



"""simple docstring"""







import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info

def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				Optional[int]:






							class   UpperCAmelCase__		:



									def __init__(			self	:     int   ,				snake_case	:     str					)      ->    None:






																'''simple docstring'''

																assert isinstance(_lowerCamelCase   ,				(bytes, str)					)
																A    =       content



									def        A_		(			self	:     List[str]					)      ->    Optional[Any]:






																'''simple docstring'''

																return json.loads(self.content					)

							def mock_response(*lowerCamelCase__					,   **lowerCamelCase__      ):
														assert args[0] == AUTHENTICATED_USER_ENDPOINT
														assert "Authorization" in kwargs["headers"]
														assert kwargs["headers"]["Authorization"].startswith('token '      )
														assert "Accept" in kwargs["headers"]
														return FakeResponse(B'{"login":"test","id":1}'      )

							monkeypatch.setattr(a__					,   'get'					,   a__      )
							A    =       fetch_github_info('token'      )
							assert result["login"] == "test"
							assert result["id"] == 1





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_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Any				      = TimesformerConfig()

    if "large" in model_name:
        SCREAMING_SNAKE_CASE				:				Dict				      = 96

    if "hr" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 16
        SCREAMING_SNAKE_CASE				:				str				      = 448

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

    return config







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    return name







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = orig_state_dict.pop(a__   )

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

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

    return orig_state_dict







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video''' ,						filename='''eating_spaghetti.npy''' ,						repo_type='''dataset'''   )
    SCREAMING_SNAKE_CASE				:				int				      = np.load(a__   )
    return list(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = get_timesformer_config(a__   )

    SCREAMING_SNAKE_CASE				:				Tuple				      = TimesformerForVideoClassification(a__   )

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

    model.load_state_dict(a__   )
    model.eval()

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

    SCREAMING_SNAKE_CASE				:				Any				      = model(**a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = outputs.logits

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [
        # 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":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.3_016, -0.7_713, -0.4_205]   )
    elif model_name == "timesformer-base-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-0.7_267, -0.7_466, 3.2_404]   )
    elif model_name == "timesformer-base-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Dict				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_059, 0.6_433, -3.1_457]   )
    elif model_name == "timesformer-large-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				Any				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				str				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-hr-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				List[str]				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_617, -3.7_311, -3.7_708]   )
    elif model_name == "timesformer-hr-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor([2.5_273, 0.7_127, 1.8_848]   )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-3.6_756, -0.7_513, 0.7_180]   )
    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] ,						a__ ,						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(a__   )
        model.save_pretrained(a__   )

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


if __name__ == "__main__":
      a__				:      Optional[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.'''
      )

      a__				:      str    		=     parser.parse_args()
      convert_timesformer_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
      )
