
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__   (	A_ ):






		'''simple docstring'''





		__magic_name__    					=	TimesformerConfig()

		if "large" in model_name:
				__magic_name__    					=	96

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

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

		return config

def a__   (	A_ ):






		'''simple docstring'''





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

		return name

def a__   (	A_,					A_ ):






		'''simple docstring'''





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

				if key.startswith("""model.""" ):
						__magic_name__    					=	key.replace("""model.""",					"""""" )

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

		return orig_state_dict

def a__   (	):






		'''simple docstring'''





		__magic_name__    					=	hf_hub_download(
		    repo_id="""hf-internal-testing/spaghetti-video""",					filename="""eating_spaghetti.npy""",					repo_type="""dataset""" )
		__magic_name__    					=	np.load(_lowerCAmelCase )
		return list(_lowerCAmelCase )

def a__   (	A_,					A_,					A_,					A_ ):






		'''simple docstring'''





		__magic_name__    					=	get_timesformer_config(_lowerCAmelCase )

		__magic_name__    					=	TimesformerForVideoClassification(_lowerCAmelCase )

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

		model.load_state_dict(_lowerCAmelCase )
		model.eval()

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

		__magic_name__    					=	model(**_lowerCAmelCase )
		__magic_name__    					=	outputs.logits

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

				__lowerCAmelCase      :					Optional[Any]   	=       parser.parse_args()
				convert_timesformer_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
				)



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""image_processor""", """tokenizer"""]
  a__   	=		"""LayoutLMv2ImageProcessor"""
  a__   	=		("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  def __init__(			self      :			int      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			int=None      ,							**UpperCamelCase__      :			str   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
          """ instead."""      ,							UpperCamelCase__      ,							)
      __magic_name__    					=	kwargs.pop("""feature_extractor"""   )

    __magic_name__    					=	image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError("""You need to specify an `image_processor`."""   )
    if tokenizer is None:
      raise ValueError("""You need to specify a `tokenizer`."""   )

    super().__init__(UpperCamelCase__      ,							UpperCamelCase__   )
  def __call__(			self      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,							UpperCamelCase__      :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,							UpperCamelCase__      :			Union[List[List[int]], List[List[List[int]]]] = None      ,							UpperCamelCase__      :			Optional[Union[List[int], List[List[int]]]] = None      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__      :			Union[bool, str, TruncationStrategy] = False      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			int = 0      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     BatchEncoding:







    """simple docstring"""






    if self.image_processor.apply_ocr and (boxes is not None):
      raise ValueError(
          """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""   )

    if self.image_processor.apply_ocr and (word_labels is not None):
      raise ValueError(
          """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

    # first, apply the image processor
    __magic_name__    					=	self.image_processor(images=UpperCamelCase__      ,							return_tensors=UpperCamelCase__   )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
      __magic_name__    					=	features["""words"""]

    __magic_name__    					=	self.tokenizer(
        text=text if text is not None else features["""words"""]      ,							text_pair=text_pair if text_pair is not None else None      ,							boxes=boxes if boxes is not None else features["""boxes"""]      ,							word_labels=UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							stride=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_token_type_ids=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							return_overflowing_tokens=UpperCamelCase__      ,							return_special_tokens_mask=UpperCamelCase__      ,							return_offsets_mapping=UpperCamelCase__      ,							return_length=UpperCamelCase__      ,							verbose=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__      ,							)

    # add pixel values
    __magic_name__    					=	features.pop("""pixel_values"""   )
    if return_overflowing_tokens is True:
      __magic_name__    					=	self.get_overflowing_images(UpperCamelCase__      ,							encoded_inputs["""overflow_to_sample_mapping"""]   )
    __magic_name__    					=	images

    return encoded_inputs
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]   )

    if len(UpperCamelCase__   ) != len(UpperCamelCase__   ):
      raise ValueError(
          """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
          F''' {len(UpperCamelCase__   )} and {len(UpperCamelCase__   )}'''   )

    return images_with_overflow
  def 							_lowercase							(			self      :			Optional[int]      ,							*UpperCamelCase__      :			str      ,							**UpperCamelCase__      :			int   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict      ,							*UpperCamelCase__      :			Optional[int]      ,							**UpperCamelCase__      :			str   )      ->     str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  @property
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,							UpperCamelCase__      ,							)
    return self.image_processor_class





  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,							UpperCamelCase__      ,							)
    return self.image_processor

