


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		)  ->    Tuple:







 """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		)  ->    List[str]:







 """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		)  ->    str:







 """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_      (							)  ->    str:







 """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		)  ->    Optional[int]:







 """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
  )





from ..utils import DummyObject, requires_backends







class    lowerCamelCase__     ( metaclass=lowerCAmelCase):
     SCREAMING_SNAKE_CASE__       =    ['''tensorflow_text''']





     def __init__(self	,  *UpperCAmelCase	,  **UpperCAmelCase )  ->     Any:
      requires_backends(self	,  ['''tensorflow_text'''] )

