
+            md5='6f83eeb1f24d99cab4eb377263132c91',
+        )
+
+    def extra_repr(self) -> str:
+        return 'Target type: {target_type}'.format(**self.__dict__)
+
+
+class Caltech256(VisionDataset):
+    """`Caltech 256 <http://www.vision.caltech.edu/Image_Datasets/Caltech256/>`_ Dataset.
+
+    Args:
+        root (string): Root directory of dataset where directory
+            ``caltech256`` exists or will be saved to if download is set to True.
+        transform (callable, optional): A function/transform that takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+    """
+
+    def __init__(
+            self,
+            root: str,
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+            download: bool = False,
+    ) -> None:
+        super().__init__(os.path.join(root, 'caltech256'), transform=transform, target_transform=target_transform)
+        os.makedirs(self.root, exist_ok=True)
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError('Dataset not found or corrupted. You can use download=True to download it')
+
+        self.categories = sorted(os.listdir(os.path.join(self.root, '256_ObjectCategories')))
+        self.index: List[int] = []
+        self.y = []
+        for (i, c) in enumerate(self.categories):
+            n = len(
+                [
+                    item
+                    for item in os.listdir(os.path.join(self.root, '256_ObjectCategories', c))
+                    if item.endswith('.jpg')
+                ]
+            )
+            self.index.extend(range(1, n + 1))
+            self.y.extend(n * [i])
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img = Image.open(
+            os.path.join(
+                self.root,
+                '256_ObjectCategories',
+                self.categories[self.y[index]],
+                f'{self.y[index] + 1:03d}_{self.index[index]:04d}.jpg',
+            )
+        )
+
+        target = self.y[index]
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def _check_integrity(self) -> bool:
+        # can be more robust and check hash of files
+        return os.path.exists(os.path.join(self.root, '256_ObjectCategories'))
+
+    def __len__(self) -> int:
+        return len(self.index)
+
+    def download(self) -> None:
+        if self._check_integrity():
+            print('Files already downloaded and verified')
+            return
+
+        download_and_extract_archive(
+            'https://drive.google.com/file/d/1r6o0pSROcV1_VwT4oSjA2FBUSCWGuxLK',
+            self.root,
+            filename='256_ObjectCategories.tar',
+            md5='67b4f42ca05d46448c6bb8ecd2220f6d',
+        )

InternVL-main/clip_benchmark/clip_benchmark/datasets/cn_classnames.json

@@ -0,0 +1,1004 @@
+{
+  "imagenet1k": [
+    "\u4e01\u9cb7",
+    "\u91d1\u9c7c",
+    "\u5927\u767d\u9ca8",
+    "\u864e\u9ca8",
+    "\u9524\u5934\u9ca8",
+    "\u7535\u9cd0",
+    "\u9ec4\u8c82\u9c7c",
+    "\u516c\u9e21",
+    "\u6bcd\u9e21",
+    "\u9e35\u9e1f",
+    "\u71d5\u96c0",
+    "\u91d1\u7fc5\u96c0",
+    "\u5bb6\u6731\u96c0",
+    "\u706f\u82af\u8349\u96c0",
+    "\u975b\u84dd\u96c0",
+    "\u84dd\u9e40",
+    "\u591c\u83ba",
+    "\u677e\u9e26",
+    "\u559c\u9e4a",
+    "\u5c71\u96c0",
+    "\u6cb3\u9e1f",
+    "\u9e22\uff08\u731b\u79bd\uff09",
+    "\u79c3\u5934\u9e70",
+    "\u79c3\u9e6b",
+    "\u5927\u7070\u732b\u5934\u9e70",
+    "\u6b27\u6d32\u706b\u877e\u8788",
+    "\u666e\u901a\u877e\u8788",
+    "\u6c34\u8725",
+    "\u6591\u70b9\u877e\u8788",
+    "\u877e\u8788",
+    "\u725b\u86d9",
+    "\u6811\u86d9",
+    "\u5c3e\u86d9",
+    "\u7ea2\u6d77\u9f9f",
+    "\u76ae\u9769\u9f9f",
+    "\u6ce5\u9f9f",
+    "\u6de1\u6c34\u9f9f",
+    "\u7bb1\u9f9f",
+    "\u5e26\u72b6\u58c1\u864e",
+    "\u666e\u901a\u9b23\u8725",
+    "\u7f8e\u56fd\u53d8\u8272\u9f99",
+    "\u97ad\u5c3e\u8725\u8734",
+    "\u98de\u9f99\u79d1\u8725\u8734",
+    "\u8936\u8fb9\u8725\u8734",
+    "\u9cc4\u9c7c\u8725\u8734",
+    "\u6bd2\u8725",
+    "\u7eff\u8725\u8734",
+    "\u975e\u6d32\u53d8\u8272\u9f99",
+    "\u79d1\u83ab\u591a\u8725\u8734",
+    "\u975e\u6d32\u9cc4",
+    "\u7f8e\u56fd\u9cc4\u9c7c",
+    "\u4e09\u89d2\u9f99",
+    "\u96f7\u86c7",
+    "\u73af\u86c7",
+    "\u5e0c\u814a\u86c7",
+    "\u7eff\u86c7",
+    "\u56fd\u738b\u86c7",
+    "\u889c\u5e26\u86c7",
+    "\u6c34\u86c7",
+    "\u85e4\u86c7",
+    "\u591c\u86c7",
+    "\u5927\u87d2\u86c7",
+    "\u5ca9\u77f3\u87d2\u86c7",
+    "\u5370\u5ea6\u773c\u955c\u86c7",
+    "\u7eff\u66fc\u5df4",
+    "\u6d77\u86c7",
+    "\u89d2\u8179\u86c7",
+    "\u83f1\u7eb9\u54cd\u5c3e\u86c7",
+    "\u89d2\u54cd\u5c3e\u86c7",
+    "\u4e09\u53f6\u866b",
+    "\u76f2\u8718\u86db",
+    "\u874e\u5b50",
+    "\u9ed1\u91d1\u82b1\u56ed\u8718\u86db",
+    "\u8c37\u4ed3\u8718\u86db",
+    "\u82b1\u56ed\u8718\u86db",
+    "\u9ed1\u5be1\u5987\u8718\u86db",
+    "\u72fc\u86db",
+    "\u72fc\u8718\u86db",
+    "\u58c1\u8671",
+    "\u8708\u86a3",
+    "\u9ed1\u677e\u9e21",
+    "\u677e\u9e21",
+    "\u62ab\u80a9\u9e21",
+    "\u8349\u539f\u9e21",
+    "\u5b54\u96c0",
+    "\u9e4c\u9e51",
+    "\u9e67\u9e2a",
+    "\u975e\u6d32\u7070\u9e66\u9e49",
+    "\u91d1\u521a\u9e66\u9e49",
+    "\u786b\u51a0\u9e66\u9e49",
+    "\u77ed\u5c3e\u9e66\u9e49",
+    "\u8910\u7fc5\u9e26\u9e43",
+    "\u98df\u8702\u9e1f\uff1b\u8702\u864e",
+    "\u7280\u9e1f",
+    "\u8702\u9e1f",
+    "\u9e5f\u4d15",
+    "\u5de8\u5634\u9e1f\uff1b\u5927\u5634\u9e1f",
+    "\u91ce\u9e2d",
+    "\u7ea2\u80f8\u79cb\u6c99\u9e2d",
+    "\u9e45",
+    "\u9ed1\u5929\u9e45",
+    "\u5927\u8c61",
+    "\u9488\u9f39\u9f20",
+    "\u9e2d\u5634\u517d",
+    "\u6c99\u888b\u9f20",
+    "\u8003\u62c9",
+    "\u888b\u718a",
+    "\u6c34\u6bcd",
+    "\u6d77\u8475",
+    "\u8111\u73ca\u745a",
+    "\u6241\u5f62\u866b\u6241\u866b",
+    "\u7ebf\u866b",
+    "\u6d77\u87ba",
+    "\u8717\u725b",
+    "\u9f3b\u6d95\u866b",
+    "\u6d77\u86de\u8753\uff1b\u6d77\u53c2",
+    "\u77f3\u9cd6",
+    "\u9e66\u9e49\u87ba",
+    "\u73cd\u5b9d\u87f9",
+    "\u77f3\u87f9",
+    "\u62db\u6f6e\u87f9",
+    "\u5e1d\u738b\u87f9",
+    "\u7f8e\u56fd\u9f99\u867e",
+    "\u5927\u87af\u867e",
+    "\u5c0f\u9f99\u867e",
+    "\u5bc4\u5c45\u87f9",
+    "\u7b49\u8db3\u76ee\u52a8\u7269\uff08\u660e\u867e\u548c\u8783\u87f9\u8fd1\u4eb2\uff09",
+    "\u767d\u9e73",
+    "\u9ed1\u9e73",
+    "\u9e6d",
+    "\u706b\u70c8\u9e1f",
+    "\u5c0f\u84dd\u9e6d",
+    "\u7f8e\u56fd\u9e6d",
+    "\u9ebb\u9e26",
+    "\u9e64",
+    "\u79e7\u9e64",
+    "\u6b27\u6d32\u6c34\u9e21",
+    "\u6cbc\u6cfd\u6ce5\u6bcd\u9e21",
+    "\u9e28",
+    "\u7ea2\u7ffb\u77f3\u9e6c",
+    "\u7ea2\u80cc\u9e6c",
+    "\u7ea2\u811a\u9e6c",
+    "\u534a\u8e7c\u9e6c",
+    "\u86ce\u9e6c",
+    "\u9e48\u9e55",
+    "\u56fd\u738b\u4f01\u9e45",
+    "\u4fe1\u5929\u7fc1",
+    "\u7070\u9cb8",
+    "\u6740\u4eba\u9cb8",
+    "\u6d77\u725b",
+    "\u6d77\u72ee",
+    "\u5409\u5a03\u5a03",
+    "\u65e5\u672c\u72c6\u72ac",
+    "\u9a6c\u5c14\u6d4e\u65af\u72ac",
+    "\u72ee\u5b50\u72d7",
+    "\u897f\u65bd\u72ac",
+    "\u5e03\u83b1\u5c3c\u59c6\u730e\u72ac",
+    "\u5df4\u6bd4\u72d7",
+    "\u73a9\u5177\u72ac",
+    "\u7f57\u5f97\u897f\u4e9a\u957f\u80cc\u730e\u72d7",
+    "\u963f\u5bcc\u6c57\u730e\u72ac",
+    "\u5df4\u5409\u5ea6\u730e\u72ac",
+    "\u6bd4\u683c\u72ac",
+    "\u4fa6\u63a2\u72ac",
+    "\u84dd\u8272\u5feb\u72d7",
+    "\u9ed1\u8910\u730e\u6d63\u718a\u72ac",
+    "\u6c83\u514b\u730e\u72ac",
+    "\u82f1\u56fd\u730e\u72d0\u72ac",
+    "\u7f8e\u6d32\u8d64\u72d7",
+    "\u4fc4\u7f57\u65af\u730e\u72fc\u72ac",
+    "\u7231\u5c14\u5170\u730e\u72fc\u72ac",
+    "\u610f\u5927\u5229\u7070\u72d7",
+    "\u60e0\u6bd4\u7279\u72ac",
+    "\u4f9d\u6bd4\u6c99\u730e\u72ac",
+    "\u632a\u5a01\u730e\u72ac",
+    "\u5965\u8fbe\u730e\u72ac",
+    "\u6c99\u514b\u72ac",
+    "\u82cf\u683c\u5170\u730e\u9e7f\u72ac",
+    "\u5a01\u739b\u730e\u72ac",
+    "\u65af\u5854\u798f\u5fb7\u90e1\u6597\u725b\u72ac",
+    "\u7f8e\u56fd\u65af\u5854\u798f\u5fb7\u90e1\u6897",
+    "\u8d1d\u5fb7\u7075\u987f\u6897",
+    "\u8fb9\u5883\u6897",
+    "\u51ef\u4e3d\u84dd\u6897",
+    "\u7231\u5c14\u5170\u6897",
+    "\u8bfa\u798f\u514b\u6897",
+    "\u8bfa\u7ef4\u5947\u6897",
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+    "\u521a\u6bdb\u730e\u72d0\u6897",
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+    "\u6fb3\u5927\u5229\u4e9a\u6897",
+    "\u4e39\u8fea\u4e01\u8499\u6897",
+    "\u6ce2\u58eb\u987f\u6897",
+    "\u8ff7\u4f60\u96ea\u7eb3\u745e\u72ac",
+    "\u5de8\u578b\u96ea\u7eb3\u745e\u72ac",
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+    "\u5e73\u6bdb\u5bfb\u56de\u72ac",
+    "\u5377\u6bdb\u5bfb\u56de\u72ac",
+    "\u91d1\u6bdb\u730e\u72ac",
+    "\u62c9\u5e03\u62c9\u591a\u730e\u72ac",
+    "\u4e5e\u6c99\u6bd4\u514b\u730e\u72ac",
+    "\u5fb7\u56fd\u77ed\u6bdb\u6307\u793a\u72ac",
+    "\u7ef4\u5179\u62c9\u72ac",
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+    "\u6208\u767b\u96ea\u8fbe\u72ac",
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+    "\u9ec4\u6bdb",
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+    "\u7f57\u7279\u97e6\u5c14\u72ac",
+    "\u5fb7\u56fd\u7267\u7f8a\u72ac",
+    "\u591a\u4f2f\u66fc\u72ac",
+    "\u9e7f\u72ac\uff1b\u8ff7\u4f60\u675c\u5bbe\u72ac",
+    "\u5927\u745e\u58eb\u5c71\u5730\u72ac",
+    "\u4f2f\u6069\u5c71\u72ac",
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+    "\u6069\u7279\u5c14\u5e03\u8d6b\u5c71\u72ac",
+    "\u62f3\u5e08\u72d7",
+    "\u6597\u725b\u7352",
+    "\u85cf\u7352",
+    "\u6cd5\u56fd\u6597\u725b\u72ac",
+    "\u5927\u4e39\u72ac",
+    "\u5723\u4f2f\u7eb3\u5fb7\u72d7",
+    "\u7231\u65af\u57fa\u6469\u72ac",
+    "\u963f\u62c9\u65af\u52a0\u96ea\u6a47\u72ac",
+    "\u54c8\u58eb\u5947",
+    "\u8fbe\u5c14\u9a6c\u63d0\u4e9a",
+    "\u72ee\u6bdb\u72d7",
+    "\u5df4\u8f9b\u5409\u72d7",
+    "\u516b\u54e5\u72ac",
+    "\u83b1\u6602\u8d1d\u683c\u72d7",
+    "\u7ebd\u82ac\u5170\u72ac",
+    "\u5927\u767d\u718a\u72ac",
+    "\u8428\u6469\u8036\u72ac",
+    "\u535a\u7f8e\u72ac",
+    "\u677e\u72ee",
+    "\u51ef\u65af\u72ac",
+    "\u5e03\u9c81\u585e\u5c14\u683c\u6797\u82ac\u72ac",
+    "\u5f6d\u5e03\u6d1b\u514b\u5a01\u5c14\u58eb\u79d1\u57fa\u72ac",
+    "\u5a01\u5c14\u58eb\u67ef\u57fa\u72ac",
+    "\u73a9\u5177\u8d35\u5bbe\u72ac",
+    "\u8ff7\u4f60\u8d35\u5bbe\u72ac",
+    "\u6807\u51c6\u8d35\u5bbe\u72ac",
+    "\u58a8\u897f\u54e5\u65e0\u6bdb\u72ac",
+    "\u7070\u72fc",
+    "\u767d\u72fc",
+    "\u7ea2\u592a\u72fc",
+    "\u72fc",
+    "\u6fb3\u6d32\u91ce\u72d7",
+    "\u8c7a",
+    "\u975e\u6d32\u730e\u72ac",
+    "\u9b23\u72d7",
+    "\u7ea2\u72d0\u72f8",
+    "\u6c99\u72d0",
+    "\u5317\u6781\u72d0\u72f8",
+    "\u7070\u72d0\u72f8",
+    "\u864e\u6591\u732b",
+    "\u5c71\u732b",
+    "\u6ce2\u65af\u732b",
+    "\u66b9\u7f57\u732b",
+    "\u57c3\u53ca\u732b",
+    "\u7f8e\u6d32\u72ee",
+    "\u731e\u7301",
+    "\u8c79\u5b50",
+    "\u96ea\u8c79",
+    "\u7f8e\u6d32\u864e",
+    "\u72ee\u5b50",
+    "\u8001\u864e",
+    "\u730e\u8c79",
+    "\u68d5\u718a",
+    "\u7f8e\u6d32\u9ed1\u718a",
+    "\u51b0\u718a",
+    "\u61d2\u718a",
+    "\u7374",
+    "\u732b\u9f2c",
+    "\u864e\u7532\u866b",
+    "\u74e2\u866b",
+    "\u571f\u9cd6\u866b",
+    "\u5929\u725b",
+    "\u9f9f\u7532\u866b",
+    "\u7caa\u7532\u866b",
+    "\u7280\u725b\u7532\u866b",
+    "\u8c61\u7532",
+    "\u82cd\u8747",
+    "\u871c\u8702",
+    "\u8682\u8681",
+    "\u86b1\u8722",
+    "\u87cb\u87c0",
+    "\u7af9\u8282\u866b",
+    "\u87d1\u8782",
+    "\u87b3\u8782",
+    "\u8749",
+    "\u53f6\u8749",
+    "\u8349\u873b\u86c9",
+    "\u873b\u8713",
+    "\u8c46\u5a18",
+    "\u4f18\u7ea2\u86f1\u8776",
+    "\u5c0f\u73af\u8774\u8776",
+    "\u541b\u4e3b\u8774\u8776",
+    "\u83dc\u7c89\u8776",
+    "\u767d\u8774\u8776",
+    "\u7070\u8776",
+    "\u6d77\u661f",
+    "\u6d77\u80c6",
+    "\u6d77\u9ec4\u74dc\uff1b\u6d77\u53c2",
+    "\u91ce\u5154",
+    "\u5154",
+    "\u5b89\u54e5\u62c9\u5154",
+    "\u4ed3\u9f20",
+    "\u523a\u732c",
+    "\u9ed1\u677e\u9f20",
+    "\u571f\u62e8\u9f20",
+    "\u6d77\u72f8",
+    "\u8c5a\u9f20",
+    "\u6817\u8272\u9a6c",
+    "\u6591\u9a6c",
+    "\u732a",
+    "\u91ce\u732a",
+    "\u75a3\u732a",
+    "\u6cb3\u9a6c",
+    "\u725b",
+    "\u6c34\u725b",
+    "\u91ce\u725b",
+    "\u516c\u7f8a",
+    "\u5927\u89d2\u7f8a",
+    "\u5c71\u7f8a",
+    "\u72f7\u7f9a",
+    "\u9ed1\u6591\u7f9a",
+    "\u77aa\u7f9a",
+    "\u963f\u62c9\u4f2f\u5355\u5cf0\u9a86\u9a7c",
+    "\u9a86\u9a7c",
+    "\u9ec4\u9f20\u72fc",
+    "\u6c34\u8c82",
+    "\u81ed\u732b",
+    "\u9ed1\u8db3\u9f2c",
+    "\u6c34\u736d",
+    "\u81ed\u9f2c",
+    "\u737e",
+    "\u72b0\u72f3",
+    "\u6811\u61d2",
+    "\u7329\u7329",
+    "\u5927\u7329\u7329",
+    "\u9ed1\u7329\u7329",
+    "\u957f\u81c2\u733f",
+    "\u5408\u8dbe\u733f\u957f\u81c2\u733f",
+    "\u957f\u5c3e\u7334",
+    "\u8d64\u7334",
+    "\u72d2\u72d2",
+    "\u6052\u6cb3\u7334",
+    "\u767d\u5934\u53f6\u7334",
+    "\u75a3\u7334",
+    "\u957f\u9f3b\u7334",
+    "\u72e8\uff08\u7f8e\u6d32\u4ea7\u5c0f\u578b\u957f\u5c3e\u7334\uff09",
+    "\u5377\u5c3e\u7334",
+    "\u543c\u7334",
+    "\u4f36\u7334",
+    "\u8718\u86db\u7334",
+    "\u677e\u9f20\u7334",
+    "\u9a6c\u8fbe\u52a0\u65af\u52a0\u73af\u5c3e\u72d0\u7334",
+    "\u5927\u72d0\u7334",
+    "\u5370\u5ea6\u5927\u8c61",
+    "\u975e\u6d32\u8c61",
+    "\u5c0f\u718a\u732b",
+    "\u5927\u718a\u732b",
+    "\u6756\u9c7c",
+    "\u9cd7\u9c7c",
+    "\u94f6\u9c91",
+    "\u4e09\u8272\u523a\u8776\u9c7c",
+    "\u6d77\u8475\u9c7c",
+    "\u9c9f\u9c7c",
+    "\u96c0\u9cdd",
+    "\u72ee\u5b50\u9c7c",
+    "\u6cb3\u8c5a",
+    "\u7b97\u76d8",
+    "\u957f\u888d",
+    "\u5b66\u4f4d\u888d",
+    "\u624b\u98ce\u7434",
+    "\u539f\u58f0\u5409\u4ed6",
+    "\u822a\u7a7a\u6bcd\u8230",
+    "\u5ba2\u673a",
+    "\u98de\u8247",
+    "\u796d\u575b",
+    "\u6551\u62a4\u8f66",
+    "\u6c34\u9646\u4e24\u7528\u8f66",
+    "\u6a21\u62df\u65f6\u949f",
+    "\u8702\u623f",
+    "\u56f4\u88d9",
+    "\u5783\u573e\u6876",
+    "\u653b\u51fb\u6b65\u67aa",
+    "\u80cc\u5305",
+    "\u9762\u5305\u5e97",
+    "\u5e73\u8861\u6728",
+    "\u70ed\u6c14\u7403",
+    "\u5706\u73e0\u7b14",
+    "\u521b\u53ef\u8d34",
+    "\u73ed\u5353\u7434",
+    "\u680f\u6746",
+    "\u6760\u94c3",
+    "\u7406\u53d1\u5e08\u7684\u6905\u5b50",
+    "\u7406\u53d1\u5e97",
+    "\u7272\u53e3\u68da",
+    "\u6674\u96e8\u8868",
+    "\u5706\u7b52",
+    "\u56ed\u5730\u5c0f\u8f66",
+    "\u68d2\u7403",
+    "\u7bee\u7403",
+    "\u5a74\u513f\u5e8a",
+    "\u5df4\u677e\u7ba1",
+    "\u6e38\u6cf3\u5e3d",
+    "\u6c90\u6d74\u6bdb\u5dfe",
+    "\u6d74\u7f38",
+    "\u6c99\u6ee9\u8f66",
+    "\u706f\u5854",
+    "\u70e7\u676f",
+    "\u718a\u76ae\u9ad8\u5e3d",
+    "\u5564\u9152\u74f6",
+    "\u5564\u9152\u676f",
+    "\u949f\u5854",
+    "\uff08\u5c0f\u513f\u7528\u7684\uff09\u56f4\u5634",
+    "\u4e32\u8054\u81ea\u884c\u8f66",
+    "\u6bd4\u57fa\u5c3c",
+    "\u88c5\u8ba2\u518c",
+    "\u53cc\u7b52\u671b\u8fdc\u955c",
+    "\u9e1f\u820d",
+    "\u8239\u5e93",
+    "\u53cc\u4eba\u96ea\u6a47",
+    "\u9970\u6263\u5f0f\u9886\u5e26",
+    "\u9614\u8fb9\u5973\u5e3d",
+    "\u4e66\u6a71",
+    "\u4e66\u5e97",
+    "\u74f6\u76d6",
+    "\u5f13\u7bad",
+    "\u8774\u8776\u7ed3\u9886\u7ed3",
+    "\u94dc\u5236\u724c\u4f4d",
+    "\u5976\u7f69",
+    "\u9632\u6ce2\u5824",
+    "\u94e0\u7532",
+    "\u626b\u5e1a",
+    "\u6876",
+    "\u6263\u73af",
+    "\u9632\u5f39\u80cc\u5fc3",
+    "\u52a8\u8f66",
+    "\u8089\u94fa",
+    "\u51fa\u79df\u8f66",
+    "\u5927\u9505",
+    "\u8721\u70db",
+    "\u5927\u70ae",
+    "\u72ec\u6728\u821f",
+    "\u5f00\u74f6\u5668",
+    "\u5f00\u886b",
+    "\u8f66\u955c",
+    "\u65cb\u8f6c\u6728\u9a6c",
+    "\u6728\u5320\u7684\u5de5\u5177\u5305",
+    "\u7eb8\u7bb1",
+    "\u8f66\u8f6e",
+    "\u53d6\u6b3e\u673a",
+    "\u76d2\u5f0f\u5f55\u97f3\u5e26",
+    "\u5361\u5e26\u64ad\u653e\u5668",
+    "\u57ce\u5821",
+    "\u53cc\u4f53\u8239",
+    "CD\u64ad\u653e\u5668",
+    "\u5927\u63d0\u7434",
+    "\u79fb\u52a8\u7535\u8bdd",
+    "\u94c1\u94fe",
+    "\u56f4\u680f",
+    "\u94fe\u7532",
+    "\u7535\u952f",
+    "\u7bb1\u5b50",
+    "\u68b3\u5986\u53f0",
+    "\u7f16\u949f",
+    "\u4e2d\u56fd\u6a71\u67dc",
+    "\u5723\u8bde\u889c",
+    "\u6559\u5802",
+    "\u7535\u5f71\u9662",
+    "\u5207\u8089\u5200",
+    "\u60ac\u5d16\u5c4b",
+    "\u6597\u7bf7",
+    "\u6728\u5c50",
+    "\u9e21\u5c3e\u9152\u8c03\u9152\u5668",
+    "\u5496\u5561\u676f",
+    "\u5496\u5561\u58f6",
+    "\u87ba\u65cb\u7ed3\u6784\uff08\u697c\u68af\uff09",
+    "\u7ec4\u5408\u9501",
+    "\u7535\u8111\u952e\u76d8",
+    "\u7cd6\u679c",
+    "\u96c6\u88c5\u7bb1\u8239",
+    "\u655e\u7bf7\u8f66",
+    "\u74f6\u585e\u94bb",
+    "\u77ed\u53f7",
+    "\u725b\u4ed4\u9774",
+    "\u725b\u4ed4\u5e3d",
+    "\u6447\u7bee",
+    "\u8d77\u91cd\u673a",
+    "\u5934\u76d4",
+    "\u677f\u6761\u7bb1",
+    "\u5c0f\u513f\u5e8a",
+    "\u7802\u9505",
+    "\u69cc\u7403",
+    "\u62d0\u6756",
+    "\u80f8\u7532",
+    "\u5927\u575d",
+    "\u4e66\u684c",
+    "\u53f0\u5f0f\u7535\u8111",
+    "\u6709\u7ebf\u7535\u8bdd",
+    "\u5c3f\u5e03\u6e7f",
+    "\u6570\u5b57\u65f6\u949f",
+    "\u6570\u5b57\u624b\u8868",
+    "\u9910\u684c\u677f",
+    "\u62b9\u5e03",
+    "\u6d17\u7897\u673a",
+    "\u76d8\u5f0f\u5236\u52a8\u5668",
+    "\u7801\u5934",
+    "\u72d7\u62c9\u96ea\u6a47",
+    "\u5706\u9876",
+    "\u95e8\u57ab",
+    "\u94bb\u4e95\u5e73\u53f0",
+    "\u9f13",
+    "\u9f13\u69cc",
+    "\u54d1\u94c3",
+    "\u8377\u5170\u70e4\u7bb1",
+    "\u7535\u98ce\u6247",
+    "\u7535\u5409\u4ed6",
+    "\u7535\u529b\u673a\u8f66",
+    "\u7ec4\u5408\u7535\u89c6\u67dc",
+    "\u4fe1\u5c01",
+    "\u6d53\u7f29\u5496\u5561\u673a",
+    "\u6251\u9762\u7c89",
+    "\u5973\u7528\u957f\u56f4\u5dfe",
+    "\u6587\u4ef6",
+    "\u6d88\u9632\u8239",
+    "\u6d88\u9632\u8f66",
+    "\u706b\u7089\u680f",
+    "\u65d7\u6746",
+    "\u957f\u7b1b",
+    "\u6298\u53e0\u6905",
+    "\u6a44\u6984\u7403\u5934\u76d4",
+    "\u53c9\u8f66",
+    "\u55b7\u6cc9",
+    "\u94a2\u7b14",
+    "\u6709\u56db\u6839\u5e37\u67f1\u7684\u5e8a",
+    "\u8fd0\u8d27\u8f66\u53a2",
+    "\u5706\u53f7",
+    "\u714e\u9505",
+    "\u88d8\u76ae\u5927\u8863",
+    "\u5783\u573e\u8f66",
+    "\u9632\u6bd2\u9762\u5177",
+    "\u6c7d\u6cb9\u6cf5",
+    "\u9ad8\u811a\u676f",
+    "\u5361\u4e01\u8f66",
+    "\u9ad8\u5c14\u592b\u7403",
+    "\u9ad8\u5c14\u592b\u7403\u8f66",
+    "\u72ed\u957f\u5c0f\u8239",
+    "\u9523",
+    "\u793c\u670d",
+    "\u94a2\u7434",
+    "\u6e29\u5ba4",
+    "\u6563\u70ed\u5668\u683c\u6805",
+    "\u6742\u8d27\u5e97",
+    "\u65ad\u5934\u53f0",
+    "\u5c0f\u53d1\u5939",
+    "\u5934\u53d1\u55b7\u96fe",
+    "\u534a\u5c65\u5e26\u88c5\u7532\u8f66",
+    "\u9524\u5b50",
+    "\u5927\u7bee\u5b50",
+    "\u624b\u6447\u9f13\u98ce\u673a",
+    "\u624b\u63d0\u7535\u8111",
+    "\u624b\u5e15",
+    "\u786c\u76d8",
+    "\u53e3\u7434",
+    "\u7ad6\u7434",
+    "\u6536\u5272\u673a",
+    "\u65a7\u5934",
+    "\u624b\u67aa\u76ae\u5957",
+    "\u5bb6\u5ead\u5f71\u9662",
+    "\u8702\u7a9d",
+    "\u94a9\u722a",
+    "\u886c\u88d9",
+    "\u5355\u6760",
+    "\u9a6c\u8f66",
+    "\u6c99\u6f0f",
+    "iPod",
+    "\u71a8\u6597",
+    "\u5357\u74dc\u706f\u7b3c",
+    "\u725b\u4ed4\u88e4",
+    "\u5409\u666e\u8f66",
+    "T\u6064\u886b",
+    "\u62fc\u56fe",
+    "\u4eba\u529b\u8f66",
+    "\u64cd\u7eb5\u6746",
+    "\u548c\u670d",
+    "\u62a4\u819d",
+    "\u8774\u8776\u7ed3",
+    "\u5927\u8902",
+    "\u957f\u67c4\u52fa",
+    "\u706f\u7f69",
+    "\u7b14\u8bb0\u672c\u7535\u8111",
+    "\u5272\u8349\u673a",
+    "\u955c\u5934\u76d6",
+    "\u5f00\u4fe1\u5200\uff1b\u62c6\u4fe1\u5200",
+    "\u56fe\u4e66\u9986",
+    "\u6551\u751f\u8247",
+    "\u70b9\u706b\u5668",
+    "\u8c6a\u534e\u8f7f\u8f66",
+    "\u8fdc\u6d0b\u73ed\u8f6e",
+    "\u5507\u818f",
+    "\u5e73\u5e95\u4fbf\u978b",
+    "\u6d17\u5242",
+    "\u626c\u58f0\u5668",
+    "\u653e\u5927\u955c",
+    "\u952f\u6728\u5382",
+    "\u78c1\u7f57\u76d8",
+    "\u90ae\u888b",
+    "\u4fe1\u7bb1",
+    "\u5973\u6e38\u6cf3\u8863",
+    "\u6709\u80a9\u5e26\u6d74\u8863",
+    "\u7aa8\u4e95\u76d6",
+    "\u6c99\u7403\uff08\u4e00\u79cd\u6253\u51fb\u4e50\u5668\uff09",
+    "\u9a6c\u6797\u5df4\u6728\u7434",
+    "\u9762\u819c",
+    "\u706b\u67f4",
+    "\u82b1\u67f1",
+    "\u8ff7\u5bab",
+    "\u91cf\u676f",
+    "\u836f\u7bb1",
+    "\u5de8\u77f3",
+    "\u9ea6\u514b\u98ce",
+    "\u5fae\u6ce2\u7089",
+    "\u519b\u88c5",
+    "\u5976\u6876",
+    "\u8ff7\u4f60\u5df4\u58eb",
+    "\u8ff7\u4f60\u88d9",
+    "\u9762\u5305\u8f66\uff1b\u5c0f\u578b\u8d27\u8f66",
+    "\u5bfc\u5f39",
+    "\u8fde\u6307\u624b\u5957",
+    "\u6405\u62cc\u94b5",
+    "\u6d3b\u52a8\u623f\u5c4b\uff08\u7531\u6c7d\u8f66\u62d6\u62c9\u7684\uff09",
+    "\u798f\u7279T\u578b\u8f66",
+    "\u8c03\u5236\u89e3\u8c03\u5668\uff1b\u5149\u732b",
+    "\u4fee\u9053\u9662",
+    "\u663e\u793a\u5668",
+    "\u7535\u74f6\u8f66",
+    "\u7802\u6d46",
+    "\u5b66\u58eb",
+    "\u6e05\u771f\u5bfa",
+    "\u868a\u5e10",
+    "\u6469\u6258\u8f66",
+    "\u5c71\u5730\u81ea\u884c\u8f66",
+    "\u767b\u5c71\u5e10",
+    "\u9f20\u6807",
+    "\u6355\u9f20\u5668",
+    "\u642c\u5bb6\u8d27\u8f66",
+    "\u52a8\u7269\u7684\u53e3\u5957",
+    "\u91d1\u5c5e\u9489\u5b50",
+    "\u9888\u6258",
+    "\u9879\u94fe",
+    "\u4e73\u5934\uff08\u74f6\uff09",
+    "\u5e73\u677f\u7535\u8111",
+    "\u65b9\u5c16\u7891",
+    "\u53cc\u7c27\u7ba1",
+    "\u5c0f\u9e45\u7b1b\uff1b\u7403\u5f62\u7b1b(\u7ba1\u8eab\u692d\u5706\u5f62)",
+    "\u91cc\u7a0b\u8868",
+    "\u6ee4\u6cb9\u5668",
+    "\u98ce\u7434",
+    "\u793a\u6ce2\u5668",
+    "\u7f69\u88d9",
+    "\u725b\u8f66",
+    "\u6c27\u6c14\u9762\u7f69",
+    "\u5305\u88c5",
+    "\u8239\u6868",
+    "\u660e\u8f6e",
+    "\u6302\u9501",
+    "\u753b\u7b14",
+    "\u7761\u8863",
+    "\u5bab\u6bbf",
+    "\u6392\u7bab",
+    "\u7eb8\u5dfe",
+    "\u964d\u843d\u4f1e",
+    "\u53cc\u6760",
+    "\u516c\u56ed\u957f\u6905",
+    "\u505c\u8f66\u6536\u8d39\u8868",
+    "\u5ba2\u8f66",
+    "\u9732\u53f0",
+    "\u4ed8\u8d39\u7535\u8bdd",
+    "\u57fa\u5ea7",
+    "\u94c5\u7b14\u76d2",
+    "\u5377\u7b14\u5200",
+    "\u9999\u6c34\uff08\u74f6\uff09",
+    "\u57f9\u517b\u76bf",
+    "\u590d\u5370\u673a",
+    "\u62e8\u5f26\u7247",
+    "\u5c16\u9876\u5934\u76d4",
+    "\u7528\u5c16\u677f\u6761\u8fde\u6210\u7684\u5c16\u6869\u7bf1\u6805",
+    "\u76ae\u5361",
+    "\u6865\u58a9",
+    "\u5b58\u94b1\u7f50",
+    "\u836f\u74f6",
+    "\u6795\u5934",
+    "\u4e52\u4e53\u7403",
+    "\u98ce\u8f66",
+    "\u6d77\u76d7\u8239",
+    "\u6c34\u7f50",
+    "\u6728\u5de5\u5228",
+    "\u5929\u6587\u9986",
+    "\u5851\u6599\u888b",
+    "\u677f\u67b6",
+    "\u7281\u578b\u94f2\u96ea\u673a",
+    "\u624b\u538b\u76ae\u7897\u6cf5",
+    "\u5b9d\u4e3d\u6765\u76f8\u673a",
+    "\u7535\u7ebf\u6746",
+    "\u8b66\u8f66",
+    "\u96e8\u62ab",
+    "\u53f0\u7403\u684c",
+    "\u5145\u6c14\u996e\u6599\u74f6",
+    "\u82b1\u76c6",
+    "\u9676\u5de5\u65cb\u76d8",
+    "\u7535\u94bb",
+    "\u7948\u7977\u57ab",
+    "\u6253\u5370\u673a",
+    "\u76d1\u72f1",
+    "\u70ae\u5f39",
+    "\u6295\u5f71\u4eea",
+    "\u51b0\u7403",
+    "\u6c99\u5305",
+    "\u5c0f\u94b1\u888b\uff1b\u624b\u888b",
+    "\u7fbd\u7ba1\u7b14",
+    "\u88ab\u5b50",
+    "\u8d5b\u8f66",
+    "\u7403\u62cd",
+    "\u6563\u70ed\u5668",
+    "\u6536\u97f3\u673a",
+    "\u5c04\u7535\u671b\u8fdc\u955c",
+    "\u96e8\u6876",
+    "\u4f11\u95f2\u8f66",
+    "\u5377\u8f74",
+    "\u53cd\u5c04\u5f0f\u7167\u76f8\u673a",
+    "\u51b0\u7bb1",
+    "\u9065\u63a7\u5668",
+    "\u9910\u5385",
+    "\u5de6\u8f6e\u624b\u67aa",
+    "\u6b65\u67aa",
+    "\u6447\u6905",
+    "\u7535\u8f6c\u70e4\u8089\u67b6",
+    "\u6a61\u76ae",
+    "\u6a44\u6984\u7403",
+    "\u76f4\u5c3a",
+    "\u8dd1\u6b65\u978b",
+    "\u4fdd\u9669\u67dc",
+    "\u5b89\u5168\u522b\u9488",
+    "\u76d0\u74f6\uff08\u8c03\u5473\u7528\uff09",
+    "\u51c9\u978b",
+    "\u7eb1\u7b3c",
+    "\u8428\u514b\u65af\u7ba1",
+    "\u5251\u9798",
+    "\u79e4",
+    "\u6821\u8f66",
+    "\u5e06\u8239",
+    "\u8bb0\u5206\u724c",
+    "\u5c4f\u5e55",
+    "\u87ba\u4e1d",
+    "\u87ba\u4e1d\u5200",
+    "\u5b89\u5168\u5e26",
+    "\u7f1d\u7eab\u673a",
+    "\u76fe\u724c",
+    "\u76ae\u978b\u5e97",
+    "\u969c\u5b50",
+    "\u8d2d\u7269\u7bee",
+    "\u8d2d\u7269\u8f66",
+    "\u94c1\u9539",
+    "\u6d74\u5e3d",
+    "\u6d74\u5e18",
+    "\u6ed1\u96ea\u677f",
+    "\u6ed1\u96ea\u9762\u7f69",
+    "\u7761\u888b",
+    "\u6ed1\u5c3a",
+    "\u6ed1\u52a8\u95e8",
+    "\u89d2\u5b50\u8001\u864e\u673a",
+    "\u6f5c\u6c34\u901a\u6c14\u7ba1",
+    "\u6469\u6258\u96ea\u6a47\uff1b\u96ea\u5730\u673a\u52a8\u8f66",
+    "\u626b\u96ea\u673a",
+    "\u7682\u6db2\u5668",
+    "\u8db3\u7403",
+    "\u889c\u5b50",
+    "\u789f\u5f0f\u592a\u9633\u80fd",
+    "\u5bbd\u8fb9\u5e3d",
+    "\u6c64\u7897",
+    "\u7a7a\u683c\u952e",
+    "\u7a7a\u95f4\u52a0\u70ed\u5668",
+    "\u822a\u5929\u98de\u673a",
+    "\u9505\u94f2\uff1b\u505a\u996d\u7684\u94f2\u5b50",
+    "\u5feb\u8247",
+    "\u8718\u86db\u7f51",
+    "\u7eba\u9524\uff1b\u624b\u7eba\u7528\u7684\u7ed5\u7ebf\u6746",
+    "\u8dd1\u8f66",
+    "\u805a\u5149\u706f",
+    "\u821e\u53f0",
+    "\u84b8\u6c7d\u673a\u8f66",
+    "\u94a2\u62f1\u6865",
+    "\u94a2\u6eda\u7b52",
+    "\u542c\u8bca\u5668",
+    "\u5973\u7528\u62ab\u80a9",
+    "\u77f3\u5934\u5899",
+    "\u79d2\u8868",
+    "\u706b\u7089",
+    "\u8fc7\u6ee4\u5668",
+    "\u6709\u8f68\u7535\u8f66",
+    "\u62c5\u67b6",
+    "\u6c99\u53d1\u5e8a",
+    "\u4f5b\u5854",
+    "\u6f5c\u8247",
+    "\u5957\u88c5",
+    "\u65e5\u6677",
+    "\u592a\u9633\u955c",
+    "\u592a\u9633\u955c",
+    "\u9632\u6652\u971c",
+    "\u60ac\u7d22\u6865",
+    "\u62d6\u628a",
+    "\u8fd0\u52a8\u886b",
+    "\u6e38\u6cf3\u88e4",
+    "\u79cb\u5343",
+    "\u5f00\u5173",
+    "\u6ce8\u5c04\u5668\uff1b\u5438\u7ba1",
+    "\u53f0\u706f",
+    "\u5766\u514b",
+    "\u5f55\u97f3\u673a",
+    "\u8336\u58f6",
+    "\u6cf0\u8fea",
+    "\u7535\u89c6",
+    "\u7f51\u7403\uff1b\u6253\u7f51\u7403\u7684\u7403",
+    "\u8305\u8349",
+    "\u5e55\u5e03",
+    "\u9876\u9488",
+    "\u6253\u8c37\u673a\uff1b\u8131\u7c92\u673a",
+    "\u5b9d\u5ea7",
+    "\u74e6\u5c4b\u9876",
+    "\u70e4\u9762\u5305\u673a",
+    "\u70df\u8349\u5e97",
+    "\u9a6c\u6876",
+    "\u706b\u70ac",
+    "\u56fe\u817e\u67f1",
+    "\u62d6\u8f66\uff1b\u7275\u5f15\u8f66",
+    "\u73a9\u5177\u5e97",
+    "\u62d6\u62c9\u673a",
+    "\u534a\u6302\u6c7d\u8f66",
+    "\u6258\u76d8",
+    "\u98ce\u8863",
+    "\u4e09\u8f6e\u8f66",
+    "\u4e09\u4f53\u8239",
+    "\u4e09\u811a\u67b6",
+    "\u51ef\u65cb\u95e8",
+    "\u65e0\u8f68\u7535\u8f66",
+    "\u957f\u53f7",
+    "\u6d74\u76c6",
+    "\u65cb\u8f6c\u5f0f\u6805\u95e8",
+    "\u6253\u5b57\u673a\u952e\u76d8",
+    "\u4f1e",
+    "\u72ec\u8f6e\u8f66",
+    "\u76f4\u7acb\u5f0f\u94a2\u7434",
+    "\u5438\u5c18\u5668",
+    "\u82b1\u74f6\uff1b\u88c5\u9970\u74f6",
+    "\u62f1\u9876",
+    "\u5929\u9e45\u7ed2",
+    "\u81ea\u52a8\u552e\u8d27\u673a",
+    "\u6cd5\u8863\uff1b\u796d\u8863\uff1b\u796d\u670d",
+    "\u9ad8\u67b6\u6865",
+    "\u5c0f\u63d0\u7434",
+    "\u6392\u7403",
+    "\u677e\u997c\u673a",
+    "\u6302\u949f",
+    "\u94b1\u5305\uff1b\u94b1\u5939",
+    "\u8863\u67dc\u8863\u6a71",
+    "\u519b\u7528\u98de\u673a",
+    "\u6d17\u8138\u76c6",
+    "\u6d17\u8863\u673a",
+    "\u6c34\u74f6",
+    "\u6c34\u58f6",
+    "\u6c34\u5854",
+    "\u5a01\u58eb\u5fcc\u58f6",
+    "\u54e8\u5b50",
+    "\u5047\u53d1",
+    "\u7eb1\u7a97",
+    "\u767e\u53f6\u7a97",
+    "\u6e29\u838e\u9886\u5e26",
+    "\u8461\u8404\u9152\u74f6",
+    "\u98de\u673a\u7fc5\u8180",
+    "\u7092\u83dc\u9505",
+    "\u6728\u52fa\u5b50\uff1b\u6728\u5934\u52fa\u5b50",
+    "\u6bdb\u7ec7\u54c1",
+    "\u539f\u6728\u6805\u680f",
+    "\u6c89\u8239",
+    "\u53cc\u6845\u8239",
+    "\u8499\u53e4\u5305",
+    "\u7f51\u7ad9\uff1b\u7f51\u9875",
+    "\u6f2b\u753b",
+    "\u7eb5\u6a2a\u5b57\u8c1c",
+    "\u8def\u6807",
+    "\u4ea4\u901a\u4fe1\u53f7\u706f",
+    "\u9632\u5c18\u7f69",
+    "\u83dc\u5355",
+    "\u76d8\u5b50",
+    "\u58a8\u897f\u54e5\u9cc4\u68a8\u9171\uff1b\u58a8\u897f\u54e5\u725b\u6cb9\u679c\u9171",
+    "\u6e05\u7096\u8089\u6c64",
+    "\u706b\u9505",
+    "\u4e73\u8102\u86cb\u7cd5\uff1b\u82f1\u56fd\u751c\u70b9",
+    "\u51b0\u6dc7\u6dcb",
+    "\u51b0\u68cd\uff1b\u96ea\u7cd5",
+    "\u6cd5\u5f0f\u9762\u5305",
+    "\u767e\u5409\u997c",
+    "\u6912\u76d0\u8106\u997c",
+    "\u829d\u58eb\u6c49\u5821",
+    "\u70ed\u72d7",
+    "\u571f\u8c46\u6ce5",
+    "\u7ed3\u7403\u7518\u84dd",
+    "\u897f\u5170\u82b1\uff1b\u7eff\u83dc\u82b1",
+    "\u83dc\u82b1\uff1b\u82b1\u6930\u83dc",
+    "\u897f\u846b\u82a6",
+    "\u91d1\u4e1d\u74dc\uff1b\u610f\u9762\u5357\u74dc\uff1b\u9762\u6761\u74dc",
+    "\u7eff\u8272\u5c0f\u5357\u74dc\uff1b\u9752\u5357\u74dc",
+    "\u5357\u74dc",
+    "\u9ec4\u74dc",
+    "\u6d0b\u84df\uff1b\u7403\u84df",
+    "\u751c\u6912",
+    "\u523a\u68d8\u84df",
+    "\u8611\u83c7",
+    "\u7eff\u82f9\u679c",
+    "\u8349\u8393",
+    "\u6a58\u5b50",
+    "\u67e0\u6aac",
+    "\u65e0\u82b1\u679c",
+    "\u83e0\u841d",
+    "\u9999\u8549",
+    "\u83e0\u841d\u871c",
+    "\u756a\u8354\u679d",
+    "\u77f3\u69b4",
+    "\u5e72\u8349",
+    "\u57f9\u6839\u86cb\u9171\u610f\u5927\u5229\u9762",
+    "\u5de7\u514b\u529b\u9171",
+    "\u751f\u9762\uff1b\u9762\u56e2",
+    "\u745e\u58eb\u8089\u5305",
+    "\u62ab\u8428",
+    "\u9985\u997c",
+    "\u5377\u997c",
+    "\u7ea2\u8461\u8404\u9152",
+    "\u610f\u5f0f\u6d53\u7f29\u5496\u5561",
+    "\u676f\u5b50",
+    "\u86cb\u9152",
+    "\u9ad8\u5c71",
+    "\u6ce1\u6ce1",
+    "\u60ac\u5d16",
+    "\u73ca\u745a\u7901",
+    "\u95f4\u6b47\u6cc9\uff1b\u95f4\u65ad\u55b7\u53d1\u7684\u6e29\u6cc9",
+    "\u6e56\u8fb9",
+    "\u5cac\u89d2\uff1b\u6df1\u5165\u6d77\u4e2d\u7684\u72ed\u957f\u9ad8\u5730",
+    "\u6c99\u6d32",
+    "\u6c99\u6ee9",
+    "\u5ce1\u8c37",
+    "\u706b\u5c71",
+    "\u68d2\u7403\u8fd0\u52a8\u5458",
+    "\u65b0\u90ce",
+    "\u6f5c\u6c34\u5458",
+    "\u6cb9\u83dc",
+    "\u96cf\u83ca",
+    "\u9ec4\u8272\u6753\u5170",
+    "\u7389\u7c73",
+    "\u6a61\u5b50",
+    "\u73ab\u7470\u679c",
+    "\u4e03\u53f6\u6811\u679c\u5b9e",
+    "\u73ca\u745a\u83cc",
+    "\u6728\u8033",
+    "\u9e7f\u82b1\u83cc",
+    "\u81ed\u89d2\u83c7",
+    "\u5730\u661f",
+    "\u591a\u53f6\u5947\u679c\u83cc",
+    "\u725b\u809d\u83cc",
+    "\u7389\u7c73\u68d2\u5b50",
+    "\u536b\u751f\u7eb8"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/cn_zeroshot_classification_templates.json

@@ -0,0 +1,84 @@
+{
+  "imagenet1k": [
+    "{c}\u7684\u7167\u7247\u3002",
+    "\u8d28\u91cf\u5dee\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u8bb8\u591a{c}\u7684\u7167\u7247\u3002",
+    "{c}\u7684\u96d5\u5851\u3002",
+    "\u96be\u4ee5\u770b\u5230{c}\u7684\u7167\u7247\u3002",
+    "{c}\u7684\u4f4e\u5206\u8fa8\u7387\u7167\u7247\u3002",
+    "{c}\u7684\u6e32\u67d3\u3002",
+    "\u6d82\u9e26{c}\u3002",
+    "{c}\u7684\u7cdf\u7cd5\u7167\u7247\u3002",
+    "{c}\u7684\u88c1\u526a\u7167\u7247\u3002",
+    "{c}\u7684\u7eb9\u8eab\u3002",
+    "{c}\u7684\u523a\u7ee3\u7167\u7247\u3002",
+    "\u5f88\u96be\u770b\u5230{c}\u7684\u7167\u7247\u3002",
+    "{c}\u7684\u660e\u4eae\u7167\u7247\u3002",
+    "\u4e00\u5f20\u5e72\u51c0\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u4e00\u5f20\u5305\u542b{c}\u7684\u7167\u7247\u3002",
+    "{c}\u7684\u6df1\u8272\u7167\u7247\u3002",
+    "{c}\u7684\u624b\u7ed8\u753b\u3002",
+    "\u6211\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u4e0d\u81ea\u7136\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u4e00\u5f20\u9177\u7684{c}\u7684\u7167\u7247\u3002",
+    "{c}\u7684\u7279\u5199\u7167\u7247\u3002",
+    "{c}\u7684\u9ed1\u767d\u7167\u7247\u3002",
+    "\u4e00\u5e45{c}\u7684\u753b\u3002",
+    "\u4e00\u5e45{c}\u7684\u7ed8\u753b\u3002",
+    "\u4e00\u5f20{c}\u7684\u50cf\u7d20\u7167\u7247\u3002",
+    "{c}\u7684\u96d5\u50cf\u3002",
+    "\u4e00\u5f20{c}\u7684\u660e\u4eae\u7167\u7247\u3002",
+    "{c}\u7684\u88c1\u526a\u7167\u7247\u3002",
+    "\u4eba\u9020\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u4e00\u5f20\u5173\u4e8e{c}\u7684\u7167\u7247\u3002",
+    "\u635f\u574f\u7684{c}\u7684jpeg\u7167\u7247\u3002",
+    "{c}\u7684\u6a21\u7cca\u7167\u7247\u3002",
+    "{c}\u7684\u76f8\u7247\u3002",
+    "\u4e00\u5f20{c}\u7684\u597d\u7167\u7247\u3002",
+    "{c}\u7684\u6e32\u67d3\u7167\u3002",
+    "\u89c6\u9891\u6e38\u620f\u4e2d\u7684{c}\u3002",
+    "\u4e00\u5f20{c}\u7684\u7167\u7247\u3002",
+    "{c}\u7684\u6d82\u9e26\u3002",
+    "{c}\u7684\u8fd1\u8ddd\u79bb\u7167\u7247\u3002",
+    "{c}\u7684\u6298\u7eb8\u3002",
+    "{c}\u5728\u89c6\u9891\u6e38\u620f\u4e2d\u3002",
+    "{c}\u7684\u8349\u56fe\u3002",
+    "{c}\u7684\u6d82\u9e26\u7167\u3002",
+    "{c}\u7684\u6298\u7eb8\u5f62\u72b6\u3002",
+    "\u4f4e\u5206\u8fa8\u7387\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u73a9\u5177{c}\u3002",
+    "{c}\u7684\u526f\u672c\u3002",
+    "{c}\u7684\u5e72\u51c0\u7684\u7167\u7247\u3002",
+    "\u4e00\u5f20\u5927{c}\u7684\u7167\u7247\u3002",
+    "{c}\u7684\u91cd\u73b0\u3002",
+    "\u4e00\u5f20\u6f02\u4eae\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u4e00\u5f20\u5947\u602a\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u6a21\u7cca\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u5361\u901a{c}\u3002",
+    "{c}\u7684\u827a\u672f\u4f5c\u54c1\u3002",
+    "{c}\u7684\u7d20\u63cf\u3002",
+    "\u523a\u7ee3{c}\u3002",
+    "{c}\u7684\u50cf\u7d20\u7167\u3002",
+    "{c}\u7684\u62cd\u7167\u3002",
+    "{c}\u7684\u635f\u574f\u7684\u7167\u7247\u3002",
+    "\u9ad8\u8d28\u91cf\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u6bdb\u7ed2\u73a9\u5177{c}\u3002",
+    "\u6f02\u4eae\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u5c0f{c}\u7684\u7167\u7247\u3002",
+    "\u7167\u7247\u662f\u5947\u602a\u7684{c}\u3002",
+    "\u6f2b\u753b{c}\u3002",
+    "{c}\u7684\u827a\u672f\u7167\u3002",
+    "{c}\u7684\u56fe\u5f62\u3002",
+    "\u5927{c}\u7684\u7167\u7247\u3002",
+    "\u9ed1\u767d\u7684{c}\u7684\u7167\u7247\u3002",
+    "{c}\u6bdb\u7ed2\u73a9\u5177\u3002",
+    "\u4e00\u5f20{c}\u7684\u6df1\u8272\u7167\u7247\u3002",
+    "{c}\u7684\u6444\u5f71\u56fe\u3002",
+    "{c}\u7684\u6d82\u9e26\u7167\u3002",
+    "\u73a9\u5177\u5f62\u72b6\u7684{c}\u3002",
+    "\u62cd\u4e86{c}\u7684\u7167\u7247\u3002",
+    "\u9177\u9177\u7684{c}\u7684\u7167\u7247\u3002",
+    "\u7167\u7247\u91cc\u7684\u5c0f{c}\u3002",
+    "{c}\u7684\u523a\u9752\u3002"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/en_classnames.json

@@ -0,0 +1,1701 @@
+{
+  "flowers": [
+    "pink primrose",
+    "hard-leaved pocket orchid",
+    "canterbury bells",
+    "sweet pea",
+    "english marigold",
+    "tiger lily",
+    "moon orchid",
+    "bird of paradise",
+    "monkshood",
+    "globe thistle",
+    "snapdragon",
+    "colt's foot",
+    "king protea",
+    "spear thistle",
+    "yellow iris",
+    "globe flower",
+    "purple coneflower",
+    "peruvian lily",
+    "balloon flower",
+    "giant white arum lily",
+    "fire lily",
+    "pincushion flower",
+    "fritillary",
+    "red ginger",
+    "grape hyacinth",
+    "corn poppy",
+    "prince of wales feathers",
+    "stemless gentian",
+    "artichoke",
+    "sweet william",
+    "carnation",
+    "garden phlox",
+    "love in the mist",
+    "mexican aster",
+    "alpine sea holly",
+    "ruby-lipped cattleya",
+    "cape flower",
+    "great masterwort",
+    "siam tulip",
+    "lenten rose",
+    "barbeton daisy",
+    "daffodil",
+    "sword lily",
+    "poinsettia",
+    "bolero deep blue",
+    "wallflower",
+    "marigold",
+    "buttercup",
+    "oxeye daisy",
+    "common dandelion",
+    "petunia",
+    "wild pansy",
+    "primula",
+    "sunflower",
+    "pelargonium",
+    "bishop of llandaff",
+    "gaura",
+    "geranium",
+    "orange dahlia",
+    "pink and yellow dahlia",
+    "cautleya spicata",
+    "japanese anemone",
+    "black-eyed susan",
+    "silverbush",
+    "californian poppy",
+    "osteospermum",
+    "spring crocus",
+    "bearded iris",
+    "windflower",
+    "tree poppy",
+    "gazania",
+    "azalea",
+    "water lily",
+    "rose",
+    "thorn apple",
+    "morning glory",
+    "passion flower",
+    "lotus",
+    "toad lily",
+    "anthurium",
+    "frangipani",
+    "clematis",
+    "hibiscus",
+    "columbine",
+    "desert-rose",
+    "tree mallow",
+    "magnolia",
+    "cyclamen",
+    "watercress",
+    "canna lily",
+    "hippeastrum",
+    "bee balm",
+    "air plant",
+    "foxglove",
+    "bougainvillea",
+    "camellia",
+    "mallow",
+    "mexican petunia",
+    "bromelia",
+    "blanket flower",
+    "trumpet creeper",
+    "blackberry lily"
+  ],
+  "gtsrb": [
+    "red and white circle 20 kph speed limit",
+    "red and white circle 30 kph speed limit",
+    "red and white circle 50 kph speed limit",
+    "red and white circle 60 kph speed limit",
+    "red and white circle 70 kph speed limit",
+    "red and white circle 80 kph speed limit",
+    "end / de-restriction of 80 kph speed limit",
+    "red and white circle 100 kph speed limit",
+    "red and white circle 120 kph speed limit",
+    "red and white circle red car and black car no passing",
+    "red and white circle red truck and black car no passing",
+    "red and white triangle road intersection warning",
+    "white and yellow diamond priority road",
+    "red and white upside down triangle yield right-of-way",
+    "stop",
+    "empty red and white circle",
+    "red and white circle no truck entry",
+    "red circle with white horizonal stripe no entry",
+    "red and white triangle with exclamation mark warning",
+    "red and white triangle with black left curve approaching warning",
+    "red and white triangle with black right curve approaching warning",
+    "red and white triangle with black double curve approaching warning",
+    "red and white triangle rough / bumpy road warning",
+    "red and white triangle car skidding / slipping warning",
+    "red and white triangle with merging / narrow lanes warning",
+    "red and white triangle with person digging / construction / road work warning",
+    "red and white triangle with traffic light approaching warning",
+    "red and white triangle with person walking warning",
+    "red and white triangle with child and person walking warning",
+    "red and white triangle with bicyle warning",
+    "red and white triangle with snowflake / ice warning",
+    "red and white triangle with deer warning",
+    "white circle with gray strike bar no speed limit",
+    "blue circle with white right turn arrow mandatory",
+    "blue circle with white left turn arrow mandatory",
+    "blue circle with white forward arrow mandatory",
+    "blue circle with white forward or right turn arrow mandatory",
+    "blue circle with white forward or left turn arrow mandatory",
+    "blue circle with white keep right arrow mandatory",
+    "blue circle with white keep left arrow mandatory",
+    "blue circle with white arrows indicating a traffic circle",
+    "white circle with gray strike bar indicating no passing for cars has ended",
+    "white circle with gray strike bar indicating no passing for trucks has ended"
+  ],
+  "country211": [
+    "Andorra",
+    "United Arab Emirates",
+    "Afghanistan",
+    "Antigua and Barbuda",
+    "Anguilla",
+    "Albania",
+    "Armenia",
+    "Angola",
+    "Antarctica",
+    "Argentina",
+    "Austria",
+    "Australia",
+    "Aruba",
+    "Aland Islands",
+    "Azerbaijan",
+    "Bosnia and Herzegovina",
+    "Barbados",
+    "Bangladesh",
+    "Belgium",
+    "Burkina Faso",
+    "Bulgaria",
+    "Bahrain",
+    "Benin",
+    "Bermuda",
+    "Brunei Darussalam",
+    "Bolivia",
+    "Bonaire, Saint Eustatius and Saba",
+    "Brazil",
+    "Bahamas",
+    "Bhutan",
+    "Botswana",
+    "Belarus",
+    "Belize",
+    "Canada",
+    "DR Congo",
+    "Central African Republic",
+    "Switzerland",
+    "Cote d'Ivoire",
+    "Cook Islands",
+    "Chile",
+    "Cameroon",
+    "China",
+    "Colombia",
+    "Costa Rica",
+    "Cuba",
+    "Cabo Verde",
+    "Curacao",
+    "Cyprus",
+    "Czech Republic",
+    "Germany",
+    "Denmark",
+    "Dominica",
+    "Dominican Republic",
+    "Algeria",
+    "Ecuador",
+    "Estonia",
+    "Egypt",
+    "Spain",
+    "Ethiopia",
+    "Finland",
+    "Fiji",
+    "Falkland Islands",
+    "Faeroe Islands",
+    "France",
+    "Gabon",
+    "United Kingdom",
+    "Grenada",
+    "Georgia",
+    "French Guiana",
+    "Guernsey",
+    "Ghana",
+    "Gibraltar",
+    "Greenland",
+    "Gambia",
+    "Guadeloupe",
+    "Greece",
+    "South Georgia and South Sandwich Is.",
+    "Guatemala",
+    "Guam",
+    "Guyana",
+    "Hong Kong",
+    "Honduras",
+    "Croatia",
+    "Haiti",
+    "Hungary",
+    "Indonesia",
+    "Ireland",
+    "Israel",
+    "Isle of Man",
+    "India",
+    "Iraq",
+    "Iran",
+    "Iceland",
+    "Italy",
+    "Jersey",
+    "Jamaica",
+    "Jordan",
+    "Japan",
+    "Kenya",
+    "Kyrgyz Republic",
+    "Cambodia",
+    "St. Kitts and Nevis",
+    "North Korea",
+    "South Korea",
+    "Kuwait",
+    "Cayman Islands",
+    "Kazakhstan",
+    "Laos",
+    "Lebanon",
+    "St. Lucia",
+    "Liechtenstein",
+    "Sri Lanka",
+    "Liberia",
+    "Lithuania",
+    "Luxembourg",
+    "Latvia",
+    "Libya",
+    "Morocco",
+    "Monaco",
+    "Moldova",
+    "Montenegro",
+    "Saint-Martin",
+    "Madagascar",
+    "Macedonia",
+    "Mali",
+    "Myanmar",
+    "Mongolia",
+    "Macau",
+    "Martinique",
+    "Mauritania",
+    "Malta",
+    "Mauritius",
+    "Maldives",
+    "Malawi",
+    "Mexico",
+    "Malaysia",
+    "Mozambique",
+    "Namibia",
+    "New Caledonia",
+    "Nigeria",
+    "Nicaragua",
+    "Netherlands",
+    "Norway",
+    "Nepal",
+    "New Zealand",
+    "Oman",
+    "Panama",
+    "Peru",
+    "French Polynesia",
+    "Papua New Guinea",
+    "Philippines",
+    "Pakistan",
+    "Poland",
+    "Puerto Rico",
+    "Palestine",
+    "Portugal",
+    "Palau",
+    "Paraguay",
+    "Qatar",
+    "Reunion",
+    "Romania",
+    "Serbia",
+    "Russia",
+    "Rwanda",
+    "Saudi Arabia",
+    "Solomon Islands",
+    "Seychelles",
+    "Sudan",
+    "Sweden",
+    "Singapore",
+    "St. Helena",
+    "Slovenia",
+    "Svalbard and Jan Mayen Islands",
+    "Slovakia",
+    "Sierra Leone",
+    "San Marino",
+    "Senegal",
+    "Somalia",
+    "South Sudan",
+    "El Salvador",
+    "Sint Maarten",
+    "Syria",
+    "Eswatini",
+    "Togo",
+    "Thailand",
+    "Tajikistan",
+    "Timor-Leste",
+    "Turkmenistan",
+    "Tunisia",
+    "Tonga",
+    "Turkey",
+    "Trinidad and Tobago",
+    "Taiwan",
+    "Tanzania",
+    "Ukraine",
+    "Uganda",
+    "United States",
+    "Uruguay",
+    "Uzbekistan",
+    "Vatican",
+    "Venezuela",
+    "British Virgin Islands",
+    "United States Virgin Islands",
+    "Vietnam",
+    "Vanuatu",
+    "Samoa",
+    "Kosovo",
+    "Yemen",
+    "South Africa",
+    "Zambia",
+    "Zimbabwe"
+  ],
+  "eurosat": [
+    "annual crop land",
+    "forest",
+    "brushland or shrubland",
+    "highway or road",
+    "industrial buildings or commercial buildings",
+    "pasture land",
+    "permanent crop land",
+    "residential buildings or homes or apartments",
+    "river",
+    "lake or sea"
+  ],
+  "fer2013": [
+    "angry",
+    "disgusted",
+    "fearful",
+    "happy",
+    "neutral",
+    "sad",
+    "surprised"
+  ],
+  "caltech101": [
+    "background",
+    "off-center face",
+    "centered face",
+    "leopard",
+    "motorbike",
+    "accordion",
+    "airplane",
+    "anchor",
+    "ant",
+    "barrel",
+    "bass",
+    "beaver",
+    "binocular",
+    "bonsai",
+    "brain",
+    "brontosaurus",
+    "buddha",
+    "butterfly",
+    "camera",
+    "cannon",
+    "side of a car",
+    "ceiling fan",
+    "cellphone",
+    "chair",
+    "chandelier",
+    "body of a cougar cat",
+    "face of a cougar cat",
+    "crab",
+    "crayfish",
+    "crocodile",
+    "head of a  crocodile",
+    "cup",
+    "dalmatian",
+    "dollar bill",
+    "dolphin",
+    "dragonfly",
+    "electric guitar",
+    "elephant",
+    "emu",
+    "euphonium",
+    "ewer",
+    "ferry",
+    "flamingo",
+    "head of a flamingo",
+    "garfield",
+    "gerenuk",
+    "gramophone",
+    "grand piano",
+    "hawksbill",
+    "headphone",
+    "hedgehog",
+    "helicopter",
+    "ibis",
+    "inline skate",
+    "joshua tree",
+    "kangaroo",
+    "ketch",
+    "lamp",
+    "laptop",
+    "llama",
+    "lobster",
+    "lotus",
+    "mandolin",
+    "mayfly",
+    "menorah",
+    "metronome",
+    "minaret",
+    "nautilus",
+    "octopus",
+    "okapi",
+    "pagoda",
+    "panda",
+    "pigeon",
+    "pizza",
+    "platypus",
+    "pyramid",
+    "revolver",
+    "rhino",
+    "rooster",
+    "saxophone",
+    "schooner",
+    "scissors",
+    "scorpion",
+    "sea horse",
+    "snoopy (cartoon beagle)",
+    "soccer ball",
+    "stapler",
+    "starfish",
+    "stegosaurus",
+    "stop sign",
+    "strawberry",
+    "sunflower",
+    "tick",
+    "trilobite",
+    "umbrella",
+    "watch",
+    "water lilly",
+    "wheelchair",
+    "wild cat",
+    "windsor chair",
+    "wrench",
+    "yin and yang symbol"
+  ],
+  "caltech101_vtab": [
+    "accordion",
+    "airplane",
+    "anchor",
+    "ant",
+    "background",
+    "barrel",
+    "bass",
+    "beaver",
+    "binocular",
+    "bonsai",
+    "brain",
+    "brontosaurus",
+    "buddha",
+    "butterfly",
+    "camera",
+    "cannon",
+    "side of a car",
+    "ceiling fan",
+    "cellphone",
+    "chair",
+    "chandelier",
+    "body of a cougar cat",
+    "face of a cougar cat",
+    "crab",
+    "crayfish",
+    "crocodile",
+    "head of a  crocodile",
+    "cup",
+    "dalmatian",
+    "dollar bill",
+    "dolphin",
+    "dragonfly",
+    "electric guitar",
+    "elephant",
+    "emu",
+    "euphonium",
+    "ewer",
+    "off-center face",
+    "centered face",
+    "ferry",
+    "flamingo",
+    "head of a flamingo",
+    "garfield",
+    "gerenuk",
+    "gramophone",
+    "grand piano",
+    "hawksbill",
+    "headphone",
+    "hedgehog",
+    "helicopter",
+    "ibis",
+    "inline skate",
+    "joshua tree",
+    "kangaroo",
+    "ketch",
+    "lamp",
+    "laptop",
+    "leopard",
+    "llama",
+    "lobster",
+    "lotus",
+    "mandolin",
+    "mayfly",
+    "menorah",
+    "metronome",
+    "minaret",
+    "motorbike",
+    "nautilus",
+    "octopus",
+    "okapi",
+    "pagoda",
+    "panda",
+    "pigeon",
+    "pizza",
+    "platypus",
+    "pyramid",
+    "revolver",
+    "rhino",
+    "rooster",
+    "saxophone",
+    "schooner",
+    "scissors",
+    "scorpion",
+    "sea horse",
+    "snoopy (cartoon beagle)",
+    "soccer ball",
+    "stapler",
+    "starfish",
+    "stegosaurus",
+    "stop sign",
+    "strawberry",
+    "sunflower",
+    "tick",
+    "trilobite",
+    "umbrella",
+    "watch",
+    "water lilly",
+    "wheelchair",
+    "wild cat",
+    "windsor chair",
+    "wrench",
+    "yin and yang symbol"
+  ],
+  "imagenet1k": [
+    "tench",
+    "goldfish",
+    "great white shark",
+    "tiger shark",
+    "hammerhead shark",
+    "electric ray",
+    "stingray",
+    "rooster",
+    "hen",
+    "ostrich",
+    "brambling",
+    "goldfinch",
+    "house finch",
+    "junco",
+    "indigo bunting",
+    "American robin",
+    "bulbul",
+    "jay",
+    "magpie",
+    "chickadee",
+    "American dipper",
+    "kite (bird of prey)",
+    "bald eagle",
+    "vulture",
+    "great grey owl",
+    "fire salamander",
+    "smooth newt",
+    "newt",
+    "spotted salamander",
+    "axolotl",
+    "American bullfrog",
+    "tree frog",
+    "tailed frog",
+    "loggerhead sea turtle",
+    "leatherback sea turtle",
+    "mud turtle",
+    "terrapin",
+    "box turtle",
+    "banded gecko",
+    "green iguana",
+    "Carolina anole",
+    "desert grassland whiptail lizard",
+    "agama",
+    "frilled-necked lizard",
+    "alligator lizard",
+    "Gila monster",
+    "European green lizard",
+    "chameleon",
+    "Komodo dragon",
+    "Nile crocodile",
+    "American alligator",
+    "triceratops",
+    "worm snake",
+    "ring-necked snake",
+    "eastern hog-nosed snake",
+    "smooth green snake",
+    "kingsnake",
+    "garter snake",
+    "water snake",
+    "vine snake",
+    "night snake",
+    "boa constrictor",
+    "African rock python",
+    "Indian cobra",
+    "green mamba",
+    "sea snake",
+    "Saharan horned viper",
+    "eastern diamondback rattlesnake",
+    "sidewinder rattlesnake",
+    "trilobite",
+    "harvestman",
+    "scorpion",
+    "yellow garden spider",
+    "barn spider",
+    "European garden spider",
+    "southern black widow",
+    "tarantula",
+    "wolf spider",
+    "tick",
+    "centipede",
+    "black grouse",
+    "ptarmigan",
+    "ruffed grouse",
+    "prairie grouse",
+    "peafowl",
+    "quail",
+    "partridge",
+    "african grey parrot",
+    "macaw",
+    "sulphur-crested cockatoo",
+    "lorikeet",
+    "coucal",
+    "bee eater",
+    "hornbill",
+    "hummingbird",
+    "jacamar",
+    "toucan",
+    "duck",
+    "red-breasted merganser",
+    "goose",
+    "black swan",
+    "tusker",
+    "echidna",
+    "platypus",
+    "wallaby",
+    "koala",
+    "wombat",
+    "jellyfish",
+    "sea anemone",
+    "brain coral",
+    "flatworm",
+    "nematode",
+    "conch",
+    "snail",
+    "slug",
+    "sea slug",
+    "chiton",
+    "chambered nautilus",
+    "Dungeness crab",
+    "rock crab",
+    "fiddler crab",
+    "red king crab",
+    "American lobster",
+    "spiny lobster",
+    "crayfish",
+    "hermit crab",
+    "isopod",
+    "white stork",
+    "black stork",
+    "spoonbill",
+    "flamingo",
+    "little blue heron",
+    "great egret",
+    "bittern bird",
+    "crane bird",
+    "limpkin",
+    "common gallinule",
+    "American coot",
+    "bustard",
+    "ruddy turnstone",
+    "dunlin",
+    "common redshank",
+    "dowitcher",
+    "oystercatcher",
+    "pelican",
+    "king penguin",
+    "albatross",
+    "grey whale",
+    "killer whale",
+    "dugong",
+    "sea lion",
+    "Chihuahua",
+    "Japanese Chin",
+    "Maltese",
+    "Pekingese",
+    "Shih Tzu",
+    "King Charles Spaniel",
+    "Papillon",
+    "toy terrier",
+    "Rhodesian Ridgeback",
+    "Afghan Hound",
+    "Basset Hound",
+    "Beagle",
+    "Bloodhound",
+    "Bluetick Coonhound",
+    "Black and Tan Coonhound",
+    "Treeing Walker Coonhound",
+    "English foxhound",
+    "Redbone Coonhound",
+    "borzoi",
+    "Irish Wolfhound",
+    "Italian Greyhound",
+    "Whippet",
+    "Ibizan Hound",
+    "Norwegian Elkhound",
+    "Otterhound",
+    "Saluki",
+    "Scottish Deerhound",
+    "Weimaraner",
+    "Staffordshire Bull Terrier",
+    "American Staffordshire Terrier",
+    "Bedlington Terrier",
+    "Border Terrier",
+    "Kerry Blue Terrier",
+    "Irish Terrier",
+    "Norfolk Terrier",
+    "Norwich Terrier",
+    "Yorkshire Terrier",
+    "Wire Fox Terrier",
+    "Lakeland Terrier",
+    "Sealyham Terrier",
+    "Airedale Terrier",
+    "Cairn Terrier",
+    "Australian Terrier",
+    "Dandie Dinmont Terrier",
+    "Boston Terrier",
+    "Miniature Schnauzer",
+    "Giant Schnauzer",
+    "Standard Schnauzer",
+    "Scottish Terrier",
+    "Tibetan Terrier",
+    "Australian Silky Terrier",
+    "Soft-coated Wheaten Terrier",
+    "West Highland White Terrier",
+    "Lhasa Apso",
+    "Flat-Coated Retriever",
+    "Curly-coated Retriever",
+    "Golden Retriever",
+    "Labrador Retriever",
+    "Chesapeake Bay Retriever",
+    "German Shorthaired Pointer",
+    "Vizsla",
+    "English Setter",
+    "Irish Setter",
+    "Gordon Setter",
+    "Brittany dog",
+    "Clumber Spaniel",
+    "English Springer Spaniel",
+    "Welsh Springer Spaniel",
+    "Cocker Spaniel",
+    "Sussex Spaniel",
+    "Irish Water Spaniel",
+    "Kuvasz",
+    "Schipperke",
+    "Groenendael dog",
+    "Malinois",
+    "Briard",
+    "Australian Kelpie",
+    "Komondor",
+    "Old English Sheepdog",
+    "Shetland Sheepdog",
+    "collie",
+    "Border Collie",
+    "Bouvier des Flandres dog",
+    "Rottweiler",
+    "German Shepherd Dog",
+    "Dobermann",
+    "Miniature Pinscher",
+    "Greater Swiss Mountain Dog",
+    "Bernese Mountain Dog",
+    "Appenzeller Sennenhund",
+    "Entlebucher Sennenhund",
+    "Boxer",
+    "Bullmastiff",
+    "Tibetan Mastiff",
+    "French Bulldog",
+    "Great Dane",
+    "St. Bernard",
+    "husky",
+    "Alaskan Malamute",
+    "Siberian Husky",
+    "Dalmatian",
+    "Affenpinscher",
+    "Basenji",
+    "pug",
+    "Leonberger",
+    "Newfoundland dog",
+    "Great Pyrenees dog",
+    "Samoyed",
+    "Pomeranian",
+    "Chow Chow",
+    "Keeshond",
+    "brussels griffon",
+    "Pembroke Welsh Corgi",
+    "Cardigan Welsh Corgi",
+    "Toy Poodle",
+    "Miniature Poodle",
+    "Standard Poodle",
+    "Mexican hairless dog (xoloitzcuintli)",
+    "grey wolf",
+    "Alaskan tundra wolf",
+    "red wolf or maned wolf",
+    "coyote",
+    "dingo",
+    "dhole",
+    "African wild dog",
+    "hyena",
+    "red fox",
+    "kit fox",
+    "Arctic fox",
+    "grey fox",
+    "tabby cat",
+    "tiger cat",
+    "Persian cat",
+    "Siamese cat",
+    "Egyptian Mau",
+    "cougar",
+    "lynx",
+    "leopard",
+    "snow leopard",
+    "jaguar",
+    "lion",
+    "tiger",
+    "cheetah",
+    "brown bear",
+    "American black bear",
+    "polar bear",
+    "sloth bear",
+    "mongoose",
+    "meerkat",
+    "tiger beetle",
+    "ladybug",
+    "ground beetle",
+    "longhorn beetle",
+    "leaf beetle",
+    "dung beetle",
+    "rhinoceros beetle",
+    "weevil",
+    "fly",
+    "bee",
+    "ant",
+    "grasshopper",
+    "cricket insect",
+    "stick insect",
+    "cockroach",
+    "praying mantis",
+    "cicada",
+    "leafhopper",
+    "lacewing",
+    "dragonfly",
+    "damselfly",
+    "red admiral butterfly",
+    "ringlet butterfly",
+    "monarch butterfly",
+    "small white butterfly",
+    "sulphur butterfly",
+    "gossamer-winged butterfly",
+    "starfish",
+    "sea urchin",
+    "sea cucumber",
+    "cottontail rabbit",
+    "hare",
+    "Angora rabbit",
+    "hamster",
+    "porcupine",
+    "fox squirrel",
+    "marmot",
+    "beaver",
+    "guinea pig",
+    "common sorrel horse",
+    "zebra",
+    "pig",
+    "wild boar",
+    "warthog",
+    "hippopotamus",
+    "ox",
+    "water buffalo",
+    "bison",
+    "ram (adult male sheep)",
+    "bighorn sheep",
+    "Alpine ibex",
+    "hartebeest",
+    "impala (antelope)",
+    "gazelle",
+    "arabian camel",
+    "llama",
+    "weasel",
+    "mink",
+    "European polecat",
+    "black-footed ferret",
+    "otter",
+    "skunk",
+    "badger",
+    "armadillo",
+    "three-toed sloth",
+    "orangutan",
+    "gorilla",
+    "chimpanzee",
+    "gibbon",
+    "siamang",
+    "guenon",
+    "patas monkey",
+    "baboon",
+    "macaque",
+    "langur",
+    "black-and-white colobus",
+    "proboscis monkey",
+    "marmoset",
+    "white-headed capuchin",
+    "howler monkey",
+    "titi monkey",
+    "Geoffroy's spider monkey",
+    "common squirrel monkey",
+    "ring-tailed lemur",
+    "indri",
+    "Asian elephant",
+    "African bush elephant",
+    "red panda",
+    "giant panda",
+    "snoek fish",
+    "eel",
+    "silver salmon",
+    "rock beauty fish",
+    "clownfish",
+    "sturgeon",
+    "gar fish",
+    "lionfish",
+    "pufferfish",
+    "abacus",
+    "abaya",
+    "academic gown",
+    "accordion",
+    "acoustic guitar",
+    "aircraft carrier",
+    "airliner",
+    "airship",
+    "altar",
+    "ambulance",
+    "amphibious vehicle",
+    "analog clock",
+    "apiary",
+    "apron",
+    "trash can",
+    "assault rifle",
+    "backpack",
+    "bakery",
+    "balance beam",
+    "balloon",
+    "ballpoint pen",
+    "Band-Aid",
+    "banjo",
+    "baluster / handrail",
+    "barbell",
+    "barber chair",
+    "barbershop",
+    "barn",
+    "barometer",
+    "barrel",
+    "wheelbarrow",
+    "baseball",
+    "basketball",
+    "bassinet",
+    "bassoon",
+    "swimming cap",
+    "bath towel",
+    "bathtub",
+    "station wagon",
+    "lighthouse",
+    "beaker",
+    "military hat (bearskin or shako)",
+    "beer bottle",
+    "beer glass",
+    "bell tower",
+    "baby bib",
+    "tandem bicycle",
+    "bikini",
+    "ring binder",
+    "binoculars",
+    "birdhouse",
+    "boathouse",
+    "bobsleigh",
+    "bolo tie",
+    "poke bonnet",
+    "bookcase",
+    "bookstore",
+    "bottle cap",
+    "hunting bow",
+    "bow tie",
+    "brass memorial plaque",
+    "bra",
+    "breakwater",
+    "breastplate",
+    "broom",
+    "bucket",
+    "buckle",
+    "bulletproof vest",
+    "high-speed train",
+    "butcher shop",
+    "taxicab",
+    "cauldron",
+    "candle",
+    "cannon",
+    "canoe",
+    "can opener",
+    "cardigan",
+    "car mirror",
+    "carousel",
+    "tool kit",
+    "cardboard box / carton",
+    "car wheel",
+    "automated teller machine",
+    "cassette",
+    "cassette player",
+    "castle",
+    "catamaran",
+    "CD player",
+    "cello",
+    "mobile phone",
+    "chain",
+    "chain-link fence",
+    "chain mail",
+    "chainsaw",
+    "storage chest",
+    "chiffonier",
+    "bell or wind chime",
+    "china cabinet",
+    "Christmas stocking",
+    "church",
+    "movie theater",
+    "cleaver",
+    "cliff dwelling",
+    "cloak",
+    "clogs",
+    "cocktail shaker",
+    "coffee mug",
+    "coffeemaker",
+    "spiral or coil",
+    "combination lock",
+    "computer keyboard",
+    "candy store",
+    "container ship",
+    "convertible",
+    "corkscrew",
+    "cornet",
+    "cowboy boot",
+    "cowboy hat",
+    "cradle",
+    "construction crane",
+    "crash helmet",
+    "crate",
+    "infant bed",
+    "Crock Pot",
+    "croquet ball",
+    "crutch",
+    "cuirass",
+    "dam",
+    "desk",
+    "desktop computer",
+    "rotary dial telephone",
+    "diaper",
+    "digital clock",
+    "digital watch",
+    "dining table",
+    "dishcloth",
+    "dishwasher",
+    "disc brake",
+    "dock",
+    "dog sled",
+    "dome",
+    "doormat",
+    "drilling rig",
+    "drum",
+    "drumstick",
+    "dumbbell",
+    "Dutch oven",
+    "electric fan",
+    "electric guitar",
+    "electric locomotive",
+    "entertainment center",
+    "envelope",
+    "espresso machine",
+    "face powder",
+    "feather boa",
+    "filing cabinet",
+    "fireboat",
+    "fire truck",
+    "fire screen",
+    "flagpole",
+    "flute",
+    "folding chair",
+    "football helmet",
+    "forklift",
+    "fountain",
+    "fountain pen",
+    "four-poster bed",
+    "freight car",
+    "French horn",
+    "frying pan",
+    "fur coat",
+    "garbage truck",
+    "gas mask or respirator",
+    "gas pump",
+    "goblet",
+    "go-kart",
+    "golf ball",
+    "golf cart",
+    "gondola",
+    "gong",
+    "gown",
+    "grand piano",
+    "greenhouse",
+    "radiator grille",
+    "grocery store",
+    "guillotine",
+    "hair clip",
+    "hair spray",
+    "half-track",
+    "hammer",
+    "hamper",
+    "hair dryer",
+    "hand-held computer",
+    "handkerchief",
+    "hard disk drive",
+    "harmonica",
+    "harp",
+    "combine harvester",
+    "hatchet",
+    "holster",
+    "home theater",
+    "honeycomb",
+    "hook",
+    "hoop skirt",
+    "gymnastic horizontal bar",
+    "horse-drawn vehicle",
+    "hourglass",
+    "iPod",
+    "clothes iron",
+    "carved pumpkin",
+    "jeans",
+    "jeep",
+    "T-shirt",
+    "jigsaw puzzle",
+    "rickshaw",
+    "joystick",
+    "kimono",
+    "knee pad",
+    "knot",
+    "lab coat",
+    "ladle",
+    "lampshade",
+    "laptop computer",
+    "lawn mower",
+    "lens cap",
+    "letter opener",
+    "library",
+    "lifeboat",
+    "lighter",
+    "limousine",
+    "ocean liner",
+    "lipstick",
+    "slip-on shoe",
+    "lotion",
+    "music speaker",
+    "loupe magnifying glass",
+    "sawmill",
+    "magnetic compass",
+    "messenger bag",
+    "mailbox",
+    "tights",
+    "one-piece bathing suit",
+    "manhole cover",
+    "maraca",
+    "marimba",
+    "mask",
+    "matchstick",
+    "maypole",
+    "maze",
+    "measuring cup",
+    "medicine cabinet",
+    "megalith",
+    "microphone",
+    "microwave oven",
+    "military uniform",
+    "milk can",
+    "minibus",
+    "miniskirt",
+    "minivan",
+    "missile",
+    "mitten",
+    "mixing bowl",
+    "mobile home",
+    "ford model t",
+    "modem",
+    "monastery",
+    "monitor",
+    "moped",
+    "mortar and pestle",
+    "graduation cap",
+    "mosque",
+    "mosquito net",
+    "vespa",
+    "mountain bike",
+    "tent",
+    "computer mouse",
+    "mousetrap",
+    "moving van",
+    "muzzle",
+    "metal nail",
+    "neck brace",
+    "necklace",
+    "baby pacifier",
+    "notebook computer",
+    "obelisk",
+    "oboe",
+    "ocarina",
+    "odometer",
+    "oil filter",
+    "pipe organ",
+    "oscilloscope",
+    "overskirt",
+    "bullock cart",
+    "oxygen mask",
+    "product packet / packaging",
+    "paddle",
+    "paddle wheel",
+    "padlock",
+    "paintbrush",
+    "pajamas",
+    "palace",
+    "pan flute",
+    "paper towel",
+    "parachute",
+    "parallel bars",
+    "park bench",
+    "parking meter",
+    "railroad car",
+    "patio",
+    "payphone",
+    "pedestal",
+    "pencil case",
+    "pencil sharpener",
+    "perfume",
+    "Petri dish",
+    "photocopier",
+    "plectrum",
+    "Pickelhaube",
+    "picket fence",
+    "pickup truck",
+    "pier",
+    "piggy bank",
+    "pill bottle",
+    "pillow",
+    "ping-pong ball",
+    "pinwheel",
+    "pirate ship",
+    "drink pitcher",
+    "block plane",
+    "planetarium",
+    "plastic bag",
+    "plate rack",
+    "farm plow",
+    "plunger",
+    "Polaroid camera",
+    "pole",
+    "police van",
+    "poncho",
+    "pool table",
+    "soda bottle",
+    "plant pot",
+    "potter's wheel",
+    "power drill",
+    "prayer rug",
+    "printer",
+    "prison",
+    "missile",
+    "projector",
+    "hockey puck",
+    "punching bag",
+    "purse",
+    "quill",
+    "quilt",
+    "race car",
+    "racket",
+    "radiator",
+    "radio",
+    "radio telescope",
+    "rain barrel",
+    "recreational vehicle",
+    "fishing casting reel",
+    "reflex camera",
+    "refrigerator",
+    "remote control",
+    "restaurant",
+    "revolver",
+    "rifle",
+    "rocking chair",
+    "rotisserie",
+    "eraser",
+    "rugby ball",
+    "ruler measuring stick",
+    "sneaker",
+    "safe",
+    "safety pin",
+    "salt shaker",
+    "sandal",
+    "sarong",
+    "saxophone",
+    "scabbard",
+    "weighing scale",
+    "school bus",
+    "schooner",
+    "scoreboard",
+    "CRT monitor",
+    "screw",
+    "screwdriver",
+    "seat belt",
+    "sewing machine",
+    "shield",
+    "shoe store",
+    "shoji screen / room divider",
+    "shopping basket",
+    "shopping cart",
+    "shovel",
+    "shower cap",
+    "shower curtain",
+    "ski",
+    "balaclava ski mask",
+    "sleeping bag",
+    "slide rule",
+    "sliding door",
+    "slot machine",
+    "snorkel",
+    "snowmobile",
+    "snowplow",
+    "soap dispenser",
+    "soccer ball",
+    "sock",
+    "solar thermal collector",
+    "sombrero",
+    "soup bowl",
+    "keyboard space bar",
+    "space heater",
+    "space shuttle",
+    "spatula",
+    "motorboat",
+    "spider web",
+    "spindle",
+    "sports car",
+    "spotlight",
+    "stage",
+    "steam locomotive",
+    "through arch bridge",
+    "steel drum",
+    "stethoscope",
+    "scarf",
+    "stone wall",
+    "stopwatch",
+    "stove",
+    "strainer",
+    "tram",
+    "stretcher",
+    "couch",
+    "stupa",
+    "submarine",
+    "suit",
+    "sundial",
+    "sunglasses",
+    "sunglasses",
+    "sunscreen",
+    "suspension bridge",
+    "mop",
+    "sweatshirt",
+    "swim trunks / shorts",
+    "swing",
+    "electrical switch",
+    "syringe",
+    "table lamp",
+    "tank",
+    "tape player",
+    "teapot",
+    "teddy bear",
+    "television",
+    "tennis ball",
+    "thatched roof",
+    "front curtain",
+    "thimble",
+    "threshing machine",
+    "throne",
+    "tile roof",
+    "toaster",
+    "tobacco shop",
+    "toilet seat",
+    "torch",
+    "totem pole",
+    "tow truck",
+    "toy store",
+    "tractor",
+    "semi-trailer truck",
+    "tray",
+    "trench coat",
+    "tricycle",
+    "trimaran",
+    "tripod",
+    "triumphal arch",
+    "trolleybus",
+    "trombone",
+    "hot tub",
+    "turnstile",
+    "typewriter keyboard",
+    "umbrella",
+    "unicycle",
+    "upright piano",
+    "vacuum cleaner",
+    "vase",
+    "vaulted or arched ceiling",
+    "velvet fabric",
+    "vending machine",
+    "vestment",
+    "viaduct",
+    "violin",
+    "volleyball",
+    "waffle iron",
+    "wall clock",
+    "wallet",
+    "wardrobe",
+    "military aircraft",
+    "sink",
+    "washing machine",
+    "water bottle",
+    "water jug",
+    "water tower",
+    "whiskey jug",
+    "whistle",
+    "hair wig",
+    "window screen",
+    "window shade",
+    "Windsor tie",
+    "wine bottle",
+    "airplane wing",
+    "wok",
+    "wooden spoon",
+    "wool",
+    "split-rail fence",
+    "shipwreck",
+    "sailboat",
+    "yurt",
+    "website",
+    "comic book",
+    "crossword",
+    "traffic or street sign",
+    "traffic light",
+    "dust jacket",
+    "menu",
+    "plate",
+    "guacamole",
+    "consomme",
+    "hot pot",
+    "trifle",
+    "ice cream",
+    "popsicle",
+    "baguette",
+    "bagel",
+    "pretzel",
+    "cheeseburger",
+    "hot dog",
+    "mashed potatoes",
+    "cabbage",
+    "broccoli",
+    "cauliflower",
+    "zucchini",
+    "spaghetti squash",
+    "acorn squash",
+    "butternut squash",
+    "cucumber",
+    "artichoke",
+    "bell pepper",
+    "cardoon",
+    "mushroom",
+    "Granny Smith apple",
+    "strawberry",
+    "orange",
+    "lemon",
+    "fig",
+    "pineapple",
+    "banana",
+    "jackfruit",
+    "cherimoya (custard apple)",
+    "pomegranate",
+    "hay",
+    "carbonara",
+    "chocolate syrup",
+    "dough",
+    "meatloaf",
+    "pizza",
+    "pot pie",
+    "burrito",
+    "red wine",
+    "espresso",
+    "tea cup",
+    "eggnog",
+    "mountain",
+    "bubble",
+    "cliff",
+    "coral reef",
+    "geyser",
+    "lakeshore",
+    "promontory",
+    "sandbar",
+    "beach",
+    "valley",
+    "volcano",
+    "baseball player",
+    "bridegroom",
+    "scuba diver",
+    "rapeseed",
+    "daisy",
+    "yellow lady's slipper",
+    "corn",
+    "acorn",
+    "rose hip",
+    "horse chestnut seed",
+    "coral fungus",
+    "agaric",
+    "gyromitra",
+    "stinkhorn mushroom",
+    "earth star fungus",
+    "hen of the woods mushroom",
+    "bolete",
+    "corn cob",
+    "toilet paper"
+  ],
+  "clevr_count_all": [
+    "three",
+    "four",
+    "five",
+    "six",
+    "seven",
+    "eight",
+    "nine",
+    "ten"
+  ],
+  "clevr_closest_object_distance": [
+    "very nearby",
+    "nearby",
+    "near",
+    "",
+    "distant",
+    "very distant"
+  ],
+  "mnist": [
+    "0",
+    "1",
+    "2",
+    "3",
+    "4",
+    "5",
+    "6",
+    "7",
+    "8",
+    "9"
+  ],
+  "svhn": [
+    "zero",
+    "one",
+    "two",
+    "three",
+    "four",
+    "five",
+    "six",
+    "seven",
+    "eight",
+    "nine"
+  ],
+  "kitti_closest_vehicle_distance": [
+    "a photo i took of a car on my left or right side.",
+    "a photo i took with a car nearby.",
+    "a photo i took with a car in the distance.",
+    "a photo i took with no car."
+  ],
+  "dmlab": [
+    "nearby apple/melon",
+    "far apple/melon",
+    "very far apple/melon",
+    "nearby lemon",
+    "far lemon",
+    "very far lemon"
+  ],
+  "pets": [
+    "Abyssinian",
+    "American Bulldog",
+    "American Pit Bull Terrier",
+    "Basset Hound",
+    "Beagle",
+    "Bengal",
+    "Birman",
+    "Bombay",
+    "Boxer",
+    "British Shorthair",
+    "Chihuahua",
+    "Egyptian Mau",
+    "English Cocker Spaniel",
+    "English Setter",
+    "German Shorthaired",
+    "Great Pyrenees",
+    "Havanese",
+    "Japanese Chin",
+    "Keeshond",
+    "Leonberger",
+    "Maine Coon",
+    "Miniature Pinscher",
+    "Newfoundland",
+    "Persian",
+    "Pomeranian",
+    "Pug",
+    "Ragdoll",
+    "Russian Blue",
+    "Saint Bernard",
+    "Samoyed",
+    "Scottish Terrier",
+    "Shiba Inu",
+    "Siamese",
+    "Sphynx",
+    "Staffordshire Bull Terrier",
+    "Wheaten Terrier",
+    "Yorkshire Terrier"
+  ],
+  "pcam": [
+    "lymph node",
+    "lymph node containing metastatic tumor tissue"
+  ],
+  "diabetic_retinopathy": [
+    "no diabetic retinopathy",
+    "mild diabetic retinopathy",
+    "moderate diabetic retinopathy",
+    "severe diabetic retinopathy",
+    "proliferative diabetic retinopathy"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/en_zeroshot_classification_templates.json

@@ -0,0 +1,298 @@
+{
+  "cifar10": [
+    "a photo of a {c}.",
+    "a blurry photo of a {c}.",
+    "a black and white photo of a {c}.",
+    "a low contrast photo of a {c}.",
+    "a high contrast photo of a {c}.",
+    "a bad photo of a {c}.",
+    "a good photo of a {c}.",
+    "a photo of a small {c}.",
+    "a photo of a big {c}.",
+    "a photo of the {c}.",
+    "a blurry photo of the {c}.",
+    "a black and white photo of the {c}.",
+    "a low contrast photo of the {c}.",
+    "a high contrast photo of the {c}.",
+    "a bad photo of the {c}.",
+    "a good photo of the {c}.",
+    "a photo of the small {c}.",
+    "a photo of the big {c}."
+  ],
+  "cifar100": [
+    "a photo of a {c}.",
+    "a blurry photo of a {c}.",
+    "a black and white photo of a {c}.",
+    "a low contrast photo of a {c}.",
+    "a high contrast photo of a {c}.",
+    "a bad photo of a {c}.",
+    "a good photo of a {c}.",
+    "a photo of a small {c}.",
+    "a photo of a big {c}.",
+    "a photo of the {c}.",
+    "a blurry photo of the {c}.",
+    "a black and white photo of the {c}.",
+    "a low contrast photo of the {c}.",
+    "a high contrast photo of the {c}.",
+    "a bad photo of the {c}.",
+    "a good photo of the {c}.",
+    "a photo of the small {c}.",
+    "a photo of the big {c}."
+  ],
+  "imagenet1k": [
+    "a bad photo of a {c}.",
+    "a photo of many {c}.",
+    "a sculpture of a {c}.",
+    "a photo of the hard to see {c}.",
+    "a low resolution photo of the {c}.",
+    "a rendering of a {c}.",
+    "graffiti of a {c}.",
+    "a bad photo of the {c}.",
+    "a cropped photo of the {c}.",
+    "a tattoo of a {c}.",
+    "the embroidered {c}.",
+    "a photo of a hard to see {c}.",
+    "a bright photo of a {c}.",
+    "a photo of a clean {c}.",
+    "a photo of a dirty {c}.",
+    "a dark photo of the {c}.",
+    "a drawing of a {c}.",
+    "a photo of my {c}.",
+    "the plastic {c}.",
+    "a photo of the cool {c}.",
+    "a close-up photo of a {c}.",
+    "a black and white photo of the {c}.",
+    "a painting of the {c}.",
+    "a painting of a {c}.",
+    "a pixelated photo of the {c}.",
+    "a sculpture of the {c}.",
+    "a bright photo of the {c}.",
+    "a cropped photo of a {c}.",
+    "a plastic {c}.",
+    "a photo of the dirty {c}.",
+    "a jpeg corrupted photo of a {c}.",
+    "a blurry photo of the {c}.",
+    "a photo of the {c}.",
+    "a good photo of the {c}.",
+    "a rendering of the {c}.",
+    "a {c} in a video game.",
+    "a photo of one {c}.",
+    "a doodle of a {c}.",
+    "a close-up photo of the {c}.",
+    "a photo of a {c}.",
+    "the origami {c}.",
+    "the {c} in a video game.",
+    "a sketch of a {c}.",
+    "a doodle of the {c}.",
+    "a origami {c}.",
+    "a low resolution photo of a {c}.",
+    "the toy {c}.",
+    "a rendition of the {c}.",
+    "a photo of the clean {c}.",
+    "a photo of a large {c}.",
+    "a rendition of a {c}.",
+    "a photo of a nice {c}.",
+    "a photo of a weird {c}.",
+    "a blurry photo of a {c}.",
+    "a cartoon {c}.",
+    "art of a {c}.",
+    "a sketch of the {c}.",
+    "a embroidered {c}.",
+    "a pixelated photo of a {c}.",
+    "itap of the {c}.",
+    "a jpeg corrupted photo of the {c}.",
+    "a good photo of a {c}.",
+    "a plushie {c}.",
+    "a photo of the nice {c}.",
+    "a photo of the small {c}.",
+    "a photo of the weird {c}.",
+    "the cartoon {c}.",
+    "art of the {c}.",
+    "a drawing of the {c}.",
+    "a photo of the large {c}.",
+    "a black and white photo of a {c}.",
+    "the plushie {c}.",
+    "a dark photo of a {c}.",
+    "itap of a {c}.",
+    "graffiti of the {c}.",
+    "a toy {c}.",
+    "itap of my {c}.",
+    "a photo of a cool {c}.",
+    "a photo of a small {c}.",
+    "a tattoo of the {c}."
+  ],
+  "food101": [
+    "a photo of {c}, a type of food."
+  ],
+  "sun397": [
+    "a photo of a {c}.",
+    "a photo of the {c}."
+  ],
+  "cars": [
+    "a photo of a {c}.",
+    "a photo of the {c}.",
+    "a photo of my {c}.",
+    "i love my {c}!",
+    "a photo of my dirty {c}.",
+    "a photo of my clean {c}.",
+    "a photo of my new {c}.",
+    "a photo of my old {c}."
+  ],
+  "fgvc_aircraft": [
+    "a photo of a {c}, a type of aircraft.",
+    "a photo of the {c}, a type of aircraft."
+  ],
+  "dtd": [
+    "a photo of a {c} texture.",
+    "a photo of a {c} pattern.",
+    "a photo of a {c} thing.",
+    "a photo of a {c} object.",
+    "a photo of the {c} texture.",
+    "a photo of the {c} pattern.",
+    "a photo of the {c} thing.",
+    "a photo of the {c} object."
+  ],
+  "pets": [
+    "a photo of a {c}, a type of pet."
+  ],
+  "birdsnap": [
+    "a photo of a {c}, a type of bird."
+  ],
+  "caltech101": [
+    "a photo of a {c}.",
+    "a painting of a {c}.",
+    "a plastic {c}.",
+    "a sculpture of a {c}.",
+    "a sketch of a {c}.",
+    "a tattoo of a {c}.",
+    "a toy {c}.",
+    "a rendition of a {c}.",
+    "a embroidered {c}.",
+    "a cartoon {c}.",
+    "a {c} in a video game.",
+    "a plushie {c}.",
+    "a origami {c}.",
+    "art of a {c}.",
+    "graffiti of a {c}.",
+    "a drawing of a {c}.",
+    "a doodle of a {c}.",
+    "a photo of the {c}.",
+    "a painting of the {c}.",
+    "the plastic {c}.",
+    "a sculpture of the {c}.",
+    "a sketch of the {c}.",
+    "a tattoo of the {c}.",
+    "the toy {c}.",
+    "a rendition of the {c}.",
+    "the embroidered {c}.",
+    "the cartoon {c}.",
+    "the {c} in a video game.",
+    "the plushie {c}.",
+    "the origami {c}.",
+    "art of the {c}.",
+    "graffiti of the {c}.",
+    "a drawing of the {c}.",
+    "a doodle of the {c}."
+  ],
+  "flowers": [
+    "a photo of a {c}, a type of flower."
+  ],
+  "mnist": [
+    "a photo of the number: \"{c}\"."
+  ],
+  "stl10": [
+    "a photo of a {c}.",
+    "a photo of the {c}."
+  ],
+  "eurosat": [
+    "a centered satellite photo of {c}.",
+    "a centered satellite photo of a {c}.",
+    "a centered satellite photo of the {c}."
+  ],
+  "gtsrb": [
+    "a zoomed in photo of a \"{c}\" traffic sign.",
+    "a centered photo of a \"{c}\" traffic sign.",
+    "a close up photo of a \"{c}\" traffic sign."
+  ],
+  "country211": [
+    "a photo i took in {c}.",
+    "a photo i took while visiting {c}.",
+    "a photo from my home country of {c}.",
+    "a photo from my visit to {c}.",
+    "a photo showing the country of {c}."
+  ],
+  "renderedsst2": [
+    "a {c} review of a movie."
+  ],
+  "voc2007": [
+    "a photo of a {c}."
+  ],
+  "voc2007_multilabel": [
+    "a photo of a {c}."
+  ],
+  "fer2013": [
+    "a photo of a {c} looking face.",
+    "a photo of a face showing the emotion: {c}.",
+    "a photo of a face looking {c}.",
+    "a face that looks {c}.",
+    "they look {c}.",
+    "look at how {c} they are."
+  ],
+  "clevr_count_all": [
+    "a picture of {c} objects"
+  ],
+  "clevr_closest_object_distance": [
+    "{c} shapes."
+  ],
+  "pcam": [
+    "a histopathology slide showing {c}",
+    "histopathology image of {c}"
+  ],
+  "svhn": [
+    "a photo of the number {c} written on a sign",
+    "an outdoor house number {c}",
+    "the number {c} in the center of the image",
+    "an outdoor number {c} writte on a sign",
+    "an outdoor number {c}",
+    "a centered image of the number {c}"
+  ],
+  "resisc45": [
+    "a sattelite image of {c}",
+    "an aerial view of {c}",
+    "a sattelite photo of {c}",
+    "{c} from above"
+  ],
+  "kitti_closest_vehicle_distance": [
+    "{c}"
+  ],
+  "smallnorb_label_azimuth": [
+    "an object rotated at {c}",
+    "something rotated at {c}",
+    "{c} rotation",
+    "something at a {c} angle"
+  ],
+  "smallnorb_label_elevation": [
+    "an object rotated at {c}",
+    "something rotated at {c}",
+    "{c} rotation",
+    "something at a {c} angle"
+  ],
+  "dsprites_label_x_position": [
+    "an object located at position {c}% on the horizontal axis"
+  ],
+  "dsprites_label_orientation": [
+    "an object rotated at {c}",
+    "something rotated at {c}",
+    "{c} rotation",
+    "something at a {c} angle"
+  ],
+  "dmlab": [
+    "{c}"
+  ],
+  "diabetic_retinopathy": [
+    "a retinal image with {c}"
+  ],
+  "dummy": [
+    "a photo of a {c}"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/flickr.py

@@ -0,0 +1,59 @@
+"""
+Adapted from https://github.com/pytorch/vision/blob/main/torchvision/datasets/flickr.py
+Thanks to the authors of torchvision
+"""
+import os
+from collections import defaultdict
+from typing import Any, Callable, Optional, Tuple
+
+from PIL import Image
+from torchvision.datasets import VisionDataset
+
+
+class Flickr(VisionDataset):
+
+    def __init__(
+            self,
+            root: str,
+            ann_file: str,
+            transform: Optional[Callable] = None,
+            target_transform: Optional[Callable] = None,
+    ) -> None:
+        super().__init__(root, transform=transform, target_transform=target_transform)
+        self.ann_file = os.path.expanduser(ann_file)
+        data = defaultdict(list)
+        with open(ann_file) as fd:
+            fd.readline()
+            for line in fd:
+                line = line.strip()
+                if line:
+                    # some lines have comma in the caption, se we make sure we do the split correctly
+                    img, caption = line.strip().split('.jpg,')
+                    img = img + '.jpg'
+                    data[img].append(caption)
+        self.data = list(data.items())
+
+    def __getitem__(self, index: int) -> Tuple[Any, Any]:
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: Tuple (image, target). target is a list of captions for the image.
+        """
+        img, captions = self.data[index]
+
+        # Image
+        img = Image.open(os.path.join(self.root, img)).convert('RGB')
+        if self.transform is not None:
+            img = self.transform(img)
+
+        # Captions
+        target = captions
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self) -> int:
+        return len(self.data)

InternVL-main/clip_benchmark/clip_benchmark/datasets/imagenetv2.py

@@ -0,0 +1,98 @@
+"""
+Code from https://github.com/mlfoundations/wise-ft/blob/master/src/datasets/imagenetv2.py
+Thanks to the authors of wise-ft
+"""
+import pathlib
+import shutil
+import tarfile
+
+import requests
+from PIL import Image
+from torch.utils.data import Dataset
+from torchvision.datasets import ImageFolder
+from tqdm import tqdm
+
+URLS = {'matched-frequency': 'https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenetv2-matched-frequency.tar.gz',
+        'threshold-0.7': 'https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenetv2-threshold0.7.tar.gz',
+        'top-images': 'https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenetv2-top-images.tar.gz',
+        'val': 'https://imagenetv2public.s3-us-west-2.amazonaws.com/imagenet_validation.tar.gz'}
+
+FNAMES = {'matched-frequency': 'imagenetv2-matched-frequency-format-val',
+          'threshold-0.7': 'imagenetv2-threshold0.7-format-val',
+          'top-images': 'imagenetv2-top-images-format-val',
+          'val': 'imagenet_validation'}
+
+V2_DATASET_SIZE = 10000
+VAL_DATASET_SIZE = 50000
+
+
+class ImageNetValDataset(Dataset):
+    def __init__(self, transform=None, location='.'):
+        self.dataset_root = pathlib.Path(f'{location}/imagenet_validation/')
+        self.tar_root = pathlib.Path(f'{location}/imagenet_validation.tar.gz')
+        self.fnames = list(self.dataset_root.glob('**/*.JPEG'))
+        self.transform = transform
+        if not self.dataset_root.exists() or len(self.fnames) != VAL_DATASET_SIZE:
+            if not self.tar_root.exists():
+                print(f'Dataset imagenet-val not found on disk, downloading....')
+                response = requests.get(URLS['val'], stream=True)
+                total_size_in_bytes = int(response.headers.get('content-length', 0))
+                block_size = 1024  # 1 Kibibyte
+                progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
+                with open(self.tar_root, 'wb') as f:
+                    for data in response.iter_content(block_size):
+                        progress_bar.update(len(data))
+                        f.write(data)
+                progress_bar.close()
+                if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
+                    assert False, f'Downloading from {URLS[variant]} failed'
+            print('Extracting....')
+            tarfile.open(self.tar_root).extractall(f'{location}')
+            shutil.move(f"{location}/{FNAMES['val']}", self.dataset_root)
+
+        self.dataset = ImageFolder(self.dataset_root)
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, i):
+        img, label = self.dataset[i]
+        if self.transform is not None:
+            img = self.transform(img)
+        return img, label
+
+
+class ImageNetV2Dataset(Dataset):
+    def __init__(self, variant='matched-frequency', transform=None, location='.'):
+        self.dataset_root = pathlib.Path(f'{location}/ImageNetV2-{variant}/')
+        self.tar_root = pathlib.Path(f'{location}/ImageNetV2-{variant}.tar.gz')
+        self.fnames = list(self.dataset_root.glob('**/*.jpeg'))
+        self.transform = transform
+        assert variant in URLS, f'unknown V2 Variant: {variant}'
+        if not self.dataset_root.exists() or len(self.fnames) != V2_DATASET_SIZE:
+            if not self.tar_root.exists():
+                print(f'Dataset {variant} not found on disk, downloading....')
+                response = requests.get(URLS[variant], stream=True)
+                total_size_in_bytes = int(response.headers.get('content-length', 0))
+                block_size = 1024  # 1 Kibibyte
+                progress_bar = tqdm(total=total_size_in_bytes, unit='iB', unit_scale=True)
+                with open(self.tar_root, 'wb') as f:
+                    for data in response.iter_content(block_size):
+                        progress_bar.update(len(data))
+                        f.write(data)
+                progress_bar.close()
+                if total_size_in_bytes != 0 and progress_bar.n != total_size_in_bytes:
+                    assert False, f'Downloading from {URLS[variant]} failed'
+            print('Extracting....')
+            tarfile.open(self.tar_root).extractall(f'{location}')
+            shutil.move(f'{location}/{FNAMES[variant]}', self.dataset_root)
+            self.fnames = list(self.dataset_root.glob('**/*.jpeg'))
+
+    def __len__(self):
+        return len(self.fnames)
+
+    def __getitem__(self, i):
+        img, label = Image.open(self.fnames[i]), int(self.fnames[i].parent.name)
+        if self.transform is not None:
+            img = self.transform(img)
+        return img, label

InternVL-main/clip_benchmark/clip_benchmark/datasets/it_classnames.json

@@ -0,0 +1,1004 @@
+{
+  "imagenet1k": [
+    "una tinca",
+    "un pesce rosso",
+    "un grande squalo bianco",
+    "uno squalo tigre",
+    "un pesce martello",
+    "un raggio elettrico",
+    "una pastinaca",
+    "un gallo",
+    "una gallina",
+    "uno struzzo",
+    "un rovo",
+    "un cardellino",
+    "un fringuello di casa",
+    "un giunco",
+    "uno zigolo indaco",
+    "un pettirosso",
+    "un bulbul",
+    "una ghiandaia",
+    "una gazza",
+    "una cinciallegra",
+    "un'ouzel d'acqua",
+    "un aquilone",
+    "un'aquila calva",
+    "un avvoltoio",
+    "un grande gufo grigio",
+    "una salamandra da fuoco europea",
+    "un tritone comune",
+    "a eft",
+    "una salamandra pezzata",
+    "un axolotl",
+    "una rana toro",
+    "una raganella",
+    "una rana con la coda",
+    "una testa di toro",
+    "una tartaruga di cuoio",
+    "una tartaruga di fango",
+    "una tartaruga",
+    "una tartaruga di scatola",
+    "un geco a bande",
+    "un'iguana comune",
+    "un camaleonte americano",
+    "una coda di frusta",
+    "a agama",
+    "una lucertola con le ali",
+    "una lucertola alligatore",
+    "un mostro di Gila",
+    "una lucertola verde",
+    "un camaleonte africano",
+    "un drago di Komodo",
+    "un coccodrillo africano",
+    "un alligatore americano",
+    "un triceratopo",
+    "un serpente di tuono",
+    "un serpente ringneck",
+    "un serpente hognose",
+    "un serpente verde",
+    "un serpente re",
+    "un serpente giarrettiera",
+    "un serpente d'acqua",
+    "un serpente a forma di vite",
+    "un serpente notturno",
+    "un boa constrictor",
+    "un pitone delle rocce",
+    "un cobra indiano",
+    "un mamba verde",
+    "un serpente di mare",
+    "una vipera cornuta",
+    "un diamondback",
+    "un sidewinder",
+    "un trilobite",
+    "un raccoglitore",
+    "uno scorpione",
+    "un ragno da giardino nero e oro",
+    "un ragno da fienile",
+    "un ragno del giardino",
+    "una vedova nera",
+    "una tarantola",
+    "un ragno lupo",
+    "un segno di spunta",
+    "un millepiedi",
+    "un fagiano di monte",
+    "pernice bianca",
+    "un gallo cedrone",
+    "un pollo della prateria",
+    "un pavone",
+    "una quaglia",
+    "una pernice",
+    "un grigio africano",
+    "un'ara",
+    "un cacatua dalla cresta sulfurea",
+    "un lorichetto",
+    "una coucal",
+    "un mangiatore di api",
+    "un bucerotide",
+    "un colibr\u00ec",
+    "un jacamar",
+    "un tucano",
+    "un drake",
+    "un merganser dal petto rosso",
+    "un'oca",
+    "un cigno nero",
+    "un tusker",
+    "un echidna",
+    "un ornitorinco",
+    "un wallaby",
+    "un koala",
+    "un vombato",
+    "una medusa",
+    "un anemone di mare",
+    "un corallo del cervello",
+    "un verme piatto",
+    "un nematode",
+    "uno strombo",
+    "una lumaca",
+    "una lumaca",
+    "una lumaca di mare",
+    "un chitone",
+    "un nautilus a camera",
+    "un granchio di Dungeness",
+    "un granchio di roccia",
+    "un granchio violinista",
+    "un granchio reale",
+    "un'aragosta americana",
+    "un'aragosta spinosa",
+    "un gambero di fiume",
+    "un paguro",
+    "un isopode",
+    "una cicogna bianca",
+    "una cicogna nera",
+    "una spatola",
+    "un fenicottero",
+    "un piccolo airone blu",
+    "una garzetta americana",
+    "un tarabuso",
+    "una gru",
+    "un moscerino",
+    "un gallinaccio europeo",
+    "una folaga americana",
+    "un'otarda",
+    "una pietra focaia rubiconda",
+    "un piovanello dal dorso rosso",
+    "una pettegola",
+    "una dowitcher",
+    "una beccaccia di mare",
+    "un pellicano",
+    "un pinguino reale",
+    "un albatros",
+    "una balena grigia",
+    "un'orca assassina",
+    "un dugongo",
+    "un leone marino",
+    "un chihuahua",
+    "uno spaniel giapponese",
+    "un cane maltese",
+    "un pechinese",
+    "uno Shih-Tzu",
+    "uno spaniel Blenheim",
+    "un papillon",
+    "un terrier giocattolo",
+    "un Rhodesian ridgeback",
+    "un segugio afgano",
+    "un bassotto",
+    "un beagle",
+    "un segugio",
+    "un bluetick",
+    "un coonhound nero e marrone",
+    "un segugio Walker",
+    "un foxhound inglese",
+    "un osso rosso",
+    "un borzoi",
+    "un cane lupo irlandese",
+    "un levriero italiano",
+    "un whippet",
+    "un segugio ibizenco",
+    "un elkhound norvegese",
+    "una lontra",
+    "un Saluki",
+    "un deerhound scozzese",
+    "un Weimaraner",
+    "uno Staffordshire bullterrier",
+    "un American Staffordshire terrier",
+    "un Bedlington terrier",
+    "un Border terrier",
+    "un Kerry blue terrier",
+    "un terrier irlandese",
+    "un Norfolk terrier",
+    "un Norwich terrier",
+    "uno Yorkshire terrier",
+    "un fox terrier a pelo corto",
+    "un Lakeland terrier",
+    "un Sealyham terrier",
+    "un Airedale",
+    "un tumulo",
+    "un terrier australiano",
+    "un Dandie Dinmont",
+    "un toro di Boston",
+    "uno schnauzer in miniatura",
+    "uno schnauzer gigante",
+    "uno schnauzer standard",
+    "un terrier scozzese",
+    "un terrier tibetano",
+    "un terrier di seta",
+    "un wheaten terrier a pelo morbido",
+    "un West Highland white terrier",
+    "a Lhasa",
+    "un flat-coated retriever",
+    "un retriever a pelo riccio",
+    "un golden retriever",
+    "un Labrador retriever",
+    "un Chesapeake Bay retriever",
+    "un pointer tedesco a pelo corto",
+    "un vizsla",
+    "un setter inglese",
+    "un setter irlandese",
+    "un setter Gordon",
+    "un Brittany spaniel",
+    "un idraulico",
+    "uno springer inglese",
+    "uno springer spaniel gallese",
+    "un cocker spaniel",
+    "uno spaniel del Sussex",
+    "uno spaniel d'acqua irlandese",
+    "un kuvasz",
+    "uno schipperke",
+    "una groenendael",
+    "un malinois",
+    "una briarda",
+    "un kelpie",
+    "un komondor",
+    "un vecchio cane da pastore inglese",
+    "un cane da pastore Shetland",
+    "un collie",
+    "un Border collie",
+    "un Bouvier des Flandres",
+    "un Rottweiler",
+    "un pastore tedesco",
+    "un dobermann",
+    "un pinscher in miniatura",
+    "un cane da montagna svizzero maggiore",
+    "un cane di montagna bernese",
+    "un Appenzeller",
+    "a EntleBucher",
+    "un pugile",
+    "un mastino toro",
+    "un mastino tibetano",
+    "un bulldog francese",
+    "un alano",
+    "un San Bernardo",
+    "un cane eschimese",
+    "un malamute",
+    "un Siberian husky",
+    "un dalmata",
+    "un affenpinscher",
+    "un basenji",
+    "un carlino",
+    "a Leonberg",
+    "un Terranova",
+    "un Grande Pireneo",
+    "un samoiedo",
+    "un Pomerania",
+    "un chow",
+    "un keeshond",
+    "un grifone di Brabancon",
+    "un Pembroke",
+    "un cardigan",
+    "un barboncino giocattolo",
+    "un barboncino in miniatura",
+    "un barboncino standard",
+    "un messicano senza capelli",
+    "un lupo di legno",
+    "un lupo bianco",
+    "un lupo rosso",
+    "un coyote",
+    "un dingo",
+    "un dhole",
+    "un cane da caccia africano",
+    "una iena",
+    "una volpe rossa",
+    "una volpe di kit",
+    "una volpe artica",
+    "una volpe grigia",
+    "un soriano",
+    "un gatto tigrato",
+    "un gatto persiano",
+    "un gatto siamese",
+    "un gatto egiziano",
+    "un puma",
+    "una lince",
+    "un leopardo",
+    "un leopardo delle nevi",
+    "un giaguaro",
+    "un leone",
+    "una tigre",
+    "un ghepardo",
+    "un orso bruno",
+    "un orso nero americano",
+    "un orso di ghiaccio",
+    "un orso bradipo",
+    "una mangusta",
+    "un suricato",
+    "uno scarabeo tigre",
+    "una coccinella",
+    "uno scarabeo di terra",
+    "uno scarabeo dalle lunghe corna",
+    "uno scarabeo delle foglie",
+    "uno scarabeo stercorario",
+    "uno scarabeo rinoceronte",
+    "un tonchio",
+    "una mosca",
+    "un'ape",
+    "una formica",
+    "una cavalletta",
+    "un grillo",
+    "un bastone da passeggio",
+    "uno scarafaggio",
+    "una mantide",
+    "una cicala",
+    "una cavalletta",
+    "un pizzo",
+    "una libellula",
+    "una damigella",
+    "un ammiraglio",
+    "un ricciolo",
+    "un monarca",
+    "una farfalla cavolo",
+    "una farfalla sulfurea",
+    "un licaenide",
+    "una stella marina",
+    "un riccio di mare",
+    "un cetriolo di mare",
+    "un coniglio di legno",
+    "una lepre",
+    "un angora",
+    "un criceto",
+    "un porcospino",
+    "uno scoiattolo volpe",
+    "una marmotta",
+    "un castoro",
+    "un porcellino d'India",
+    "un'acetosa",
+    "una zebra",
+    "un maiale",
+    "un cinghiale",
+    "un facocero",
+    "un ippopotamo",
+    "un bue",
+    "un bufalo d'acqua",
+    "un bisonte",
+    "ariete",
+    "un bighorn",
+    "uno stambecco",
+    "un alcefalo",
+    "un impala",
+    "una gazzella",
+    "un cammello arabo",
+    "un lama",
+    "una donnola",
+    "un visone",
+    "una puzzola",
+    "un furetto dai piedi neri",
+    "una lontra",
+    "una puzzola",
+    "un tasso",
+    "un armadillo",
+    "un bradipo a tre dita",
+    "un orango",
+    "un gorilla",
+    "uno scimpanz\u00e9",
+    "un gibbone",
+    "un siamang",
+    "un cercopiteco",
+    "un patas",
+    "un babbuino",
+    "un macaco",
+    "un langur",
+    "un colobo",
+    "una scimmia proboscide",
+    "uno uistit\u00ec",
+    "un cappuccino",
+    "una scimmia urlatrice",
+    "un titi",
+    "una scimmia ragno",
+    "una scimmia scoiattolo",
+    "un gatto del Madagascar",
+    "un indri",
+    "un elefante indiano",
+    "un elefante africano",
+    "un panda minore",
+    "un panda gigante",
+    "un barracuda",
+    "un'anguilla",
+    "un coho",
+    "una bellezza di roccia",
+    "un pesce anemone",
+    "uno storione",
+    "un capo d'abbigliamento",
+    "un pesce leone",
+    "un puffer",
+    "un abaco",
+    "un abaya",
+    "un abito accademico",
+    "una fisarmonica",
+    "una chitarra acustica",
+    "una portaerei",
+    "un aereo di linea",
+    "un dirigibile",
+    "un altare",
+    "un'ambulanza",
+    "un anfibio",
+    "un orologio analogico",
+    "un apiario",
+    "un grembiule",
+    "un frassino",
+    "un fucile d'assalto",
+    "uno zaino",
+    "un panificio",
+    "una trave di equilibrio",
+    "un pallone",
+    "una penna a sfera",
+    "un cerotto",
+    "un banjo",
+    "una balaustra",
+    "un bilanciere",
+    "una sedia da barbiere",
+    "un barbiere",
+    "un fienile",
+    "un barometro",
+    "un barile",
+    "un carretto",
+    "una palla da baseball",
+    "una pallacanestro",
+    "una culla",
+    "un fagotto",
+    "un berretto da bagno",
+    "un asciugamano da bagno",
+    "una vasca da bagno",
+    "un carro da spiaggia",
+    "un faro",
+    "un bicchiere",
+    "una pelle d'orso",
+    "una bottiglia di birra",
+    "un bicchiere di birra",
+    "un campanile",
+    "un bavaglino",
+    "una bicicletta costruita per due",
+    "un bikini",
+    "un raccoglitore",
+    "un binocolo",
+    "una casetta per uccelli",
+    "una rimessa per barche",
+    "un bob",
+    "una cravatta bolo",
+    "un cofano",
+    "una libreria",
+    "una libreria",
+    "un tappo di bottiglia",
+    "un arco",
+    "un papillon",
+    "un ottone",
+    "un reggiseno",
+    "un frangiflutti",
+    "una corazza",
+    "una scopa",
+    "un secchio",
+    "una fibbia",
+    "un giubbotto antiproiettile",
+    "un treno proiettile",
+    "una macelleria",
+    "un taxi",
+    "un calderone",
+    "una candela",
+    "un cannone",
+    "una canoa",
+    "un apriscatole",
+    "un cardigan",
+    "uno specchio per auto",
+    "una giostra",
+    "un kit da falegname",
+    "un cartone",
+    "una ruota di automobile",
+    "un bancomat",
+    "una cassetta",
+    "un lettore di cassette",
+    "un castello",
+    "un catamarano",
+    "un lettore CD",
+    "un violoncello",
+    "un telefono cellulare",
+    "una catena",
+    "una recinzione di rete metallica",
+    "una cotta di maglia",
+    "una motosega",
+    "un petto",
+    "una chiffoniera",
+    "una suoneria",
+    "una vetrina per porcellane",
+    "una calza di Natale",
+    "una chiesa",
+    "un cinema",
+    "una mannaia",
+    "una dimora sulla scogliera",
+    "un mantello",
+    "un intasamento",
+    "uno shaker da cocktail",
+    "una tazza da caff\u00e8",
+    "una caffettiera",
+    "una bobina",
+    "una serratura a combinazione",
+    "una tastiera di computer",
+    "una pasticceria",
+    "una nave container",
+    "una convertibile",
+    "un cavatappi",
+    "una cornetta",
+    "uno stivale da cowboy",
+    "un cappello da cowboy",
+    "una culla",
+    "una gru",
+    "un casco di protezione",
+    "una cassa",
+    "una culla",
+    "una pentola di coccio",
+    "una palla da croquet",
+    "una stampella",
+    "una corazza",
+    "una diga",
+    "una scrivania",
+    "un computer da tavolo",
+    "un telefono a selezione",
+    "un pannolino",
+    "un orologio digitale",
+    "un orologio digitale",
+    "un tavolo da pranzo",
+    "uno strofinaccio",
+    "una lavastoviglie",
+    "un freno a disco",
+    "un molo",
+    "una slitta trainata da cani",
+    "una cupola",
+    "uno zerbino",
+    "una piattaforma di perforazione",
+    "un tamburo",
+    "una bacchetta",
+    "un manubrio",
+    "un forno olandese",
+    "un ventilatore elettrico",
+    "una chitarra elettrica",
+    "una locomotiva elettrica",
+    "un centro di intrattenimento",
+    "una busta",
+    "una macchina per il caff\u00e8 espresso",
+    "una polvere per il viso",
+    "un boa di piume",
+    "un file",
+    "una barca antincendio",
+    "un'autopompa",
+    "uno schermo per il fuoco",
+    "un pennone",
+    "un flauto",
+    "una sedia pieghevole",
+    "un casco da calcio",
+    "un carrello elevatore",
+    "una fontana",
+    "una penna stilografica",
+    "un baldacchino",
+    "un vagone merci",
+    "un corno francese",
+    "una padella",
+    "una pelliccia",
+    "un camion della spazzatura",
+    "una maschera antigas",
+    "una pompa di benzina",
+    "un calice",
+    "un go-kart",
+    "una pallina da golf",
+    "un golfcart",
+    "una gondola",
+    "un gong",
+    "un abito",
+    "un pianoforte a coda",
+    "una serra",
+    "una griglia",
+    "un negozio di alimentari",
+    "una ghigliottina",
+    "uno scivolo per capelli",
+    "una lacca per capelli",
+    "una mezza traccia",
+    "un martello",
+    "un cesto regalo",
+    "un soffiatore a mano",
+    "un computer portatile",
+    "un fazzoletto",
+    "un disco rigido",
+    "un'armonica",
+    "un'arpa",
+    "una mietitrice",
+    "un'accetta",
+    "una fondina",
+    "un home theater",
+    "un nido d'ape",
+    "un gancio",
+    "una gonna a cerchio",
+    "una barra orizzontale",
+    "un carro di cavalli",
+    "una clessidra",
+    "un iPod",
+    "un ferro da stiro",
+    "una zucca",
+    "un jeans",
+    "una jeep",
+    "una maglia",
+    "un puzzle",
+    "a jinrikisha",
+    "un joystick",
+    "un kimono",
+    "una ginocchiera",
+    "un nodo",
+    "un camice da laboratorio",
+    "un mestolo",
+    "un paralume",
+    "un computer portatile",
+    "un tosaerba",
+    "un copriobiettivo",
+    "un tagliacarte",
+    "una biblioteca",
+    "una scialuppa di salvataggio",
+    "un accendino",
+    "una limousine",
+    "una fodera",
+    "un rossetto",
+    "un mocassino",
+    "una lozione",
+    "un altoparlante",
+    "una lente d'ingrandimento",
+    "una segheria",
+    "una bussola magnetica",
+    "una borsa della posta",
+    "una cassetta postale",
+    "un maillot",
+    "un maillot",
+    "un tombino",
+    "una maraca",
+    "una marimba",
+    "una maschera",
+    "un fiammifero",
+    "un palo di maggio",
+    "un labirinto",
+    "un misurino",
+    "una cassetta dei medicinali",
+    "un megalite",
+    "un microfono",
+    "un microonde",
+    "un'uniforme militare",
+    "una lattina di latte",
+    "un minibus",
+    "una minigonna",
+    "un minivan",
+    "un missile",
+    "un guanto",
+    "una ciotola di miscelazione",
+    "una casa mobile",
+    "un Modello T",
+    "un modem",
+    "un monastero",
+    "un monitor",
+    "un ciclomotore",
+    "un mortaio",
+    "una mortarboard",
+    "una moschea",
+    "una zanzariera",
+    "uno scooter",
+    "una bicicletta di montagna",
+    "una tenda di montagna",
+    "un topo",
+    "una trappola per topi",
+    "un furgone per traslochi",
+    "una museruola",
+    "un chiodo",
+    "un tutore per il collo",
+    "una collana",
+    "un capezzolo",
+    "un quaderno",
+    "un obelisco",
+    "un oboe",
+    "un'ocarina",
+    "un contachilometri",
+    "un filtro dell'olio",
+    "un organo",
+    "un oscilloscopio",
+    "una sopragonna",
+    "un carro da buoi",
+    "una maschera di ossigeno",
+    "un pacchetto",
+    "una pagaia",
+    "una ruota a pale",
+    "un lucchetto",
+    "un pennello",
+    "un pigiama",
+    "un palazzo",
+    "una panpipe",
+    "un tovagliolo di carta",
+    "un paracadute",
+    "una barra parallela",
+    "una panchina del parco",
+    "un parchimetro",
+    "un'autovettura",
+    "un patio",
+    "un telefono a pagamento",
+    "un piedistallo",
+    "una scatola di matite",
+    "un temperamatite",
+    "un profumo",
+    "una capsula di Petri",
+    "una fotocopiatrice",
+    "un grimaldello",
+    "un picconatore",
+    "una staccionata",
+    "un prelievo",
+    "un molo",
+    "un salvadanaio",
+    "una bottiglia di pillole",
+    "un cuscino",
+    "una pallina da ping-pong",
+    "una girandola",
+    "un pirata",
+    "un lanciatore",
+    "un aereo",
+    "un planetario",
+    "un sacchetto di plastica",
+    "un portapiatti",
+    "un aratro",
+    "uno stantuffo",
+    "una macchina fotografica Polaroid",
+    "un palo",
+    "un furgone della polizia",
+    "un poncho",
+    "un tavolo da biliardo",
+    "una bottiglia pop",
+    "una pentola",
+    "un tornio da vasaio",
+    "un trapano elettrico",
+    "un tappeto di preghiera",
+    "una stampante",
+    "una prigione",
+    "un proiettile",
+    "un proiettore",
+    "un disco",
+    "un sacco da boxe",
+    "una borsa",
+    "una penna d'oca",
+    "una trapunta",
+    "un corridore",
+    "una racchetta",
+    "un radiatore",
+    "una radio",
+    "un radiotelescopio",
+    "un barile per la pioggia",
+    "un veicolo ricreativo",
+    "una bobina",
+    "una macchina fotografica reflex",
+    "un frigorifero",
+    "un telecomando",
+    "un ristorante",
+    "un revolver",
+    "un fucile",
+    "una sedia a dondolo",
+    "un girarrosto",
+    "una gomma da cancellare",
+    "un pallone da rugby",
+    "una regola",
+    "una scarpa da corsa",
+    "una cassaforte",
+    "una spilla da balia",
+    "una saliera",
+    "un sandalo",
+    "un sarong",
+    "un sassofono",
+    "un fodero",
+    "una scala",
+    "uno scuolabus",
+    "una goletta",
+    "un tabellone segnapunti",
+    "uno schermo",
+    "una vite",
+    "un cacciavite",
+    "una cintura di sicurezza",
+    "una macchina da cucire",
+    "uno scudo",
+    "un negozio di scarpe",
+    "uno shoji",
+    "un cestino della spesa",
+    "un carrello della spesa",
+    "una pala",
+    "una cuffia da doccia",
+    "una tenda da doccia",
+    "uno sci",
+    "un passamontagna",
+    "un sacco a pelo",
+    "un regolo calcolatore",
+    "una porta scorrevole",
+    "una fessura",
+    "un boccaglio",
+    "una motoslitta",
+    "uno spazzaneve",
+    "un distributore di sapone",
+    "un pallone da calcio",
+    "un calzino",
+    "un piatto solare",
+    "un sombrero",
+    "una ciotola per la zuppa",
+    "una barra spaziatrice",
+    "una stufa per ambienti",
+    "una navetta spaziale",
+    "una spatola",
+    "un motoscafo",
+    "una ragnatela",
+    "un mandrino",
+    "un'auto sportiva",
+    "un riflettore",
+    "una fase",
+    "una locomotiva a vapore",
+    "un ponte ad arco in acciaio",
+    "un tamburo d'acciaio",
+    "uno stetoscopio",
+    "una stola",
+    "un muro di pietra",
+    "un cronometro",
+    "una stufa",
+    "un colino",
+    "un tram",
+    "una barella",
+    "un divano da studio",
+    "uno stupa",
+    "un sottomarino",
+    "un vestito",
+    "una meridiana",
+    "un occhiale da sole",
+    "occhiali da sole",
+    "una protezione solare",
+    "un ponte sospeso",
+    "un tampone",
+    "una felpa",
+    "un costume da bagno",
+    "un'altalena",
+    "un interruttore",
+    "una siringa",
+    "una lampada da tavolo",
+    "un carro armato",
+    "un lettore di nastri",
+    "una teiera",
+    "un orsacchiotto",
+    "una televisione",
+    "una palla da tennis",
+    "una paglia",
+    "un sipario teatrale",
+    "un ditale",
+    "una trebbiatrice",
+    "un trono",
+    "un tetto di tegole",
+    "un tostapane",
+    "un negozio di tabacco",
+    "un sedile del water",
+    "una torcia",
+    "un totem",
+    "un carro attrezzi",
+    "un negozio di giocattoli",
+    "un trattore",
+    "un camion con rimorchio",
+    "un vassoio",
+    "un trench",
+    "un triciclo",
+    "un trimarano",
+    "un treppiede",
+    "un arco di trionfo",
+    "un filobus",
+    "un trombone",
+    "una vasca da bagno",
+    "un tornello",
+    "una tastiera per macchina da scrivere",
+    "un ombrello",
+    "un monociclo",
+    "un montante",
+    "un vuoto",
+    "un vaso",
+    "una volta",
+    "un velluto",
+    "un distributore automatico",
+    "un paramento",
+    "un viadotto",
+    "un violino",
+    "una pallavolo",
+    "una piastra per cialde",
+    "un orologio da parete",
+    "un portafoglio",
+    "un armadio",
+    "un aereo da guerra",
+    "un lavandino",
+    "una rondella",
+    "una bottiglia d'acqua",
+    "una brocca d'acqua",
+    "una torre d'acqua",
+    "una brocca di whisky",
+    "un fischio",
+    "una parrucca",
+    "uno schermo per finestre",
+    "una tenda per finestre",
+    "una cravatta Windsor",
+    "una bottiglia di vino",
+    "un'ala",
+    "un wok",
+    "un cucchiaio di legno",
+    "una lana",
+    "un recinto di vermi",
+    "un relitto",
+    "uno yawl",
+    "una yurta",
+    "un sito web",
+    "un fumetto",
+    "un cruciverba",
+    "un cartello stradale",
+    "un semaforo",
+    "una giacca del libro",
+    "un menu",
+    "un piatto",
+    "un guacamole",
+    "un consomme",
+    "una pentola calda",
+    "un'inezia",
+    "un gelato",
+    "un ghiacciolo",
+    "una pagnotta francese",
+    "un bagel",
+    "un pretzel",
+    "un cheeseburger",
+    "un hotdog",
+    "un pur\u00e8 di patate",
+    "una testa di cavolo",
+    "un broccolo",
+    "un cavolfiore",
+    "una zucchina",
+    "una zucca per spaghetti",
+    "una zucca",
+    "una zucca butternut",
+    "un cetriolo",
+    "un carciofo",
+    "un peperone",
+    "un cardo",
+    "un fungo",
+    "una Granny Smith",
+    "una fragola",
+    "un'arancia",
+    "un limone",
+    "un fico",
+    "un ananas",
+    "una banana",
+    "un jackfruit",
+    "una mela custard",
+    "un melograno",
+    "un fieno",
+    "una carbonara",
+    "una salsa al cioccolato",
+    "un impasto",
+    "un polpettone",
+    "una pizza",
+    "una torta salata",
+    "un burrito",
+    "un vino rosso",
+    "un espresso",
+    "una tazza",
+    "uno zabaione",
+    "a alpe",
+    "una bolla",
+    "una scogliera",
+    "una barriera corallina",
+    "un geyser",
+    "un lago",
+    "un promontorio",
+    "un banco di sabbia",
+    "una riva del mare",
+    "una valle",
+    "un vulcano",
+    "un giocatore di pallone",
+    "uno sposo",
+    "un subacqueo",
+    "un seme di colza",
+    "una margherita",
+    "una pantofola gialla da donna",
+    "un mais",
+    "una ghianda",
+    "un'anca",
+    "un buckeye",
+    "un fungo corallino",
+    "un agarico",
+    "un gyromitra",
+    "una spina dorsale",
+    "una stella di terra",
+    "una gallina dei boschi",
+    "un boleto",
+    "un orecchio",
+    "una carta igienica"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/it_zeroshot_classification_templates.json

@@ -0,0 +1,53 @@
+{
+  "imagenet1k": [
+    "una brutta foto di {c}",
+    "una scultura di {c}",
+    "una foto di {c} difficilmente visibile",
+    "una foto a bassa risoluzione di {c}",
+    "un rendering di {c}",
+    "graffiti di {c}",
+    "una pessima foto di {c}",
+    "una foto ritagliata di {c}",
+    "un tatuaggio di {c}",
+    "{c} ricamato",
+    "{c} ricamata",
+    "una foto luminosa di {c}",
+    "una foto di {c} pulito",
+    "una foto di {c} pulita",
+    "una foto di {c} sporco",
+    "una foto di {c} sporca",
+    "una foto di {c}\u00a0carino",
+    "una foto di {c} carina",
+    "una foto di {c} strano",
+    "una foto di {c} strana",
+    "una foto di {c} piccolo",
+    "una foto di {c} piccola",
+    "una foto di {c} largo",
+    "una foto di {c} larga",
+    "una foto di {c} grande",
+    "una foto scura di {c}",
+    "un disegno di {c}",
+    "{c} di plastica",
+    "una foto del {c} bella",
+    "una foto ravvicinata di {c}",
+    "una foto in bianco e nero di {c}",
+    "un dipinto di {c}",
+    "una foto sgranata di {c}",
+    "una foto ritagliata di {c}",
+    "una foto sfocata di {c}",
+    "una buona foto di {c}",
+    "una riproduzione di {c}",
+    "un rendering di {c}",
+    "{c} in un video gioco",
+    "uno scarabocchio di {c}",
+    "un origami di {c}",
+    "uno sketch di {c}",
+    "una bozza di {c}",
+    "una foto a bassa risoluzione di {c}",
+    "un giocattolo di {c}",
+    "una resa di {c}",
+    "{c} come cartone animato",
+    "un'opera di {c}",
+    "un peluche di {c}"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/jp_classnames.json

@@ -0,0 +1,1004 @@
+{
+  "imagenet1k": [
+    "\u30c6\u30f3\u30c1",
+    "\u91d1\u9b5a",
+    "\u30db\u30db\u30b8\u30ed\u30b6\u30e1",
+    "\u30a4\u30bf\u30c1\u30b6\u30e1",
+    "\u30cf\u30f3\u30de\u30fc\u30d8\u30c3\u30c9",
+    "\u30b7\u30d3\u30ec\u30a8\u30a4",
+    "\u30a2\u30ab\u30a8\u30a4",
+    "\u30b3\u30c3\u30af",
+    "\u3081\u3093\u3069\u308a",
+    "\u30c0\u30c1\u30e7\u30a6",
+    "\u30a2\u30c8\u30ea",
+    "\u30b4\u30b7\u30ad\u30d2\u30ef",
+    "\u30cf\u30a6\u30b9\u30d5\u30a3\u30f3\u30c1",
+    "\u30e6\u30ad\u30d2\u30e1\u30c9\u30ea",
+    "\u30a4\u30f3\u30c7\u30a3\u30b4\u30db\u30aa\u30b8\u30ed",
+    "\u30ed\u30d3\u30f3",
+    "\u30d6\u30eb\u30d6\u30eb",
+    "\u30ab\u30b1\u30b9",
+    "\u30ab\u30b5\u30b5\u30ae",
+    "\u56db\u5341\u96c0",
+    "\u6c34\u30af\u30ed\u30a6\u30bf\u30c9\u30ea",
+    "\u51e7",
+    "\u767d\u982d\u30ef\u30b7",
+    "\u30cf\u30b2\u30ef\u30b7",
+    "\u30ab\u30e9\u30d5\u30c8\u30d5\u30af\u30ed\u30a6",
+    "\u6b27\u5dde\u30d5\u30a1\u30a4\u30a2\u30b5\u30e9\u30de\u30f3\u30c0\u30fc",
+    "\u5171\u901a\u30a4\u30e2\u30ea",
+    "\u30a4\u30e2\u30ea",
+    "\u30b5\u30f3\u30b7\u30e7\u30a6\u30a6\u30aa\u3092\u767a\u898b",
+    "\u30a2\u30db\u30ed\u30fc\u30c8\u30eb",
+    "\u30a6\u30b7\u30ac\u30a8\u30eb",
+    "\u30a2\u30de\u30ac\u30a8\u30eb",
+    "\u3064\u304b\u308c\u305f\u30ab\u30a8\u30eb",
+    "\u3068\u3093\u3061\u304d",
+    "\u30aa\u30b5\u30ac\u30e1",
+    "\u9f08",
+    "\u30c6\u30e9\u30d4\u30f3",
+    "\u30cf\u30b3\u30ac\u30e1",
+    "\u7e1e\u6a21\u69d8\u306e\u30e4\u30e2\u30ea",
+    "\u5171\u901a\u30a4\u30b0\u30a2\u30ca",
+    "\u30a2\u30e1\u30ea\u30ab\u30f3\u30ab\u30e1\u30ec\u30aa\u30f3",
+    "\u30a6\u30a3\u30c3\u30da\u30a4\u30eb",
+    "\u30a2\u30ac\u30de\u30c8\u30ab\u30b2",
+    "\u30d5\u30ea\u30eb\u30c8\u30ab\u30b2",
+    "\u30a2\u30ea\u30b2\u30fc\u30bf\u30fc\u30c8\u30ab\u30b2",
+    "\u30a2\u30e1\u30ea\u30ab\u30c9\u30af\u30c8\u30ab\u30b2",
+    "\u7dd1\u306e\u30c8\u30ab\u30b2",
+    "\u30a2\u30d5\u30ea\u30ab\u306e\u30ab\u30e1\u30ec\u30aa\u30f3",
+    "\u30b3\u30e2\u30c9\u30c9\u30e9\u30b4\u30f3",
+    "\u30a2\u30d5\u30ea\u30ab\u306e\u30ef\u30cb",
+    "\u30a2\u30e1\u30ea\u30ab\u30ef\u30cb",
+    "\u30c8\u30ea\u30b1\u30e9\u30c8\u30d7\u30b9",
+    "\u96f7\u306e\u30d8\u30d3",
+    "\u30ea\u30f3\u30b0\u30cd\u30c3\u30af\u30b9\u30cd\u30fc\u30af",
+    "\u30db\u30fc\u30ce\u30fc\u30b9\u30d8\u30d3",
+    "\u7dd1\u306e\u30d8\u30d3",
+    "\u30ad\u30f3\u30b0\u30b9\u30cd\u30fc\u30af",
+    "\u30ac\u30fc\u30bf\u30fc\u30b9\u30cd\u30fc\u30af",
+    "\u6c34\u86c7",
+    "\u3064\u308b\u30d8\u30d3",
+    "\u591c\u306e\u30d8\u30d3",
+    "\u30dc\u30a2\u30fb\u30b3\u30f3\u30b9\u30c8\u30ea\u30af\u30bf\u30fc",
+    "\u30ed\u30c3\u30af\u30d1\u30a4\u30bd\u30f3",
+    "\u30a4\u30f3\u30c9\u30b3\u30d6\u30e9",
+    "\u30b0\u30ea\u30fc\u30f3\u30de\u30f3\u30d0",
+    "\u30a6\u30df\u30d8\u30d3",
+    "\u30c4\u30ce\u30af\u30b5\u30ea\u30d8\u30d3",
+    "\u30c0\u30a4\u30e4",
+    "\u30b5\u30a4\u30c9\u30ef\u30a4\u30f3\u30c0\u30fc",
+    "\u4e09\u8449\u866b",
+    "\u5208\u308a\u5165\u308c\u4f5c\u696d\u8005",
+    "\u30b5\u30bd\u30ea",
+    "\u9ed2\u3068\u91d1\u306e\u5ead\u30af\u30e2",
+    "\u7d0d\u5c4b\u30af\u30e2",
+    "\u5ead\u30af\u30e2",
+    "\u30af\u30ed\u30b4\u30b1\u30b0\u30e2",
+    "\u30bf\u30e9\u30f3\u30c1\u30e5\u30e9",
+    "\u30aa\u30aa\u30ab\u30df\u306e\u30af\u30e2",
+    "\u30c0\u30cb",
+    "\u767e\u8db3",
+    "\u30af\u30ed\u30e9\u30a4\u30c1\u30e7\u30a6",
+    "\u96f7\u9ce5",
+    "\u3072\u3060\u3048\u308a\u306e\u4ed8\u3044\u305f\u30e9\u30a4\u30c1\u30e7\u30a6",
+    "\u8349\u539f\u30c1\u30ad\u30f3",
+    "\u5b54\u96c0",
+    "\u30a6\u30ba\u30e9",
+    "\u30e4\u30de\u30a6\u30ba\u30e9",
+    "\u30a2\u30d5\u30ea\u30ab\u306e\u7070\u8272",
+    "\u30b3\u30f3\u30b4\u30a6\u30a4\u30f3\u30b3",
+    "\u786b\u9ec4\u30c8\u30ad\u30aa\u30a6\u30e0",
+    "\u30a4\u30f3\u30b3",
+    "\u30d0\u30f3\u30b1\u30f3",
+    "\u8702\u98df\u3079\u308b\u4eba",
+    "\u30b5\u30a4\u30c1\u30e7\u30a6",
+    "\u30cf\u30c1\u30c9\u30ea",
+    "\u9310\u5634",
+    "\u30aa\u30aa\u30cf\u30b7",
+    "\u30c9\u30ec\u30a4\u30af",
+    "\u8d64\u30d6\u30ec\u30b9\u30c8\u30a2\u30a4\u30b5\u5c5e\u306e\u30ac\u30e2",
+    "\u30ac\u30c1\u30e7\u30a6",
+    "\u9ed2\u3044\u767d\u9ce5",
+    "\u30bf\u30b9\u30ab\u30fc\u30d3\u30fc\u30eb",
+    "\u30cf\u30ea\u30e2\u30b0\u30e9",
+    "\u30ab\u30e2\u30ce\u30cf\u30b7",
+    "\u30ef\u30e9\u30d3\u30fc",
+    "\u30b3\u30a2\u30e9",
+    "\u30a6\u30a9\u30f3\u30d0\u30c3\u30c8",
+    "\u30af\u30e9\u30b2",
+    "\u30a4\u30bd\u30ae\u30f3\u30c1\u30e3\u30af",
+    "\u8133\u30b5\u30f3\u30b4",
+    "\u6241\u5f62\u52d5\u7269",
+    "\u7dda\u866b",
+    "\u5dfb\u304d\u8c9d",
+    "\u30ab\u30bf\u30c4\u30e0\u30ea",
+    "\u30ca\u30e1\u30af\u30b8",
+    "\u30a6\u30df\u30a6\u30b7",
+    "\u30ad\u30c8\u30f3",
+    "\u30aa\u30a6\u30e0\u30ac\u30a4",
+    "\u30a2\u30e1\u30ea\u30ab\u30a4\u30c1\u30e7\u30a6\u30ac\u30cb",
+    "\u5ca9\u30ab\u30cb",
+    "\u30b7\u30aa\u30de\u30cd\u30ad",
+    "\u30bf\u30e9\u30d0\u30ac\u30cb",
+    "\u30a2\u30e1\u30ea\u30ab\u30f3\u30ed\u30d6\u30b9\u30bf\u30fc",
+    "\u4f0a\u52e2\u30a8\u30d3",
+    "\u30b6\u30ea\u30ac\u30cb",
+    "\u30e4\u30c9\u30ab\u30ea",
+    "\u7b49\u811a\u985e",
+    "\u30b3\u30a6\u30ce\u30c8\u30ea",
+    "\u30ca\u30d9\u30b3\u30a6",
+    "\u30d8\u30e9\u30b5\u30ae",
+    "\u30d5\u30e9\u30df\u30f3\u30b4",
+    "\u5c0f\u3055\u306a\u9752\u3044\u30b5\u30ae",
+    "\u30a2\u30e1\u30ea\u30ab\u30f3\u767d\u9dfa",
+    "\u306b\u304c\u308a",
+    "\u30af\u30ec\u30fc\u30f3",
+    "\u30c4\u30eb\u30e2\u30c9\u30ad\u79d1\u306e\u9ce5",
+    "\u30e8\u30fc\u30ed\u30d4\u30a2\u30f3\u6c34\u9ce5",
+    "\u30a2\u30e1\u30ea\u30ab\u30aa\u30aa\u30d0\u30f3",
+    "\u30ce\u30ac\u30f3",
+    "\u30ad\u30e7\u30a6\u30b8\u30e7\u30b7\u30ae",
+    "\u8d64\u62c5\u4fdd\u30b7\u30ae",
+    "\u30a2\u30ab\u30a2\u30b7\u30b7\u30ae",
+    "\u30aa\u30aa\u30cf\u30b7\u30b7\u30ae",
+    "\u30df\u30e4\u30b3\u30c9\u30ea",
+    "\u30da\u30ea\u30ab\u30f3",
+    "\u30ad\u30f3\u30b0\u30da\u30f3\u30ae\u30f3",
+    "\u30a2\u30eb\u30d0\u30c8\u30ed\u30b9",
+    "\u30b3\u30af\u30af\u30b8\u30e9",
+    "\u30b7\u30e3\u30c1",
+    "\u30b8\u30e5\u30b4\u30f3",
+    "\u30a2\u30b7\u30ab",
+    "\u30c1\u30ef\u30ef",
+    "\u72c6",
+    "\u30de\u30eb\u30c1\u30fc\u30ba\u72ac",
+    "\u72c6",
+    "\u30b7\u30fc\u30ba\u30fc\u3001\u30b7\u30fc\u30ba\u30fc",
+    "\u30d6\u30ec\u30ca\u30e0\u30b9\u30d1\u30cb\u30a8\u30eb",
+    "\u30d1\u30d4\u30e8\u30f3",
+    "\u30c8\u30a4\u30c6\u30ea\u30a2",
+    "\u30ed\u30fc\u30c7\u30b7\u30a2\u30f3\u30fb\u30ea\u30c3\u30b8\u30d0\u30c3\u30af",
+    "\u30a2\u30d5\u30ac\u30f3\u30cf\u30a6\u30f3\u30c9",
+    "\u30d0\u30bb\u30c3\u30c8\u72ac",
+    "\u30d3\u30fc\u30b0\u30eb",
+    "\u30d6\u30e9\u30c3\u30c9\u30cf\u30a6\u30f3\u30c9",
+    "\u30d6\u30eb\u30fc\u30c6\u30a3\u30c3\u30af",
+    "\u9ed2\u3068\u9ec4\u8910\u8272\u306e\u731f\u72ac",
+    "\u30a6\u30a9\u30fc\u30ab\u30fc\u30cf\u30a6\u30f3\u30c9",
+    "\u30a4\u30f3\u30b0\u30ea\u30c3\u30b7\u30e5\u30d5\u30a9\u30c3\u30af\u30b9\u30cf\u30a6\u30f3\u30c9",
+    "\u30ec\u30c3\u30c9\u30dc\u30fc\u30f3",
+    "\u30dc\u30eb\u30be\u30a4",
+    "\u30a2\u30a4\u30ea\u30c3\u30b7\u30e5\u30fb\u30a6\u30eb\u30d5\u30cf\u30a6\u30f3\u30c9",
+    "\u30a4\u30bf\u30ea\u30a2\u30f3\u30b0\u30ec\u30fc\u30cf\u30a6\u30f3\u30c9",
+    "\u30a6\u30a3\u30da\u30c3\u30c8",
+    "\u30a4\u30d3\u30b5\u30cf\u30a6\u30f3\u30c9",
+    "\u30ce\u30eb\u30a6\u30a7\u30fc\u30a8\u30eb\u30af\u30cf\u30a6\u30f3\u30c9",
+    "\u30aa\u30c3\u30bf\u30fc\u30cf\u30a6\u30f3\u30c9",
+    "\u30b5\u30eb\u30fc\u30ad",
+    "\u30b9\u30b3\u30c6\u30a3\u30c3\u30b7\u30e5\u30fb\u30c7\u30a3\u30a2\u30cf\u30a6\u30f3\u30c9",
+    "\u30ef\u30a4\u30de\u30e9\u30ca\u30fc",
+    "\u30b9\u30bf\u30d5\u30a9\u30fc\u30c9\u30b7\u30e3\u30fc\u30d6\u30eb\u30c6\u30ea\u30a2",
+    "\u30a2\u30e1\u30ea\u30ab\u30f3\u30fb\u30b9\u30bf\u30c3\u30d5\u30a9\u30fc\u30c9\u30b7\u30e3\u30fc\u30fb\u30c6\u30ea\u30a2",
+    "\u30d9\u30c9\u30ea\u30f3\u30c8\u30f3\u30c6\u30ea\u30a2",
+    "\u30dc\u30fc\u30c0\u30fc\u30c6\u30ea\u30a2",
+    "\u30b1\u30ea\u30fc\u30d6\u30eb\u30fc\u30c6\u30ea\u30a2",
+    "\u30a2\u30a4\u30ea\u30c3\u30b7\u30e5\u30c6\u30ea\u30a2",
+    "\u30ce\u30fc\u30d5\u30a9\u30fc\u30af\u30c6\u30ea\u30a2",
+    "\u30ce\u30fc\u30ea\u30c3\u30c1\u30fb\u30c6\u30ea\u30a2",
+    "\u30e8\u30fc\u30af\u30b7\u30e3\u30fc\u30c6\u30ea\u30a2",
+    "\u30ef\u30a4\u30e4\u30fc\u30d8\u30a2\u30fc\u30fb\u30d5\u30a9\u30c3\u30af\u30b9\u30c6\u30ea\u30a2",
+    "\u30ec\u30fc\u30af\u30e9\u30f3\u30c9\u30c6\u30ea\u30a2",
+    "\u30b7\u30fc\u30ea\u30fc\u30cf\u30e0\u30c6\u30ea\u30a2",
+    "\u30a8\u30a2\u30c7\u30fc\u30eb",
+    "\u30b1\u30eb\u30f3",
+    "\u30aa\u30fc\u30b9\u30c8\u30e9\u30ea\u30a2\u30c6\u30ea\u30a2",
+    "\u30c0\u30f3\u30c7\u30a3\u30c7\u30a3\u30f3\u30e2\u30f3\u30c8\u30c6\u30ea\u30a2",
+    "\u30dc\u30b9\u30c8\u30f3\u30d6\u30eb",
+    "\u30df\u30cb\u30c1\u30e5\u30a2\u30b7\u30e5\u30ca\u30a6\u30b6\u30fc",
+    "\u30b8\u30e3\u30a4\u30a2\u30f3\u30c8\u30b7\u30e5\u30ca\u30a6\u30b6\u30fc",
+    "\u30b9\u30bf\u30f3\u30c0\u30fc\u30c9\u30b7\u30e5\u30ca\u30a6\u30b6\u30fc",
+    "\u30b9\u30b3\u30c3\u30c1\u30c6\u30ea\u30a2",
+    "\u30c1\u30d9\u30bf\u30f3\u30c6\u30ea\u30a2",
+    "\u30b7\u30eb\u30ad\u30fc\u30c6\u30ea\u30a2",
+    "\u30bd\u30d5\u30c8\u30b3\u30fc\u30c6\u30c3\u30c9\u30fb\u30a6\u30a3\u30fc\u30c8\u30f3\u30fb\u30c6\u30ea\u30a2",
+    "\u30a6\u30a7\u30b9\u30c8\u30cf\u30a4\u30e9\u30f3\u30c9\u30db\u30ef\u30a4\u30c8\u30c6\u30ea\u30a2",
+    "\u30e9\u30b5",
+    "\u30d5\u30e9\u30c3\u30c8\u30b3\u30fc\u30c6\u30c3\u30c9\u30fb\u30ec\u30c8\u30ea\u30fc\u30d0\u30fc",
+    "\u30ab\u30fc\u30ea\u30fc\u30b3\u30fc\u30c6\u30a3\u30f3\u30b0\u3055\u308c\u305f\u30ec\u30c8\u30ea\u30fc\u30d0\u30fc",
+    "\u30b4\u30fc\u30eb\u30c7\u30f3\u30ec\u30c8\u30ea\u30d0\u30fc",
+    "\u30e9\u30d6\u30e9\u30c9\u30eb\u30fb\u30ec\u30c8\u30ea\u30fc\u30d0\u30fc\u72ac",
+    "\u30c1\u30a7\u30b5\u30d4\u30fc\u30af\u6e7e\u30ec\u30c8\u30ea\u30fc\u30d0\u30fc",
+    "\u30b8\u30e3\u30fc\u30de\u30f3\u30fb\u30b7\u30e7\u30fc\u30c8\u30d8\u30a2\u30fb\u30dd\u30a4\u30f3\u30bf",
+    "\u30d3\u30ba\u30e9",
+    "\u30a4\u30f3\u30b0\u30ea\u30c3\u30b7\u30e5\u30bb\u30c3\u30bf\u30fc",
+    "\u30a2\u30a4\u30ea\u30c3\u30b7\u30e5\u30bb\u30c3\u30bf\u30fc",
+    "\u30b4\u30fc\u30c9\u30f3\u30bb\u30c3\u30bf\u30fc",
+    "\u30d6\u30ea\u30bf\u30cb\u30fc\u30b9\u30d1\u30cb\u30a8\u30eb",
+    "\u30af\u30e9\u30f3\u30d0\u30fc",
+    "\u30a4\u30f3\u30b0\u30ea\u30c3\u30b7\u30e5\u30b9\u30d7\u30ea\u30f3\u30ac\u30fc",
+    "\u30a6\u30a7\u30eb\u30b7\u30e5\u30b9\u30d7\u30ea\u30f3\u30ac\u30fc\u30b9\u30d1\u30cb\u30a8\u30eb",
+    "\u30b3\u30c3\u30ab\u30fc\u30b9\u30d1\u30cb\u30a8\u30eb",
+    "\u30b5\u30bb\u30c3\u30af\u30b9\u30b9\u30d1\u30cb\u30a8\u30eb",
+    "\u30a2\u30a4\u30eb\u30e9\u30f3\u30c9\u306e\u30a6\u30a9\u30fc\u30bf\u30fc\u30b9\u30d1\u30cb\u30a8\u30eb",
+    "\u30af\u30d0\u30fc\u30b9\u72ac",
+    "\u30b9\u30ad\u30c3\u30d1\u30fc\u30ad\u30fc",
+    "\u30d9\u30eb\u30b8\u30a2\u30f3\u30fb\u30b7\u30a7\u30d1\u30fc\u30c9\u30fb\u30c9\u30c3\u30b0\u30fb\u30b0\u30ed\u30fc\u30cd\u30f3\u30c0\u30fc\u30eb",
+    "\u30de\u30ea\u30ce\u30a2",
+    "\u30d6\u30ea\u30a2\u30fc\u30eb",
+    "\u30b1\u30eb\u30d4\u30fc",
+    "\u30b3\u30e2\u30f3\u30c9\u30fc\u30eb",
+    "\u30aa\u30fc\u30eb\u30c9\u30a4\u30f3\u30b0\u30ea\u30c3\u30b7\u30e5\u30b7\u30fc\u30d7\u30c9\u30c3\u30b0",
+    "\u30b7\u30a7\u30c8\u30e9\u30f3\u30c9\u30b7\u30fc\u30d7\u30c9\u30c3\u30b0",
+    "\u30b3\u30ea\u30fc",
+    "\u30dc\u30fc\u30c0\u30fc\u30b3\u30ea\u30fc",
+    "\u30d6\u30fc\u30f4\u30a3\u30a8\u30fb\u30c7\u30fb\u30d5\u30e9\u30f3\u30c9\u30eb",
+    "\u30ed\u30c3\u30c8\u30ef\u30a4\u30e9\u30fc",
+    "\u30b8\u30e3\u30fc\u30de\u30f3\u30b7\u30a7\u30d1\u30fc\u30c9",
+    "\u30c9\u30fc\u30d9\u30eb\u30de\u30f3\u72ac",
+    "\u30df\u30cb\u30c1\u30e5\u30a2\u30d4\u30f3\u30b7\u30e3\u30fc",
+    "\u30b0\u30ec\u30fc\u30bf\u30fc\u30b9\u30a4\u30b9\u30de\u30a6\u30f3\u30c6\u30f3\u30c9\u30c3\u30b0",
+    "\u30d0\u30fc\u30cd\u30fc\u30ba\u30de\u30a6\u30f3\u30c6\u30f3\u30c9\u30c3\u30b0",
+    "\u30a2\u30c3\u30da\u30f3\u30c4\u30a7\u30eb",
+    "\u30a8\u30f3\u30c8\u30ec\u30d6\u30c3\u30b7\u30e3\u30fc",
+    "\u30dc\u30af\u30b5\u30fc",
+    "\u30d6\u30eb\u30de\u30b9\u30c1\u30d5",
+    "\u30c1\u30d9\u30c3\u30c8\u30de\u30b9\u30c1\u30d5",
+    "\u30d5\u30ec\u30f3\u30c1\u30d6\u30eb\u30c9\u30c3\u30b0",
+    "\u30b0\u30ec\u30fc\u30c8\u30c7\u30fc\u30f3",
+    "\u30bb\u30f3\u30c8\u30d0\u30fc\u30ca\u30fc\u30c9",
+    "\u30a8\u30b9\u30ad\u30e2\u30fc\u72ac",
+    "\u30de\u30e9\u30df\u30e5\u30fc\u30c8",
+    "\u30b7\u30d9\u30ea\u30a2\u30f3\u30cf\u30b9\u30ad\u30fc",
+    "\u30c0\u30eb\u30e1\u30b7\u30a2\u30f3",
+    "\u30a2\u30fc\u30d5\u30a7\u30f3\u30d4\u30f3\u30b7\u30e3\u30fc",
+    "\u30d0\u30bb\u30f3\u30b8\u30fc",
+    "\u30d1\u30b0",
+    "\u30ec\u30aa\u30f3\u30d0\u30fc\u30b0",
+    "\u30cb\u30e5\u30fc\u30d5\u30a1\u30f3\u30c9\u30e9\u30f3\u30c9\u5cf6",
+    "\u30b0\u30ec\u30fc\u30c8\u30d4\u30ec\u30cb\u30fc\u30ba",
+    "\u30b5\u30e2\u30a8\u30c9",
+    "\u30dd\u30e1\u30e9\u30cb\u30a2\u30f3",
+    "\u30c1\u30e3\u30a6",
+    "\u30ad\u30fc\u30b9\u30db\u30f3\u30c9",
+    "\u30d6\u30e9\u30d0\u30f3\u30bd\u30f3\u30b0\u30ea\u30d5\u30a9\u30f3",
+    "\u30da\u30f3\u30d6\u30ed\u30fc\u30af",
+    "\u30ab\u30fc\u30c7\u30a3\u30ac\u30f3",
+    "\u30c8\u30a4\u30d7\u30fc\u30c9\u30eb",
+    "\u30df\u30cb\u30c1\u30e5\u30a2\u30d7\u30fc\u30c9\u30eb",
+    "\u30b9\u30bf\u30f3\u30c0\u30fc\u30c9\u30d7\u30fc\u30c9\u30eb",
+    "\u30e1\u30ad\u30b7\u30ab\u30f3\u30fb\u30d8\u30a2\u30fc\u30ec\u30b9",
+    "\u30b7\u30f3\u30ea\u30f3\u30aa\u30aa\u30ab\u30df",
+    "\u767d\u3044\u30aa\u30aa\u30ab\u30df",
+    "\u30ec\u30c3\u30c9\u30a6\u30eb\u30d5",
+    "\u30b3\u30e8\u30fc\u30c6",
+    "\u30c7\u30a3\u30f3\u30b4",
+    "\u30c9\u30fc\u30eb",
+    "\u30ea\u30ab\u30aa\u30f3",
+    "\u30cf\u30a4\u30a8\u30ca",
+    "\u30a2\u30ab\u30ae\u30c4\u30cd",
+    "\u30ad\u30c3\u30c8\u30ad\u30c4\u30cd",
+    "\u30db\u30c3\u30ad\u30e7\u30af\u30ae\u30c4\u30cd",
+    "\u7070\u8272\u306e\u30ad\u30c4\u30cd",
+    "\u30bf\u30d3\u30fc",
+    "\u864e\u732b",
+    "\u30da\u30eb\u30b7\u30e3\u732b",
+    "\u30b7\u30e3\u30e0\u732b",
+    "\u30a8\u30b8\u30d7\u30c8\u306e\u732b",
+    "\u30af\u30fc\u30ac\u30fc",
+    "\u30aa\u30aa\u30e4\u30de\u30cd\u30b3",
+    "\u30d2\u30e7\u30a6",
+    "\u30e6\u30ad\u30d2\u30e7\u30a6",
+    "\u30b8\u30e3\u30ac\u30fc",
+    "\u30e9\u30a4\u30aa\u30f3",
+    "\u864e",
+    "\u30c1\u30fc\u30bf\u30fc",
+    "\u30d2\u30b0\u30de",
+    "\u30a2\u30e1\u30ea\u30ab\u30af\u30ed\u30af\u30de",
+    "\u6c37\u306e\u30af\u30de",
+    "\u30ca\u30de\u30b1\u30b0\u30de",
+    "\u30de\u30f3\u30b0\u30fc\u30b9",
+    "\u30df\u30fc\u30a2\u30ad\u30e3\u30c3\u30c8",
+    "\u30cf\u30f3\u30df\u30e7\u30a6",
+    "\u3066\u3093\u3068\u3046\u866b",
+    "\u30b0\u30e9\u30f3\u30c9\u30d3\u30fc\u30c8\u30eb",
+    "\u30ab\u30df\u30ad\u30ea\u30e0\u30b7",
+    "\u30cf\u30e0\u30b7",
+    "\u30d5\u30f3\u30b3\u30ed\u30ac\u30b7",
+    "\u30b5\u30a4\u30cf\u30e0\u30b7",
+    "\u30be\u30a6\u30e0\u30b7",
+    "\u30cf\u30a8",
+    "\u8702",
+    "\u87fb",
+    "\u30d0\u30c3\u30bf",
+    "\u30af\u30ea\u30b1\u30c3\u30c8",
+    "\u6756",
+    "\u30b4\u30ad\u30d6\u30ea",
+    "\u30ab\u30de\u30ad\u30ea",
+    "\u8749",
+    "\u30e8\u30b3\u30d0\u30a4",
+    "\u30af\u30b5\u30ab\u30b2\u30ed\u30a6",
+    "\u30c8\u30f3\u30dc",
+    "\u30a4\u30c8\u30c8\u30f3\u30dc",
+    "\u63d0\u7763",
+    "\u30ea\u30f3\u30b0\u30ec\u30c3\u30c8",
+    "\u541b\u4e3b",
+    "\u30e2\u30f3\u30b7\u30ed\u30c1\u30e7\u30a6",
+    "\u786b\u9ec4\u8776",
+    "\u30b7\u30b8\u30df\u30c1\u30e7\u30a6",
+    "\u30d2\u30c8\u30c7",
+    "\u3046\u306b",
+    "\u30ca\u30de\u30b3",
+    "\u6728\u306e\u30a6\u30b5\u30ae",
+    "\u91ce\u30a6\u30b5\u30ae",
+    "\u30a2\u30f3\u30b4\u30e9",
+    "\u30cf\u30e0\u30b9\u30bf\u30fc",
+    "\u30e4\u30de\u30a2\u30e9\u30b7",
+    "\u30ad\u30c4\u30cd\u30ea\u30b9",
+    "\u30de\u30fc\u30e2\u30c3\u30c8",
+    "\u30d3\u30fc\u30d0\u30fc",
+    "\u30e2\u30eb\u30e2\u30c3\u30c8",
+    "\u6817\u8272",
+    "\u30b7\u30de\u30a6\u30de",
+    "\u8c5a",
+    "\u30a4\u30ce\u30b7\u30b7",
+    "\u30a4\u30dc\u30a4\u30ce\u30b7\u30b7",
+    "\u30ab\u30d0",
+    "\u96c4\u725b",
+    "\u6c34\u725b",
+    "\u30d0\u30a4\u30bd\u30f3",
+    "\u30e9\u30e0",
+    "\u30d3\u30c3\u30b0\u30db\u30fc\u30f3",
+    "\u30a2\u30a4\u30d9\u30c3\u30af\u30b9",
+    "\u30cf\u30fc\u30c6\u30d3\u30fc\u30b9\u30c8",
+    "\u30a4\u30f3\u30d1\u30e9",
+    "\u30ac\u30bc\u30eb",
+    "\u30a2\u30e9\u30d3\u30a2\u30e9\u30af\u30c0",
+    "\u30e9\u30de",
+    "\u30a4\u30bf\u30c1",
+    "\u30df\u30f3\u30af",
+    "\u30b1\u30ca\u30ac\u30a4\u30bf\u30c1",
+    "\u30af\u30ed\u30a2\u30b7\u30a4\u30bf\u30c1",
+    "\u30ab\u30ef\u30a6\u30bd",
+    "\u30b9\u30ab\u30f3\u30af",
+    "\u72f8",
+    "\u30a2\u30eb\u30de\u30b8\u30ed",
+    "\u30df\u30e6\u30d3\u30ca\u30de\u30b1\u30e2\u30ce",
+    "\u30aa\u30e9\u30f3\u30a6\u30fc\u30bf\u30f3",
+    "\u30b4\u30ea\u30e9",
+    "\u30c1\u30f3\u30d1\u30f3\u30b8\u30fc",
+    "\u30c6\u30ca\u30ac\u30b6\u30eb",
+    "\u30d5\u30af\u30ed\u30c6\u30ca\u30ac\u30b6\u30eb",
+    "\u30aa\u30ca\u30ac\u30b6\u30eb",
+    "\u30d1\u30bf\u30b9",
+    "\u30d2\u30d2",
+    "\u30de\u30ab\u30af",
+    "\u30e4\u30bb\u30b6\u30eb",
+    "\u30b3\u30ed\u30d6\u30b9\u5c5e",
+    "\u30c6\u30f3\u30b0\u30b6\u30eb",
+    "\u30de\u30fc\u30e2\u30bb\u30c3\u30c8",
+    "\u30aa\u30de\u30ad\u30b6\u30eb",
+    "\u30db\u30a8\u30b6\u30eb",
+    "\u30c6\u30a3\u30c6\u30a3",
+    "\u30af\u30e2\u30b6\u30eb",
+    "\u30ea\u30b9\u30b6\u30eb",
+    "\u30de\u30c0\u30ac\u30b9\u30ab\u30eb\u732b",
+    "\u30a4\u30f3\u30c9\u30ea",
+    "\u30a4\u30f3\u30c9\u30be\u30a6",
+    "\u30a2\u30d5\u30ea\u30ab\u30be\u30a6",
+    "\u30ec\u30c3\u30b5\u30fc\u30d1\u30f3\u30c0",
+    "\u30b8\u30e3\u30a4\u30a2\u30f3\u30c8\u30d1\u30f3\u30c0",
+    "\u30d0\u30e9\u30af\u30fc\u30bf",
+    "\u30a6\u30ca\u30ae",
+    "\u30ae\u30f3\u30b6\u30b1",
+    "\u5ca9\u306e\u7f8e\u3057\u3055",
+    "\u30af\u30de\u30ce\u30df",
+    "\u30c1\u30e7\u30a6\u30b6\u30e1",
+    "\u30ac\u30fc",
+    "\u30df\u30ce\u30ab\u30b5\u30b4",
+    "\u30d5\u30b0",
+    "\u305d\u308d\u3070\u3093",
+    "\u30a2\u30d0\u30e4",
+    "\u30a2\u30ab\u30c7\u30df\u30c3\u30af\u30ac\u30a6\u30f3",
+    "\u30a2\u30b3\u30fc\u30c7\u30a3\u30aa\u30f3",
+    "\u30a2\u30b3\u30fc\u30b9\u30c6\u30a3\u30c3\u30af\u30ae\u30bf\u30fc",
+    "\u7a7a\u6bcd",
+    "\u65c5\u5ba2\u6a5f",
+    "\u98db\u884c\u8239",
+    "\u796d\u58c7",
+    "\u6551\u6025\u8eca",
+    "\u4e21\u751f\u985e",
+    "\u30a2\u30ca\u30ed\u30b0\u6642\u8a08",
+    "\u990a\u8702\u5834",
+    "\u30a8\u30d7\u30ed\u30f3",
+    "\u3054\u307f\u5165\u308c",
+    "\u30a2\u30b5\u30eb\u30c8\u30e9\u30a4\u30d5\u30eb",
+    "\u30d0\u30c3\u30af\u30d1\u30c3\u30af",
+    "\u30d9\u30fc\u30ab\u30ea\u30fc",
+    "\u5e73\u5747\u53f0",
+    "\u30d0\u30eb\u30fc\u30f3",
+    "\u30dc\u30fc\u30eb\u30da\u30f3",
+    "\u30d0\u30f3\u30c9\u30a8\u30a4\u30c9",
+    "\u30d0\u30f3\u30b8\u30e7\u30fc",
+    "\u30d0\u30cb\u30b9\u30bf\u30fc",
+    "\u30d0\u30fc\u30d9\u30eb",
+    "\u7406\u9aea\u5e97\u306e\u6905\u5b50",
+    "\u7406\u9aea\u5e97",
+    "\u7d0d\u5c4b",
+    "\u30d0\u30ed\u30e1\u30fc\u30bf\u30fc",
+    "\u30d0\u30ec\u30eb",
+    "\u30d0\u30ed\u30fc",
+    "\u91ce\u7403",
+    "\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb",
+    "\u30d0\u30b7\u30cd\u30c3\u30c8",
+    "\u30d5\u30a1\u30b4\u30c3\u30c8",
+    "\u6c34\u6cf3\u5e3d",
+    "\u30d0\u30b9\u30bf\u30aa\u30eb",
+    "\u30d0\u30b9\u30bf\u30d6",
+    "\u30d3\u30fc\u30c1\u30ef\u30b4\u30f3",
+    "\u30d3\u30fc\u30b3\u30f3",
+    "\u30d3\u30fc\u30ab\u30fc",
+    "\u30d9\u30a2\u30b9\u30ad\u30f3",
+    "\u30d3\u30fc\u30eb\u74f6",
+    "\u30d3\u30fc\u30eb\u30b0\u30e9\u30b9",
+    "\u30d9\u30eb\u30b3\u30fc\u30c8",
+    "\u30d3\u30d6",
+    "\u81ea\u8ee2\u8eca",
+    "\u30d3\u30ad\u30cb",
+    "\u30d0\u30a4\u30f3\u30c0\u30fc",
+    "\u53cc\u773c\u93e1",
+    "\u5de3\u7bb1",
+    "\u30dc\u30fc\u30c8\u30cf\u30a6\u30b9",
+    "\u30dc\u30d6\u30b9\u30ec\u30fc",
+    "\u30eb\u30fc\u30d7\u30bf\u30a4",
+    "\u30dc\u30f3\u30cd\u30c3\u30c8",
+    "\u672c\u68da",
+    "\u66f8\u5e97",
+    "\u74f6\u306e\u30ad\u30e3\u30c3\u30d7",
+    "\u5f13",
+    "\u3061\u3087\u3046\u30cd\u30af\u30bf\u30a4",
+    "\u771f\u936e",
+    "\u30d6\u30e9\u30b8\u30e3\u30fc",
+    "\u9632\u6ce2\u5824",
+    "\u80f8\u5f53\u3066",
+    "\u307b\u3046\u304d",
+    "\u30d0\u30b1\u30c4",
+    "\u30d0\u30c3\u30af\u30eb",
+    "\u9632\u5f3e\u30c1\u30e7\u30c3\u30ad",
+    "\u65b0\u5e79\u7dda",
+    "\u7cbe\u8089\u5e97",
+    "\u30bf\u30af\u30b7\u30fc",
+    "\u5927\u91dc",
+    "\u30ad\u30e3\u30f3\u30c9\u30eb",
+    "\u5927\u7832",
+    "\u30ab\u30cc\u30fc",
+    "\u7f36\u5207\u308a",
+    "\u30ab\u30fc\u30c7\u30a3\u30ac\u30f3",
+    "\u8eca\u306e\u30df\u30e9\u30fc",
+    "\u56de\u8ee2\u6728\u99ac",
+    "\u5927\u5de5\u306e\u30ad\u30c3\u30c8",
+    "\u30ab\u30fc\u30c8\u30f3",
+    "\u8eca\u306e\u30db\u30a4\u30fc\u30eb",
+    "\u73fe\u91d1\u81ea\u52d5\u9810\u3051\u6255\u3044\u6a5f",
+    "\u30ab\u30bb\u30c3\u30c8",
+    "\u30ab\u30bb\u30c3\u30c8\u30fb\u30d7\u30ec\u30fc\u30e4\u30fc",
+    "\u57ce",
+    "\u30ab\u30bf\u30de\u30e9\u30f3",
+    "CD\u30d7\u30ec\u30fc\u30e4\u30fc",
+    "\u30c1\u30a7\u30ed",
+    "\u30b9\u30de\u30fc\u30c8\u30d5\u30a9\u30f3",
+    "\u9396",
+    "\u30c1\u30a7\u30fc\u30f3\u30ea\u30f3\u30af\u30d5\u30a7\u30f3\u30b9",
+    "\u30c1\u30a7\u30fc\u30f3\u30e1\u30fc\u30eb",
+    "\u30c1\u30a7\u30fc\u30f3\u30bd\u30fc",
+    "\u80f8",
+    "\u30b7\u30d5\u30a9\u30cb\u30a2",
+    "\u30c1\u30e3\u30a4\u30e0",
+    "\u4e2d\u56fd\u30ad\u30e3\u30d3\u30cd\u30c3\u30c8",
+    "\u30af\u30ea\u30b9\u30de\u30b9\u306e\u9774\u4e0b",
+    "\u6559\u4f1a",
+    "\u6620\u753b",
+    "\u30af\u30ea\u30fc\u30d0\u30fc",
+    "\u5d16\u306e\u4f4f\u5c45",
+    "\u30de\u30f3\u30c8",
+    "\u30af\u30ed\u30c3\u30b0",
+    "\u30ab\u30af\u30c6\u30eb\u30b7\u30a7\u30fc\u30ab\u30fc",
+    "\u30b3\u30fc\u30d2\u30fc\u30de\u30b0",
+    "\u30b3\u30fc\u30d2\u30fc\u30dd\u30c3\u30c8",
+    "\u30b3\u30a4\u30eb",
+    "\u30c0\u30a4\u30e4\u30eb\u9320",
+    "\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u306e\u30ad\u30fc\u30dc\u30fc\u30c9",
+    "\u88fd\u83d3",
+    "\u30b3\u30f3\u30c6\u30ca\u8239",
+    "\u30b3\u30f3\u30d0\u30fc\u30c1\u30d6\u30eb",
+    "\u30b3\u30fc\u30af\u30b9\u30af\u30ea\u30e5\u30fc",
+    "\u30b3\u30eb\u30cd\u30c3\u30c8",
+    "\u30ab\u30a6\u30dc\u30fc\u30a4\u30d6\u30fc\u30c4",
+    "\u30ab\u30a6\u30dc\u30fc\u30a4\u30cf\u30c3\u30c8",
+    "\u30af\u30ec\u30fc\u30c9\u30eb",
+    "\u30af\u30ec\u30fc\u30f3",
+    "\u30af\u30e9\u30c3\u30b7\u30e5\u30d8\u30eb\u30e1\u30c3\u30c8",
+    "\u6728\u7bb1",
+    "\u30d9\u30d3\u30fc\u30d9\u30c3\u30c9",
+    "\u30af\u30ed\u30fc\u30af\u30dd\u30c3\u30c8",
+    "\u30af\u30ed\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb",
+    "\u677e\u8449\u6756",
+    "\u80f8\u5f53\u3066",
+    "\u30c0\u30e0",
+    "\u673a",
+    "\u30c7\u30b9\u30af\u30c8\u30c3\u30d7\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc",
+    "\u30c0\u30a4\u30e4\u30eb\u96fb\u8a71",
+    "\u304a\u3080\u3064",
+    "\u30c7\u30b8\u30bf\u30eb\u6642\u8a08",
+    "\u30c7\u30b8\u30bf\u30eb\u8155\u6642\u8a08",
+    "\u30c0\u30a4\u30cb\u30f3\u30b0\u30c6\u30fc\u30d6\u30eb",
+    "\u610f\u6c17\u5730\u306a\u3057",
+    "\u98df\u5668\u6d17\u3044\u6a5f",
+    "\u30c7\u30a3\u30b9\u30af\u30d6\u30ec\u30fc\u30ad",
+    "\u30c9\u30c3\u30af",
+    "\u72ac\u305e\u308a",
+    "\u30c9\u30fc\u30e0",
+    "\u7384\u95a2\u30de\u30c3\u30c8",
+    "\u6398\u524a\u57fa\u5730",
+    "\u30c9\u30e9\u30e0",
+    "\u30c9\u30e9\u30e0\u30b9\u30c6\u30a3\u30c3\u30af",
+    "\u30c0\u30f3\u30d9\u30eb",
+    "\u30c0\u30c3\u30c1\u30aa\u30fc\u30d6\u30f3",
+    "\u6247\u98a8\u6a5f",
+    "\u30a8\u30ec\u30ad\u30ae\u30bf\u30fc",
+    "\u96fb\u6c17\u6a5f\u95a2\u8eca",
+    "\u5a2f\u697d\u65bd\u8a2d",
+    "\u5c01\u7b52",
+    "\u30a8\u30b9\u30d7\u30ec\u30c3\u30bd\u30de\u30b7\u30fc\u30f3",
+    "\u30d5\u30a7\u30fc\u30b9\u30d1\u30a6\u30c0\u30fc",
+    "\u30d5\u30a7\u30b6\u30fc\u30dc\u30a2",
+    "\u30d5\u30a1\u30a4\u30eb",
+    "\u6d88\u9632\u8247",
+    "\u6d88\u9632\u8eca",
+    "\u30d5\u30a1\u30a4\u30a2\u30fc\u30b9\u30af\u30ea\u30fc\u30f3",
+    "\u65d7\u7aff",
+    "\u30d5\u30eb\u30fc\u30c8",
+    "\u6298\u308a\u7573\u307f\u5f0f\u6905\u5b50",
+    "\u30d5\u30c3\u30c8\u30dc\u30fc\u30eb\u30d8\u30eb\u30e1\u30c3\u30c8",
+    "\u30d5\u30a9\u30fc\u30af\u30ea\u30d5\u30c8",
+    "\u5674\u6c34",
+    "\u4e07\u5e74\u7b46",
+    "\u56db\u67f1",
+    "\u8ca8\u8eca",
+    "\u30d5\u30ec\u30f3\u30c1\u30db\u30eb\u30f3",
+    "\u30d5\u30e9\u30a4\u30d1\u30f3",
+    "\u6bdb\u76ae\u306e\u30b3\u30fc\u30c8",
+    "\u3054\u307f\u53ce\u96c6\u8eca",
+    "\u30ac\u30b9\u30de\u30b9\u30af",
+    "\u30ac\u30bd\u30ea\u30f3\u30dd\u30f3\u30d7",
+    "\u30b4\u30d6\u30ec\u30c3\u30c8",
+    "\u30b4\u30fc\u30ab\u30fc\u30c8",
+    "\u30b4\u30eb\u30d5\u30dc\u30fc\u30eb",
+    "\u30b4\u30eb\u30d5\u30ab\u30fc\u30c8",
+    "\u30b4\u30f3\u30c9\u30e9",
+    "\u30b4\u30f3\u30b0",
+    "\u30ac\u30a6\u30f3",
+    "\u30b0\u30e9\u30f3\u30c9\u30d4\u30a2\u30ce",
+    "\u6e29\u5ba4",
+    "\u30b0\u30ea\u30eb",
+    "\u98df\u6599\u54c1\u5e97",
+    "\u30ae\u30ed\u30c1\u30f3",
+    "\u30d8\u30a2\u30b9\u30e9\u30a4\u30c9",
+    "\u30d8\u30a2\u30b9\u30d7\u30ec\u30fc",
+    "\u534a\u30c8\u30e9\u30c3\u30af",
+    "\u30cf\u30f3\u30de\u30fc",
+    "\u59a8\u3052\u307e\u3059",
+    "\u30cf\u30f3\u30c9\u30d6\u30ed\u30ef\u30fc",
+    "\u30bf\u30d6\u30ec\u30c3\u30c8",
+    "\u30cf\u30f3\u30ab\u30c1",
+    "\u30cf\u30fc\u30c9\u30c7\u30a3\u30b9\u30af",
+    "\u30cf\u30fc\u30e2\u30cb\u30ab",
+    "\u30cf\u30fc\u30d7",
+    "\u30cf\u30fc\u30d9\u30b9\u30bf",
+    "\u65a7",
+    "\u30db\u30eb\u30b9\u30bf\u30fc",
+    "\u30db\u30fc\u30e0\u30b7\u30a2\u30bf\u30fc",
+    "\u30cf\u30cb\u30ab\u30e0",
+    "\u30d5\u30c3\u30af",
+    "\u30d5\u30fc\u30d7\u30b9\u30ab\u30fc\u30c8",
+    "\u6c34\u5e73\u30d0\u30fc",
+    "\u99ac\u8eca",
+    "\u7802\u6642\u8a08",
+    "\u30a2\u30a4\u30d5\u30a9\u30fc\u30f3",
+    "\u9244",
+    "\u30b8\u30e3\u30c3\u30af\u30aa\u30fc\u30e9\u30f3\u30bf\u30f3",
+    "\u30b8\u30fc\u30f3\u30ba",
+    "\u30b8\u30fc\u30d7",
+    "\u30b8\u30e3\u30fc\u30b8\u30fc",
+    "\u30b8\u30b0\u30bd\u30fc\u30d1\u30ba\u30eb",
+    "\u4eba\u529b\u8eca",
+    "\u30b8\u30e7\u30a4\u30b9\u30c6\u30a3\u30c3\u30af",
+    "\u7740\u7269",
+    "\u819d\u30d1\u30c3\u30c9",
+    "\u7d50\u3073\u76ee",
+    "\u767d\u8863",
+    "\u3072\u3057\u3083\u304f",
+    "\u30e9\u30f3\u30d7\u306e\u304b\u3055",
+    "\u30ce\u30fc\u30c8\u30d1\u30bd\u30b3\u30f3",
+    "\u829d\u5208\u308a\u6a5f",
+    "\u30ec\u30f3\u30ba\u30ad\u30e3\u30c3\u30d7",
+    "\u30ec\u30bf\u30fc\u30aa\u30fc\u30d7\u30ca\u30fc",
+    "\u30e9\u30a4\u30d6\u30e9\u30ea",
+    "\u6551\u547d\u30dc\u30fc\u30c8",
+    "\u30e9\u30a4\u30bf\u30fc",
+    "\u30ea\u30e0\u30b8\u30f3",
+    "\u30e9\u30a4\u30ca\u30fc",
+    "\u53e3\u7d05",
+    "\u30ed\u30fc\u30d5\u30a1\u30fc",
+    "\u30ed\u30fc\u30b7\u30e7\u30f3",
+    "\u30b9\u30d4\u30fc\u30ab\u30fc",
+    "\u30eb\u30fc\u30da",
+    "\u88fd\u6750\u6240",
+    "\u78c1\u6c17\u30b3\u30f3\u30d1\u30b9",
+    "\u90f5\u888b",
+    "\u30e1\u30fc\u30eb\u30dc\u30c3\u30af\u30b9",
+    "\u30de\u30a4\u30e8",
+    "\u30de\u30a4\u30e8",
+    "\u30de\u30f3\u30db\u30fc\u30eb\u306e\u84cb",
+    "\u30de\u30e9\u30ab\u30b9",
+    "\u30de\u30ea\u30f3\u30d0",
+    "\u30de\u30b9\u30af",
+    "\u30de\u30c3\u30c1\u68d2",
+    "\u30e1\u30a4\u30dd\u30fc\u30eb",
+    "\u8ff7\u8def",
+    "\u8a08\u91cf\u30ab\u30c3\u30d7",
+    "\u85ac\u7bb1",
+    "\u5de8\u77f3",
+    "\u30de\u30a4\u30af",
+    "\u30de\u30a4\u30af\u30ed\u6ce2",
+    "\u8ecd\u670d",
+    "\u30df\u30eb\u30af\u7f36",
+    "\u30df\u30cb\u30d0\u30b9",
+    "\u30df\u30cb\u30b9\u30ab\u30fc\u30c8",
+    "\u30df\u30cb\u30d0\u30f3",
+    "\u30df\u30b5\u30a4\u30eb",
+    "\u30df\u30c8\u30f3",
+    "\u30df\u30ad\u30b7\u30f3\u30b0\u30dc\u30a6\u30eb",
+    "\u79fb\u52d5\u4f4f\u5b85",
+    "\u30e2\u30c7\u30ebT",
+    "\u30e2\u30c7\u30e0",
+    "\u4fee\u9053\u9662",
+    "\u30e2\u30cb\u30bf\u30fc",
+    "\u30e2\u30da\u30c3\u30c8",
+    "\u30e2\u30eb\u30bf\u30eb",
+    "\u30e2\u30eb\u30bf\u30eb\u30dc\u30fc\u30c9",
+    "\u30e2\u30b9\u30af",
+    "\u868a\u5e33",
+    "\u30b9\u30af\u30fc\u30bf\u30fc",
+    "\u30de\u30a6\u30f3\u30c6\u30f3\u30d0\u30a4\u30af",
+    "\u5c71\u306e\u30c6\u30f3\u30c8",
+    "\u30de\u30a6\u30b9",
+    "\u30cd\u30ba\u30df\u6355\u308a",
+    "\u5f15\u3063\u8d8a\u3057\u30c8\u30e9\u30c3\u30af",
+    "\u9283\u53e3",
+    "\u30cd\u30a4\u30eb",
+    "\u30cd\u30c3\u30af\u30d6\u30ec\u30fc\u30b9",
+    "\u30cd\u30c3\u30af\u30ec\u30b9",
+    "\u4e73\u9996",
+    "\u30ce\u30fc\u30c8",
+    "\u30aa\u30d9\u30ea\u30b9\u30af",
+    "\u30aa\u30fc\u30dc\u30a8",
+    "\u30aa\u30ab\u30ea\u30ca",
+    "\u30aa\u30c9\u30e1\u30fc\u30bf\u30fc",
+    "\u30aa\u30a4\u30eb\u30d5\u30a3\u30eb\u30bf\u30fc",
+    "\u5668\u5b98",
+    "\u30aa\u30b7\u30ed\u30b9\u30b3\u30fc\u30d7",
+    "\u30aa\u30fc\u30d0\u30fc\u30b9\u30ab\u30fc\u30c8",
+    "\u725b\u8eca",
+    "\u9178\u7d20\u30de\u30b9\u30af",
+    "\u30d1\u30b1\u30c3\u30c8",
+    "\u30d1\u30c9\u30eb",
+    "\u30d1\u30c9\u30eb\u30db\u30a4\u30fc\u30eb",
+    "\u5357\u4eac\u9320",
+    "\u7d75\u7b46",
+    "\u30d1\u30b8\u30e3\u30de",
+    "\u5bae\u6bbf",
+    "\u30d1\u30f3\u30d1\u30a4\u30d7",
+    "\u30da\u30fc\u30d1\u30fc\u30bf\u30aa\u30eb",
+    "\u30d1\u30e9\u30b7\u30e5\u30fc\u30c8",
+    "\u5e73\u884c\u68d2",
+    "\u516c\u5712\u306e\u30d9\u30f3\u30c1",
+    "\u30d1\u30fc\u30ad\u30f3\u30b0\u30e1\u30fc\u30bf\u30fc",
+    "\u4e57\u7528\u8eca",
+    "\u30d1\u30c6\u30a3\u30aa",
+    "\u6709\u6599\u96fb\u8a71",
+    "\u53f0\u5ea7",
+    "\u7b46\u7bb1",
+    "\u925b\u7b46\u524a\u308a",
+    "\u9999\u6c34",
+    "\u30da\u30c8\u30ea\u76bf",
+    "\u30b3\u30d4\u30fc\u6a5f",
+    "\u9078\u3076",
+    "\u30b9\u30d1\u30a4\u30af\u4ed8\u304d\u9244\u304b\u3076\u3068",
+    "\u676d\u67f5",
+    "\u62fe\u3046",
+    "\u685f\u6a4b",
+    "\u8caf\u91d1\u7bb1",
+    "\u9320\u5264\u74f6",
+    "\u6795",
+    "\u30d4\u30f3\u30dd\u30f3\u7403",
+    "\u98a8\u8eca",
+    "\u6d77\u8cca",
+    "\u30d4\u30c3\u30c1\u30e3\u30fc",
+    "\u98db\u884c\u6a5f",
+    "\u30d7\u30e9\u30cd\u30bf\u30ea\u30a6\u30e0",
+    "\u30d3\u30cb\u30fc\u30eb\u888b",
+    "\u76bf\u7acb\u3066",
+    "\u30d7\u30e9\u30a6",
+    "\u30d7\u30e9\u30f3\u30b8\u30e3\u30fc",
+    "\u30dd\u30e9\u30ed\u30a4\u30c9\u30ab\u30e1\u30e9",
+    "\u30dd\u30fc\u30eb",
+    "\u8b66\u5bdf\u8eca",
+    "\u30dd\u30f3\u30c1\u30e7",
+    "\u30d3\u30ea\u30e4\u30fc\u30c9\u53f0",
+    "\u30dd\u30c3\u30d7\u30fb\u30dc\u30c8\u30eb",
+    "\u30dd\u30c3\u30c8",
+    "\u308d\u304f\u308d",
+    "\u30d1\u30ef\u30fc\u30c9\u30ea\u30eb",
+    "\u793c\u62dd\u7528\u6577\u7269",
+    "\u30d7\u30ea\u30f3\u30bf",
+    "\u5211\u52d9\u6240",
+    "\u767a\u5c04\u4f53",
+    "\u30d7\u30ed\u30b8\u30a7\u30af\u30bf\u30fc",
+    "\u30d1\u30c3\u30af",
+    "\u30b5\u30f3\u30c9\u30d0\u30c3\u30b0",
+    "\u8ca1\u5e03",
+    "\u30af\u30a4\u30eb",
+    "\u30ad\u30eb\u30c8",
+    "\u30ec\u30fc\u30b5\u30fc",
+    "\u30e9\u30b1\u30c3\u30c8",
+    "\u30e9\u30b8\u30a8\u30fc\u30bf\u30fc",
+    "\u7121\u7dda",
+    "\u96fb\u6ce2\u671b\u9060\u93e1",
+    "\u5929\u6c34\u6876",
+    "RV\u8eca",
+    "\u30ea\u30fc\u30eb",
+    "\u30ec\u30d5\u30ec\u30c3\u30af\u30b9\u30ab\u30e1\u30e9",
+    "\u51b7\u8535\u5eab",
+    "\u30ea\u30e2\u30b3\u30f3",
+    "\u30ec\u30b9\u30c8\u30e9\u30f3",
+    "\u30ea\u30dc\u30eb\u30d0\u30fc",
+    "\u30e9\u30a4\u30d5\u30eb",
+    "\u30ed\u30c3\u30ad\u30f3\u30b0\u30c1\u30a7\u30a2",
+    "\u713c\u8089\u6599\u7406\u5e97",
+    "\u6d88\u3057\u30b4\u30e0",
+    "\u30e9\u30b0\u30d3\u30fc\u30dc\u30fc\u30eb",
+    "\u30eb\u30fc\u30eb",
+    "\u30e9\u30f3\u30cb\u30f3\u30b0\u30b7\u30e5\u30fc\u30ba",
+    "\u5b89\u5168",
+    "\u5b89\u5168\u30d4\u30f3",
+    "\u5869\u306e\u5165\u308c\u7269",
+    "\u30b5\u30f3\u30c0\u30eb",
+    "\u30b5\u30ed\u30f3",
+    "\u30b5\u30c3\u30af\u30b9",
+    "\u9798",
+    "\u898f\u6a21",
+    "\u30b9\u30af\u30fc\u30eb\u30d0\u30b9",
+    "\u30b9\u30af\u30fc\u30ca\u30fc",
+    "\u30b9\u30b3\u30a2\u30dc\u30fc\u30c9",
+    "\u753b\u9762",
+    "\u30b9\u30af\u30ea\u30e5\u30fc",
+    "\u30c9\u30e9\u30a4\u30d0\u30fc",
+    "\u30b7\u30fc\u30c8\u30d9\u30eb\u30c8",
+    "\u30df\u30b7\u30f3",
+    "\u30b7\u30fc\u30eb\u30c9",
+    "\u9774\u5c4b",
+    "\u969c\u5b50",
+    "\u8cb7\u3044\u7269\u304b\u3054",
+    "\u30b7\u30e7\u30c3\u30d4\u30f3\u30b0\u30ab\u30fc\u30c8",
+    "\u30b7\u30e3\u30d9\u30eb",
+    "\u30b7\u30e3\u30ef\u30fc\u30ad\u30e3\u30c3\u30d7",
+    "\u30b7\u30e3\u30ef\u30fc\u30ab\u30fc\u30c6\u30f3",
+    "\u30b9\u30ad\u30fc",
+    "\u30b9\u30ad\u30fc\u30de\u30b9\u30af",
+    "\u5bdd\u888b",
+    "\u8a08\u7b97\u5c3a",
+    "\u5f15\u304d\u6238",
+    "\u30b9\u30ed\u30c3\u30c8",
+    "\u30b9\u30ce\u30fc\u30b1\u30eb",
+    "\u30b9\u30ce\u30fc\u30e2\u30fc\u30d3\u30eb",
+    "\u9664\u96ea\u6a5f",
+    "\u30bd\u30fc\u30d7\u30c7\u30a3\u30b9\u30da\u30f3\u30b5\u30fc",
+    "\u30b5\u30c3\u30ab\u30fc\u30dc\u30fc\u30eb",
+    "\u9774\u4e0b",
+    "\u592a\u967d\u306e\u76bf",
+    "\u30bd\u30f3\u30d6\u30ec\u30ed",
+    "\u30b9\u30fc\u30d7\u76bf",
+    "\u30b9\u30da\u30fc\u30b9\u30ad\u30fc",
+    "\u30b9\u30da\u30fc\u30b9\u30d2\u30fc\u30bf\u30fc",
+    "\u30b9\u30da\u30fc\u30b9\u30b7\u30e3\u30c8\u30eb",
+    "\u3078\u3089",
+    "\u30b9\u30d4\u30fc\u30c9\u30dc\u30fc\u30c8",
+    "\u30af\u30e2\u306e\u5de3",
+    "\u30b9\u30d4\u30f3\u30c9\u30eb",
+    "\u30b9\u30dd\u30fc\u30c4\u30ab\u30fc",
+    "\u30b9\u30dd\u30c3\u30c8\u30e9\u30a4\u30c8",
+    "\u30b9\u30c6\u30fc\u30b8",
+    "\u84b8\u6c17\u6a5f\u95a2\u8eca",
+    "\u92fc\u30a2\u30fc\u30c1\u6a4b",
+    "\u30b9\u30c1\u30fc\u30eb\u30c9\u30e9\u30e0",
+    "\u8074\u8a3a\u5668",
+    "\u30b9\u30c8\u30fc\u30eb",
+    "\u77f3\u57a3",
+    "\u30b9\u30c8\u30c3\u30d7\u30a6\u30a9\u30c3\u30c1",
+    "\u30ec\u30f3\u30b8",
+    "\u30b9\u30c8\u30ec\u30fc\u30ca\u30fc",
+    "\u8def\u9762\u96fb\u8eca",
+    "\u30b9\u30c8\u30ec\u30c3\u30c1\u30e3\u30fc",
+    "\u30b9\u30bf\u30b8\u30aa\u30bd\u30d5\u30a1",
+    "\u4ecf\u820e\u5229\u5854",
+    "\u6f5c\u6c34\u8266",
+    "\u30b9\u30fc\u30c4",
+    "\u65e5\u6642\u8a08",
+    "\u30b5\u30f3\u30b0\u30e9\u30b9",
+    "\u30b5\u30f3\u30b0\u30e9\u30b9",
+    "\u65e5\u713c\u3051\u6b62\u3081\u5264",
+    "\u3064\u308a\u6a4b",
+    "\u7dbf\u68d2",
+    "\u30c8\u30ec\u30fc\u30ca\u30fc",
+    "\u6d77\u30d1\u30f3",
+    "\u30b9\u30a4\u30f3\u30b0",
+    "\u30b9\u30a4\u30c3\u30c1",
+    "\u6ce8\u5c04\u5668",
+    "\u96fb\u6c17\u30b9\u30bf\u30f3\u30c9",
+    "\u30bf\u30f3\u30af",
+    "\u30c6\u30fc\u30d7\u30d7\u30ec\u30fc\u30e4\u30fc",
+    "\u30c6\u30a3\u30fc\u30dd\u30c3\u30c8",
+    "\u30c6\u30c7\u30a3",
+    "\u30c6\u30ec\u30d3",
+    "\u30c6\u30cb\u30b9\u30dc\u30fc\u30eb",
+    "\u30b5\u30c3\u30c1",
+    "\u5287\u5834\u306e\u30ab\u30fc\u30c6\u30f3",
+    "\u6307\u306c\u304d",
+    "\u8131\u7a40\u6a5f",
+    "\u738b\u4f4d",
+    "\u74e6\u5c4b\u6839",
+    "\u30c8\u30fc\u30b9\u30bf\u30fc",
+    "\u30bf\u30d0\u30b3\u5c4b",
+    "\u4fbf\u5ea7",
+    "\u30c8\u30fc\u30c1",
+    "\u30c8\u30fc\u30c6\u30e0\u30dd\u30fc\u30eb",
+    "\u30ec\u30c3\u30ab\u30fc\u8eca",
+    "\u73a9\u5177\u5c4b",
+    "\u30c8\u30e9\u30af\u30bf\u30fc",
+    "\u30c8\u30ec\u30fc\u30e9\u30fc\u30c8\u30e9\u30c3\u30af",
+    "\u30c8\u30ec\u30a4",
+    "\u30c8\u30ec\u30f3\u30c1\u30b3\u30fc\u30c8",
+    "\u4e09\u8f2a\u8eca",
+    "\u4e09\u80f4\u8239",
+    "\u4e09\u811a",
+    "\u51f1\u65cb\u9580",
+    "\u30c8\u30ed\u30ea\u30fc\u30d0\u30b9",
+    "\u30c8\u30ed\u30f3\u30dc\u30fc\u30f3",
+    "\u30d0\u30b9\u30bf\u30d6",
+    "\u56de\u8ee2\u30c9\u30a2",
+    "\u30bf\u30a4\u30d7\u30e9\u30a4\u30bf\u30fc\u306e\u30ad\u30fc\u30dc\u30fc\u30c9",
+    "\u5098",
+    "\u4e00\u8f2a\u8eca",
+    "\u76f4\u7acb",
+    "\u771f\u7a7a",
+    "\u82b1\u74f6",
+    "\u30dc\u30fc\u30eb\u30c8",
+    "\u30d9\u30eb\u30d9\u30c3\u30c8",
+    "\u81ea\u52d5\u8ca9\u58f2\u6a5f",
+    "\u796d\u670d",
+    "\u9ad8\u67b6\u6a4b",
+    "\u30d0\u30a4\u30aa\u30ea\u30f3",
+    "\u30d0\u30ec\u30fc\u30dc\u30fc\u30eb",
+    "\u30ef\u30c3\u30d5\u30eb\u713c\u304d\u578b",
+    "\u58c1\u6642\u8a08",
+    "\u8ca1\u5e03",
+    "\u30ef\u30fc\u30c9\u30ed\u30fc\u30d6",
+    "\u6226\u95d8\u6a5f",
+    "\u6d17\u9762\u5668",
+    "\u30ef\u30c3\u30b7\u30e3\u30fc",
+    "\u6c34\u7b52",
+    "\u6c34\u5dee\u3057",
+    "\u7d66\u6c34\u5854",
+    "\u30a6\u30a4\u30b9\u30ad\u30fc\u30b8\u30e3\u30b0",
+    "\u30db\u30a4\u30c3\u30b9\u30eb",
+    "\u304b\u3064\u3089",
+    "\u7a93\u7db2\u6238",
+    "\u30d6\u30e9\u30a4\u30f3\u30c9",
+    "\u30a6\u30a3\u30f3\u30b6\u30fc\u30cd\u30af\u30bf\u30a4",
+    "\u30ef\u30a4\u30f3\u30dc\u30c8\u30eb",
+    "\u7ffc",
+    "\u4e2d\u83ef\u934b",
+    "\u6728\u88fd\u30b9\u30d7\u30fc\u30f3",
+    "\u30a6\u30fc\u30eb",
+    "\u30ef\u30fc\u30e0\u30d5\u30a7\u30f3\u30b9",
+    "\u96e3\u7834\u8239",
+    "\u30e8\u30fc\u30eb",
+    "\u30d1\u30aa",
+    "\u30b5\u30a4\u30c8",
+    "\u30b3\u30df\u30c3\u30af\u30d6\u30c3\u30af",
+    "\u30af\u30ed\u30b9\u30ef\u30fc\u30c9\u30d1\u30ba\u30eb",
+    "\u9053\u8def\u6a19\u8b58",
+    "\u4ea4\u901a\u4fe1\u53f7\u706f",
+    "\u30d6\u30c3\u30af\u30ab\u30d0\u30fc",
+    "\u30e1\u30cb\u30e5\u30fc",
+    "\u30d7\u30ec\u30fc\u30c8",
+    "\u30b0\u30a2\u30ab\u30e2\u30fc\u30ec",
+    "\u30b3\u30f3\u30bd\u30e1",
+    "\u30db\u30c3\u30c8\u30dd\u30c3\u30c8",
+    "\u30d1\u30d5\u30a7",
+    "\u30a2\u30a4\u30b9\u30af\u30ea\u30fc\u30e0",
+    "\u30a2\u30a4\u30b9\u30ad\u30e3\u30f3\u30c7\u30a3\u30fc",
+    "\u30d5\u30e9\u30f3\u30b9\u30d1\u30f3",
+    "\u30d9\u30fc\u30b0\u30eb",
+    "\u30d7\u30ec\u30c3\u30c4\u30a7\u30eb",
+    "\u30c1\u30fc\u30ba\u30d0\u30fc\u30ac\u30fc",
+    "\u30db\u30c3\u30c8\u30c9\u30c3\u30b0",
+    "\u30de\u30c3\u30b7\u30e5\u30dd\u30c6\u30c8",
+    "\u30ad\u30e3\u30d9\u30c4",
+    "\u30d6\u30ed\u30c3\u30b3\u30ea\u30fc",
+    "\u30ab\u30ea\u30d5\u30e9\u30ef\u30fc",
+    "\u30ba\u30c3\u30ad\u30fc\u30cb",
+    "\u305d\u3046\u3081\u3093\u304b\u307c\u3061\u3083",
+    "\u30c9\u30f3\u30b0\u30ea\u304b\u307c\u3061\u3083",
+    "\u30ab\u30dc\u30c1\u30e3",
+    "\u30ad\u30e5\u30a6\u30ea",
+    "\u30a2\u30fc\u30c6\u30a3\u30c1\u30e7\u30fc\u30af",
+    "\u30d4\u30fc\u30de\u30f3",
+    "\u30ab\u30eb\u30c9\u30f3",
+    "\u30ad\u30ce\u30b3",
+    "\u30ea\u30f3\u30b4",
+    "\u30a4\u30c1\u30b4",
+    "\u30aa\u30ec\u30f3\u30b8",
+    "\u30ec\u30e2\u30f3",
+    "\u30a4\u30c1\u30b8\u30af",
+    "\u30d1\u30a4\u30ca\u30c3\u30d7\u30eb",
+    "\u30d0\u30ca\u30ca",
+    "\u30d1\u30e9\u30df\u30c4",
+    "\u30ab\u30b9\u30bf\u30fc\u30c9\u30a2\u30c3\u30d7\u30eb",
+    "\u30b6\u30af\u30ed",
+    "\u5e72\u3057\u8349",
+    "\u30ab\u30eb\u30dc\u30ca\u30fc\u30e9",
+    "\u30c1\u30e7\u30b3\u30ec\u30fc\u30c8\u30bd\u30fc\u30b9",
+    "\u30d1\u30f3\u751f\u5730",
+    "\u30df\u30fc\u30c8\u30ed\u30fc\u30d5",
+    "\u30d4\u30b6",
+    "\u30dd\u30c3\u30c8\u30d1\u30a4",
+    "\u30d6\u30ea\u30c8\u30fc",
+    "\u8d64\u30ef\u30a4\u30f3",
+    "\u30a8\u30b9\u30d7\u30ec\u30c3\u30bd",
+    "\u30ab\u30c3\u30d7",
+    "\u30a8\u30c3\u30b0\u30ce\u30c3\u30b0",
+    "\u30a2\u30eb\u30d7\u30b9",
+    "\u30d0\u30d6\u30eb",
+    "\u5d16",
+    "\u30b5\u30f3\u30b4\u7901",
+    "\u9593\u6b20\u6cc9",
+    "\u6e56\u7554",
+    "\u5cac",
+    "\u7802\u5dde",
+    "\u6d77\u5cb8",
+    "\u8c37",
+    "\u706b\u5c71",
+    "\u91ce\u7403\u9078\u624b",
+    "\u65b0\u90ce",
+    "\u30b9\u30ad\u30e5\u30fc\u30d0\u30c0\u30a4\u30d0\u30fc",
+    "\u83dc\u7a2e",
+    "\u30c7\u30a4\u30b8\u30fc",
+    "\u862d",
+    "\u30c8\u30a6\u30e2\u30ed\u30b3\u30b7",
+    "\u30c9\u30f3\u30b0\u30ea",
+    "\u30d2\u30c3\u30d7",
+    "\u30c8\u30c1\u30ce\u30ad",
+    "\u30b5\u30f3\u30b4\u83cc",
+    "\u30cf\u30e9\u30bf\u30b1",
+    "\u30b7\u30e3\u30b0\u30de\u30a2\u30df\u30ac\u30b5\u30bf\u30b1",
+    "\u30b9\u30c3\u30dd\u30f3\u30bf\u30b1",
+    "\u30cf\u30e9\u30bf\u30b1",
+    "\u821e\u8338",
+    "\u304d\u306e\u3053",
+    "\u8033",
+    "\u30c8\u30a4\u30ec\u30c3\u30c8\u30da\u30fc\u30d1\u30fc"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/jp_zeroshot_classification_templates.json

@@ -0,0 +1,41 @@
+{
+  "imagenet1k": [
+    "{c}\u306e\u60aa\u3044\u5199\u771f",
+    "\u591a\u304f\u306e{c}\u306e\u5199\u771f",
+    "{c}\u306e\u5f6b\u523b",
+    "\u898b\u3065\u3089\u3044{c}\u306e\u5199\u771f",
+    "{c}\u306e\u4f4e\u89e3\u50cf\u5ea6\u5199\u771f",
+    "{c}\u306e\u30ec\u30f3\u30c0\u30ea\u30f3\u30b0",
+    "{c}\u306e\u843d\u66f8\u304d",
+    "{c}\u306e\u30c8\u30ea\u30df\u30f3\u30b0\u5199\u771f",
+    "{c}\u306e\u30bf\u30c8\u30a5\u30fc",
+    "\u523a\u7e4d\u3055\u308c\u305f{c}",
+    "{c}\u306e\u660e\u308b\u3044\u5199\u771f",
+    "\u304d\u308c\u3044\u306a{c}\u306e\u5199\u771f",
+    "\u6c5a\u308c\u305f{c}\u306e\u5199\u771f",
+    "{c}\u306e\u6697\u3044\u5199\u771f",
+    "{c}\u306e\u7d75",
+    "\u79c1\u306e{c}\u306e\u5199\u771f",
+    "\u30d7\u30e9\u30b9\u30c1\u30c3\u30af\u88fd\u306e{c}",
+    "\u304b\u3063\u3053\u3044\u3044{c}\u306e\u5199\u771f",
+    "{c}\u306e\u30af\u30ed\u30fc\u30ba\u30a2\u30c3\u30d7\u5199\u771f",
+    "{c}\u306e\u767d\u9ed2\u5199\u771f",
+    "{c}\u306e\u30d4\u30af\u30bb\u30eb\u5199\u771f",
+    "jpeg\u3067\u52a0\u5de5\u3057\u305f{c}\u306e\u5199\u771f",
+    "{c}\u306e\u307c\u3084\u3051\u305f\u5199\u771f",
+    "{c}\u306e\u5199\u771f",
+    "{c}\u306e\u826f\u3044\u5199\u771f",
+    "\u30b2\u30fc\u30e0\u306b\u767b\u5834\u3059\u308b{c}",
+    "\u6298\u308a\u7d19\u3067\u4f5c\u3063\u305f{c}",
+    "{c}\u306e\u30b9\u30b1\u30c3\u30c1",
+    "\u304a\u3082\u3061\u3083\u306e{c}",
+    "{c}\u306e\u6f14\u51fa",
+    "\u5927\u304d\u306a{c}\u306e\u5199\u771f",
+    "\u7d20\u6575\u306a{c}\u306e\u5199\u771f",
+    "\u5947\u5999\u306a{c}\u306e\u5199\u771f",
+    "\u6f2b\u753b\u306e{c}",
+    "{c}\u306e\u82b8\u8853",
+    "{c}\u306e\u306c\u3044\u3050\u308b\u307f",
+    "\u5c0f\u3055\u306a{c}\u306e\u5199\u771f"
+  ]
+}

InternVL-main/clip_benchmark/clip_benchmark/datasets/kitti.py

@@ -0,0 +1,205 @@
+# Copyright 2019 Google LLC.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Implements Kitti data class."""
+
+from __future__ import absolute_import, division, print_function
+
+import numpy as np
+import task_adaptation.data.base as base
+import tensorflow.compat.v1 as tf
+import tensorflow_datasets as tfds
+from task_adaptation.registry import Registry
+
+
+def _count_all_pp(x):
+    """Count all objects."""
+    # Count distribution (thresholded at 15):
+
+    label = tf.math.minimum(tf.size(x['objects']['type']) - 1, 8)
+    return {'image': x['image'], 'label': label}
+
+
+def _count_vehicles_pp(x):
+    """Counting vehicles."""
+    # Label distribution:
+
+    vehicles = tf.where(x['objects']['type'] < 3)  # Car, Van, Truck.
+    # Cap at 3.
+    label = tf.math.minimum(tf.size(vehicles), 3)
+    return {'image': x['image'], 'label': label}
+
+
+def _count_left_pp(x):
+    """Count objects on the left hand side of the camera."""
+    # Count distribution (thresholded at 15):
+
+    # Location feature contains (x, y, z) in meters w.r.t. the camera.
+    objects_on_left = tf.where(x['objects']['location'][:, 0] < 0)
+    label = tf.math.minimum(tf.size(objects_on_left), 8)
+    return {'image': x['image'], 'label': label}
+
+
+def _count_far_pp(x):
+    """Counts objects far from the camera."""
+    # Threshold removes ~half of the objects.
+    # Count distribution (thresholded at 15):
+
+    # Location feature contains (x, y, z) in meters w.r.t. the camera.
+    distant_objects = tf.where(x['objects']['location'][:, 2] >= 25)
+    label = tf.math.minimum(tf.size(distant_objects), 8)
+    return {'image': x['image'], 'label': label}
+
+
+def _count_near_pp(x):
+    """Counts objects close to the camera."""
+    # Threshold removes ~half of the objects.
+    # Count distribution:
+
+    # Location feature contains (x, y, z) in meters w.r.t. the camera.
+    close_objects = tf.where(x['objects']['location'][:, 2] < 25)
+    label = tf.math.minimum(tf.size(close_objects), 8)
+    return {'image': x['image'], 'label': label}
+
+
+def _closest_object_distance_pp(x):
+    """Predict the distance to the closest object."""
+    # Label distribution:
+
+    # Location feature contains (x, y, z) in meters w.r.t. the camera.
+    dist = tf.reduce_min(x['objects']['location'][:, 2])
+    thrs = np.array([-100, 5.6, 8.4, 13.4, 23.4])
+    label = tf.reduce_max(tf.where((thrs - dist) < 0))
+    return {'image': x['image'], 'label': label}
+
+
+def _closest_vehicle_distance_pp(x):
+    """Predict the distance to the closest vehicle."""
+    # Label distribution:
+
+    # Location feature contains (x, y, z) in meters w.r.t. the camera.
+    vehicles = tf.where(x['objects']['type'] < 3)  # Car, Van, Truck.
+    vehicle_z = tf.gather(params=x['objects']['location'][:, 2], indices=vehicles)
+    vehicle_z = tf.concat([vehicle_z, tf.constant([[1000.0]])], axis=0)
+    dist = tf.reduce_min(vehicle_z)
+    # Results in a uniform distribution over three distances, plus one class for
+    # "no vehicle".
+    thrs = np.array([-100.0, 8.0, 20.0, 999.0])
+    label = tf.reduce_max(tf.where((thrs - dist) < 0))
+    return {'image': x['image'], 'label': label}
+
+
+def _closest_object_x_location_pp(x):
+    """Predict the absolute x position of the closest object."""
+    # Label distribution:
+
+    # Location feature contains (x, y, z) in meters w.r.t. the camera.
+    idx = tf.math.argmin(x['objects']['location'][:, 2])
+    xloc = x['objects']['location'][idx, 0]
+    thrs = np.array([-100, -6.4, -3.5, 0.0, 3.3, 23.9])
+    label = tf.reduce_max(tf.where((thrs - xloc) < 0))
+    return {'image': x['image'], 'label': label}
+
+
+_TASK_DICT = {
+    'count_all': {
+        'preprocess_fn': _count_all_pp,
+        'num_classes': 16,
+    },
+    'count_left': {
+        'preprocess_fn': _count_left_pp,
+        'num_classes': 16,
+    },
+    'count_far': {
+        'preprocess_fn': _count_far_pp,
+        'num_classes': 16,
+    },
+    'count_near': {
+        'preprocess_fn': _count_near_pp,
+        'num_classes': 16,
+    },
+    'closest_object_distance': {
+        'preprocess_fn': _closest_object_distance_pp,
+        'num_classes': 5,
+    },
+    'closest_object_x_location': {
+        'preprocess_fn': _closest_object_x_location_pp,
+        'num_classes': 5,
+    },
+    'count_vehicles': {
+        'preprocess_fn': _count_vehicles_pp,
+        'num_classes': 4,
+    },
+    'closest_vehicle_distance': {
+        'preprocess_fn': _closest_vehicle_distance_pp,
+        'num_classes': 4,
+    },
+}
+
+
+@Registry.register('data.kitti', 'class')
+class KittiData(base.ImageTfdsData):
+    """Provides Kitti dataset.
+
+    Six tasks are supported:
+      1. Count the number of objects.
+      2. Count the number of objects on the left hand side of the camera.
+      3. Count the number of objects in the foreground.
+      4. Count the number of objects in the background.
+      5. Predict the distance of the closest object.
+      6. Predict the x-location (w.r.t. the camera) of the closest object.
+    """
+
+    def __init__(self, task, data_dir=None):
+        if task not in _TASK_DICT:
+            raise ValueError('Unknown task: %s' % task)
+
+        dataset_builder = tfds.builder('kitti:3.3.0', data_dir=data_dir)
+        dataset_builder.download_and_prepare()
+
+        tfds_splits = {
+            'train': 'train',
+            'val': 'validation',
+            'trainval': 'train+validation',
+            'test': 'test',
+            'train800': 'train[:800]',
+            'val200': 'validation[:200]',
+            'train800val200': 'train[:800]+validation[:200]',
+        }
+
+        # Example counts are retrieved from the tensorflow dataset info.
+        train_count = dataset_builder.info.splits[tfds.Split.TRAIN].num_examples
+        val_count = dataset_builder.info.splits[tfds.Split.VALIDATION].num_examples
+        test_count = dataset_builder.info.splits[tfds.Split.TEST].num_examples
+        # Creates a dict with example counts for each split.
+        num_samples_splits = {
+            'train': train_count,
+            'val': val_count,
+            'trainval': train_count + val_count,
+            'test': test_count,
+            'train800': 800,
+            'val200': 200,
+            'train800val200': 1000,
+        }
+
+        task = _TASK_DICT[task]
+        base_preprocess_fn = task['preprocess_fn']
+        super(KittiData, self).__init__(
+            dataset_builder=dataset_builder,
+            tfds_splits=tfds_splits,
+            num_samples_splits=num_samples_splits,
+            num_preprocessing_threads=400,
+            shuffle_buffer_size=10000,
+            base_preprocess_fn=base_preprocess_fn,
+            num_classes=task['num_classes'])

InternVL-main/clip_benchmark/clip_benchmark/datasets/multilingual_mscoco.py

@@ -0,0 +1,95 @@
+import json
+import os
+from subprocess import call
+
+from PIL import Image
+from torchvision.datasets import VisionDataset
+
+GITHUB_MAIN_ORIGINAL_ANNOTATION_PATH = 'https://github.com/mehdidc/retrieval_annotations/releases/download/1.0.0/coco_{}_karpathy.json'
+GITHUB_MAIN_PATH = 'https://raw.githubusercontent.com/adobe-research/Cross-lingual-Test-Dataset-XTD10/main/XTD10/'
+SUPPORTED_LANGUAGES = ['es', 'it', 'ko', 'pl', 'ru', 'tr', 'zh', 'en', 'jp', 'fr']
+
+IMAGE_INDEX_FILE = 'mscoco-multilingual_index.json'
+IMAGE_INDEX_FILE_DOWNLOAD_NAME = 'test_image_names.txt'
+
+CAPTIONS_FILE_DOWNLOAD_NAME = 'test_1kcaptions_{}.txt'
+CAPTIONS_FILE_NAME = 'multilingual_mscoco_captions-{}.json'
+
+ORIGINAL_ANNOTATION_FILE_NAME = 'coco_{}_karpathy.json'
+
+
+class Multilingual_MSCOCO(VisionDataset):
+
+    def __init__(self, root, ann_file, transform=None, target_transform=None):
+        super().__init__(root, transform=transform, target_transform=target_transform)
+        self.ann_file = os.path.expanduser(ann_file)
+        with open(ann_file, 'r') as fp:
+            data = json.load(fp)
+
+        self.data = [(img_path, txt) for img_path, txt in zip(data['image_paths'], data['annotations'])]
+
+    def __getitem__(self, index):
+        img, captions = self.data[index]
+
+        # Image
+        img = Image.open(os.path.join(self.root, img)).convert('RGB')
+        if self.transform is not None:
+            img = self.transform(img)
+
+        # Captions
+        target = [captions, ]
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+
+def _get_downloadable_file(filename, download_url, is_json=True):
+    if (os.path.exists(filename) is False):
+        print('Downloading', download_url)
+        call('wget {} -O {}'.format(download_url, filename), shell=True)
+    with open(filename, 'r') as fp:
+        if (is_json):
+            return json.load(fp)
+        return [line.strip() for line in fp.readlines()]
+
+
+def create_annotation_file(root, lang_code):
+    print('Downloading multilingual_ms_coco index file')
+    download_path = os.path.join(GITHUB_MAIN_PATH, IMAGE_INDEX_FILE_DOWNLOAD_NAME)
+    save_path = os.path.join(root, 'multilingual_coco_images.txt')
+    target_images = _get_downloadable_file(save_path, download_path, False)
+
+    print('Downloading multilingual_ms_coco captions:', lang_code)
+    download_path = os.path.join(GITHUB_MAIN_PATH, CAPTIONS_FILE_DOWNLOAD_NAME.format(lang_code))
+    if lang_code == 'jp':
+        download_path = 'https://github.com/adobe-research/Cross-lingual-Test-Dataset-XTD10/raw/main/STAIR/test_1kcaptions_jp.txt'
+    if lang_code == 'fr':
+        download_path = 'https://github.com/adobe-research/Cross-lingual-Test-Dataset-XTD10/raw/main/MIC/test_1kcaptions_fr.txt'
+    save_path = os.path.join(root, 'raw_multilingual_coco_captions_{}.txt'.format(lang_code))
+    target_captions = _get_downloadable_file(save_path, download_path, False)
+
+    number_of_missing_images = 0
+    valid_images, valid_annotations, valid_indicies = [], [], []
+    for i, (img, txt) in enumerate(zip(target_images, target_captions)):
+        # Create a new file name that includes the root split
+        root_split = 'val2014' if 'val' in img else 'train2014'
+        filename_with_root_split = '{}/{}'.format(root_split, img)
+
+        if (os.path.exists(filename_with_root_split)):
+            print('Missing image file', img)
+            number_of_missing_images += 1
+            continue
+
+        valid_images.append(filename_with_root_split)
+        valid_annotations.append(txt)
+        valid_indicies.append(i)
+
+    if (number_of_missing_images > 0):
+        print('*** WARNING *** missing {} files.'.format(number_of_missing_images))
+
+    with open(os.path.join(root, CAPTIONS_FILE_NAME.format(lang_code)), 'w') as fp:
+        json.dump({'image_paths': valid_images, 'annotations': valid_annotations, 'indicies': valid_indicies}, fp)

InternVL-main/clip_benchmark/clip_benchmark/datasets/objectnet.py

@@ -0,0 +1,71 @@
+"""
+Code adapted from https://github.com/mlfoundations/wise-ft/blob/master/src/datasets/objectnet.py
+Thanks to the authors of wise-ft
+"""
+
+import json
+import os
+from pathlib import Path
+
+from torchvision import datasets
+
+
+def get_metadata(folder):
+    metadata = Path(folder)
+
+    with open(metadata / 'folder_to_objectnet_label.json', 'r') as f:
+        folder_map = json.load(f)
+        folder_map = {v: k for k, v in folder_map.items()}
+    with open(metadata / 'objectnet_to_imagenet_1k.json', 'r') as f:
+        objectnet_map = json.load(f)
+
+    with open(metadata / 'pytorch_to_imagenet_2012_id.json', 'r') as f:
+        pytorch_map = json.load(f)
+        pytorch_map = {v: k for k, v in pytorch_map.items()}
+
+    with open(metadata / 'imagenet_to_label_2012_v2', 'r') as f:
+        imagenet_map = {v.strip(): str(pytorch_map[i]) for i, v in enumerate(f)}
+
+    folder_to_ids, class_sublist = {}, []
+    classnames = []
+    for objectnet_name, imagenet_names in objectnet_map.items():
+        imagenet_names = imagenet_names.split('; ')
+        imagenet_ids = [int(imagenet_map[imagenet_name]) for imagenet_name in imagenet_names]
+        class_sublist.extend(imagenet_ids)
+        folder_to_ids[folder_map[objectnet_name]] = imagenet_ids
+
+    class_sublist = sorted(class_sublist)
+    class_sublist_mask = [(i in class_sublist) for i in range(1000)]
+    classname_map = {v: k for k, v in folder_map.items()}
+    return class_sublist, class_sublist_mask, folder_to_ids, classname_map
+
+
+class ObjectNetDataset(datasets.ImageFolder):
+
+    def __init__(self, root, transform):
+        (self._class_sublist,
+         self.class_sublist_mask,
+         self.folders_to_ids,
+         self.classname_map) = get_metadata(root)
+        subdir = os.path.join(root, 'objectnet-1.0', 'images')
+        label_map = {name: idx for idx, name in enumerate(sorted(list(self.folders_to_ids.keys())))}
+        self.label_map = label_map
+        super().__init__(subdir, transform=transform)
+        self.samples = [
+            d for d in self.samples
+            if os.path.basename(os.path.dirname(d[0])) in self.label_map
+        ]
+        self.imgs = self.samples
+        self.classes = sorted(list(self.folders_to_ids.keys()))
+        self.classes = [self.classname_map[c].lower() for c in self.classes]
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, index):
+        path, target = self.samples[index]
+        sample = self.loader(path)
+        if self.transform is not None:
+            sample = self.transform(sample)
+        label = os.path.basename(os.path.dirname(path))
+        return sample, self.label_map[label]

InternVL-main/clip_benchmark/clip_benchmark/datasets/tfds.py

@@ -0,0 +1,51 @@
+import torch
+from PIL import Image
+
+
+def download_tfds_dataset(name, data_dir=None):
+    import tensorflow_datasets as tfds
+    builder = tfds.builder(name, data_dir=data_dir)
+    builder.download_and_prepare()
+
+
+def disable_gpus_on_tensorflow():
+    import tensorflow as tf
+    tf.config.set_visible_devices([], 'GPU')
+
+
+class VTABIterableDataset(torch.utils.data.IterableDataset):
+
+    def __init__(self, tfds_dataset, split='test', input_name='image', label_name='label', input_mode='RGB',
+                 transform=None, target_transform=None, classes=None):
+        self.tfds_dataset = tfds_dataset
+        self.input_name = input_name
+        self.label_name = label_name
+        self.transform = transform
+        self.target_transform = target_transform
+        self.input_mode = input_mode
+        self.num_examples = tfds_dataset.get_num_samples(split)
+        self.split = split
+        if classes is None:
+            self.classes = tfds_dataset._dataset_builder.info.features['label'].names
+        else:
+            self.classes = classes
+
+    def __iter__(self):
+        worker_info = torch.utils.data.get_worker_info()
+        iterator = self.tfds_dataset.get_tf_data(self.split, batch_size=1, epochs=1, for_eval=True)
+        if worker_info is not None:
+            iterator = iterator.shard(index=worker_info.id, num_shards=worker_info.num_workers)
+        nb = 0
+        for data in iterator:
+            inputs = (data[self.input_name].numpy())
+            labels = data[self.label_name].numpy()
+            for input, label in zip(inputs, labels):
+                input = Image.fromarray(input, mode=self.input_mode)
+                if self.transform is not None:
+                    input = self.transform(input)
+                if self.target_transform is not None:
+                    label = self.target_transform(label)
+                yield input, label
+
+    def __len__(self):
+        return self.num_examples

InternVL-main/clip_benchmark/clip_benchmark/datasets/tools.py

@@ -0,0 +1,22 @@
+import re
+
+
+def process_single_caption(caption, max_words=50):
+    caption = re.sub(r"([.!\"()*#:;~])", ' ', caption.lower())
+    caption = re.sub(r'\s{2,}', ' ', caption)
+    caption = caption.rstrip('\n')
+    caption = caption.strip(' ')
+
+    # truncate caption
+    caption_words = caption.split(' ')
+    if len(caption_words) > max_words:
+        caption = ' '.join(caption_words[: max_words])
+    return caption
+
+
+def pre_caption(caption, max_words=50):
+    if type(caption) == str:
+        caption = process_single_caption(caption, max_words)
+    else:
+        caption = [process_single_caption(c, max_words) for c in caption]
+    return caption

InternVL-main/clip_benchmark/clip_benchmark/datasets/voc2007.py

@@ -0,0 +1,251 @@
+# Code from https://github.com/SsnL/dataset-distillation/blob/master/datasets/pascal_voc.py , thanks to the authors
+"""Dataset setting and data loader for PASCAL VOC 2007 as a classification task.
+
+Modified from
+https://github.com/Cadene/pretrained-models.pytorch/blob/56aa8c921819d14fb36d7248ab71e191b37cb146/pretrainedmodels/datasets/voc.py
+"""
+
+import os
+import os.path
+import tarfile
+import xml.etree.ElementTree as ET
+from urllib.parse import urlparse
+
+import torch
+import torch.utils.data as data
+import torchvision
+from PIL import Image
+
+object_categories = ['aeroplane', 'bicycle', 'bird', 'boat',
+                     'bottle', 'bus', 'car', 'cat', 'chair',
+                     'cow', 'diningtable', 'dog', 'horse',
+                     'motorbike', 'person', 'pottedplant',
+                     'sheep', 'sofa', 'train', 'tvmonitor']
+
+category_to_idx = {c: i for i, c in enumerate(object_categories)}
+
+urls = {
+    'devkit': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar',
+    'trainval_2007': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',
+    'test_images_2007': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',
+    'test_anno_2007': 'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtestnoimgs_06-Nov-2007.tar',
+}
+
+
+def download_url(url, path):
+    root, filename = os.path.split(path)
+    torchvision.datasets.utils.download_url(url, root=root, filename=filename, md5=None)
+
+
+def download_voc2007(root):
+    path_devkit = os.path.join(root, 'VOCdevkit')
+    path_images = os.path.join(root, 'VOCdevkit', 'VOC2007', 'JPEGImages')
+    tmpdir = os.path.join(root, 'tmp')
+
+    # create directory
+    if not os.path.exists(root):
+        os.makedirs(root)
+
+    if not os.path.exists(path_devkit):
+
+        if not os.path.exists(tmpdir):
+            os.makedirs(tmpdir)
+
+        parts = urlparse(urls['devkit'])
+        filename = os.path.basename(parts.path)
+        cached_file = os.path.join(tmpdir, filename)
+
+        if not os.path.exists(cached_file):
+            download_url(urls['devkit'], cached_file)
+
+        # extract file
+        print('[dataset] Extracting tar file {file} to {path}'.format(file=cached_file, path=root))
+        cwd = os.getcwd()
+        tar = tarfile.open(cached_file, 'r')
+        os.chdir(root)
+        tar.extractall()
+        tar.close()
+        os.chdir(cwd)
+        print('[dataset] Done!')
+
+    # train/val images/annotations
+    if not os.path.exists(path_images):
+
+        # download train/val images/annotations
+        parts = urlparse(urls['trainval_2007'])
+        filename = os.path.basename(parts.path)
+        cached_file = os.path.join(tmpdir, filename)
+
+        if not os.path.exists(cached_file):
+            download_url(urls['trainval_2007'], cached_file)
+
+        # extract file
+        print('[dataset] Extracting tar file {file} to {path}'.format(file=cached_file, path=root))
+        cwd = os.getcwd()
+        tar = tarfile.open(cached_file, 'r')
+        os.chdir(root)
+        tar.extractall()
+        tar.close()
+        os.chdir(cwd)
+        print('[dataset] Done!')
+
+    # test annotations
+    test_anno = os.path.join(path_devkit, 'VOC2007/ImageSets/Main/aeroplane_test.txt')
+    if not os.path.exists(test_anno):
+
+        # download test annotations
+        parts = urlparse(urls['test_images_2007'])
+        filename = os.path.basename(parts.path)
+        cached_file = os.path.join(tmpdir, filename)
+
+        if not os.path.exists(cached_file):
+            download_url(urls['test_images_2007'], cached_file)
+
+        # extract file
+        print('[dataset] Extracting tar file {file} to {path}'.format(file=cached_file, path=root))
+        cwd = os.getcwd()
+        tar = tarfile.open(cached_file, 'r')
+        os.chdir(root)
+        tar.extractall()
+        tar.close()
+        os.chdir(cwd)
+        print('[dataset] Done!')
+
+    # test images
+    test_image = os.path.join(path_devkit, 'VOC2007/JPEGImages/000001.jpg')
+    if not os.path.exists(test_image):
+
+        # download test images
+        parts = urlparse(urls['test_anno_2007'])
+        filename = os.path.basename(parts.path)
+        cached_file = os.path.join(tmpdir, filename)
+
+        if not os.path.exists(cached_file):
+            download_url(urls['test_anno_2007'], cached_file)
+
+        # extract file
+        print('[dataset] Extracting tar file {file} to {path}'.format(file=cached_file, path=root))
+        cwd = os.getcwd()
+        tar = tarfile.open(cached_file, 'r')
+        os.chdir(root)
+        tar.extractall()
+        tar.close()
+        os.chdir(cwd)
+        print('[dataset] Done!')
+
+
+def read_split(root, dataset, split):
+    base_path = os.path.join(root, 'VOCdevkit', dataset, 'ImageSets', 'Main')
+    filename = os.path.join(base_path, object_categories[0] + '_' + split + '.txt')
+
+    with open(filename, 'r') as f:
+        paths = []
+        for line in f.readlines():
+            line = line.strip().split()
+            if len(line) > 0:
+                assert len(line) == 2
+                paths.append(line[0])
+
+        return tuple(paths)
+
+
+def read_bndbox(root, dataset, paths):
+    xml_base = os.path.join(root, 'VOCdevkit', dataset, 'Annotations')
+    instances = []
+    for path in paths:
+        xml = ET.parse(os.path.join(xml_base, path + '.xml'))
+        for obj in xml.findall('object'):
+            c = obj[0]
+            assert c.tag == 'name', c.tag
+            c = category_to_idx[c.text]
+            bndbox = obj.find('bndbox')
+            xmin = int(bndbox[0].text)  # left
+            ymin = int(bndbox[1].text)  # top
+            xmax = int(bndbox[2].text)  # right
+            ymax = int(bndbox[3].text)  # bottom
+            instances.append((path, (xmin, ymin, xmax, ymax), c))
+    return instances
+
+
+class PASCALVoc2007(data.Dataset):
+    """
+    Multi-label classification problem for voc2007
+    labels are of one hot of shape (C,), denoting the presence/absence
+    of each class in each image, where C is the number of classes.
+    """
+
+    def __init__(self, root, set, transform=None, download=False, target_transform=None):
+        self.root = root
+        self.path_devkit = os.path.join(root, 'VOCdevkit')
+        self.path_images = os.path.join(root, 'VOCdevkit', 'VOC2007', 'JPEGImages')
+        self.transform = transform
+        self.target_transform = target_transform
+
+        # download dataset
+        if download:
+            download_voc2007(self.root)
+
+        paths = read_split(self.root, 'VOC2007', set)
+        bndboxes = read_bndbox(self.root, 'VOC2007', paths)
+        labels = torch.zeros(len(paths), len(object_categories))
+        path_index = {}
+        for i, p in enumerate(paths):
+            path_index[p] = i
+        for path, bbox, c in bndboxes:
+            labels[path_index[path], c] = 1
+        self.labels = labels
+        self.classes = object_categories
+        self.paths = paths
+
+    def __getitem__(self, index):
+        path = self.paths[index]
+        img = Image.open(os.path.join(self.path_images, path + '.jpg')).convert('RGB')
+        target = self.labels[index]
+        if self.transform is not None:
+            img = self.transform(img)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+        return img, target
+
+    def __len__(self):
+        return len(self.paths)
+
+
+class PASCALVoc2007Cropped(data.Dataset):
+    """
+    voc2007 is originally object detection and multi-label.
+    In this version, we just convert it to single-label per image classification
+    problem by looping over bounding boxes in the dataset and cropping the relevant
+    object.
+    """
+
+    def __init__(self, root, set, transform=None, download=False, target_transform=None):
+        self.root = root
+        self.path_devkit = os.path.join(root, 'VOCdevkit')
+        self.path_images = os.path.join(root, 'VOCdevkit', 'VOC2007', 'JPEGImages')
+        self.transform = transform
+        self.target_transform = target_transform
+
+        # download dataset
+        if download:
+            download_voc2007(self.root)
+
+        paths = read_split(self.root, 'VOC2007', set)
+        self.bndboxes = read_bndbox(self.root, 'VOC2007', paths)
+        self.classes = object_categories
+
+        print('[dataset] VOC 2007 classification set=%s number of classes=%d  number of bndboxes=%d' % (
+            set, len(self.classes), len(self.bndboxes)))
+
+    def __getitem__(self, index):
+        path, crop, target = self.bndboxes[index]
+        img = Image.open(os.path.join(self.path_images, path + '.jpg')).convert('RGB')
+        img = img.crop(crop)
+        if self.transform is not None:
+            img = self.transform(img)
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+        return img, target
+
+    def __len__(self):
+        return len(self.bndboxes)

InternVL-main/clip_benchmark/clip_benchmark/models/intern_vit_6b/configuration_intern_vit.py

@@ -0,0 +1,117 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class InternVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
+    instantiate a vision encoder according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of color channels in the input images (e.g., 3 for RGB).
+        patch_size (`int`, *optional*, defaults to 14):
+            The size (resolution) of each patch.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to add a bias to the queries and values in the self-attention layers.
+        hidden_size (`int`, *optional*, defaults to 3200):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_attention_heads (`int`, *optional*, defaults to 25):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 12800):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        qk_normalization (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the queries and keys in the self-attention layers.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        use_flash_attn (`bool`, *optional*, defaults to `True`):
+            Whether to use flash attention mechanism.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Dropout rate for stochastic depth.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_factor (`float`, *optional*, defaults to 0.1):
+            A factor for layer scale.
+    """
+
+    model_type = 'intern_vit_6b'
+
+    def __init__(
+            self,
+            num_channels=3,
+            patch_size=14,
+            image_size=224,
+            qkv_bias=False,
+            hidden_size=3200,
+            num_attention_heads=25,
+            intermediate_size=12800,
+            qk_normalization=True,
+            num_hidden_layers=48,
+            use_flash_attn=True,
+            hidden_act='gelu',
+            layer_norm_eps=1e-6,
+            dropout=0.0,
+            drop_path_rate=0.0,
+            attention_dropout=0.0,
+            initializer_range=0.02,
+            initializer_factor=0.1,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if 'vision_config' in config_dict:
+            config_dict = config_dict['vision_config']
+
+        if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
+            )
+
+        return cls.from_dict(config_dict, **kwargs)

InternVL-main/clip_benchmark/clip_benchmark/models/intern_vit_6b/flash_attention.py

@@ -0,0 +1,75 @@
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+try:  # v1
+    from flash_attn.flash_attn_interface import \
+        flash_attn_unpadded_qkvpacked_func
+except:  # v2
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
+
+from flash_attn.bert_padding import pad_input, unpad_input
+
+
+class FlashAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
+                max_s=None, need_weights=False):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+                if unpadded: (nnz, 3, h, d)
+            key_padding_mask: a bool tensor of shape (B, S)
+        """
+        assert not need_weights
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        if cu_seqlens is None:
+            batch_size = qkv.shape[0]
+            seqlen = qkv.shape[1]
+            if key_padding_mask is None:
+                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
+                max_s = seqlen
+                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
+                                          device=qkv.device)
+                output = flash_attn_unpadded_qkvpacked_func(
+                    qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+            else:
+                nheads = qkv.shape[-2]
+                x = rearrange(qkv, 'b s three h d -> b s (three h d)')
+                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+                x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
+                output_unpad = flash_attn_unpadded_qkvpacked_func(
+                    x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
+                                             indices, batch_size, seqlen),
+                                   'b s (h d) -> b s h d', h=nheads)
+        else:
+            assert max_s is not None
+            output = flash_attn_unpadded_qkvpacked_func(
+                qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                softmax_scale=self.softmax_scale, causal=causal
+            )
+
+        return output, None

InternVL-main/clip_benchmark/clip_benchmark/models/intern_vit_6b/modeling_intern_vit.py

@@ -0,0 +1,342 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from timm.models.layers import DropPath
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutput,
+                                           BaseModelOutputWithPooling)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+try:
+    from .flash_attention import FlashAttention
+    has_flash_attn = True
+except:
+    print('FlashAttention is not installed.')
+    has_flash_attn = False
+
+
+logger = logging.get_logger(__name__)
+
+
+class InternRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+try:
+    from apex.normalization import FusedRMSNorm
+
+    InternRMSNorm = FusedRMSNorm  # noqa
+
+    logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
+except ImportError:
+    # using the normal InternRMSNorm
+    pass
+except Exception:
+    logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
+    pass
+
+
+class InternVisionEmbeddings(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(
+            torch.randn(1, 1, self.embed_dim),
+        )
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+
+        self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        batch_size = pixel_values.shape[0]
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        embeddings = embeddings + self.position_embedding.to(target_dtype)
+        return embeddings
+
+
+class InternAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_flash_attn = config.use_flash_attn and has_flash_attn
+        if config.use_flash_attn and not has_flash_attn:
+            print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
+                f' {self.num_heads}).'
+            )
+
+        self.scale = self.head_dim ** -0.5
+        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
+        self.attn_drop = nn.Dropout(config.attention_dropout)
+        self.proj_drop = nn.Dropout(config.dropout)
+
+        self.qk_normalization = config.qk_normalization
+
+        if self.qk_normalization:
+            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        if self.use_flash_attn:
+            self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
+        self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def _naive_attn(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        if self.qk_normalization:
+            B_, H_, N_, D_ = q.shape
+            q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+            k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+
+        attn = ((q * self.scale) @ k.transpose(-2, -1))
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
+        qkv = self.qkv(x)
+        qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
+
+        if self.qk_normalization:
+            q, k, v = qkv.unbind(2)
+            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
+            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
+            qkv = torch.stack([q, k, v], dim=2)
+
+        context, _ = self.inner_attn(
+            qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
+        )
+        outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
+        return x
+
+
+class InternMLP(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.act = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class InternVisionEncoderLayer(nn.Module):
+    def __init__(self, config: InternVisionConfig, drop_path_rate: float):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        self.attn = InternAttention(config)
+        self.mlp = InternMLP(config)
+        self.norm1 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.norm2 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+        self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
+        """
+        hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
+
+        hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
+
+        return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`InternEncoderLayer`].
+
+    Args:
+        config (`InternConfig`):
+            The corresponding vision configuration for the `InternEncoder`.
+    """
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+        self.layers = nn.ModuleList([
+            InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = True
+
+    def forward(
+            self,
+            inputs_embeds,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Embedded representation of the inputs. Should be float, not int tokens.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_states = () if output_hidden_states else None
+        hidden_states = inputs_embeds
+
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    encoder_layer,
+                    hidden_states)
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                )
+            hidden_states = layer_outputs
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states
+        )
+
+
+class InternVisionModel(PreTrainedModel):
+    main_input_name = 'pixel_values'
+    config_class = InternVisionConfig
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = InternVisionEmbeddings(config)
+        self.encoder = InternVisionEncoder(config)
+
+    def resize_pos_embeddings(self, old_size, new_size, patch_size):
+        pos_emb = self.embeddings.position_embedding
+        _, num_positions, embed_dim = pos_emb.shape
+        cls_emb = pos_emb[:, :1, :]
+        pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
+        pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
+        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
+        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
+        self.embeddings.position_embedding = nn.Parameter(pos_emb)
+        logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def forward(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            pixel_embeds: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None and pixel_embeds is None:
+            raise ValueError('You have to specify pixel_values or pixel_embeds')
+
+        if pixel_embeds is not None:
+            hidden_states = pixel_embeds
+        else:
+            if len(pixel_values.shape) == 4:
+                hidden_states = self.embeddings(pixel_values)
+            else:
+                raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = last_hidden_state[:, 0, :]
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

InternVL-main/clip_benchmark/clip_benchmark/models/internvl_huggingface/configuration_intern_vit.py

@@ -0,0 +1,117 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class InternVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
+    instantiate a vision encoder according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of color channels in the input images (e.g., 3 for RGB).
+        patch_size (`int`, *optional*, defaults to 14):
+            The size (resolution) of each patch.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to add a bias to the queries and values in the self-attention layers.
+        hidden_size (`int`, *optional*, defaults to 3200):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_attention_heads (`int`, *optional*, defaults to 25):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 12800):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        qk_normalization (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the queries and keys in the self-attention layers.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        use_flash_attn (`bool`, *optional*, defaults to `True`):
+            Whether to use flash attention mechanism.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Dropout rate for stochastic depth.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_factor (`float`, *optional*, defaults to 0.1):
+            A factor for layer scale.
+    """
+
+    model_type = 'intern_vit_6b'
+
+    def __init__(
+            self,
+            num_channels=3,
+            patch_size=14,
+            image_size=224,
+            qkv_bias=False,
+            hidden_size=3200,
+            num_attention_heads=25,
+            intermediate_size=12800,
+            qk_normalization=True,
+            num_hidden_layers=48,
+            use_flash_attn=True,
+            hidden_act='gelu',
+            layer_norm_eps=1e-6,
+            dropout=0.0,
+            drop_path_rate=0.0,
+            attention_dropout=0.0,
+            initializer_range=0.02,
+            initializer_factor=0.1,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if 'vision_config' in config_dict:
+            config_dict = config_dict['vision_config']
+
+        if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
+            )
+
+        return cls.from_dict(config_dict, **kwargs)

InternVL-main/clip_benchmark/clip_benchmark/models/internvl_huggingface/flash_attention.py

@@ -0,0 +1,76 @@
+# https://github.com/Dao-AILab/flash-attention/blob/v0.2.8/flash_attn/flash_attention.py
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+try:  # v1
+    from flash_attn.flash_attn_interface import \
+        flash_attn_unpadded_qkvpacked_func
+except:  # v2
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func
+
+from flash_attn.bert_padding import pad_input, unpad_input
+
+
+class FlashAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
+                max_s=None, need_weights=False):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+                if unpadded: (nnz, 3, h, d)
+            key_padding_mask: a bool tensor of shape (B, S)
+        """
+        assert not need_weights
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        if cu_seqlens is None:
+            batch_size = qkv.shape[0]
+            seqlen = qkv.shape[1]
+            if key_padding_mask is None:
+                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
+                max_s = seqlen
+                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
+                                          device=qkv.device)
+                output = flash_attn_unpadded_qkvpacked_func(
+                    qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+            else:
+                nheads = qkv.shape[-2]
+                x = rearrange(qkv, 'b s three h d -> b s (three h d)')
+                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+                x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
+                output_unpad = flash_attn_unpadded_qkvpacked_func(
+                    x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
+                                             indices, batch_size, seqlen),
+                                   'b s (h d) -> b s h d', h=nheads)
+        else:
+            assert max_s is not None
+            output = flash_attn_unpadded_qkvpacked_func(
+                qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                softmax_scale=self.softmax_scale, causal=causal
+            )
+
+        return output, None

InternVL-main/clip_benchmark/clip_benchmark/models/internvl_huggingface/modeling_intern_vit.py

@@ -0,0 +1,342 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2023 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from timm.models.layers import DropPath
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutput,
+                                           BaseModelOutputWithPooling)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+try:
+    from .flash_attention import FlashAttention
+    has_flash_attn = True
+except:
+    print('FlashAttention is not installed.')
+    has_flash_attn = False
+
+
+logger = logging.get_logger(__name__)
+
+
+class InternRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+try:
+    from apex.normalization import FusedRMSNorm
+
+    InternRMSNorm = FusedRMSNorm  # noqa
+
+    logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
+except ImportError:
+    # using the normal InternRMSNorm
+    pass
+except Exception:
+    logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
+    pass
+
+
+class InternVisionEmbeddings(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(
+            torch.randn(1, 1, self.embed_dim),
+        )
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+
+        self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        batch_size = pixel_values.shape[0]
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, width, grid, grid]
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        embeddings = embeddings + self.position_embedding.to(target_dtype)
+        return embeddings
+
+
+class InternAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_flash_attn = config.use_flash_attn and has_flash_attn
+        if config.use_flash_attn and not has_flash_attn:
+            print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
+                f' {self.num_heads}).'
+            )
+
+        self.scale = self.head_dim ** -0.5
+        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
+        self.attn_drop = nn.Dropout(config.attention_dropout)
+        self.proj_drop = nn.Dropout(config.dropout)
+
+        self.qk_normalization = config.qk_normalization
+
+        if self.qk_normalization:
+            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        if self.use_flash_attn:
+            self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
+        self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def _naive_attn(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        if self.qk_normalization:
+            B_, H_, N_, D_ = q.shape
+            q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+            k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+
+        attn = ((q * self.scale) @ k.transpose(-2, -1))
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
+        qkv = self.qkv(x)
+        qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
+
+        if self.qk_normalization:
+            q, k, v = qkv.unbind(2)
+            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
+            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
+            qkv = torch.stack([q, k, v], dim=2)
+
+        context, _ = self.inner_attn(
+            qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
+        )
+        outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
+        return x
+
+
+class InternMLP(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.act = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class InternVisionEncoderLayer(nn.Module):
+    def __init__(self, config: InternVisionConfig, drop_path_rate: float):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        self.attn = InternAttention(config)
+        self.mlp = InternMLP(config)
+        self.norm1 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.norm2 = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+        self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
+        """
+        hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states)) * self.ls1)
+
+        hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states)) * self.ls2)
+
+        return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`InternEncoderLayer`].
+
+    Args:
+        config (`InternConfig`):
+            The corresponding vision configuration for the `InternEncoder`.
+    """
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+        self.layers = nn.ModuleList([
+            InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = True
+
+    def forward(
+            self,
+            inputs_embeds,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Embedded representation of the inputs. Should be float, not int tokens.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_states = () if output_hidden_states else None
+        hidden_states = inputs_embeds
+
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    encoder_layer,
+                    hidden_states)
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                )
+            hidden_states = layer_outputs
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states
+        )
+
+
+class InternVisionModel(PreTrainedModel):
+    main_input_name = 'pixel_values'
+    config_class = InternVisionConfig
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = InternVisionEmbeddings(config)
+        self.encoder = InternVisionEncoder(config)
+
+    def resize_pos_embeddings(self, old_size, new_size, patch_size):
+        pos_emb = self.embeddings.position_embedding
+        _, num_positions, embed_dim = pos_emb.shape
+        cls_emb = pos_emb[:, :1, :]
+        pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
+        pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
+        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
+        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
+        self.embeddings.position_embedding = nn.Parameter(pos_emb)
+        logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def forward(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            pixel_embeds: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None and pixel_embeds is None:
+            raise ValueError('You have to specify pixel_values or pixel_embeds')
+
+        if pixel_embeds is not None:
+            hidden_states = pixel_embeds
+        else:
+            if len(pixel_values.shape) == 4:
+                hidden_states = self.embeddings(pixel_values)
+            else:
+                raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = last_hidden_state[:, 0, :]
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

InternVL/InternVL_2_5/.gitattributes

@@ -0,0 +1,37 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/red-panda.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/image2.jpg filter=lfs diff=lfs merge=lfs -text

InternVL/InternVL_2_5/README.md

@@ -0,0 +1,677 @@
+---
+license: mit
+pipeline_tag: image-text-to-text
+library_name: transformers
+base_model:
+  - OpenGVLab/InternViT-300M-448px-V2_5
+  - internlm/internlm2_5-1_8b-chat
+base_model_relation: merge
+language:
+  - multilingual
+tags:
+  - internvl
+  - custom_code
+datasets:
+  - HuggingFaceFV/finevideo
+---
+
+# InternVL2_5-2B
+
+[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL)  [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238)  [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821)  [\[📜 Mini-InternVL\]](https://arxiv.org/abs/2410.16261)  [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271)
+
+[\[🆕 Blog\]](https://internvl.github.io/blog/)  [\[🗨️ Chat Demo\]](https://internvl.opengvlab.com/)  [\[🤗 HF Demo\]](https://huggingface.co/spaces/OpenGVLab/InternVL)  [\[🚀 Quick Start\]](#quick-start)  [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/)
+
+<div align="center">
+  <img width="500" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/zJsd2hqd3EevgXo6fNgC-.png">
+</div>
+
+## Introduction
+
+We are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/5HDAGOQOZvS1EtI107Ac-.png)
+
+## InternVL 2.5 Family
+
+In the following table, we provide an overview of the InternVL 2.5 series.
+
+|   Model Name    |                                       Vision Part                                       |                                 Language Part                                  |                           HF Link                           |
+| :-------------: | :-------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :---------------------------------------------------------: |
+| InternVL2_5-1B  | [InternViT-300M-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-300M-448px-V2_5) |   [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)   | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2_5-1B)  |
+| InternVL2_5-2B  | [InternViT-300M-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-300M-448px-V2_5) | [internlm2_5-1_8b-chat](https://huggingface.co/internlm/internlm2_5-1_8b-chat) | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2_5-2B)  |
+| InternVL2_5-4B  | [InternViT-300M-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-300M-448px-V2_5) |     [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)     | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2_5-4B)  |
+| InternVL2_5-8B  | [InternViT-300M-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-300M-448px-V2_5) |   [internlm2_5-7b-chat](https://huggingface.co/internlm/internlm2_5-7b-chat)   | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2_5-8B)  |
+| InternVL2_5-26B |   [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5)   |  [internlm2_5-20b-chat](https://huggingface.co/internlm/internlm2_5-20b-chat)  | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2_5-26B) |
+| InternVL2_5-38B |   [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5)   |    [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)    | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2_5-38B) |
+| InternVL2_5-78B |   [InternViT-6B-448px-V2_5](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V2_5)   |    [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)    | [🤗 link](https://huggingface.co/OpenGVLab/InternVL2_5-78B) |
+
+## Model Architecture
+
+As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/BiiyXN6NOk0p-3rl3ueyL.png)
+
+As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.
+
+## Training Strategy
+
+### Dynamic High-Resolution for Multimodal Data
+
+In InternVL 2.0 and 2.5, we extend the dynamic high-resolution training approach, enhancing its capabilities to handle multi-image and video datasets.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/xoMY6rwRrNxbAGYPNyU8g.png)
+
+- For single-image datasets, the total number of tiles `n_max` are allocated to a single image for maximum resolution. Visual tokens are enclosed in `<img>` and `</img>` tags.
+
+- For multi-image datasets, the total number of tiles `n_max` are distributed across all images in a sample. Each image is labeled with auxiliary tags like `Image-1` and enclosed in `<img>` and `</img>` tags.
+
+- For videos, each frame is resized to 448×448. Frames are labeled with tags like `Frame-1` and enclosed in `<img>` and `</img>` tags, similar to images.
+
+### Single Model Training Pipeline
+
+The training pipeline for a single model in InternVL 2.5 is structured across three stages, designed to enhance the model's visual perception and multimodal capabilities.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/5NduZeCPLgPJTFr0RGTq3.png)
+
+- **Stage 1: MLP Warmup.** In this stage, only the MLP projector is trained while the vision encoder and language model are frozen. A dynamic high-resolution training strategy is applied for better performance, despite increased cost. This phase ensures robust cross-modal alignment and prepares the model for stable multimodal training.
+
+- **Stage 1.5: ViT Incremental Learning (Optional).** This stage allows incremental training of the vision encoder and MLP projector using the same data as Stage 1. It enhances the encoder’s ability to handle rare domains like multilingual OCR and mathematical charts. Once trained, the encoder can be reused across LLMs without retraining, making this stage optional unless new domains are introduced.
+
+- **Stage 2: Full Model Instruction Tuning.** The entire model is trained on high-quality multimodal instruction datasets. Strict data quality controls are enforced to prevent degradation of the LLM, as noisy data can cause issues like repetitive or incorrect outputs. After this stage, the training process is complete.
+
+### Progressive Scaling Strategy
+
+We introduce a progressive scaling strategy to align the vision encoder with LLMs efficiently. This approach trains with smaller LLMs first (e.g., 20B) to optimize foundational visual capabilities and cross-modal alignment before transferring the vision encoder to larger LLMs (e.g., 72B) without retraining. This reuse skips intermediate stages for larger models.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/UoNUyS7ctN5pBxNv9KnzH.png)
+
+Compared to Qwen2-VL's 1.4 trillion tokens, InternVL2.5-78B uses only 120 billion tokens—less than one-tenth. This strategy minimizes redundancy, maximizes pre-trained component reuse, and enables efficient training for complex vision-language tasks.
+
+### Training Enhancements
+
+To improve real-world adaptability and performance, we introduce two key techniques:
+
+- **Random JPEG Compression**: Random JPEG compression with quality levels between 75 and 100 is applied as a data augmentation technique. This simulates image degradation from internet sources, enhancing the model's robustness to noisy images.
+
+- **Loss Reweighting**: To balance the NTP loss across responses of different lengths, we use a reweighting strategy called **square averaging**. This method balances contributions from responses of varying lengths, mitigating biases toward longer or shorter responses.
+
+### Data Organization
+
+#### Dataset Configuration
+
+In InternVL 2.0 and 2.5, the organization of the training data is controlled by several key parameters to optimize the balance and distribution of datasets during training.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/2LJe24b1ua3gjI9gDitVl.png)
+
+- **Data Augmentation:** JPEG compression is applied conditionally: enabled for image datasets to enhance robustness and disabled for video datasets to maintain consistent frame quality.
+
+- **Maximum Tile Number:** The parameter `n_max` controls the maximum tiles per dataset. For example, higher values (24–36) are used for multi-image or high-resolution data, lower values (6–12) for standard images, and 1 for videos.
+
+- **Repeat Factor:** The repeat factor `r` adjusts dataset sampling frequency. Values below 1 reduce a dataset's weight, while values above 1 increase it. This ensures balanced training across tasks and prevents overfitting or underfitting.
+
+#### Data Filtering Pipeline
+
+During development, we found that LLMs are highly sensitive to data noise, with even small anomalies—like outliers or repetitive data—causing abnormal behavior during inference. Repetitive generation, especially in long-form or CoT reasoning tasks, proved particularly harmful.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/aka8ZRiKF3ajdyZBnNFZI.png)
+
+To address this challenge and support future research, we designed an efficient data filtering pipeline to remove low-quality samples.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/70l1UxnX-Arn0NoOGwpth.png)
+
+The pipeline includes two modules, for **pure-text data**, three key strategies are used:
+
+1. **LLM-Based Quality Scoring**: Each sample is scored (0–10) using a pre-trained LLM with domain-specific prompts. Samples scoring below a threshold (e.g., 7) are removed to ensure high-quality data.
+2. **Repetition Detection**: Repetitive samples are flagged using LLM-based prompts and manually reviewed. Samples scoring below a stricter threshold (e.g., 3) are excluded to avoid repetitive patterns.
+3. **Heuristic Rule-Based Filtering**: Anomalies like abnormal sentence lengths or duplicate lines are detected using rules. Flagged samples undergo manual verification to ensure accuracy before removal.
+
+For **multimodal data**, two strategies are used:
+
+1. **Repetition Detection**: Repetitive samples in non-academic datasets are flagged and manually reviewed to prevent pattern loops. High-quality datasets are exempt from this process.
+2. **Heuristic Rule-Based Filtering**: Similar rules are applied to detect visual anomalies, with flagged data verified manually to maintain integrity.
+
+#### Training Data
+
+As shown in the following figure, from InternVL 1.5 to 2.0 and then to 2.5, the fine-tuning data mixture has undergone iterative improvements in scale, quality, and diversity. For more information about the training data, please refer to our technical report.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/GaTY9Lde02YzclASMthDa.png)
+
+## Evaluation on Multimodal Capability
+
+### Multimodal Reasoning and Mathematics
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/ihFWMRHbF0lpFTkLqnnj1.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/Nrzq0kjlitjp_jrJCqtwX.png)
+
+### OCR, Chart, and Document Understanding
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/3yCMoLjlbsqY7ZJViGzih.png)
+
+### Multi-Image & Real-World Comprehension
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/DSnalmEyhDVQ9GE0GPCla.png)
+
+### Comprehensive Multimodal & Hallucination Evaluation
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/Z7Raj3TGDiV1H81pDHtoG.png)
+
+### Visual Grounding
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/lPcIrng8MPSg_PM1hpDPt.png)
+
+### Multimodal Multilingual Understanding
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/BPpbAOX36RV8RTnm3j-gs.png)
+
+### Video Understanding
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/tcwH-i1qc8H16En-7AZ5M.png)
+
+## Evaluation on Language Capability
+
+Training InternVL 2.0 models led to a decline in pure language capabilities. InternVL 2.5 addresses this by collecting more high-quality open-source data and filtering out low-quality data, achieving better preservation of pure language performance.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64119264f0f81eb569e0d569/mxuSKvSY-kfI8zePpXj6y.png)
+
+## Quick Start
+
+We provide an example code to run `InternVL2_5-2B` using `transformers`.
+
+> Please use transformers>=4.37.2 to ensure the model works normally.
+
+### Model Loading
+
+#### 16-bit (bf16 / fp16)
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+path = "OpenGVLab/InternVL2_5-2B"
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=True,
+    trust_remote_code=True).eval().cuda()
+```
+
+#### BNB 8-bit Quantization
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+path = "OpenGVLab/InternVL2_5-2B"
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    load_in_8bit=True,
+    low_cpu_mem_usage=True,
+    use_flash_attn=True,
+    trust_remote_code=True).eval()
+```
+
+#### Multiple GPUs
+
+The reason for writing the code this way is to avoid errors that occur during multi-GPU inference due to tensors not being on the same device. By ensuring that the first and last layers of the large language model (LLM) are on the same device, we prevent such errors.
+
+```python
+import math
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+def split_model(model_name):
+    device_map = {}
+    world_size = torch.cuda.device_count()
+    num_layers = {
+        'InternVL2_5-1B': 24, 'InternVL2_5-2B': 24, 'InternVL2_5-4B': 36, 'InternVL2_5-8B': 32,
+        'InternVL2_5-26B': 48, 'InternVL2_5-38B': 64, 'InternVL2_5-78B': 80}[model_name]
+    # Since the first GPU will be used for ViT, treat it as half a GPU.
+    num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
+    num_layers_per_gpu = [num_layers_per_gpu] * world_size
+    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
+    layer_cnt = 0
+    for i, num_layer in enumerate(num_layers_per_gpu):
+        for j in range(num_layer):
+            device_map[f'language_model.model.layers.{layer_cnt}'] = i
+            layer_cnt += 1
+    device_map['vision_model'] = 0
+    device_map['mlp1'] = 0
+    device_map['language_model.model.tok_embeddings'] = 0
+    device_map['language_model.model.embed_tokens'] = 0
+    device_map['language_model.output'] = 0
+    device_map['language_model.model.norm'] = 0
+    device_map['language_model.model.rotary_emb'] = 0
+    device_map['language_model.lm_head'] = 0
+    device_map[f'language_model.model.layers.{num_layers - 1}'] = 0
+
+    return device_map
+
+path = "OpenGVLab/InternVL2_5-2B"
+device_map = split_model('InternVL2_5-2B')
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=True,
+    trust_remote_code=True,
+    device_map=device_map).eval()
+```
+
+### Inference with Transformers
+
+```python
+import numpy as np
+import torch
+import torchvision.transforms as T
+from decord import VideoReader, cpu
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image_file, input_size=448, max_num=12):
+    image = Image.open(image_file).convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.
+path = 'OpenGVLab/InternVL2_5-2B'
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    use_flash_attn=True,
+    trust_remote_code=True).eval().cuda()
+tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
+
+# set the max number of tiles in `max_num`
+pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
+generation_config = dict(max_new_tokens=1024, do_sample=True)
+
+# pure-text conversation (纯文本对话)
+question = 'Hello, who are you?'
+response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+question = 'Can you tell me a story?'
+response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+# single-image single-round conversation (单图单轮对话)
+question = '<image>\nPlease describe the image shortly.'
+response = model.chat(tokenizer, pixel_values, question, generation_config)
+print(f'User: {question}\nAssistant: {response}')
+
+# single-image multi-round conversation (单图多轮对话)
+question = '<image>\nPlease describe the image in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+question = 'Please write a poem according to the image.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
+pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
+pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
+pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
+
+question = '<image>\nDescribe the two images in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               history=None, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+question = 'What are the similarities and differences between these two images.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               history=history, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
+pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
+pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
+pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
+num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
+
+question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               num_patches_list=num_patches_list,
+                               history=None, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+question = 'What are the similarities and differences between these two images.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               num_patches_list=num_patches_list,
+                               history=history, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+# batch inference, single image per sample (单图批处理)
+pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
+pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
+num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
+pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
+
+questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
+responses = model.batch_chat(tokenizer, pixel_values,
+                             num_patches_list=num_patches_list,
+                             questions=questions,
+                             generation_config=generation_config)
+for question, response in zip(questions, responses):
+    print(f'User: {question}\nAssistant: {response}')
+
+# video multi-round conversation (视频多轮对话)
+def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
+    if bound:
+        start, end = bound[0], bound[1]
+    else:
+        start, end = -100000, 100000
+    start_idx = max(first_idx, round(start * fps))
+    end_idx = min(round(end * fps), max_frame)
+    seg_size = float(end_idx - start_idx) / num_segments
+    frame_indices = np.array([
+        int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
+        for idx in range(num_segments)
+    ])
+    return frame_indices
+
+def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
+    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
+    max_frame = len(vr) - 1
+    fps = float(vr.get_avg_fps())
+
+    pixel_values_list, num_patches_list = [], []
+    transform = build_transform(input_size=input_size)
+    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
+    for frame_index in frame_indices:
+        img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
+        img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
+        pixel_values = [transform(tile) for tile in img]
+        pixel_values = torch.stack(pixel_values)
+        num_patches_list.append(pixel_values.shape[0])
+        pixel_values_list.append(pixel_values)
+    pixel_values = torch.cat(pixel_values_list)
+    return pixel_values, num_patches_list
+
+video_path = './examples/red-panda.mp4'
+pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1)
+pixel_values = pixel_values.to(torch.bfloat16).cuda()
+video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
+question = video_prefix + 'What is the red panda doing?'
+# Frame1: <image>\nFrame2: <image>\n...\nFrame8: <image>\n{question}
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               num_patches_list=num_patches_list, history=None, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+
+question = 'Describe this video in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               num_patches_list=num_patches_list, history=history, return_history=True)
+print(f'User: {question}\nAssistant: {response}')
+```
+
+#### Streaming Output
+
+Besides this method, you can also use the following code to get streamed output.
+
+```python
+from transformers import TextIteratorStreamer
+from threading import Thread
+
+# Initialize the streamer
+streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=10)
+# Define the generation configuration
+generation_config = dict(max_new_tokens=1024, do_sample=False, streamer=streamer)
+# Start the model chat in a separate thread
+thread = Thread(target=model.chat, kwargs=dict(
+    tokenizer=tokenizer, pixel_values=pixel_values, question=question,
+    history=None, return_history=False, generation_config=generation_config,
+))
+thread.start()
+
+# Initialize an empty string to store the generated text
+generated_text = ''
+# Loop through the streamer to get the new text as it is generated
+for new_text in streamer:
+    if new_text == model.conv_template.sep:
+        break
+    generated_text += new_text
+    print(new_text, end='', flush=True)  # Print each new chunk of generated text on the same line
+```
+
+## Finetune
+
+Many repositories now support fine-tuning of the InternVL series models, including [InternVL](https://github.com/OpenGVLab/InternVL), [SWIFT](https://github.com/modelscope/ms-swift), [XTurner](https://github.com/InternLM/xtuner), and others. Please refer to their documentation for more details on fine-tuning.
+
+## Deployment
+
+### LMDeploy
+
+LMDeploy is a toolkit for compressing, deploying, and serving LLMs & VLMs.
+
+```sh
+pip install lmdeploy>=0.6.4
+```
+
+LMDeploy abstracts the complex inference process of multi-modal Vision-Language Models (VLM) into an easy-to-use pipeline, similar to the Large Language Model (LLM) inference pipeline.
+
+#### A 'Hello, world' Example
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig
+from lmdeploy.vl import load_image
+
+model = 'OpenGVLab/InternVL2_5-2B'
+image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
+pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192))
+response = pipe(('describe this image', image))
+print(response.text)
+```
+
+If `ImportError` occurs while executing this case, please install the required dependency packages as prompted.
+
+#### Multi-images Inference
+
+When dealing with multiple images, you can put them all in one list. Keep in mind that multiple images will lead to a higher number of input tokens, and as a result, the size of the context window typically needs to be increased.
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig
+from lmdeploy.vl import load_image
+from lmdeploy.vl.constants import IMAGE_TOKEN
+
+model = 'OpenGVLab/InternVL2_5-2B'
+pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192))
+
+image_urls=[
+    'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg',
+    'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg'
+]
+
+images = [load_image(img_url) for img_url in image_urls]
+# Numbering images improves multi-image conversations
+response = pipe((f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\ndescribe these two images', images))
+print(response.text)
+```
+
+#### Batch Prompts Inference
+
+Conducting inference with batch prompts is quite straightforward; just place them within a list structure:
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig
+from lmdeploy.vl import load_image
+
+model = 'OpenGVLab/InternVL2_5-2B'
+pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192))
+
+image_urls=[
+    "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg",
+    "https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg"
+]
+prompts = [('describe this image', load_image(img_url)) for img_url in image_urls]
+response = pipe(prompts)
+print(response)
+```
+
+#### Multi-turn Conversation
+
+There are two ways to do the multi-turn conversations with the pipeline. One is to construct messages according to the format of OpenAI and use above introduced method, the other is to use the `pipeline.chat` interface.
+
+```python
+from lmdeploy import pipeline, TurbomindEngineConfig, GenerationConfig
+from lmdeploy.vl import load_image
+
+model = 'OpenGVLab/InternVL2_5-2B'
+pipe = pipeline(model, backend_config=TurbomindEngineConfig(session_len=8192))
+
+image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg')
+gen_config = GenerationConfig(top_k=40, top_p=0.8, temperature=0.8)
+sess = pipe.chat(('describe this image', image), gen_config=gen_config)
+print(sess.response.text)
+sess = pipe.chat('What is the woman doing?', session=sess, gen_config=gen_config)
+print(sess.response.text)
+```
+
+#### Service
+
+LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
+
+```shell
+lmdeploy serve api_server OpenGVLab/InternVL2_5-2B --server-port 23333
+```
+
+To use the OpenAI-style interface, you need to install OpenAI:
+
+```shell
+pip install openai
+```
+
+Then, use the code below to make the API call:
+
+```python
+from openai import OpenAI
+
+client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
+model_name = client.models.list().data[0].id
+response = client.chat.completions.create(
+    model=model_name,
+    messages=[{
+        'role':
+        'user',
+        'content': [{
+            'type': 'text',
+            'text': 'describe this image',
+        }, {
+            'type': 'image_url',
+            'image_url': {
+                'url':
+                'https://modelscope.oss-cn-beijing.aliyuncs.com/resource/tiger.jpeg',
+            },
+        }],
+    }],
+    temperature=0.8,
+    top_p=0.8)
+print(response)
+```
+
+## License
+
+This project is released under the MIT License. This project uses the pre-trained internlm2_5-1_8b-chat as a component, which is licensed under the Apache License 2.0.
+
+## Citation
+
+If you find this project useful in your research, please consider citing:
+
+```BibTeX
+@article{chen2024expanding,
+  title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},
+  author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},
+  journal={arXiv preprint arXiv:2412.05271},
+  year={2024}
+}
+@article{gao2024mini,
+  title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},
+  author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},
+  journal={arXiv preprint arXiv:2410.16261},
+  year={2024}
+}
+@article{chen2024far,
+  title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
+  author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
+  journal={arXiv preprint arXiv:2404.16821},
+  year={2024}
+}
+@inproceedings{chen2024internvl,
+  title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},
+  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={24185--24198},
+  year={2024}
+}
+```

InternVL/InternVL_2_5/added_tokens.json

@@ -0,0 +1,11 @@
+{
+  "</box>": 92552,
+  "</img>": 92545,
+  "</quad>": 92548,
+  "</ref>": 92550,
+  "<IMG_CONTEXT>": 92546,
+  "<box>": 92551,
+  "<img>": 92544,
+  "<quad>": 92547,
+  "<ref>": 92549
+}

InternVL/InternVL_2_5/config.json

@@ -0,0 +1,145 @@
+{
+  "_commit_hash": null,
+  "architectures": [
+    "InternVLChatModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_internvl_chat.InternVLChatConfig",
+    "AutoModel": "modeling_internvl_chat.InternVLChatModel",
+    "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel"
+  },
+  "downsample_ratio": 0.5,
+  "dynamic_image_size": true,
+  "force_image_size": 448,
+  "llm_config": {
+    "_name_or_path": "internlm/internlm2_5-1_8b-chat",
+    "add_cross_attention": false,
+    "architectures": [
+      "InternLM2ForCausalLM"
+    ],
+    "attn_implementation": "flash_attention_2",
+    "auto_map": {
+      "AutoConfig": "configuration_internlm2.InternLM2Config",
+      "AutoModel": "modeling_internlm2.InternLM2ForCausalLM",
+      "AutoModelForCausalLM": "modeling_internlm2.InternLM2ForCausalLM",
+      "AutoModelForSequenceClassification": "modeling_internlm2.InternLM2ForSequenceClassification"
+    },
+    "bad_words_ids": null,
+    "begin_suppress_tokens": null,
+    "bias": false,
+    "bos_token_id": 1,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": 2,
+    "exponential_decay_length_penalty": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 32768,
+    "min_length": 0,
+    "model_type": "internlm2",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 8,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 2,
+    "prefix": null,
+    "pretraining_tp": 1,
+    "problem_type": null,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": {
+      "factor": 2.0,
+      "type": "dynamic"
+    },
+    "rope_theta": 1000000,
+    "sep_token_id": null,
+    "suppress_tokens": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tf_legacy_loss": false,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": false,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": "bfloat16",
+    "torchscript": false,
+    "transformers_version": "4.37.2",
+    "typical_p": 1.0,
+    "use_bfloat16": true,
+    "use_cache": true,
+    "vocab_size": 92553
+  },
+  "max_dynamic_patch": 12,
+  "min_dynamic_patch": 1,
+  "model_type": "internvl_chat",
+  "ps_version": "v2",
+  "select_layer": -1,
+  "template": "internvl2_5",
+  "torch_dtype": "bfloat16",
+  "use_backbone_lora": 0,
+  "use_llm_lora": 0,
+  "use_thumbnail": true,
+  "vision_config": {
+    "architectures": [
+      "InternVisionModel"
+    ],
+    "attention_dropout": 0.0,
+    "drop_path_rate": 0.0,
+    "dropout": 0.0,
+    "hidden_act": "gelu",
+    "hidden_size": 1024,
+    "image_size": 448,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "layer_norm_eps": 1e-06,
+    "model_type": "intern_vit_6b",
+    "norm_type": "layer_norm",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 24,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "patch_size": 14,
+    "qk_normalization": false,
+    "qkv_bias": true,
+    "return_dict": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.37.2",
+    "use_bfloat16": true,
+    "use_flash_attn": true
+  }
+}

InternVL/InternVL_2_5/configuration_intern_vit.py

@@ -0,0 +1,120 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import os
+from typing import Union
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class InternVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternVisionModel`]. It is used to
+    instantiate a vision encoder according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of color channels in the input images (e.g., 3 for RGB).
+        patch_size (`int`, *optional*, defaults to 14):
+            The size (resolution) of each patch.
+        image_size (`int`, *optional*, defaults to 224):
+            The size (resolution) of each image.
+        qkv_bias (`bool`, *optional*, defaults to `False`):
+            Whether to add a bias to the queries and values in the self-attention layers.
+        hidden_size (`int`, *optional*, defaults to 3200):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_attention_heads (`int`, *optional*, defaults to 25):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 12800):
+            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        qk_normalization (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the queries and keys in the self-attention layers.
+        num_hidden_layers (`int`, *optional*, defaults to 48):
+            Number of hidden layers in the Transformer encoder.
+        use_flash_attn (`bool`, *optional*, defaults to `True`):
+            Whether to use flash attention mechanism.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the layer normalization layers.
+        dropout (`float`, *optional*, defaults to 0.0):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Dropout rate for stochastic depth.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        initializer_factor (`float`, *optional*, defaults to 0.1):
+            A factor for layer scale.
+    """
+
+    model_type = 'intern_vit_6b'
+
+    def __init__(
+            self,
+            num_channels=3,
+            patch_size=14,
+            image_size=224,
+            qkv_bias=False,
+            hidden_size=3200,
+            num_attention_heads=25,
+            intermediate_size=12800,
+            qk_normalization=True,
+            num_hidden_layers=48,
+            use_flash_attn=True,
+            hidden_act='gelu',
+            norm_type='rms_norm',
+            layer_norm_eps=1e-6,
+            dropout=0.0,
+            drop_path_rate=0.0,
+            attention_dropout=0.0,
+            initializer_range=0.02,
+            initializer_factor=0.1,
+            **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.drop_path_rate = drop_path_rate
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_range = initializer_range
+        self.initializer_factor = initializer_factor
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act
+        self.norm_type = norm_type
+        self.qkv_bias = qkv_bias
+        self.qk_normalization = qk_normalization
+        self.use_flash_attn = use_flash_attn
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if 'vision_config' in config_dict:
+            config_dict = config_dict['vision_config']
+
+        if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
+            )
+
+        return cls.from_dict(config_dict, **kwargs)

InternVL/InternVL_2_5/configuration_internlm2.py

@@ -0,0 +1,150 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" InternLM2 model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+INTERNLM2_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+# Modified from transformers.model.llama.configuration_llama.LlamaConfig
+class InternLM2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLM2Model`]. It is used to instantiate
+    an InternLM2 model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the InternLM2-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the InternLM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLM2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        Example:
+
+    """
+    model_type = 'internlm2'
+    _auto_class = 'AutoConfig'
+
+    def __init__(  # pylint: disable=W0102
+        self,
+        vocab_size=103168,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act='silu',
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        bias=True,
+        rope_theta=10000,
+        rope_scaling=None,
+        attn_implementation='eager',
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.bias = bias
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = 'eager'
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                '`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
+                f'got {self.rope_scaling}'
+            )
+        rope_scaling_type = self.rope_scaling.get('type', None)
+        rope_scaling_factor = self.rope_scaling.get('factor', None)
+        if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor < 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {rope_scaling_factor}")

InternVL/InternVL_2_5/configuration_internvl_chat.py

@@ -0,0 +1,96 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import copy
+
+from transformers import AutoConfig, LlamaConfig
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+from .configuration_internlm2 import InternLM2Config
+
+logger = logging.get_logger(__name__)
+
+
+class InternVLChatConfig(PretrainedConfig):
+    model_type = 'internvl_chat'
+    is_composition = True
+
+    def __init__(
+            self,
+            vision_config=None,
+            llm_config=None,
+            use_backbone_lora=0,
+            use_llm_lora=0,
+            select_layer=-1,
+            force_image_size=None,
+            downsample_ratio=0.5,
+            template=None,
+            dynamic_image_size=False,
+            use_thumbnail=False,
+            ps_version='v1',
+            min_dynamic_patch=1,
+            max_dynamic_patch=6,
+            **kwargs):
+        super().__init__(**kwargs)
+
+        if vision_config is None:
+            vision_config = {'architectures': ['InternVisionModel']}
+            logger.info('vision_config is None. Initializing the InternVisionConfig with default values.')
+
+        if llm_config is None:
+            llm_config = {'architectures': ['InternLM2ForCausalLM']}
+            logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
+
+        self.vision_config = InternVisionConfig(**vision_config)
+        if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
+            self.llm_config = LlamaConfig(**llm_config)
+        elif llm_config.get('architectures')[0] == 'InternLM2ForCausalLM':
+            self.llm_config = InternLM2Config(**llm_config)
+        else:
+            raise ValueError('Unsupported architecture: {}'.format(llm_config.get('architectures')[0]))
+        self.use_backbone_lora = use_backbone_lora
+        self.use_llm_lora = use_llm_lora
+        self.select_layer = select_layer
+        self.force_image_size = force_image_size
+        self.downsample_ratio = downsample_ratio
+        self.template = template
+        self.dynamic_image_size = dynamic_image_size
+        self.use_thumbnail = use_thumbnail
+        self.ps_version = ps_version  # pixel shuffle version
+        self.min_dynamic_patch = min_dynamic_patch
+        self.max_dynamic_patch = max_dynamic_patch
+
+        logger.info(f'vision_select_layer: {self.select_layer}')
+        logger.info(f'ps_version: {self.ps_version}')
+        logger.info(f'min_dynamic_patch: {self.min_dynamic_patch}')
+        logger.info(f'max_dynamic_patch: {self.max_dynamic_patch}')
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+        output['vision_config'] = self.vision_config.to_dict()
+        output['llm_config'] = self.llm_config.to_dict()
+        output['model_type'] = self.__class__.model_type
+        output['use_backbone_lora'] = self.use_backbone_lora
+        output['use_llm_lora'] = self.use_llm_lora
+        output['select_layer'] = self.select_layer
+        output['force_image_size'] = self.force_image_size
+        output['downsample_ratio'] = self.downsample_ratio
+        output['template'] = self.template
+        output['dynamic_image_size'] = self.dynamic_image_size
+        output['use_thumbnail'] = self.use_thumbnail
+        output['ps_version'] = self.ps_version
+        output['min_dynamic_patch'] = self.min_dynamic_patch
+        output['max_dynamic_patch'] = self.max_dynamic_patch
+
+        return output

InternVL/InternVL_2_5/conversation.py

@@ -0,0 +1,391 @@
+"""
+Conversation prompt templates.
+
+We kindly request that you import fastchat instead of copying this file if you wish to use it.
+If you have changes in mind, please contribute back so the community can benefit collectively and continue to maintain these valuable templates.
+
+Modified from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+"""
+
+import dataclasses
+from enum import IntEnum, auto
+from typing import Dict, List, Tuple, Union
+
+
+class SeparatorStyle(IntEnum):
+    """Separator styles."""
+
+    ADD_COLON_SINGLE = auto()
+    ADD_COLON_TWO = auto()
+    ADD_COLON_SPACE_SINGLE = auto()
+    NO_COLON_SINGLE = auto()
+    NO_COLON_TWO = auto()
+    ADD_NEW_LINE_SINGLE = auto()
+    LLAMA2 = auto()
+    CHATGLM = auto()
+    CHATML = auto()
+    CHATINTERN = auto()
+    DOLLY = auto()
+    RWKV = auto()
+    PHOENIX = auto()
+    ROBIN = auto()
+    FALCON_CHAT = auto()
+    CHATGLM3 = auto()
+    INTERNVL_ZH = auto()
+    MPT = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that manages prompt templates and keeps all conversation history."""
+
+    # The name of this template
+    name: str
+    # The template of the system prompt
+    system_template: str = '{system_message}'
+    # The system message
+    system_message: str = ''
+    # The names of two roles
+    roles: Tuple[str] = ('USER', 'ASSISTANT')
+    # All messages. Each item is (role, message).
+    messages: List[List[str]] = ()
+    # The number of few shot examples
+    offset: int = 0
+    # The separator style and configurations
+    sep_style: SeparatorStyle = SeparatorStyle.ADD_COLON_SINGLE
+    sep: str = '\n'
+    sep2: str = None
+    # Stop criteria (the default one is EOS token)
+    stop_str: Union[str, List[str]] = None
+    # Stops generation if meeting any token in this list
+    stop_token_ids: List[int] = None
+
+    def get_prompt(self) -> str:
+        """Get the prompt for generation."""
+        system_prompt = self.system_template.format(system_message=self.system_message)
+        if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ': ' + message + self.sep
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.ADD_COLON_TWO:
+            seps = [self.sep, self.sep2]
+            ret = system_prompt + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ': ' + message + seps[i % 2]
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ': ' + message + self.sep
+                else:
+                    ret += role + ': '  # must be end with a space
+            return ret
+        elif self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
+            ret = '' if system_prompt == '' else system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + '\n' + message + self.sep
+                else:
+                    ret += role + '\n'
+            return ret
+        elif self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
+            ret = system_prompt
+            for role, message in self.messages:
+                if message:
+                    ret += role + message + self.sep
+                else:
+                    ret += role
+            return ret
+        elif self.sep_style == SeparatorStyle.NO_COLON_TWO:
+            seps = [self.sep, self.sep2]
+            ret = system_prompt
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + message + seps[i % 2]
+                else:
+                    ret += role
+            return ret
+        elif self.sep_style == SeparatorStyle.RWKV:
+            ret = system_prompt
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += (
+                        role
+                        + ': '
+                        + message.replace('\r\n', '\n').replace('\n\n', '\n')
+                    )
+                    ret += '\n\n'
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.LLAMA2:
+            seps = [self.sep, self.sep2]
+            if self.system_message:
+                ret = system_prompt
+            else:
+                ret = '[INST] '
+            for i, (role, message) in enumerate(self.messages):
+                tag = self.roles[i % 2]
+                if message:
+                    if i == 0:
+                        ret += message + ' '
+                    else:
+                        ret += tag + ' ' + message + seps[i % 2]
+                else:
+                    ret += tag
+            return ret
+        elif self.sep_style == SeparatorStyle.CHATGLM:
+            # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
+            # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
+            round_add_n = 1 if self.name == 'chatglm2' else 0
+            if system_prompt:
+                ret = system_prompt + self.sep
+            else:
+                ret = ''
+
+            for i, (role, message) in enumerate(self.messages):
+                if i % 2 == 0:
+                    ret += f'[Round {i//2 + round_add_n}]{self.sep}'
+
+                if message:
+                    ret += f'{role}：{message}{self.sep}'
+                else:
+                    ret += f'{role}：'
+            return ret
+        elif self.sep_style == SeparatorStyle.CHATML:
+            ret = '' if system_prompt == '' else system_prompt + self.sep + '\n'
+            for role, message in self.messages:
+                if message:
+                    ret += role + '\n' + message + self.sep + '\n'
+                else:
+                    ret += role + '\n'
+            return ret
+        elif self.sep_style == SeparatorStyle.CHATGLM3:
+            ret = ''
+            if self.system_message:
+                ret += system_prompt
+            for role, message in self.messages:
+                if message:
+                    ret += role + '\n' + ' ' + message
+                else:
+                    ret += role
+            return ret
+        elif self.sep_style == SeparatorStyle.CHATINTERN:
+            # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
+            seps = [self.sep, self.sep2]
+            ret = system_prompt
+            for i, (role, message) in enumerate(self.messages):
+                # if i % 2 == 0:
+                #     ret += "<s>"
+                if message:
+                    ret += role + ':' + message + seps[i % 2] + '\n'
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.DOLLY:
+            seps = [self.sep, self.sep2]
+            ret = system_prompt
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ':\n' + message + seps[i % 2]
+                    if i % 2 == 1:
+                        ret += '\n\n'
+                else:
+                    ret += role + ':\n'
+            return ret
+        elif self.sep_style == SeparatorStyle.PHOENIX:
+            ret = system_prompt
+            for role, message in self.messages:
+                if message:
+                    ret += role + ': ' + '<s>' + message + '</s>'
+                else:
+                    ret += role + ': ' + '<s>'
+            return ret
+        elif self.sep_style == SeparatorStyle.ROBIN:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ':\n' + message + self.sep
+                else:
+                    ret += role + ':\n'
+            return ret
+        elif self.sep_style == SeparatorStyle.FALCON_CHAT:
+            ret = ''
+            if self.system_message:
+                ret += system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ': ' + message + self.sep
+                else:
+                    ret += role + ':'
+
+            return ret
+        elif self.sep_style == SeparatorStyle.INTERNVL_ZH:
+            seps = [self.sep, self.sep2]
+            ret = self.system_message + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ': ' + message + seps[i % 2]
+                else:
+                    ret += role + ':'
+            return ret
+        elif self.sep_style == SeparatorStyle.MPT:
+            ret = system_prompt + self.sep
+            for role, message in self.messages:
+                if message:
+                    if type(message) is tuple:
+                        message, _, _ = message
+                    ret += role + message + self.sep
+                else:
+                    ret += role
+            return ret
+        else:
+            raise ValueError(f'Invalid style: {self.sep_style}')
+
+    def set_system_message(self, system_message: str):
+        """Set the system message."""
+        self.system_message = system_message
+
+    def append_message(self, role: str, message: str):
+        """Append a new message."""
+        self.messages.append([role, message])
+
+    def update_last_message(self, message: str):
+        """Update the last output.
+
+        The last message is typically set to be None when constructing the prompt,
+        so we need to update it in-place after getting the response from a model.
+        """
+        self.messages[-1][1] = message
+
+    def to_gradio_chatbot(self):
+        """Convert the conversation to gradio chatbot format."""
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def to_openai_api_messages(self):
+        """Convert the conversation to OpenAI chat completion format."""
+        ret = [{'role': 'system', 'content': self.system_message}]
+
+        for i, (_, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append({'role': 'user', 'content': msg})
+            else:
+                if msg is not None:
+                    ret.append({'role': 'assistant', 'content': msg})
+        return ret
+
+    def copy(self):
+        return Conversation(
+            name=self.name,
+            system_template=self.system_template,
+            system_message=self.system_message,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            stop_str=self.stop_str,
+            stop_token_ids=self.stop_token_ids,
+        )
+
+    def dict(self):
+        return {
+            'template_name': self.name,
+            'system_message': self.system_message,
+            'roles': self.roles,
+            'messages': self.messages,
+            'offset': self.offset,
+        }
+
+
+# A global registry for all conversation templates
+conv_templates: Dict[str, Conversation] = {}
+
+
+def register_conv_template(template: Conversation, override: bool = False):
+    """Register a new conversation template."""
+    if not override:
+        assert (
+            template.name not in conv_templates
+        ), f'{template.name} has been registered.'
+
+    conv_templates[template.name] = template
+
+
+def get_conv_template(name: str) -> Conversation:
+    """Get a conversation template."""
+    return conv_templates[name].copy()
+
+
+# Both Hermes-2 and internlm2-chat are chatml-format conversation templates. The difference
+# is that during training, the preprocessing function for the Hermes-2 template doesn't add
+# <s> at the beginning of the tokenized sequence, while the internlm2-chat template does.
+# Therefore, they are completely equivalent during inference.
+register_conv_template(
+    Conversation(
+        name='Hermes-2',
+        system_template='<|im_start|>system\n{system_message}',
+        # note: The new system prompt was not used here to avoid changes in benchmark performance.
+        # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
+        system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+        roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|im_end|>',
+        stop_str='<|endoftext|>',
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name='internlm2-chat',
+        system_template='<|im_start|>system\n{system_message}',
+        # note: The new system prompt was not used here to avoid changes in benchmark performance.
+        # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
+        system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+        roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|im_end|>',
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name='phi3-chat',
+        system_template='<|system|>\n{system_message}',
+        # note: The new system prompt was not used here to avoid changes in benchmark performance.
+        # system_message='我是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
+        system_message='你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。',
+        roles=('<|user|>\n', '<|assistant|>\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|end|>',
+    )
+)
+
+
+register_conv_template(
+    Conversation(
+        name='internvl2_5',
+        system_template='<|im_start|>system\n{system_message}',
+        system_message='你是书生·万象，英文名是InternVL，是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型。',
+        roles=('<|im_start|>user\n', '<|im_start|>assistant\n'),
+        sep_style=SeparatorStyle.MPT,
+        sep='<|im_end|>\n',
+    )
+)

InternVL/InternVL_2_5/eval_llm_benchmark.log

@@ -0,0 +1,61 @@
+/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl_eval2/lib/python3.10/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
+  warn("The installed version of bitsandbytes was compiled without GPU support. "
+/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl_eval2/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32
+model path is /mnt/petrelfs/wangweiyun/workspace_cz/InternVL/internvl_chat_dev/share_internvl/InternVL2_5-2B
+11/22 18:39:59 - OpenCompass - WARNING - No previous results to reuse!
+11/22 18:39:59 - OpenCompass - INFO - Reusing experiements from 20241122_183959
+11/22 18:39:59 - OpenCompass - INFO - Current exp folder: /mnt/petrelfs/wangweiyun/workspace_cz/InternVL/internvl_chat_dev/share_internvl/InternVL2_5-2B/20241122_183959
+11/22 18:40:02 - OpenCompass - INFO - Partitioned into 256 tasks.
+[                                                  ] 0/256, elapsed: 0s, ETA:
+11/22 18:51:40 - OpenCompass - INFO - Partitioned into 287 tasks.
+[                                                  ] 0/287, elapsed: 0s, ETA:
+dataset                       version    metric                        mode    internvl-chat-20b
+----------------------------  ---------  ----------------------------  ------  -------------------
+mmlu                          -          naive_average                 gen     52.60
+mmlu_pro                      -          -                             -       -
+cmmlu                         -          naive_average                 gen     57.00
+ceval                         -          naive_average                 gen     56.16
+agieval                       -          -                             -       -
+GaokaoBench                   -          weighted_average              gen     52.58
+GPQA_extended                 -          -                             -       -
+GPQA_main                     -          -                             -       -
+GPQA_diamond                  -          -                             -       -
+ARC-c                         -          -                             -       -
+truthfulqa                    -          -                             -       -
+triviaqa                      2121ce     score                         gen     31.23
+triviaqa_wiki_1shot           -          -                             -       -
+nq                            3dcea1     score                         gen     11.75
+C3                            8c358f     accuracy                      gen     78.03
+race-high                     9a54b6     accuracy                      gen     77.39
+flores_100                    -          -                             -       -
+winogrande                    b36770     accuracy                      gen     59.12
+hellaswag                     e42710     accuracy                      gen     68.19
+bbh                           -          naive_average                 gen     40.85
+gsm8k                         1d7fe4     accuracy                      gen     55.12
+math                          393424     accuracy                      gen     33.54
+TheoremQA                     6f0af8     score                         gen     12.00
+MathBench                     -          -                             -       -
+openai_humaneval              8e312c     humaneval_pass@1              gen     52.44
+humaneval_plus                -          -                             -       -
+humanevalx                    -          -                             -       -
+sanitized_mbpp                a447ff     score                         gen     50.58
+mbpp_plus                     -          -                             -       -
+mbpp_cn                       6fb572     score                         gen     34.20
+leval                         -          -                             -       -
+leval_closed                  -          -                             -       -
+leval_open                    -          -                             -       -
+longbench                     -          -                             -       -
+longbench_single-document-qa  -          -                             -       -
+longbench_multi-document-qa   -          -                             -       -
+longbench_summarization       -          -                             -       -
+longbench_few-shot-learning   -          -                             -       -
+longbench_synthetic-tasks     -          -                             -       -
+longbench_code-completion     -          -                             -       -
+teval                         -          -                             -       -
+teval_zh                      -          -                             -       -
+IFEval                        3321a3     Prompt-level-strict-accuracy  gen     31.05
+IFEval                        3321a3     Inst-level-strict-accuracy    gen     43.17
+IFEval                        3321a3     Prompt-level-loose-accuracy   gen     32.72
+IFEval                        3321a3     Inst-level-loose-accuracy     gen     45.44
+11/22 18:56:09 - OpenCompass - INFO - write summary to /mnt/petrelfs/wangweiyun/workspace_cz/InternVL/internvl_chat_dev/share_internvl/InternVL2_5-2B/20241122_183959/summary/summary_20241122_183959.txt
+11/22 18:56:09 - OpenCompass - INFO - write csv to /mnt/petrelfs/wangweiyun/workspace_cz/InternVL/internvl_chat_dev/share_internvl/InternVL2_5-2B/20241122_183959/summary/summary_20241122_183959.csv

InternVL/InternVL_2_5/generation_config.json

@@ -0,0 +1,8 @@
+{
+  "_from_model_config": true,
+  "transformers_version": "4.37.2",
+  "eos_token_id": [
+    92542,
+    92543
+  ]
+}

InternVL/InternVL_2_5/modeling_intern_vit.py

@@ -0,0 +1,430 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from timm.models.layers import DropPath
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutput,
+                                           BaseModelOutputWithPooling)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_intern_vit import InternVisionConfig
+
+try:
+    from flash_attn.bert_padding import pad_input, unpad_input
+    from flash_attn.flash_attn_interface import \
+        flash_attn_varlen_qkvpacked_func
+    has_flash_attn = True
+except:
+    print('FlashAttention2 is not installed.')
+    has_flash_attn = False
+
+logger = logging.get_logger(__name__)
+
+
+class FlashAttention(nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+
+    def __init__(self, softmax_scale=None, attention_dropout=0.0, device=None, dtype=None):
+        super().__init__()
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+    def forward(self, qkv, key_padding_mask=None, causal=False, cu_seqlens=None,
+                max_s=None, need_weights=False):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            qkv: The tensor containing the query, key, and value. (B, S, 3, H, D) if key_padding_mask is None
+                if unpadded: (nnz, 3, h, d)
+            key_padding_mask: a bool tensor of shape (B, S)
+        """
+        assert not need_weights
+        assert qkv.dtype in [torch.float16, torch.bfloat16]
+        assert qkv.is_cuda
+
+        if cu_seqlens is None:
+            batch_size = qkv.shape[0]
+            seqlen = qkv.shape[1]
+            if key_padding_mask is None:
+                qkv = rearrange(qkv, 'b s ... -> (b s) ...')
+                max_s = seqlen
+                cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32,
+                                          device=qkv.device)
+                output = flash_attn_varlen_qkvpacked_func(
+                    qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+            else:
+                nheads = qkv.shape[-2]
+                x = rearrange(qkv, 'b s three h d -> b s (three h d)')
+                x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)
+                x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
+                output_unpad = flash_attn_varlen_qkvpacked_func(
+                    x_unpad, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                    softmax_scale=self.softmax_scale, causal=causal
+                )
+                output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
+                                             indices, batch_size, seqlen),
+                                   'b s (h d) -> b s h d', h=nheads)
+        else:
+            assert max_s is not None
+            output = flash_attn_varlen_qkvpacked_func(
+                qkv, cu_seqlens, max_s, self.dropout_p if self.training else 0.0,
+                softmax_scale=self.softmax_scale, causal=causal
+            )
+
+        return output, None
+
+
+class InternRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+try:
+    from apex.normalization import FusedRMSNorm
+
+    InternRMSNorm = FusedRMSNorm  # noqa
+
+    logger.info('Discovered apex.normalization.FusedRMSNorm - will use it instead of InternRMSNorm')
+except ImportError:
+    # using the normal InternRMSNorm
+    pass
+except Exception:
+    logger.warning('discovered apex but it failed to load, falling back to InternRMSNorm')
+    pass
+
+
+NORM2FN = {
+    'rms_norm': InternRMSNorm,
+    'layer_norm': nn.LayerNorm,
+}
+
+
+class InternVisionEmbeddings(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.class_embedding = nn.Parameter(
+            torch.randn(1, 1, self.embed_dim),
+        )
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=3, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size
+        )
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches + 1
+
+        self.position_embedding = nn.Parameter(torch.randn(1, self.num_positions, self.embed_dim))
+
+    def _get_pos_embed(self, pos_embed, H, W):
+        target_dtype = pos_embed.dtype
+        pos_embed = pos_embed.float().reshape(
+            1, self.image_size // self.patch_size, self.image_size // self.patch_size, -1).permute(0, 3, 1, 2)
+        pos_embed = F.interpolate(pos_embed, size=(H, W), mode='bicubic', align_corners=False). \
+            reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)
+        return pos_embed
+
+    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
+        target_dtype = self.patch_embedding.weight.dtype
+        patch_embeds = self.patch_embedding(pixel_values)  # shape = [*, channel, width, height]
+        batch_size, _, height, width = patch_embeds.shape
+        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
+        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
+        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
+        position_embedding = torch.cat([
+            self.position_embedding[:, :1, :],
+            self._get_pos_embed(self.position_embedding[:, 1:, :], height, width)
+        ], dim=1)
+        embeddings = embeddings + position_embedding.to(target_dtype)
+        return embeddings
+
+
+class InternAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_flash_attn = config.use_flash_attn and has_flash_attn
+        if config.use_flash_attn and not has_flash_attn:
+            print('Warning: Flash Attention is not available, use_flash_attn is set to False.')
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f'embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:'
+                f' {self.num_heads}).'
+            )
+
+        self.scale = self.head_dim ** -0.5
+        self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=config.qkv_bias)
+        self.attn_drop = nn.Dropout(config.attention_dropout)
+        self.proj_drop = nn.Dropout(config.dropout)
+
+        self.qk_normalization = config.qk_normalization
+
+        if self.qk_normalization:
+            self.q_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+            self.k_norm = InternRMSNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+        if self.use_flash_attn:
+            self.inner_attn = FlashAttention(attention_dropout=config.attention_dropout)
+        self.proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def _naive_attn(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
+
+        if self.qk_normalization:
+            B_, H_, N_, D_ = q.shape
+            q = self.q_norm(q.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+            k = self.k_norm(k.transpose(1, 2).flatten(-2, -1)).view(B_, N_, H_, D_).transpose(1, 2)
+
+        attn = ((q * self.scale) @ k.transpose(-2, -1))
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def _flash_attn(self, x, key_padding_mask=None, need_weights=False):
+        qkv = self.qkv(x)
+        qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=self.num_heads)
+
+        if self.qk_normalization:
+            q, k, v = qkv.unbind(2)
+            q = self.q_norm(q.flatten(-2, -1)).view(q.shape)
+            k = self.k_norm(k.flatten(-2, -1)).view(k.shape)
+            qkv = torch.stack([q, k, v], dim=2)
+
+        context, _ = self.inner_attn(
+            qkv, key_padding_mask=key_padding_mask, need_weights=need_weights, causal=False
+        )
+        outs = self.proj(rearrange(context, 'b s h d -> b s (h d)'))
+        outs = self.proj_drop(outs)
+        return outs
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        x = self._naive_attn(hidden_states) if not self.use_flash_attn else self._flash_attn(hidden_states)
+        return x
+
+
+class InternMLP(nn.Module):
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        self.act = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class InternVisionEncoderLayer(nn.Module):
+    def __init__(self, config: InternVisionConfig, drop_path_rate: float):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.norm_type = config.norm_type
+
+        self.attn = InternAttention(config)
+        self.mlp = InternMLP(config)
+        self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+        self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
+
+        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
+        self.drop_path1 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+        self.drop_path2 = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+    ) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor], Optional[Tuple[torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]`): input to the layer of shape `(batch, seq_len, embed_dim)`
+        """
+        hidden_states = hidden_states + self.drop_path1(self.attn(self.norm1(hidden_states).to(hidden_states.dtype)) * self.ls1)
+
+        hidden_states = hidden_states + self.drop_path2(self.mlp(self.norm2(hidden_states).to(hidden_states.dtype)) * self.ls2)
+
+        return hidden_states
+
+
+class InternVisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`InternEncoderLayer`].
+
+    Args:
+        config (`InternConfig`):
+            The corresponding vision configuration for the `InternEncoder`.
+    """
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__()
+        self.config = config
+        # stochastic depth decay rule
+        dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers)]
+        self.layers = nn.ModuleList([
+            InternVisionEncoderLayer(config, dpr[idx]) for idx in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = True
+
+    def forward(
+            self,
+            inputs_embeds,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutput]:
+        r"""
+        Args:
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Embedded representation of the inputs. Should be float, not int tokens.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        """
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        encoder_states = () if output_hidden_states else None
+        hidden_states = inputs_embeds
+
+        for idx, encoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                encoder_states = encoder_states + (hidden_states,)
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    encoder_layer,
+                    hidden_states)
+            else:
+                layer_outputs = encoder_layer(
+                    hidden_states,
+                )
+            hidden_states = layer_outputs
+
+        if output_hidden_states:
+            encoder_states = encoder_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, encoder_states] if v is not None)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=encoder_states
+        )
+
+
+class InternVisionModel(PreTrainedModel):
+    main_input_name = 'pixel_values'
+    _supports_flash_attn_2 = True
+    config_class = InternVisionConfig
+    _no_split_modules = ['InternVisionEncoderLayer']
+
+    def __init__(self, config: InternVisionConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = InternVisionEmbeddings(config)
+        self.encoder = InternVisionEncoder(config)
+
+    def resize_pos_embeddings(self, old_size, new_size, patch_size):
+        pos_emb = self.embeddings.position_embedding
+        _, num_positions, embed_dim = pos_emb.shape
+        cls_emb = pos_emb[:, :1, :]
+        pos_emb = pos_emb[:, 1:, :].reshape(1, old_size // patch_size, old_size // patch_size, -1).permute(0, 3, 1, 2)
+        pos_emb = F.interpolate(pos_emb.float(), size=new_size // patch_size, mode='bicubic', align_corners=False)
+        pos_emb = pos_emb.to(cls_emb.dtype).reshape(1, embed_dim, -1).permute(0, 2, 1)
+        pos_emb = torch.cat([cls_emb, pos_emb], dim=1)
+        self.embeddings.position_embedding = nn.Parameter(pos_emb)
+        self.embeddings.image_size = new_size
+        logger.info('Resized position embeddings from {} to {}'.format(old_size, new_size))
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def forward(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            pixel_embeds: Optional[torch.FloatTensor] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPooling]:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if pixel_values is None and pixel_embeds is None:
+            raise ValueError('You have to specify pixel_values or pixel_embeds')
+
+        if pixel_embeds is not None:
+            hidden_states = pixel_embeds
+        else:
+            if len(pixel_values.shape) == 4:
+                hidden_states = self.embeddings(pixel_values)
+            else:
+                raise ValueError(f'wrong pixel_values size: {pixel_values.shape}')
+        encoder_outputs = self.encoder(
+            inputs_embeds=hidden_states,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        last_hidden_state = encoder_outputs.last_hidden_state
+        pooled_output = last_hidden_state[:, 0, :]
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

InternVL/InternVL_2_5/modeling_internlm2.py

@@ -0,0 +1,1415 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/modeling_llama.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch InternLM2 model."""
+import math
+import queue
+import threading
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (BaseModelOutputWithPast,
+                                           CausalLMOutputWithPast,
+                                           SequenceClassifierOutputWithPast)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (add_start_docstrings,
+                                add_start_docstrings_to_model_forward, logging,
+                                replace_return_docstrings)
+
+try:
+    from transformers.generation.streamers import BaseStreamer
+except:  # noqa # pylint: disable=bare-except
+    BaseStreamer = None
+
+from .configuration_internlm2 import InternLM2Config
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = 'InternLM2Config'
+
+flash_attn_func, flash_attn_varlen_func = None, None
+pad_input, index_first_axis, unpad_input = None, None, None
+try:
+    from flash_attn import flash_attn_func as _flash_attn_func
+    from flash_attn import flash_attn_varlen_func as _flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis as _index_first_axis
+    from flash_attn.bert_padding import pad_input as _pad_input
+    from flash_attn.bert_padding import unpad_input as _unpad_input
+
+    flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
+    pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
+    has_flash_attn = True
+except:
+    has_flash_attn = False
+
+
+def _import_flash_attn():
+    global flash_attn_func, flash_attn_varlen_func
+    global pad_input, index_first_axis, unpad_input
+    try:
+        from flash_attn import flash_attn_func as _flash_attn_func
+        from flash_attn import \
+            flash_attn_varlen_func as _flash_attn_varlen_func
+        from flash_attn.bert_padding import \
+            index_first_axis as _index_first_axis
+        from flash_attn.bert_padding import pad_input as _pad_input
+        from flash_attn.bert_padding import unpad_input as _unpad_input
+        flash_attn_func, flash_attn_varlen_func = _flash_attn_func, _flash_attn_varlen_func
+        pad_input, index_first_axis, unpad_input = _pad_input, _index_first_axis, _unpad_input
+    except ImportError:
+        raise ImportError('flash_attn is not installed.')
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->InternLM2
+class InternLM2RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        InternLM2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaRotaryEmbedding with Llama->InternLM2
+class InternLM2RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer('inv_freq', inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
+        self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.float32)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2LinearScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
+        self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->InternLM2
+class InternLM2DynamicNTKScalingRotaryEmbedding(InternLM2RotaryEmbedding):
+    """InternLM2RotaryEmbedding extended with Dynamic NTK scaling.
+    Credits to the Reddit users /u/bloc97 and /u/emozilla.
+    """
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer('inv_freq', inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device).to(dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum('i,j->ij', t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer('cos_cached', emb.cos().to(dtype), persistent=False)
+        self.register_buffer('sin_cached', emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.model.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.model.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class InternLM2MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.w1 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w3 = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.w2 = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.w2(self.act_fn(self.w1(x)) * self.w3(x))
+
+        return down_proj
+
+
+# Copied from transformers.model.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaAttention
+class InternLM2Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f'hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}'
+                f' and `num_heads`: {self.num_heads}).'
+            )
+
+        self.wqkv = nn.Linear(
+            self.hidden_size,
+            (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=config.bias,
+        )
+
+        self.wo = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = InternLM2RotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.config.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling['type']
+            scaling_factor = self.config.rope_scaling['factor']
+            if scaling_type == 'dynamic':
+                self.rotary_emb = InternLM2DynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == 'linear':
+                self.rotary_emb = InternLM2LinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    base=self.config.rope_theta,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError("Currently we only support rotary embedding's type being 'dynamic' or 'linear'.")
+        return self.rotary_emb
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f'Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is'
+                f' {attn_weights.size()}'
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f'Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}'
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f'`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is'
+                f' {attn_output.size()}'
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Modified from transformers.model.llama.modeling_llama.InternLM2FlashAttention2
+class InternLM2FlashAttention2(InternLM2Attention):
+    """
+    InternLM2 flash attention module. This module inherits from `InternLM2Attention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # InternLM2FlashAttention2 attention does not support output_attentions
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop('padding_mask')
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv_states = self.wqkv(hidden_states)
+
+        qkv_states = rearrange(
+            qkv_states,
+            'b q (h gs d) -> b q h gs d',
+            gs=2 + self.num_key_value_groups,
+            d=self.head_dim,
+        )
+
+        query_states = qkv_states[..., : self.num_key_value_groups, :]
+        query_states = rearrange(query_states, 'b q h gs d -> b q (h gs) d')
+        key_states = qkv_states[..., -2, :]
+        value_states = qkv_states[..., -1, :]
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len
+        )
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.wo(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        # Contains at least one padding token in the sequence
+        causal = self.is_causal and query_length != 1
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._unpad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _unpad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q.to(torch.int64),
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+INTERNLM2_ATTENTION_CLASSES = {
+    'eager': InternLM2Attention,
+    'flash_attention_2': InternLM2FlashAttention2,
+}
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaDecoderLayer
+class InternLM2DecoderLayer(nn.Module):
+    def __init__(self, config: InternLM2Config):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attention = INTERNLM2_ATTENTION_CLASSES[config.attn_implementation](config=config)
+
+        self.feed_forward = InternLM2MLP(config)
+        self.attention_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.ffn_norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if 'padding_mask' in kwargs:
+            warnings.warn(
+                'Passing `padding_mask` is deprecated and will be removed in v4.37. '
+                'Please make sure use `attention_mask` instead.`'
+            )
+
+        residual = hidden_states
+
+        hidden_states = self.attention_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ffn_norm(hidden_states)
+        hidden_states = self.feed_forward(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+InternLM2_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`InternLM2Config`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->InternLM2
+@add_start_docstrings(
+    'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2PreTrainedModel(PreTrainedModel):
+    config_class = InternLM2Config
+    base_model_prefix = 'model'
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['InternLM2DecoderLayer']
+    _skip_keys_device_placement = 'past_key_values'
+    _supports_flash_attn_2 = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+InternLM2_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or
+            when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, decoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaModel
+@add_start_docstrings(
+    'The bare InternLM2 Model outputting raw hidden-states without any specific head on top.',
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2Model(InternLM2PreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLM2DecoderLayer`]
+
+    Args:
+        config: InternLM2Config
+    """
+
+    _auto_class = 'AutoModel'
+
+    def __init__(self, config: InternLM2Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.config = config
+        if not has_flash_attn:
+            self.config.attn_implementation = 'eager'
+            print('Warning: Flash attention is not available, using eager attention instead.')
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+        self.layers = nn.ModuleList([InternLM2DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = InternLM2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.tok_embeddings = value
+
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.attn_implementation == 'flash_attention_2':
+            _import_flash_attn()
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
+        else:
+            raise ValueError('You have to specify either input_ids or inputs_embeds')
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.tok_embeddings(input_ids)
+
+        if self.config.attn_implementation == 'flash_attention_2':
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        else:
+            if attention_mask is None:
+                attention_mask = torch.ones(
+                    (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+                )
+            attention_mask = self._prepare_decoder_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    '`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...'
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+# Modified from transformers.model.llama.modeling_llama.LlamaForCausalLM
+class InternLM2ForCausalLM(InternLM2PreTrainedModel):
+    _auto_class = 'AutoModelForCausalLM'
+
+    _tied_weights_keys = ['output.weight']
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = InternLM2Model(config)
+        self.vocab_size = config.vocab_size
+        self.output = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    def get_output_embeddings(self):
+        return self.output
+
+    def set_output_embeddings(self, new_embeddings):
+        self.output = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, InternLM2ForCausalLM
+
+        >>> model = InternLM2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.output(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+        output = CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+        output['logits'] = output['logits'].to(device)
+        return output
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values is not None:
+            past_length = past_key_values[0][0].shape[2]
+
+            # Some generation methods already pass only the last input ID
+            if input_ids.shape[1] > past_length:
+                remove_prefix_length = past_length
+            else:
+                # Default to old behavior: keep only final ID
+                remove_prefix_length = input_ids.shape[1] - 1
+
+            input_ids = input_ids[:, remove_prefix_length:]
+
+        position_ids = kwargs.get('position_ids', None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            model_inputs = {'input_ids': input_ids}
+
+        model_inputs.update(
+            {
+                'position_ids': position_ids,
+                'past_key_values': past_key_values,
+                'use_cache': kwargs.get('use_cache'),
+                'attention_mask': attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = [], meta_instruction=''):
+        if tokenizer.add_bos_token:
+            prompt = ''
+        else:
+            prompt = tokenizer.bos_token
+        if meta_instruction:
+            prompt += f"""<|im_start|>system\n{meta_instruction}<|im_end|>\n"""
+        for record in history:
+            prompt += f"""<|im_start|>user\n{record[0]}<|im_end|>\n<|im_start|>assistant\n{record[1]}<|im_end|>\n"""
+        prompt += f"""<|im_start|>user\n{query}<|im_end|>\n<|im_start|>assistant\n"""
+        return tokenizer([prompt], return_tensors='pt')
+
+    @torch.no_grad()
+    def chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        streamer: Optional[BaseStreamer] = None,
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        meta_instruction: str = 'You are an AI assistant whose name is InternLM (书生·浦语).\n'
+        '- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n'
+        '- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.',
+        **kwargs,
+    ):
+        inputs = self.build_inputs(tokenizer, query, history, meta_instruction)
+        inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
+        # also add end-of-assistant token in eos token id to avoid unnecessary generation
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids(['<|im_end|>'])[0]]
+        outputs = self.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            temperature=temperature,
+            top_p=top_p,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        outputs = outputs[0].cpu().tolist()[len(inputs['input_ids'][0]) :]
+        response = tokenizer.decode(outputs, skip_special_tokens=True)
+        response = response.split('<|im_end|>')[0]
+        history = history + [(query, response)]
+        return response, history
+
+    @torch.no_grad()
+    def stream_chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = [],
+        max_new_tokens: int = 1024,
+        do_sample: bool = True,
+        temperature: float = 0.8,
+        top_p: float = 0.8,
+        **kwargs,
+    ):
+        """
+        Return a generator in format: (response, history)
+        Eg.
+        ('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
+        ('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
+        """
+        if BaseStreamer is None:
+            raise ModuleNotFoundError(
+                'The version of `transformers` is too low. Please make sure '
+                'that you have installed `transformers>=4.28.0`.'
+            )
+
+        response_queue = queue.Queue(maxsize=20)
+
+        class ChatStreamer(BaseStreamer):
+            def __init__(self, tokenizer) -> None:
+                super().__init__()
+                self.tokenizer = tokenizer
+                self.queue = response_queue
+                self.query = query
+                self.history = history
+                self.response = ''
+                self.cache = []
+                self.received_inputs = False
+                self.queue.put((self.response, history + [(self.query, self.response)]))
+
+            def put(self, value):
+                if len(value.shape) > 1 and value.shape[0] > 1:
+                    raise ValueError('ChatStreamer only supports batch size 1')
+                elif len(value.shape) > 1:
+                    value = value[0]
+
+                if not self.received_inputs:
+                    # The first received value is input_ids, ignore here
+                    self.received_inputs = True
+                    return
+
+                self.cache.extend(value.tolist())
+                token = self.tokenizer.decode(self.cache, skip_special_tokens=True)
+                if token.strip() != '<|im_end|>':
+                    self.response = self.response + token
+                    history = self.history + [(self.query, self.response)]
+                    self.queue.put((self.response, history))
+                    self.cache = []
+                else:
+                    self.end()
+
+            def end(self):
+                self.queue.put(None)
+
+        def stream_producer():
+            return self.chat(
+                tokenizer=tokenizer,
+                query=query,
+                streamer=ChatStreamer(tokenizer=tokenizer),
+                history=history,
+                max_new_tokens=max_new_tokens,
+                do_sample=do_sample,
+                temperature=temperature,
+                top_p=top_p,
+                **kwargs,
+            )
+
+        def consumer():
+            producer = threading.Thread(target=stream_producer)
+            producer.start()
+            while True:
+                res = response_queue.get()
+                if res is None:
+                    return
+                yield res
+
+        return consumer()
+
+
+# Copied from transformers.model.llama.modeling_llama.LlamaForSequenceClassification with Llama->InternLM2
+@add_start_docstrings(
+    """
+    The InternLM2 Model transformer with a sequence classification head on top (linear layer).
+
+    [`InternLM2ForSequenceClassification`] uses the last token in order to do the classification,
+    as other causal models (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    InternLM2_START_DOCSTRING,
+)
+class InternLM2ForSequenceClassification(InternLM2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = InternLM2Model(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.tok_embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.tok_embeddings = value
+
+    @add_start_docstrings_to_model_forward(InternLM2_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError('Cannot handle batch sizes > 1 if no padding token is defined.')
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+                    logits.device
+                )
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = 'regression'
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = 'single_label_classification'
+                else:
+                    self.config.problem_type = 'multi_label_classification'
+
+            if self.config.problem_type == 'regression':
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == 'single_label_classification':
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == 'multi_label_classification':
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

InternVL/InternVL_2_5/modeling_internvl_chat.py

@@ -0,0 +1,349 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch.utils.checkpoint
+import transformers
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
+                          LlamaTokenizer)
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import ModelOutput, logging
+
+from .configuration_internvl_chat import InternVLChatConfig
+from .conversation import get_conv_template
+from .modeling_intern_vit import InternVisionModel, has_flash_attn
+from .modeling_internlm2 import InternLM2ForCausalLM
+
+logger = logging.get_logger(__name__)
+
+
+def version_cmp(v1, v2, op='eq'):
+    import operator
+
+    from packaging import version
+    op_func = getattr(operator, op)
+    return op_func(version.parse(v1), version.parse(v2))
+
+
+class InternVLChatModel(PreTrainedModel):
+    config_class = InternVLChatConfig
+    main_input_name = 'pixel_values'
+    base_model_prefix = 'language_model'
+    _supports_flash_attn_2 = True
+    _no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']
+
+    def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
+        super().__init__(config)
+
+        assert version_cmp(transformers.__version__, '4.37.0', 'ge')
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.select_layer = config.select_layer
+        self.template = config.template
+        self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+        use_flash_attn = use_flash_attn if has_flash_attn else False
+        config.vision_config.use_flash_attn = True if use_flash_attn else False
+        config.llm_config.attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
+
+        logger.info(f'num_image_token: {self.num_image_token}')
+        logger.info(f'ps_version: {self.ps_version}')
+        if vision_model is not None:
+            self.vision_model = vision_model
+        else:
+            self.vision_model = InternVisionModel(config.vision_config)
+        if language_model is not None:
+            self.language_model = language_model
+        else:
+            if config.llm_config.architectures[0] == 'LlamaForCausalLM':
+                self.language_model = LlamaForCausalLM(config.llm_config)
+            elif config.llm_config.architectures[0] == 'InternLM2ForCausalLM':
+                self.language_model = InternLM2ForCausalLM(config.llm_config)
+            else:
+                raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
+
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.llm_config.hidden_size
+
+        self.mlp1 = nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
+            nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
+            nn.GELU(),
+            nn.Linear(llm_hidden_size, llm_hidden_size)
+        )
+
+        self.img_context_token_id = None
+        self.conv_template = get_conv_template(self.template)
+        self.system_message = self.conv_template.system_message
+
+    def forward(
+            self,
+            pixel_values: torch.FloatTensor,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            image_flags: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        image_flags = image_flags.squeeze(-1)
+        input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()
+
+        vit_embeds = self.extract_feature(pixel_values)
+        vit_embeds = vit_embeds[image_flags == 1]
+        vit_batch_size = pixel_values.shape[0]
+
+        B, N, C = input_embeds.shape
+        input_embeds = input_embeds.reshape(B * N, C)
+
+        if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
+            print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')
+
+        input_ids = input_ids.reshape(B * N)
+        selected = (input_ids == self.img_context_token_id)
+        try:
+            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
+        except Exception as e:
+            vit_embeds = vit_embeds.reshape(-1, C)
+            print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
+                  f'vit_embeds.shape={vit_embeds.shape}')
+            n_token = selected.sum()
+            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds[:n_token]
+
+        input_embeds = input_embeds.reshape(B, N, C)
+
+        outputs = self.language_model(
+            inputs_embeds=input_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        logits = outputs.logits
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.language_model.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+                   int(c / (scale_factor * scale_factor)))
+        if self.ps_version == 'v1':
+            warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
+                          'which results in a transposed image.')
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values):
+        if self.select_layer == -1:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values,
+                output_hidden_states=False,
+                return_dict=True).last_hidden_state
+        else:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values,
+                output_hidden_states=True,
+                return_dict=True).hidden_states[self.select_layer]
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        h = w = int(vit_embeds.shape[1] ** 0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
+                   history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
+                   IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
+        if history is not None or return_history:
+            print('Now multi-turn chat is not supported in batch_chat.')
+            raise NotImplementedError
+
+        if image_counts is not None:
+            num_patches_list = image_counts
+            print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
+
+        img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+        self.img_context_token_id = img_context_token_id
+
+        if verbose and pixel_values is not None:
+            image_bs = pixel_values.shape[0]
+            print(f'dynamic ViT batch size: {image_bs}')
+
+        queries = []
+        for idx, num_patches in enumerate(num_patches_list):
+            question = questions[idx]
+            if pixel_values is not None and '<image>' not in question:
+                question = '<image>\n' + question
+            template = get_conv_template(self.template)
+            template.system_message = self.system_message
+            template.append_message(template.roles[0], question)
+            template.append_message(template.roles[1], None)
+            query = template.get_prompt()
+
+            image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
+            query = query.replace('<image>', image_tokens, 1)
+            queries.append(query)
+
+        tokenizer.padding_side = 'left'
+        model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
+        input_ids = model_inputs['input_ids'].to(self.device)
+        attention_mask = model_inputs['attention_mask'].to(self.device)
+        eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
+        generation_config['eos_token_id'] = eos_token_id
+        generation_output = self.generate(
+            pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            **generation_config
+        )
+        responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
+        responses = [response.split(template.sep.strip())[0].strip() for response in responses]
+        return responses
+
+    def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
+             num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
+             verbose=False):
+
+        if history is None and pixel_values is not None and '<image>' not in question:
+            question = '<image>\n' + question
+
+        if num_patches_list is None:
+            num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
+        assert pixel_values is None or len(pixel_values) == sum(num_patches_list)
+
+        img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
+        self.img_context_token_id = img_context_token_id
+
+        template = get_conv_template(self.template)
+        template.system_message = self.system_message
+        eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
+
+        history = [] if history is None else history
+        for (old_question, old_answer) in history:
+            template.append_message(template.roles[0], old_question)
+            template.append_message(template.roles[1], old_answer)
+        template.append_message(template.roles[0], question)
+        template.append_message(template.roles[1], None)
+        query = template.get_prompt()
+
+        if verbose and pixel_values is not None:
+            image_bs = pixel_values.shape[0]
+            print(f'dynamic ViT batch size: {image_bs}')
+
+        for num_patches in num_patches_list:
+            image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
+            query = query.replace('<image>', image_tokens, 1)
+
+        model_inputs = tokenizer(query, return_tensors='pt')
+        input_ids = model_inputs['input_ids'].to(self.device)
+        attention_mask = model_inputs['attention_mask'].to(self.device)
+        generation_config['eos_token_id'] = eos_token_id
+        generation_output = self.generate(
+            pixel_values=pixel_values,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            **generation_config
+        )
+        response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
+        response = response.split(template.sep.strip())[0].strip()
+        history.append((question, response))
+        if return_history:
+            return response, history
+        else:
+            query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
+            query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
+            if verbose:
+                print(query_to_print, response)
+            return response
+
+    @torch.no_grad()
+    def generate(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            input_ids: Optional[torch.FloatTensor] = None,
+            attention_mask: Optional[torch.LongTensor] = None,
+            visual_features: Optional[torch.FloatTensor] = None,
+            generation_config: Optional[GenerationConfig] = None,
+            output_hidden_states: Optional[bool] = None,
+            **generate_kwargs,
+    ) -> torch.LongTensor:
+
+        assert self.img_context_token_id is not None
+        if pixel_values is not None:
+            if visual_features is not None:
+                vit_embeds = visual_features
+            else:
+                vit_embeds = self.extract_feature(pixel_values)
+            input_embeds = self.language_model.get_input_embeddings()(input_ids)
+            B, N, C = input_embeds.shape
+            input_embeds = input_embeds.reshape(B * N, C)
+
+            input_ids = input_ids.reshape(B * N)
+            selected = (input_ids == self.img_context_token_id)
+            assert selected.sum() != 0
+            input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
+
+            input_embeds = input_embeds.reshape(B, N, C)
+        else:
+            input_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+        outputs = self.language_model.generate(
+            inputs_embeds=input_embeds,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            output_hidden_states=output_hidden_states,
+            use_cache=True,
+            **generate_kwargs,
+        )
+
+        return outputs