






"""simple docstring"""


import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowercase_								=	[(1,)]



def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					bool(os.getenv('''RUN_SLOW'''						,			0					)					)
  if run_slow:
    assert True
  else:
    assert False, "should have been skipped"



def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					bool(os.getenv('''RUN_SLOW'''						,			0					)					)
  if run_slow and torch_device == "cuda":
    assert True
  else:
    assert False, "should have been skipped"


@require_torch
class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   @slow
   @require_torch_gpu
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     check_slow_torch_cuda()


   @require_torch_gpu
   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     check_slow_torch_cuda()






   @parameterized.expand(_lowerCamelCase    )
   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	Any=None    ):

     '''simple docstring'''
     check_slow()





@slow
@require_torch_gpu
def    lowerCAmelCase							(       ):




  """simple docstring"""







  check_slow_torch_cuda()



@require_torch_gpu
@slow
def    lowerCAmelCase							(       ):




  """simple docstring"""







  check_slow_torch_cuda()



@slow
@pytest.mark.parametrize('''param'''						,			[1]					)
def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  check_slow()





@pytest.mark.parametrize('''param'''						,			[1]					)
@slow
def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  check_slow()








'''simple docstring'''
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__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Dict          =					TimesformerConfig()

       if "large" in model_name:
              snake_case__					:	int          =					96

       if "hr" in model_name:
              snake_case__					:	int          =					16
              snake_case__					:	int          =					448

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

       return config



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





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

       return name



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       for key in orig_state_dict.copy().keys():
              snake_case__					:	Any          =					orig_state_dict.pop(UpperCAmelCase     )

              if key.startswith("""model."""     ):
                     snake_case__					:	Tuple          =					key.replace("""model."""    ,       """"""     )

              if "qkv" in key:
                     snake_case__					:	int          =					key.split("""."""     )
                     snake_case__					:	Union[str, Any]          =					int(key_split[1]     )
                     snake_case__					:	int          =					"""timesformer.encoder.layer."""
                     if "temporal" in key:
                            snake_case__					:	Any          =					""".temporal_attention.attention.qkv."""
                     else:
                            snake_case__					:	Any          =					""".attention.attention.qkv."""
                     if "weight" in key:
                            snake_case__					:	Dict          =					val
                     else:
                            snake_case__					:	str          =					val
              else:
                     snake_case__					:	Any          =					val

       return orig_state_dict



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	Dict          =					hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video"""    ,       filename="""eating_spaghetti.npy"""    ,       repo_type="""dataset"""     )
       snake_case__					:	Any          =					np.load(UpperCAmelCase     )
       return list(UpperCAmelCase     )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Any          =					get_timesformer_config(UpperCAmelCase     )

       snake_case__					:	Optional[int]          =					TimesformerForVideoClassification(UpperCAmelCase     )

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

       model.load_state_dict(UpperCAmelCase     )
       model.eval()

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

       snake_case__					:	int          =					model(**UpperCAmelCase     )
       snake_case__					:	Tuple          =					outputs.logits

       snake_case__					:	List[str]          =					[
           # 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":
              snake_case__					:	str          =					torch.Size([1, 400]     )
              snake_case__					:	str          =					torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]     )
       elif model_name == "timesformer-base-finetuned-k600":
              snake_case__					:	Dict          =					torch.Size([1, 600]     )
              snake_case__					:	str          =					torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]     )
       elif model_name == "timesformer-base-finetuned-ssv2":
              snake_case__					:	List[Any]          =					torch.Size([1, 174]     )
              snake_case__					:	Union[str, Any]          =					torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]     )
       elif model_name == "timesformer-large-finetuned-k400":
              snake_case__					:	Optional[Any]          =					torch.Size([1, 400]     )
              snake_case__					:	Tuple          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-large-finetuned-k600":
              snake_case__					:	Optional[int]          =					torch.Size([1, 600]     )
              snake_case__					:	int          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-large-finetuned-ssv2":
              snake_case__					:	Union[str, Any]          =					torch.Size([1, 174]     )
              snake_case__					:	List[Any]          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-hr-finetuned-k400":
              snake_case__					:	str          =					torch.Size([1, 400]     )
              snake_case__					:	List[Any]          =					torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]     )
       elif model_name == "timesformer-hr-finetuned-k600":
              snake_case__					:	Optional[int]          =					torch.Size([1, 600]     )
              snake_case__					:	str          =					torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]     )
       elif model_name == "timesformer-hr-finetuned-ssv2":
              snake_case__					:	int          =					torch.Size([1, 174]     )
              snake_case__					:	Optional[Any]          =					torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]     )
       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
     )




