


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 			_a      (    __UpperCamelCase			):
     a_     :  Tuple				      =     TimesformerConfig()

     if "large" in model_name:
          a_     :  Optional[int]				      =     9_6

     if "hr" in model_name:
          a_     :  Tuple				      =     1_6
          a_     :  List[str]				      =     4_4_8

     a_     :  Tuple				      =     """huggingface/label-files"""
     if "k400" in model_name:
          a_     :  str				      =     4_0_0
          a_     :  List[Any]				      =     """kinetics400-id2label.json"""
     elif "k600" in model_name:
          a_     :  Any				      =     6_0_0
          a_     :  Any				      =     """kinetics600-id2label.json"""
     elif "ssv2" in model_name:
          a_     :  Union[str, Any]				      =     1_7_4
          a_     :  Optional[int]				      =     """something-something-v2-id2label.json"""
     else:
          raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'."""			)
     a_     :  str				      =     json.load(open(hf_hub_download(__UpperCamelCase	,	__UpperCamelCase	,	repo_type="""dataset"""			)	,	"""r"""			)			)
     a_     :  List[str]				      =     {int(__UpperCamelCase			): v for k, v in idalabel.items()}
     a_     :  Dict				      =     idalabel
     a_     :  Tuple				      =     {v: k for k, v in idalabel.items()}

     return config



def 			_a      (    __UpperCamelCase			):
     if "encoder." in name:
          a_     :  Tuple				      =     name.replace("""encoder."""	,	""""""			)
     if "cls_token" in name:
          a_     :  Tuple				      =     name.replace("""cls_token"""	,	"""timesformer.embeddings.cls_token"""			)
     if "pos_embed" in name:
          a_     :  Dict				      =     name.replace("""pos_embed"""	,	"""timesformer.embeddings.position_embeddings"""			)
     if "time_embed" in name:
          a_     :  Optional[Any]				      =     name.replace("""time_embed"""	,	"""timesformer.embeddings.time_embeddings"""			)
     if "patch_embed.proj" in name:
          a_     :  List[Any]				      =     name.replace("""patch_embed.proj"""	,	"""timesformer.embeddings.patch_embeddings.projection"""			)
     if "patch_embed.norm" in name:
          a_     :  str				      =     name.replace("""patch_embed.norm"""	,	"""timesformer.embeddings.norm"""			)
     if "blocks" in name:
          a_     :  List[Any]				      =     name.replace("""blocks"""	,	"""timesformer.encoder.layer"""			)
     if "attn.proj" in name:
          a_     :  Tuple				      =     name.replace("""attn.proj"""	,	"""attention.output.dense"""			)
     if "attn" in name and "bias" not in name and "temporal" not in name:
          a_     :  Optional[int]				      =     name.replace("""attn"""	,	"""attention.self"""			)
     if "attn" in name and "temporal" not in name:
          a_     :  List[str]				      =     name.replace("""attn"""	,	"""attention.attention"""			)
     if "temporal_norm1" in name:
          a_     :  List[Any]				      =     name.replace("""temporal_norm1"""	,	"""temporal_layernorm"""			)
     if "temporal_attn.proj" in name:
          a_     :  Optional[int]				      =     name.replace("""temporal_attn"""	,	"""temporal_attention.output.dense"""			)
     if "temporal_fc" in name:
          a_     :  Dict				      =     name.replace("""temporal_fc"""	,	"""temporal_dense"""			)
     if "norm1" in name and "temporal" not in name:
          a_     :  Optional[int]				      =     name.replace("""norm1"""	,	"""layernorm_before"""			)
     if "norm2" in name:
          a_     :  Optional[Any]				      =     name.replace("""norm2"""	,	"""layernorm_after"""			)
     if "mlp.fc1" in name:
          a_     :  Any				      =     name.replace("""mlp.fc1"""	,	"""intermediate.dense"""			)
     if "mlp.fc2" in name:
          a_     :  List[Any]				      =     name.replace("""mlp.fc2"""	,	"""output.dense"""			)
     if "norm.weight" in name and "fc" not in name and "temporal" not in name:
          a_     :  Tuple				      =     name.replace("""norm.weight"""	,	"""timesformer.layernorm.weight"""			)
     if "norm.bias" in name and "fc" not in name and "temporal" not in name:
          a_     :  Tuple				      =     name.replace("""norm.bias"""	,	"""timesformer.layernorm.bias"""			)
     if "head" in name:
          a_     :  Tuple				      =     name.replace("""head"""	,	"""classifier"""			)

     return name



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     for key in orig_state_dict.copy().keys():
          a_     :  List[Any]				      =     orig_state_dict.pop(__UpperCamelCase			)

          if key.startswith("""model."""			):
               a_     :  Dict				      =     key.replace("""model."""	,	""""""			)

          if "qkv" in key:
               a_     :  Tuple				      =     key.split("""."""			)
               a_     :  int				      =     int(key_split[1]			)
               a_     :  int				      =     """timesformer.encoder.layer."""
               if "temporal" in key:
                    a_     :  Tuple				      =     """.temporal_attention.attention.qkv."""
               else:
                    a_     :  List[str]				      =     """.attention.attention.qkv."""
               if "weight" in key:
                    a_     :  Optional[Any]				      =     val
               else:
                    a_     :  Optional[int]				      =     val
          else:
               a_     :  List[Any]				      =     val

     return orig_state_dict



def 			_a      (    ):
     a_     :  int				      =     hf_hub_download(
         repo_id="""hf-internal-testing/spaghetti-video"""	,	filename="""eating_spaghetti.npy"""	,	repo_type="""dataset"""			)
     a_     :  List[str]				      =     np.load(__UpperCamelCase			)
     return list(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  List[Any]				      =     get_timesformer_config(__UpperCamelCase			)

     a_     :  Tuple				      =     TimesformerForVideoClassification(__UpperCamelCase			)

     # download original checkpoint, hosted on Google Drive
     a_     :  Dict				      =     """pytorch_model.bin"""
     gdown.cached_download(__UpperCamelCase	,	__UpperCamelCase	,	quiet=__UpperCamelCase			)
     a_     :  Dict				      =     torch.load(__UpperCamelCase	,	map_location="""cpu"""			)
     if "model" in files:
          a_     :  List[str]				      =     files["""model"""]
     elif "module" in files:
          a_     :  Optional[Any]				      =     files["""module"""]
     else:
          a_     :  str				      =     files["""model_state"""]
     a_     :  Tuple				      =     convert_state_dict(__UpperCamelCase	,	__UpperCamelCase			)

     model.load_state_dict(__UpperCamelCase			)
     model.eval()

     # verify model on basic input
     a_     :  Optional[int]				      =     VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,	image_std=[0.5, 0.5, 0.5]			)
     a_     :  List[Any]				      =     prepare_video()
     a_     :  Union[str, Any]				      =     image_processor(video[:8]	,	return_tensors="""pt"""			)

     a_     :  Optional[Any]				      =     model(**__UpperCamelCase			)
     a_     :  Optional[Any]				      =     outputs.logits

     a_     :  Optional[int]				      =     [
         # 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":
          a_     :  List[Any]				      =     torch.Size([1, 4_0_0]			)
          a_     :  Optional[int]				      =     torch.tensor([-0.30_16, -0.77_13, -0.42_05]			)
     elif model_name == "timesformer-base-finetuned-k600":
          a_     :  Optional[Any]				      =     torch.Size([1, 6_0_0]			)
          a_     :  Dict				      =     torch.tensor([-0.72_67, -0.74_66, 3.24_04]			)
     elif model_name == "timesformer-base-finetuned-ssv2":
          a_     :  Optional[int]				      =     torch.Size([1, 1_7_4]			)
          a_     :  Optional[int]				      =     torch.tensor([-0.90_59, 0.64_33, -3.14_57]			)
     elif model_name == "timesformer-large-finetuned-k400":
          a_     :  Optional[int]				      =     torch.Size([1, 4_0_0]			)
          a_     :  Union[str, Any]				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-k600":
          a_     :  Any				      =     torch.Size([1, 6_0_0]			)
          a_     :  Optional[Any]				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-large-finetuned-ssv2":
          a_     :  int				      =     torch.Size([1, 1_7_4]			)
          a_     :  Tuple				      =     torch.tensor([0, 0, 0]			)
     elif model_name == "timesformer-hr-finetuned-k400":
          a_     :  List[str]				      =     torch.Size([1, 4_0_0]			)
          a_     :  List[Any]				      =     torch.tensor([-0.96_17, -3.73_11, -3.77_08]			)
     elif model_name == "timesformer-hr-finetuned-k600":
          a_     :  Tuple				      =     torch.Size([1, 6_0_0]			)
          a_     :  Union[str, Any]				      =     torch.tensor([2.52_73, 0.71_27, 1.88_48]			)
     elif model_name == "timesformer-hr-finetuned-ssv2":
          a_     :  List[str]				      =     torch.Size([1, 1_7_4]			)
          a_     :  Tuple				      =     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]	,	__UpperCamelCase	,	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(__UpperCamelCase			)
          model.save_pretrained(__UpperCamelCase			)

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


if __name__ == "__main__":
       __lowerCamelCase   	=  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.'''
       )

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




"""simple docstring"""




def 	UpperCAmelCase       (						UpperCamelCase__ = 100							,     UpperCamelCase__ = 100					):






							"""simple docstring"""





							return max(
							    sum(int(UpperCamelCase__					) for x in str(base**power					)					)
							    for base in range(UpperCamelCase__					)
							    for power in range(UpperCamelCase__					)					)


# Tests
if __name__ == "__main__":
				import doctest

				doctest.testmod()





