


import argparse
import json

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

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor
def     UpperCAmelCase_ (							__snake_case						)					->      Optional[Any]:







 """simple docstring"""


 _lowercase											=TimesformerConfig()

 if "large" in model_name:
  _lowercase											=96

 if "hr" in model_name:
  _lowercase											=16
  _lowercase											=448

 _lowercase											='''huggingface/label-files'''
 if "k400" in model_name:
  _lowercase											=400
  _lowercase											='''kinetics400-id2label.json'''
 elif "k600" in model_name:
  _lowercase											=600
  _lowercase											='''kinetics600-id2label.json'''
 elif "ssv2" in model_name:
  _lowercase											=174
  _lowercase											='''something-something-v2-id2label.json'''
 else:
  raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''						)
 _lowercase											=json.load(open(hf_hub_download(__snake_case					,						__snake_case					,						repo_type='''dataset'''						)					,						'''r'''						)						)
 _lowercase											={int(__snake_case						): v for k, v in idalabel.items()}
 _lowercase											=idalabel
 _lowercase											={v: k for k, v in idalabel.items()}

 return config
def     UpperCAmelCase_ (							__snake_case						)					->      Any:







 """simple docstring"""


 if "encoder." in name:
  _lowercase											=name.replace('''encoder.'''					,						''''''						)
 if "cls_token" in name:
  _lowercase											=name.replace('''cls_token'''					,						'''timesformer.embeddings.cls_token'''						)
 if "pos_embed" in name:
  _lowercase											=name.replace('''pos_embed'''					,						'''timesformer.embeddings.position_embeddings'''						)
 if "time_embed" in name:
  _lowercase											=name.replace('''time_embed'''					,						'''timesformer.embeddings.time_embeddings'''						)
 if "patch_embed.proj" in name:
  _lowercase											=name.replace('''patch_embed.proj'''					,						'''timesformer.embeddings.patch_embeddings.projection'''						)
 if "patch_embed.norm" in name:
  _lowercase											=name.replace('''patch_embed.norm'''					,						'''timesformer.embeddings.norm'''						)
 if "blocks" in name:
  _lowercase											=name.replace('''blocks'''					,						'''timesformer.encoder.layer'''						)
 if "attn.proj" in name:
  _lowercase											=name.replace('''attn.proj'''					,						'''attention.output.dense'''						)
 if "attn" in name and "bias" not in name and "temporal" not in name:
  _lowercase											=name.replace('''attn'''					,						'''attention.self'''						)
 if "attn" in name and "temporal" not in name:
  _lowercase											=name.replace('''attn'''					,						'''attention.attention'''						)
 if "temporal_norm1" in name:
  _lowercase											=name.replace('''temporal_norm1'''					,						'''temporal_layernorm'''						)
 if "temporal_attn.proj" in name:
  _lowercase											=name.replace('''temporal_attn'''					,						'''temporal_attention.output.dense'''						)
 if "temporal_fc" in name:
  _lowercase											=name.replace('''temporal_fc'''					,						'''temporal_dense'''						)
 if "norm1" in name and "temporal" not in name:
  _lowercase											=name.replace('''norm1'''					,						'''layernorm_before'''						)
 if "norm2" in name:
  _lowercase											=name.replace('''norm2'''					,						'''layernorm_after'''						)
 if "mlp.fc1" in name:
  _lowercase											=name.replace('''mlp.fc1'''					,						'''intermediate.dense'''						)
 if "mlp.fc2" in name:
  _lowercase											=name.replace('''mlp.fc2'''					,						'''output.dense'''						)
 if "norm.weight" in name and "fc" not in name and "temporal" not in name:
  _lowercase											=name.replace('''norm.weight'''					,						'''timesformer.layernorm.weight'''						)
 if "norm.bias" in name and "fc" not in name and "temporal" not in name:
  _lowercase											=name.replace('''norm.bias'''					,						'''timesformer.layernorm.bias'''						)
 if "head" in name:
  _lowercase											=name.replace('''head'''					,						'''classifier'''						)

 return name
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      Dict:







 """simple docstring"""


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

  if key.startswith('''model.'''						):
   _lowercase											=key.replace('''model.'''					,						''''''						)

  if "qkv" in key:
   _lowercase											=key.split('''.'''						)
   _lowercase											=int(key_split[1]						)
   _lowercase											='''timesformer.encoder.layer.'''
   if "temporal" in key:
    _lowercase											='''.temporal_attention.attention.qkv.'''
   else:
    _lowercase											='''.attention.attention.qkv.'''
   if "weight" in key:
    _lowercase											=val
   else:
    _lowercase											=val
  else:
   _lowercase											=val

 return orig_state_dict
def     UpperCAmelCase_ (							)					->      List[Any]:







 """simple docstring"""


 _lowercase											=hf_hub_download(
     repo_id='''hf-internal-testing/spaghetti-video'''					,						filename='''eating_spaghetti.npy'''					,						repo_type='''dataset'''						)
 _lowercase											=np.load(__snake_case						)
 return list(__snake_case						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      List[str]:







 """simple docstring"""


 _lowercase											=get_timesformer_config(__snake_case						)

 _lowercase											=TimesformerForVideoClassification(__snake_case						)

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

 model.load_state_dict(__snake_case						)
 model.eval()

 # verify model on basic input
 _lowercase											=VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]					,						image_std=[0.5, 0.5, 0.5]						)
 _lowercase											=prepare_video()
 _lowercase											=image_processor(video[:8]					,						return_tensors='''pt'''						)

 _lowercase											=model(**__snake_case						)
 _lowercase											=outputs.logits

 _lowercase											=[
     # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
     '''timesformer-base-finetuned-k400''',
     '''timesformer-large-finetuned-k400''',
     '''timesformer-hr-finetuned-k400''',
     # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
     '''timesformer-base-finetuned-k600''',
     '''timesformer-large-finetuned-k600''',
     '''timesformer-hr-finetuned-k600''',
     # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
     '''timesformer-base-finetuned-ssv2''',
     '''timesformer-large-finetuned-ssv2''',
     '''timesformer-hr-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 == "timesformer-base-finetuned-k400":
  _lowercase											=torch.Size([1, 400]						)
  _lowercase											=torch.tensor([-0.30_16, -0.77_13, -0.42_05]						)
 elif model_name == "timesformer-base-finetuned-k600":
  _lowercase											=torch.Size([1, 600]						)
  _lowercase											=torch.tensor([-0.72_67, -0.74_66, 3.24_04]						)
 elif model_name == "timesformer-base-finetuned-ssv2":
  _lowercase											=torch.Size([1, 174]						)
  _lowercase											=torch.tensor([-0.90_59, 0.64_33, -3.14_57]						)
 elif model_name == "timesformer-large-finetuned-k400":
  _lowercase											=torch.Size([1, 400]						)
  _lowercase											=torch.tensor([0, 0, 0]						)
 elif model_name == "timesformer-large-finetuned-k600":
  _lowercase											=torch.Size([1, 600]						)
  _lowercase											=torch.tensor([0, 0, 0]						)
 elif model_name == "timesformer-large-finetuned-ssv2":
  _lowercase											=torch.Size([1, 174]						)
  _lowercase											=torch.tensor([0, 0, 0]						)
 elif model_name == "timesformer-hr-finetuned-k400":
  _lowercase											=torch.Size([1, 400]						)
  _lowercase											=torch.tensor([-0.96_17, -3.73_11, -3.77_08]						)
 elif model_name == "timesformer-hr-finetuned-k600":
  _lowercase											=torch.Size([1, 600]						)
  _lowercase											=torch.tensor([2.52_73, 0.71_27, 1.88_48]						)
 elif model_name == "timesformer-hr-finetuned-ssv2":
  _lowercase											=torch.Size([1, 174]						)
  _lowercase											=torch.tensor([-3.67_56, -0.75_13, 0.71_80]						)
 else:
  raise ValueError(F"Model name not supported. Should be one of {model_names}"						)

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]					,						__snake_case					,						atol=1e-4						)
 print('''Logits 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(__snake_case						)
  model.save_pretrained(__snake_case						)

 if push_to_hub:
  print('''Pushing to the hub...'''						)
  model.push_to_hub(F"fcakyon/{model_name}"						)


if __name__ == "__main__":
  UpperCAmelCase__            =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download''',
      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='''''',
      type=str,
      help='''Path to the output PyTorch model directory.''',
  )
  parser.add_argument('''--model_name''', default='''timesformer-base-finetuned-k400''', 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.'''
  )

  UpperCAmelCase__            =							parser.parse_args()
  convert_timesformer_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
  )





def     UpperCAmelCase_ (							__snake_case						)					->      str:







 """simple docstring"""


 return " ".join(sorted(set(sentence.split()						)						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

