




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)







def 					SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Any:



       """simple docstring"""

       A__   				=					VideoMAEConfig()

       set_architecture_configs(A__				,       A__		)

       if "finetuned" not in model_name:
              A__   				=					False

       if "finetuned" in model_name:
              A__   				=					'''huggingface/label-files'''
              if "kinetics" in model_name:
                     A__   				=					400
                     A__   				=					'''kinetics400-id2label.json'''
              elif "ssv2" in model_name:
                     A__   				=					174
                     A__   				=					'''something-something-v2-id2label.json'''
              else:
                     raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'''		)
              A__   				=					json.load(open(hf_hub_download(A__				,       A__				,       repo_type='''dataset'''		)				,       '''r'''		)		)
              A__   				=					{int(A__		): v for k, v in idalabel.items()}
              A__   				=					idalabel
              A__   				=					{v: k for k, v in idalabel.items()}

       return config







def 					SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Dict:



       """simple docstring"""

       if "small" in model_name:
              A__   				=					384
              A__   				=					1_536
              A__   				=					12
              A__   				=					16
              A__   				=					12
              A__   				=					3
              A__   				=					192
              A__   				=					768
       elif "large" in model_name:
              A__   				=					1_024
              A__   				=					4_096
              A__   				=					24
              A__   				=					16
              A__   				=					12
              A__   				=					8
              A__   				=					512
              A__   				=					2_048
       elif "huge" in model_name:
              A__   				=					1_280
              A__   				=					5_120
              A__   				=					32
              A__   				=					16
              A__   				=					12
              A__   				=					8
              A__   				=					640
              A__   				=					2_560
       elif "base" not in model_name:
              raise ValueError('''Model name should include either \"small\", \"base\", \"large\", or \"huge\"'''		)







def 					SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

       if "encoder." in name:
              A__   				=					name.replace('''encoder.'''				,       ''''''		)
       if "cls_token" in name:
              A__   				=					name.replace('''cls_token'''				,       '''videomae.embeddings.cls_token'''		)
       if "decoder_pos_embed" in name:
              A__   				=					name.replace('''decoder_pos_embed'''				,       '''decoder.decoder_pos_embed'''		)
       if "pos_embed" in name and "decoder" not in name:
              A__   				=					name.replace('''pos_embed'''				,       '''videomae.embeddings.position_embeddings'''		)
       if "patch_embed.proj" in name:
              A__   				=					name.replace('''patch_embed.proj'''				,       '''videomae.embeddings.patch_embeddings.projection'''		)
       if "patch_embed.norm" in name:
              A__   				=					name.replace('''patch_embed.norm'''				,       '''videomae.embeddings.norm'''		)
       if "decoder.blocks" in name:
              A__   				=					name.replace('''decoder.blocks'''				,       '''decoder.decoder_layers'''		)
       if "blocks" in name:
              A__   				=					name.replace('''blocks'''				,       '''videomae.encoder.layer'''		)
       if "attn.proj" in name:
              A__   				=					name.replace('''attn.proj'''				,       '''attention.output.dense'''		)
       if "attn" in name and "bias" not in name:
              A__   				=					name.replace('''attn'''				,       '''attention.self'''		)
       if "attn" in name:
              A__   				=					name.replace('''attn'''				,       '''attention.attention'''		)
       if "norm1" in name:
              A__   				=					name.replace('''norm1'''				,       '''layernorm_before'''		)
       if "norm2" in name:
              A__   				=					name.replace('''norm2'''				,       '''layernorm_after'''		)
       if "mlp.fc1" in name:
              A__   				=					name.replace('''mlp.fc1'''				,       '''intermediate.dense'''		)
       if "mlp.fc2" in name:
              A__   				=					name.replace('''mlp.fc2'''				,       '''output.dense'''		)
       if "decoder_embed" in name:
              A__   				=					name.replace('''decoder_embed'''				,       '''decoder.decoder_embed'''		)
       if "decoder_norm" in name:
              A__   				=					name.replace('''decoder_norm'''				,       '''decoder.decoder_norm'''		)
       if "decoder_pred" in name:
              A__   				=					name.replace('''decoder_pred'''				,       '''decoder.decoder_pred'''		)
       if "norm.weight" in name and "decoder" not in name and "fc" not in name:
              A__   				=					name.replace('''norm.weight'''				,       '''videomae.layernorm.weight'''		)
       if "norm.bias" in name and "decoder" not in name and "fc" not in name:
              A__   				=					name.replace('''norm.bias'''				,       '''videomae.layernorm.bias'''		)
       if "head" in name and "decoder" not in name:
              A__   				=					name.replace('''head'''				,       '''classifier'''		)

       return name







def 					SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Any:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__   				=					orig_state_dict.pop(A__		)

              if key.startswith('''encoder.'''		):
                     A__   				=					key.replace('''encoder.'''				,       ''''''		)

              if "qkv" in key:
                     A__   				=					key.split('''.'''		)
                     if key.startswith('''decoder.blocks'''		):
                            A__   				=					config.decoder_hidden_size
                            A__   				=					int(key_split[2]		)
                            A__   				=					'''decoder.decoder_layers.'''
                            if "weight" in key:
                                   A__   				=					val[:dim, :]
                                   A__   				=					val[dim : dim * 2, :]
                                   A__   				=					val[-dim:, :]
                     else:
                            A__   				=					config.hidden_size
                            A__   				=					int(key_split[1]		)
                            A__   				=					'''videomae.encoder.layer.'''
                            if "weight" in key:
                                   A__   				=					val[:dim, :]
                                   A__   				=					val[dim : dim * 2, :]
                                   A__   				=					val[-dim:, :]
              else:
                     A__   				=					val

       return orig_state_dict







def 					SCREAMING_SNAKE_CASE       ( )     -> List[str]:



       """simple docstring"""

       A__   				=					hf_hub_download(
           repo_id='''hf-internal-testing/spaghetti-video'''				,       filename='''eating_spaghetti.npy'''				,       repo_type='''dataset'''		)
       A__   				=					np.load(A__		)
       return list(A__		)







def 					SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

       A__   				=					get_videomae_config(A__		)

       if "finetuned" in model_name:
              A__   				=					VideoMAEForVideoClassification(A__		)
       else:
              A__   				=					VideoMAEForPreTraining(A__		)

       # download original checkpoint, hosted on Google Drive
       A__   				=					'''pytorch_model.bin'''
       gdown.cached_download(A__				,       A__				,       quiet=A__		)
       A__   				=					torch.load(A__				,       map_location='''cpu'''		)
       if "model" in files:
              A__   				=					files['''model''']
       else:
              A__   				=					files['''module''']
       A__   				=					convert_state_dict(A__				,       A__		)

       model.load_state_dict(A__		)
       model.eval()

       # verify model on basic input
       A__   				=					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]				,       image_std=[0.5, 0.5, 0.5]		)
       A__   				=					prepare_video()
       A__   				=					image_processor(A__				,       return_tensors='''pt'''		)

       if "finetuned" not in model_name:
              A__   				=					hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''				,       filename='''bool_masked_pos.pt'''		)
              A__   				=					torch.load(A__		)

       A__   				=					model(**A__		)
       A__   				=					outputs.logits

       A__   				=					[
           '''videomae-small-finetuned-kinetics''',
           '''videomae-small-finetuned-ssv2''',
           # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
           '''videomae-base-short''',
           '''videomae-base-short-finetuned-kinetics''',
           '''videomae-base''',
           '''videomae-base-finetuned-kinetics''',
           '''videomae-large''',
           '''videomae-large-finetuned-kinetics''',
           '''videomae-huge-finetuned-kinetics''',
           # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
           '''videomae-base-short-ssv2''',
           '''videomae-base-short-finetuned-ssv2''',
           '''videomae-base-ssv2''',
           '''videomae-base-finetuned-ssv2''',
       ]

       # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
       if model_name == "videomae-small-finetuned-kinetics":
              A__   				=					torch.Size([1, 400]		)
              A__   				=					torch.tensor([-0.92_91, -0.40_61, -0.93_07]		)
       elif model_name == "videomae-small-finetuned-ssv2":
              A__   				=					torch.Size([1, 174]		)
              A__   				=					torch.tensor([0.26_71, -0.46_89, -0.82_35]		)
       elif model_name == "videomae-base":
              A__   				=					torch.Size([1, 1_408, 1_536]		)
              A__   				=					torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]]		)
       elif model_name == "videomae-base-short":
              A__   				=					torch.Size([1, 1_408, 1_536]		)
              A__   				=					torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]		)
              # we verified the loss both for normalized and unnormalized targets for this one
              A__   				=					torch.tensor([0.51_42]		) if config.norm_pix_loss else torch.tensor([0.64_69]		)
       elif model_name == "videomae-large":
              A__   				=					torch.Size([1, 1_408, 1_536]		)
              A__   				=					torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]]		)
       elif model_name == "videomae-large-finetuned-kinetics":
              A__   				=					torch.Size([1, 400]		)
              A__   				=					torch.tensor([0.07_71, 0.00_11, -0.36_25]		)
       elif model_name == "videomae-huge-finetuned-kinetics":
              A__   				=					torch.Size([1, 400]		)
              A__   				=					torch.tensor([0.24_33, 0.16_32, -0.48_94]		)
       elif model_name == "videomae-base-short-finetuned-kinetics":
              A__   				=					torch.Size([1, 400]		)
              A__   				=					torch.tensor([0.65_88, 0.09_90, -0.24_93]		)
       elif model_name == "videomae-base-finetuned-kinetics":
              A__   				=					torch.Size([1, 400]		)
              A__   				=					torch.tensor([0.36_69, -0.06_88, -0.24_21]		)
       elif model_name == "videomae-base-short-ssv2":
              A__   				=					torch.Size([1, 1_408, 1_536]		)
              A__   				=					torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]]		)
       elif model_name == "videomae-base-short-finetuned-ssv2":
              A__   				=					torch.Size([1, 174]		)
              A__   				=					torch.tensor([-0.05_37, -0.15_39, -0.32_66]		)
       elif model_name == "videomae-base-ssv2":
              A__   				=					torch.Size([1, 1_408, 1_536]		)
              A__   				=					torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]]		)
       elif model_name == "videomae-base-finetuned-ssv2":
              A__   				=					torch.Size([1, 174]		)
              A__   				=					torch.tensor([0.19_61, -0.83_37, -0.63_89]		)
       else:
              raise ValueError(f"""Model name not supported. Should be one of {model_names}"""		)

       # verify logits
       assert logits.shape == expected_shape
       if "finetuned" in model_name:
              assert torch.allclose(logits[0, :3]				,       A__				,       atol=1E-4		)
       else:
              print('''Logits:'''				,       logits[0, :3, :3]		)
              assert torch.allclose(logits[0, :3, :3]				,       A__				,       atol=1E-4		)
       print('''Logits ok!'''		)

       # verify loss, if applicable
       if model_name == "videomae-base-short":
              A__   				=					outputs.loss
              assert torch.allclose(A__				,       A__				,       atol=1E-4		)
              print('''Loss ok!'''		)

       if pytorch_dump_folder_path is not None:
              print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""		)
              image_processor.save_pretrained(A__		)
              model.save_pretrained(A__		)

       if push_to_hub:
              print('''Pushing to the hub...'''		)
              model.push_to_hub(A__				,       organization='''nielsr'''		)


if __name__ == "__main__":
  _lowerCamelCase			:	Dict							 =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
      type=str,
      help=(
          """URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct"""
          """ download link."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
      type=str,
      help="""Path to the output PyTorch model directory.""",
  )
  parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )

  _lowerCamelCase			:	Any							 =     parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> float:



       """simple docstring"""

       def get_matched_characters(lowercase_				,       lowercase_		) -> str:
              A__   				=					[]
              A__   				=					min(len(_stra		)				,       len(_stra		)		) // 2
              for i, l in enumerate(_stra		):
                     A__   				=					int(max(0				,       i - limit		)		)
                     A__   				=					int(min(i + limit + 1				,       len(_stra		)		)		)
                     if l in _stra[left:right]:
                            matched.append(lowercase_		)
                            A__   				=					f"""{_stra[0:_stra.index(lowercase_		)]} {_stra[_stra.index(lowercase_		) + 1:]}"""

              return "".join(lowercase_		)

       # matching characters
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					len(lowercase_		)

       # transposition
       A__   				=					(
           len([(ca, ca) for ca, ca in zip(lowercase_				,       lowercase_		) if ca != ca]		) // 2
       )

       if not match_count:
              A__   				=					0.0
       else:
              A__   				=					(
                  1
                  / 3
                  * (
                      match_count / len(lowercase_		)
                      + match_count / len(lowercase_		)
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       A__   				=					0
       for ca, ca in zip(stra[:4]				,       stra[:4]		):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))


