code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import (
      center_crop,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  _lowerCAmelCase       :      int							=							logging.get_logger(__name__)
  
  
  if is_vision_available():
   import PIL
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				['pixel_values']
  
  
  
        def __init__(  self  :			Tuple  ,			A  :			bool = True  ,			A  :			Dict[str, int] = None  ,			A  :			PILImageResampling = PILImageResampling.BICUBIC  ,			A  :			bool = True  ,			A  :			Dict[str, int] = None  ,			A  :			bool = True  ,			A  :			Union[int, float] = 1 / 2_5_5  ,			A  :			bool = True  ,			A  :			Optional[Union[float, List[float]]] = None  ,			A  :			Optional[Union[float, List[float]]] = None  ,			A  :			bool = True  ,			**A  :			Union[str, Any]  ,			):
             super().__init__(**A		)
             _UpperCAmelCase    :			Union[str, Any]              =					size if size is not None else {"shortest_edge": 2_2_4}
             _UpperCAmelCase    :			List[Any]              =					get_size_dict(A  ,			default_to_square=A		)
             _UpperCAmelCase    :			List[Any]              =					crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
             _UpperCAmelCase    :			Optional[int]              =					get_size_dict(A		)
  
             _UpperCAmelCase    :			Any              =					do_resize
             _UpperCAmelCase    :			Optional[int]              =					size
             _UpperCAmelCase    :			List[Any]              =					resample
             _UpperCAmelCase    :			Tuple              =					do_center_crop
             _UpperCAmelCase    :			Union[str, Any]              =					crop_size
             _UpperCAmelCase    :			Dict              =					do_rescale
             _UpperCAmelCase    :			Tuple              =					rescale_factor
             _UpperCAmelCase    :			Tuple              =					do_normalize
             _UpperCAmelCase    :			Dict              =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             _UpperCAmelCase    :			Tuple              =					image_std if image_std is not None else OPENAI_CLIP_STD
             _UpperCAmelCase    :			Optional[Any]              =					do_convert_rgb
  
  
  
        def 		snake_case_      (  self  :			Dict  ,			A  :			np.ndarray  ,			A  :			Dict[str, int]  ,			A  :			PILImageResampling = PILImageResampling.BICUBIC  ,			A  :			Optional[Union[str, ChannelDimension]] = None  ,			**A  :			Dict  ,			):
             _UpperCAmelCase    :			int              =					get_size_dict(A  ,			default_to_square=A		)
             _UpperCAmelCase    :			int              =					get_resize_output_image_size(
                 A  ,			size=(size["height"], size["width"])  ,			default_to_square=A		)
             return resize(A  ,			size=A  ,			resample=A  ,			data_format=A  ,			**A		)
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			np.ndarray  ,			A  :			Dict[str, int]  ,			A  :			Optional[Union[str, ChannelDimension]] = None  ,			**A  :			Tuple  ,			):
             _UpperCAmelCase    :			Union[str, Any]              =					get_size_dict(A		)
             return center_crop(A  ,			size=(size["height"], size["width"])  ,			data_format=A  ,			**A		)
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]  ,			A  :			np.ndarray  ,			A  :			Union[int, float]  ,			A  :			Optional[Union[str, ChannelDimension]] = None  ,			**A  :			Dict  ,			):
             return rescale(A  ,			scale=A  ,			data_format=A  ,			**A		)
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			np.ndarray  ,			A  :			Union[float, List[float]]  ,			A  :			Union[float, List[float]]  ,			A  :			Optional[Union[str, ChannelDimension]] = None  ,			**A  :			Union[str, Any]  ,			):
             return normalize(A  ,			mean=A  ,			std=A  ,			data_format=A  ,			**A		)
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			ImageInput  ,			A  :			bool = None  ,			A  :			Dict[str, int] = None  ,			A  :			PILImageResampling = None  ,			A  :			bool = None  ,			A  :			int = None  ,			A  :			bool = None  ,			A  :			float = None  ,			A  :			bool = None  ,			A  :			Optional[Union[float, List[float]]] = None  ,			A  :			Optional[Union[float, List[float]]] = None  ,			A  :			bool = None  ,			A  :			Optional[Union[str, TensorType]] = None  ,			A  :			Optional[ChannelDimension] = ChannelDimension.FIRST  ,			**A  :			Optional[int]  ,			):
             _UpperCAmelCase    :			Tuple              =					do_resize if do_resize is not None else self.do_resize
             _UpperCAmelCase    :			str              =					size if size is not None else self.size
             _UpperCAmelCase    :			List[str]              =					get_size_dict(A  ,			default_to_square=A		)
             _UpperCAmelCase    :			Tuple              =					resample if resample is not None else self.resample
             _UpperCAmelCase    :			Any              =					do_center_crop if do_center_crop is not None else self.do_center_crop
             _UpperCAmelCase    :			Optional[Any]              =					crop_size if crop_size is not None else self.crop_size
             _UpperCAmelCase    :			int              =					get_size_dict(A		)
             _UpperCAmelCase    :			List[str]              =					do_rescale if do_rescale is not None else self.do_rescale
             _UpperCAmelCase    :			Any              =					rescale_factor if rescale_factor is not None else self.rescale_factor
             _UpperCAmelCase    :			int              =					do_normalize if do_normalize is not None else self.do_normalize
             _UpperCAmelCase    :			List[Any]              =					image_mean if image_mean is not None else self.image_mean
             _UpperCAmelCase    :			Optional[Any]              =					image_std if image_std is not None else self.image_std
             _UpperCAmelCase    :			List[str]              =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
             _UpperCAmelCase    :			Optional[int]              =					make_list_of_images(A		)
  
             if not valid_images(A		):
                  raise ValueError(
                      "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                      "torch.Tensor, tf.Tensor or jax.ndarray."		)
  
             if do_resize and size is None:
                  raise ValueError("Size must be specified if do_resize is True."		)
  
             if do_center_crop and crop_size is None:
                  raise ValueError("Crop size must be specified if do_center_crop is True."		)
  
             if do_rescale and rescale_factor is None:
                  raise ValueError("Rescale factor must be specified if do_rescale is True."		)
  
             if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError("Image mean and std must be specified if do_normalize is True."		)
  
             # PIL RGBA images are converted to RGB
             if do_convert_rgb:
                  _UpperCAmelCase    :			Tuple              =					[convert_to_rgb(A		) for image in images]
  
             # All transformations expect numpy arrays.
             _UpperCAmelCase    :			Union[str, Any]              =					[to_numpy_array(A		) for image in images]
  
             if do_resize:
                  _UpperCAmelCase    :			List[str]              =					[self.resize(image=A  ,			size=A  ,			resample=A		) for image in images]
  
             if do_center_crop:
                  _UpperCAmelCase    :			Any              =					[self.center_crop(image=A  ,			size=A		) for image in images]
  
             if do_rescale:
                  _UpperCAmelCase    :			List[Any]              =					[self.rescale(image=A  ,			scale=A		) for image in images]
  
             if do_normalize:
                  _UpperCAmelCase    :			List[Any]              =					[self.normalize(image=A  ,			mean=A  ,			std=A		) for image in images]
  
             _UpperCAmelCase    :			Tuple              =					[to_channel_dimension_format(A  ,			A		) for image in images]
  
             _UpperCAmelCase    :			Any              =					{"pixel_values": images}
             return BatchFeature(data=A  ,			tensor_type=A		)
  
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
   )
  