






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   (				__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      TimesformerConfig()

						if "large" in model_name:
												_UpperCAmelCase     =      96

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

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

						return config


def __A   (				__lowerCAmelCase     )->  Tuple:





						"""simple docstring"""



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

						return name


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  Optional[int]:





						"""simple docstring"""



						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase     =      orig_state_dict.pop(__lowerCAmelCase     )

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

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

						return orig_state_dict


def __A   (				)->  Dict:





						"""simple docstring"""



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


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      get_timesformer_config(__lowerCAmelCase     )

						_UpperCAmelCase     =      TimesformerForVideoClassification(__lowerCAmelCase     )

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

						model.load_state_dict(__lowerCAmelCase     )
						model.eval()

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

						_UpperCAmelCase     =      model(**__lowerCAmelCase     )
						_UpperCAmelCase     =      outputs.logits

						_UpperCAmelCase     =      [
						    # 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":
												_UpperCAmelCase     =      torch.Size([1, 400]     )
												_UpperCAmelCase     =      torch.tensor([-0.30_16, -0.77_13, -0.42_05]     )
						elif model_name == "timesformer-base-finetuned-k600":
												_UpperCAmelCase     =      torch.Size([1, 600]     )
												_UpperCAmelCase     =      torch.tensor([-0.72_67, -0.74_66, 3.24_04]     )
						elif model_name == "timesformer-base-finetuned-ssv2":
												_UpperCAmelCase     =      torch.Size([1, 174]     )
												_UpperCAmelCase     =      torch.tensor([-0.90_59, 0.64_33, -3.14_57]     )
						elif model_name == "timesformer-large-finetuned-k400":
												_UpperCAmelCase     =      torch.Size([1, 400]     )
												_UpperCAmelCase     =      torch.tensor([0, 0, 0]     )
						elif model_name == "timesformer-large-finetuned-k600":
												_UpperCAmelCase     =      torch.Size([1, 600]     )
												_UpperCAmelCase     =      torch.tensor([0, 0, 0]     )
						elif model_name == "timesformer-large-finetuned-ssv2":
												_UpperCAmelCase     =      torch.Size([1, 174]     )
												_UpperCAmelCase     =      torch.tensor([0, 0, 0]     )
						elif model_name == "timesformer-hr-finetuned-k400":
												_UpperCAmelCase     =      torch.Size([1, 400]     )
												_UpperCAmelCase     =      torch.tensor([-0.96_17, -3.73_11, -3.77_08]     )
						elif model_name == "timesformer-hr-finetuned-k600":
												_UpperCAmelCase     =      torch.Size([1, 600]     )
												_UpperCAmelCase     =      torch.tensor([2.52_73, 0.71_27, 1.88_48]     )
						elif model_name == "timesformer-hr-finetuned-ssv2":
												_UpperCAmelCase     =      torch.Size([1, 174]     )
												_UpperCAmelCase     =      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]			,		__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__":
		_a				     =  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				     =  parser.parse_args()
		convert_timesformer_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
		)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a				     =  logging.get_logger(__name__)

_a				     =  {
    '''facebook/regnet-y-040''': '''https://huggingface.co/facebook/regnet-y-040/blob/main/config.json''',
}





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				"regnet"
	UpperCamelCase__								=				["x", "y"]

	def __init__(							self	,   UpperCAmelCase=3	,   UpperCAmelCase=32	,   UpperCAmelCase=[128, 192, 512, 1088]	,   UpperCAmelCase=[2, 6, 12, 2]	,   UpperCAmelCase=64	,   UpperCAmelCase="y"	,   UpperCAmelCase="relu"	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase   )
							if layer_type not in self.layer_types:
													raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types   )}"""   )
							_UpperCAmelCase     =      num_channels
							_UpperCAmelCase     =      embedding_size
							_UpperCAmelCase     =      hidden_sizes
							_UpperCAmelCase     =      depths
							_UpperCAmelCase     =      groups_width
							_UpperCAmelCase     =      layer_type
							_UpperCAmelCase     =      hidden_act
							# always downsample in the first stage
							_UpperCAmelCase     =      True
