
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__							(       __magic_name__						:     Tuple  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[str]  	=     TimesformerConfig()

					if "large" in model_name:
										lowerCAmelCase		:					Tuple  	=     96

					if "hr" in model_name:
										lowerCAmelCase		:					Optional[int]  	=     16
										lowerCAmelCase		:					List[Any]  	=     4_48

					lowerCAmelCase		:					str  	=     '''huggingface/label-files'''
					if "k400" in model_name:
										lowerCAmelCase		:					List[str]  	=     4_00
										lowerCAmelCase		:					Dict  	=     '''kinetics400-id2label.json'''
					elif "k600" in model_name:
										lowerCAmelCase		:					Tuple  	=     6_00
										lowerCAmelCase		:					Tuple  	=     '''kinetics600-id2label.json'''
					elif "ssv2" in model_name:
										lowerCAmelCase		:					List[Any]  	=     1_74
										lowerCAmelCase		:					List[Any]  	=     '''something-something-v2-id2label.json'''
					else:
										raise ValueError('''Model name should either contain \'k400\', \'k600\' or \'ssv2\'.'''  )
					lowerCAmelCase		:					Dict  	=     json.load(open(hf_hub_download(__magic_name__							,  __magic_name__							,  repo_type='''dataset'''  )							,  '''r'''  )  )
					lowerCAmelCase		:					Tuple  	=     {int(__magic_name__  ): v for k, v in idalabel.items()}
					lowerCAmelCase		:					Tuple  	=     idalabel
					lowerCAmelCase		:					List[str]  	=     {v: k for k, v in idalabel.items()}

					return config
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

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

					return name
def     UpperCAmelCase__							(       __magic_name__						:     List[Any]							,  __magic_name__						:     Optional[Any]  ):


					'''simple docstring'''

					for key in orig_state_dict.copy().keys():
										lowerCAmelCase		:					Any  	=     orig_state_dict.pop(__magic_name__  )

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

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

					return orig_state_dict
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					List[str]  	=     hf_hub_download(
					    repo_id='''hf-internal-testing/spaghetti-video'''							,  filename='''eating_spaghetti.npy'''							,  repo_type='''dataset'''  )
					lowerCAmelCase		:					Dict  	=     np.load(__magic_name__  )
					return list(__magic_name__  )
def     UpperCAmelCase__							(       __magic_name__						:     Optional[Any]							,  __magic_name__						:     Optional[Any]							,  __magic_name__						:     int							,  __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     get_timesformer_config(__magic_name__  )

					lowerCAmelCase		:					Union[str, Any]  	=     TimesformerForVideoClassification(__magic_name__  )

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

					model.load_state_dict(__magic_name__  )
					model.eval()

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

					lowerCAmelCase		:					Any  	=     model(**__magic_name__  )
					lowerCAmelCase		:					List[str]  	=     outputs.logits

					lowerCAmelCase		:					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":
										lowerCAmelCase		:					List[str]  	=     torch.Size([1, 4_00]  )
										lowerCAmelCase		:					Union[str, Any]  	=     torch.tensor([-0.3016, -0.7713, -0.4205]  )
					elif model_name == "timesformer-base-finetuned-k600":
										lowerCAmelCase		:					Dict  	=     torch.Size([1, 6_00]  )
										lowerCAmelCase		:					List[str]  	=     torch.tensor([-0.7267, -0.7466, 3.2404]  )
					elif model_name == "timesformer-base-finetuned-ssv2":
										lowerCAmelCase		:					Tuple  	=     torch.Size([1, 1_74]  )
										lowerCAmelCase		:					str  	=     torch.tensor([-0.9059, 0.6433, -3.1457]  )
					elif model_name == "timesformer-large-finetuned-k400":
										lowerCAmelCase		:					List[str]  	=     torch.Size([1, 4_00]  )
										lowerCAmelCase		:					Any  	=     torch.tensor([0, 0, 0]  )
					elif model_name == "timesformer-large-finetuned-k600":
										lowerCAmelCase		:					Optional[Any]  	=     torch.Size([1, 6_00]  )
										lowerCAmelCase		:					Optional[int]  	=     torch.tensor([0, 0, 0]  )
					elif model_name == "timesformer-large-finetuned-ssv2":
										lowerCAmelCase		:					Union[str, Any]  	=     torch.Size([1, 1_74]  )
										lowerCAmelCase		:					int  	=     torch.tensor([0, 0, 0]  )
					elif model_name == "timesformer-hr-finetuned-k400":
										lowerCAmelCase		:					str  	=     torch.Size([1, 4_00]  )
										lowerCAmelCase		:					Any  	=     torch.tensor([-0.9617, -3.7311, -3.7708]  )
					elif model_name == "timesformer-hr-finetuned-k600":
										lowerCAmelCase		:					Optional[Any]  	=     torch.Size([1, 6_00]  )
										lowerCAmelCase		:					str  	=     torch.tensor([2.5273, 0.7127, 1.8848]  )
					elif model_name == "timesformer-hr-finetuned-ssv2":
										lowerCAmelCase		:					int  	=     torch.Size([1, 1_74]  )
										lowerCAmelCase		:					List[Any]  	=     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]							,  __magic_name__							,  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(__magic_name__  )
										model.save_pretrained(__magic_name__  )

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


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE			:   str         =     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.'
		)

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


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
