





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__	:				Union[str, Any]  )	->       Dict:



					'''simple docstring'''




					_snake_case													= TimesformerConfig()

					if "large" in model_name:
										_snake_case													= 96

					if "hr" in model_name:
										_snake_case													= 16
										_snake_case													= 448

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

					return config





def   lowerCamelCase__   (   UpperCamelCase__	:				Optional[int]  )	->       List[str]:



					'''simple docstring'''




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

					return name





def   lowerCamelCase__   (   UpperCamelCase__	:				List[str]				,			UpperCamelCase__	:				Tuple  )	->       Dict:



					'''simple docstring'''




					for key in orig_state_dict.copy().keys():
										_snake_case													= orig_state_dict.pop(lowercase_  )

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

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

					return orig_state_dict





def   lowerCamelCase__   (   )	->       Optional[int]:



					'''simple docstring'''




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





def   lowerCamelCase__   (   UpperCamelCase__	:				Any				,			UpperCamelCase__	:				Tuple				,			UpperCamelCase__	:				Dict				,			UpperCamelCase__	:				Any  )	->       List[str]:



					'''simple docstring'''




					_snake_case													= get_timesformer_config(lowercase_  )

					_snake_case													= TimesformerForVideoClassification(lowercase_  )

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

					model.load_state_dict(lowercase_  )
					model.eval()

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

					_snake_case													= model(**lowercase_  )
					_snake_case													= outputs.logits

					_snake_case													= [
					    # 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													= torch.Size([1, 400]  )
										_snake_case													= torch.tensor([-0.3016, -0.7713, -0.4205]  )
					elif model_name == "timesformer-base-finetuned-k600":
										_snake_case													= torch.Size([1, 600]  )
										_snake_case													= torch.tensor([-0.7267, -0.7466, 3.2404]  )
					elif model_name == "timesformer-base-finetuned-ssv2":
										_snake_case													= torch.Size([1, 174]  )
										_snake_case													= torch.tensor([-0.9059, 0.6433, -3.1457]  )
					elif model_name == "timesformer-large-finetuned-k400":
										_snake_case													= torch.Size([1, 400]  )
										_snake_case													= torch.tensor([0, 0, 0]  )
					elif model_name == "timesformer-large-finetuned-k600":
										_snake_case													= torch.Size([1, 600]  )
										_snake_case													= torch.tensor([0, 0, 0]  )
					elif model_name == "timesformer-large-finetuned-ssv2":
										_snake_case													= torch.Size([1, 174]  )
										_snake_case													= torch.tensor([0, 0, 0]  )
					elif model_name == "timesformer-hr-finetuned-k400":
										_snake_case													= torch.Size([1, 400]  )
										_snake_case													= torch.tensor([-0.9617, -3.7311, -3.7708]  )
					elif model_name == "timesformer-hr-finetuned-k600":
										_snake_case													= torch.Size([1, 600]  )
										_snake_case													= torch.tensor([2.5273, 0.7127, 1.8848]  )
					elif model_name == "timesformer-hr-finetuned-ssv2":
										_snake_case													= torch.Size([1, 174]  )
										_snake_case													= 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]				,			lowercase_				,			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(lowercase_  )
										model.save_pretrained(lowercase_  )

					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
					)










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase       =	{
    '''configuration_mobilenet_v1''': [
        '''MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV1Config''',
        '''MobileNetV1OnnxConfig''',
    ],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	['''MobileNetV1FeatureExtractor''']
    _lowerCamelCase       =	['''MobileNetV1ImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _lowerCamelCase       =	[
        '''MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MobileNetV1ForImageClassification''',
        '''MobileNetV1Model''',
        '''MobileNetV1PreTrainedModel''',
        '''load_tf_weights_in_mobilenet_v1''',
    ]


if TYPE_CHECKING:
    from .configuration_mobilenet_va import (
        MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileNetVaConfig,
        MobileNetVaOnnxConfig,
    )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
        from .image_processing_mobilenet_va import MobileNetVaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilenet_va import (
            MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileNetVaForImageClassification,
            MobileNetVaModel,
            MobileNetVaPreTrainedModel,
            load_tf_weights_in_mobilenet_va,
        )


else:
    import sys

    _lowerCamelCase       =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

