




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 			lowerCAmelCase_			(  lowerCamelCase:       Any						)					->			Any:


      '''simple docstring'''




      lowerCamelCase__        =				TimesformerConfig()

      if "large" in model_name:
            lowerCamelCase__        =				96

      if "hr" in model_name:
            lowerCamelCase__        =				16
            lowerCamelCase__        =				4_48

      lowerCamelCase__        =				'huggingface/label-files'
      if "k400" in model_name:
            lowerCamelCase__        =				4_00
            lowerCamelCase__        =				'kinetics400-id2label.json'
      elif "k600" in model_name:
            lowerCamelCase__        =				6_00
            lowerCamelCase__        =				'kinetics600-id2label.json'
      elif "ssv2" in model_name:
            lowerCamelCase__        =				1_74
            lowerCamelCase__        =				'something-something-v2-id2label.json'
      else:
            raise ValueError('Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'						)
      lowerCamelCase__        =				json.load(open(hf_hub_download(lowerCamelCase     ,				lowerCamelCase     ,				repo_type='dataset'						)     ,				'r'						)						)
      lowerCamelCase__        =				{int(lowerCamelCase						): v for k, v in idalabel.items()}
      lowerCamelCase__        =				idalabel
      lowerCamelCase__        =				{v: k for k, v in idalabel.items()}

      return config

def 			lowerCAmelCase_			(  lowerCamelCase:       List[str]						)					->			List[Any]:


      '''simple docstring'''




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

      return name

def 			lowerCAmelCase_			(  lowerCamelCase:       Dict     ,				lowerCamelCase:       int						)					->			List[Any]:


      '''simple docstring'''




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

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

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

      return orig_state_dict

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


      '''simple docstring'''




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Union[str, Any]     ,				lowerCamelCase:       List[Any]     ,				lowerCamelCase:       str     ,				lowerCamelCase:       Tuple						)					->			Dict:


      '''simple docstring'''




      lowerCamelCase__        =				get_timesformer_config(lowerCamelCase						)

      lowerCamelCase__        =				TimesformerForVideoClassification(lowerCamelCase						)

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

      model.load_state_dict(lowerCamelCase						)
      model.eval()

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

      lowerCamelCase__        =				model(**lowerCamelCase						)
      lowerCamelCase__        =				outputs.logits

      lowerCamelCase__        =				[
          # 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":
            lowerCamelCase__        =				torch.Size([1, 4_00]						)
            lowerCamelCase__        =				torch.tensor([-0.3016, -0.7713, -0.4205]						)
      elif model_name == "timesformer-base-finetuned-k600":
            lowerCamelCase__        =				torch.Size([1, 6_00]						)
            lowerCamelCase__        =				torch.tensor([-0.7267, -0.7466, 3.2404]						)
      elif model_name == "timesformer-base-finetuned-ssv2":
            lowerCamelCase__        =				torch.Size([1, 1_74]						)
            lowerCamelCase__        =				torch.tensor([-0.9059, 0.6433, -3.1457]						)
      elif model_name == "timesformer-large-finetuned-k400":
            lowerCamelCase__        =				torch.Size([1, 4_00]						)
            lowerCamelCase__        =				torch.tensor([0, 0, 0]						)
      elif model_name == "timesformer-large-finetuned-k600":
            lowerCamelCase__        =				torch.Size([1, 6_00]						)
            lowerCamelCase__        =				torch.tensor([0, 0, 0]						)
      elif model_name == "timesformer-large-finetuned-ssv2":
            lowerCamelCase__        =				torch.Size([1, 1_74]						)
            lowerCamelCase__        =				torch.tensor([0, 0, 0]						)
      elif model_name == "timesformer-hr-finetuned-k400":
            lowerCamelCase__        =				torch.Size([1, 4_00]						)
            lowerCamelCase__        =				torch.tensor([-0.9617, -3.7311, -3.7708]						)
      elif model_name == "timesformer-hr-finetuned-k600":
            lowerCamelCase__        =				torch.Size([1, 6_00]						)
            lowerCamelCase__        =				torch.tensor([2.5273, 0.7127, 1.8848]						)
      elif model_name == "timesformer-hr-finetuned-ssv2":
            lowerCamelCase__        =				torch.Size([1, 1_74]						)
            lowerCamelCase__        =				torch.tensor([-3.6756, -0.7513, 0.7180]						)
      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]     ,				lowerCamelCase     ,				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(lowerCamelCase						)
            model.save_pretrained(lowerCamelCase						)

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


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE    :   int       =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."""
      )

      __SCREAMING_SNAKE_CASE    :   Dict       =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 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)