code: 
  
  
  
  """simple docstring"""
  
  
  
  
  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_						(       _UpperCamelCase		):
  						__SCREAMING_SNAKE_CASE		:     List[str]				  =				['image_processor', 'tokenizer']
  						__SCREAMING_SNAKE_CASE		:     Union[str, Any]				  =				'LayoutLMv2ImageProcessor'
  						__SCREAMING_SNAKE_CASE		:     List[Any]				  =				('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
  
  
  
  						def __init__(  self  :			Dict  ,			A  :			str=None  ,			A  :			str=None  ,			**A  :			str		):
  											_UpperCAmelCase    :			Dict              =					None
  											if "feature_extractor" in kwargs:
  																warnings.warn(
  																    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
  																    " instead."  ,			A  ,			)
  																_UpperCAmelCase    :			int              =					kwargs.pop("feature_extractor"		)
  
  											_UpperCAmelCase    :			List[Any]              =					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__(A  ,			A		)
  
  
  
  						def __call__(  self  :			str  ,			A  :			Any  ,			A  :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,			A  :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,			A  :			Union[List[List[int]], List[List[List[int]]]] = None  ,			A  :			Optional[Union[List[int], List[List[int]]]] = None  ,			A  :			bool = True  ,			A  :			Union[bool, str, PaddingStrategy] = False  ,			A  :			Union[bool, str, TruncationStrategy] = False  ,			A  :			Optional[int] = None  ,			A  :			int = 0  ,			A  :			Optional[int] = None  ,			A  :			Optional[bool] = None  ,			A  :			Optional[bool] = None  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = True  ,			A  :			Optional[Union[str, TensorType]] = None  ,			**A  :			Any  ,			):
  											# verify input
  											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
  											_UpperCAmelCase    :			Dict              =					self.image_processor(images=A  ,			return_tensors=A		)
  
  											# second, apply the tokenizer
  											if text is not None and self.image_processor.apply_ocr and text_pair is None:
  																if isinstance(A  ,			A		):
  																					_UpperCAmelCase    :			Union[str, Any]              =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
  																_UpperCAmelCase    :			List[str]              =					features["words"]
  
  											_UpperCAmelCase    :			Union[str, Any]              =					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=A  ,			add_special_tokens=A  ,			padding=A  ,			truncation=A  ,			max_length=A  ,			stride=A  ,			pad_to_multiple_of=A  ,			return_token_type_ids=A  ,			return_attention_mask=A  ,			return_overflowing_tokens=A  ,			return_special_tokens_mask=A  ,			return_offsets_mapping=A  ,			return_length=A  ,			verbose=A  ,			return_tensors=A  ,			**A  ,			)
  
  											# add pixel values
  											_UpperCAmelCase    :			Tuple              =					features.pop("pixel_values"		)
  											if return_overflowing_tokens is True:
  																_UpperCAmelCase    :			Optional[Any]              =					self.get_overflowing_images(A  ,			encoded_inputs["overflow_to_sample_mapping"]		)
  											_UpperCAmelCase    :			Dict              =					images
  
  											return encoded_inputs
  
  
  
  						def 		snake_case_      (  self  :			List[str]  ,			A  :			Union[str, Any]  ,			A  :			List[str]		):
  											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
  											_UpperCAmelCase    :			int              =					[]
  											for sample_idx in overflow_to_sample_mapping:
  																images_with_overflow.append(images[sample_idx]		)
  
  											if len(A		) != len(A		):
  																raise ValueError(
  																    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
  																    f' {len(A		)} and {len(A		)}'		)
  
  											return images_with_overflow
  
  
  
  						def 		snake_case_      (  self  :			Union[str, Any]  ,			*A  :			Tuple  ,			**A  :			Dict		):
  											return self.tokenizer.batch_decode(*A  ,			**A		)
  
  
  
  						def 		snake_case_      (  self  :			int  ,			*A  :			Union[str, Any]  ,			**A  :			List[str]		):
  											return self.tokenizer.decode(*A  ,			**A		)
  
  
  
  						@property
  						def 		snake_case_      (  self  :			str		):
  											return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  
  
  
  						@property
  						def 		snake_case_      (  self  :			Union[str, Any]		):
  											warnings.warn(
  											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,			A  ,			)
  											return self.image_processor_class
  
  
  
  						@property
  						def 		snake_case_      (  self  :			int		):
  											warnings.warn(
  											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,			A  ,			)
  											return self.image_processor
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
  from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
  
  
  logging.set_verbosity_info()
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		Optional[Any]		,						SCREAMING_SNAKE_CASE__			:		Tuple		,						SCREAMING_SNAKE_CASE__			:		Any       )						->     Dict:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Any              =					{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
       _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			str              =					MODELS[model_size]  # set model hyperparameters
       _UpperCAmelCase    :			Tuple              =					ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE__		,						n_layer=SCREAMING_SNAKE_CASE__		,						n_head=SCREAMING_SNAKE_CASE__       )
       _UpperCAmelCase    :			int              =					ImageGPTForCausalLM(SCREAMING_SNAKE_CASE__       )
  
       # Load weights from numpy
       load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
  
       # Save pytorch-model
       _UpperCAmelCase    :			Union[str, Any]              =					pytorch_dump_folder_path + "/" + WEIGHTS_NAME
       _UpperCAmelCase    :			int              =					pytorch_dump_folder_path + "/" + CONFIG_NAME
       print(f'Save PyTorch model to {pytorch_weights_dump_path}'       )
       torch.save(model.state_dict()		,						SCREAMING_SNAKE_CASE__       )
       print(f'Save configuration file to {pytorch_config_dump_path}'       )
       with open(SCREAMING_SNAKE_CASE__		,						"w"		,						encoding="utf-8"       ) as f:
            f.write(config.to_json_string()       )
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      List[str]							=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--imagegpt_checkpoint_path",
       default=None,
       type=str,
       required=True,
       help="Path to the TensorFlow checkpoint path.",
   )
   parser.add_argument(
       "--model_size",
       default=None,
       type=str,
       required=True,
       help="Size of the model (can be either 'small', 'medium' or 'large').",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _lowerCAmelCase       :      Optional[int]							=							parser.parse_args()
   convert_imagegpt_checkpoint_to_pytorch(
       args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
   )
  