



'''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     __UpperCamelCase					(				_lowercase       )			->      Optional[Any]:
	_lowercase       :							List[str]  	=		TimesformerConfig()

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

	if "hr" in model_name:
		_lowercase       :							Any  	=		16
		_lowercase       :							Optional[int]  	=		448

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

	return config







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

	return name







def     __UpperCamelCase					(				_lowercase,					_lowercase       )			->      Optional[Any]:
	for key in orig_state_dict.copy().keys():
		_lowercase       :							str  	=		orig_state_dict.pop(SCREAMING_SNAKE_CASE_       )

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

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

	return orig_state_dict







def     __UpperCamelCase					(				)			->      str:
	_lowercase       :							Tuple  	=		hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video',					filename='eating_spaghetti.npy',					repo_type='dataset'       )
	_lowercase       :							List[Any]  	=		np.load(SCREAMING_SNAKE_CASE_       )
	return list(SCREAMING_SNAKE_CASE_       )







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase,					_lowercase       )			->      List[Any]:
	_lowercase       :							Union[str, Any]  	=		get_timesformer_config(SCREAMING_SNAKE_CASE_       )

	_lowercase       :							Optional[Any]  	=		TimesformerForVideoClassification(SCREAMING_SNAKE_CASE_       )

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

	model.load_state_dict(SCREAMING_SNAKE_CASE_       )
	model.eval()

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

	_lowercase       :							int  	=		model(**SCREAMING_SNAKE_CASE_       )
	_lowercase       :							Dict  	=		outputs.logits

	_lowercase       :							int  	=		[
	    # 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":
		_lowercase       :							Any  	=		torch.Size([1, 400]       )
		_lowercase       :							List[Any]  	=		torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]       )
	elif model_name == "timesformer-base-finetuned-k600":
		_lowercase       :							List[Any]  	=		torch.Size([1, 600]       )
		_lowercase       :							List[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":
		_lowercase       :							Dict  	=		torch.Size([1, 174]       )
		_lowercase       :							int  	=		torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]       )
	elif model_name == "timesformer-large-finetuned-k400":
		_lowercase       :							Union[str, Any]  	=		torch.Size([1, 400]       )
		_lowercase       :							int  	=		torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-large-finetuned-k600":
		_lowercase       :							List[Any]  	=		torch.Size([1, 600]       )
		_lowercase       :							Any  	=		torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-large-finetuned-ssv2":
		_lowercase       :							List[str]  	=		torch.Size([1, 174]       )
		_lowercase       :							str  	=		torch.tensor([0, 0, 0]       )
	elif model_name == "timesformer-hr-finetuned-k400":
		_lowercase       :							str  	=		torch.Size([1, 400]       )
		_lowercase       :							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":
		_lowercase       :							Tuple  	=		torch.Size([1, 600]       )
		_lowercase       :							Optional[int]  	=		torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]       )
	elif model_name == "timesformer-hr-finetuned-ssv2":
		_lowercase       :							List[Any]  	=		torch.Size([1, 174]       )
		_lowercase       :							Dict  	=		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],					SCREAMING_SNAKE_CASE_,					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(SCREAMING_SNAKE_CASE_       )
		model.save_pretrained(SCREAMING_SNAKE_CASE_       )

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


if __name__ == "__main__":
		_A					:     List[Any]      =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					:     Optional[int]      =parser.parse_args()
		convert_timesformer_checkpoint(
		    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
		)


import torch

from accelerate import Accelerator, DistributedType







class 	A__				:






				def __enter__(		self   : Optional[Any]  ):






							'''simple docstring'''







							if self.enabled:
										lowerCAmelCase__ : Any   =						self.model.no_sync()
										self.model_sync_obj.__enter__()

							return self






				def __exit__(		self   : int       ,	a   : int       ,	a   : Optional[Any]       ,	a   : str  ):






							'''simple docstring'''







							if self.enabled:
										# Average all models on exit
										self._sync_and_avg_model_params()
										self.model_sync_obj.__exit__(a       ,	a       ,	a  )






				def __init__(		self   : List[str]       ,	a   : Accelerator       ,	a   : torch.nn.Module       ,	a   : int       ,	a   : bool = True  ):






							'''simple docstring'''







							if accelerator.distributed_type not in [
							    DistributedType.NO,
							    DistributedType.MULTI_CPU,
							    DistributedType.MULTI_GPU,
							]:
										raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'  )
							lowerCAmelCase__ : Union[str, Any]   =						enabled and accelerator.distributed_type != DistributedType.NO
							lowerCAmelCase__ : str   =						0
							if self.enabled:
										lowerCAmelCase__ : Dict   =						accelerator
										lowerCAmelCase__ : Dict   =						model
										lowerCAmelCase__ : Optional[int]   =						local_sgd_steps






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							self.num_steps += 1
							if not self.enabled:
										return

							if self.num_steps % self.local_sgd_steps == 0:
										self._sync_and_avg_model_params()






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							self.accelerator.wait_for_everyone()
							with self.accelerator.autocast():
										for param in self.model.parameters():
													lowerCAmelCase__ : Dict   =						self.accelerator.reduce(param.data       ,	reduction='mean'  )