
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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


lowerCamelCase__        =						logging.get_logger(__name__)







class 	A__				(	__magic_name__      ):
				lowercase            =    ['pixel_values']






				def __init__(		self   : Dict       ,	a   : bool = True       ,	a   : Dict[str, int] = None       ,	a   : PILImageResampling = PIL.Image.NEAREST       ,	a   : bool = False       ,	a   : Dict[str, int] = None       ,	a   : Union[int, float] = 1 / 255       ,	a   : bool = False       ,	a   : bool = True       ,	a   : bool = True       ,	a   : Optional[Union[float, List[float]]] = None       ,	a   : Optional[Union[float, List[float]]] = None       ,	a   : bool = True       ,	**a   : List[str]       ,	):






							'''simple docstring'''







							super().__init__(**a  )
							lowerCAmelCase__ : Union[str, Any]   =						size if size is not None else {'height': 346, 'width': 346}
							lowerCAmelCase__ : List[Any]   =						get_size_dict(a  )
							lowerCAmelCase__ : List[Any]   =						crop_size if crop_size is not None else {'height': 289, 'width': 289}
							lowerCAmelCase__ : Dict   =						get_size_dict(a       ,	param_name='crop_size'  )

							lowerCAmelCase__ : int   =						do_resize
							lowerCAmelCase__ : Optional[int]   =						size
							lowerCAmelCase__ : int   =						resample
							lowerCAmelCase__ : Optional[Any]   =						do_center_crop
							lowerCAmelCase__ : Optional[Any]   =						crop_size
							lowerCAmelCase__ : Union[str, Any]   =						do_rescale
							lowerCAmelCase__ : str   =						rescale_factor
							lowerCAmelCase__ : Optional[Any]   =						rescale_offset
							lowerCAmelCase__ : List[str]   =						do_normalize
							lowerCAmelCase__ : Optional[Any]   =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCAmelCase__ : List[str]   =						image_std if image_std is not None else IMAGENET_STANDARD_STD
							lowerCAmelCase__ : Optional[Any]   =						include_top






				def 				_lowerCamelCase						(		self   : Union[str, Any]       ,	a   : np.ndarray       ,	a   : Dict[str, int]       ,	a   : PILImageResampling = PIL.Image.NEAREST       ,	a   : Optional[Union[str, ChannelDimension]] = None       ,	**a   : Union[str, Any]       ,	):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						get_size_dict(a  )
							if "height" not in size or "width" not in size:
										raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''  )
							return resize(
							    a       ,	size=(size['height'], size['width'])       ,	resample=a       ,	data_format=a       ,	**a  )






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : np.ndarray       ,	a   : Dict[str, int]       ,	a   : Optional[Union[str, ChannelDimension]] = None       ,	**a   : str       ,	):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						get_size_dict(a  )
							if "height" not in size or "width" not in size:
										raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''  )
							return center_crop(a       ,	size=(size['height'], size['width'])       ,	data_format=a       ,	**a  )






				def 				_lowerCamelCase						(		self   : Any       ,	a   : np.ndarray       ,	a   : Union[int, float]       ,	a   : bool = True       ,	a   : Optional[Union[str, ChannelDimension]] = None       ,	**a   : Union[str, Any]       ,	):






							'''simple docstring'''







							if offset:
										lowerCAmelCase__ : List[Any]   =						(image - 1_2_7.5) * scale
										if data_format is not None:
													lowerCAmelCase__ : List[str]   =						to_channel_dimension_format(a       ,	a  )
										lowerCAmelCase__ : Optional[int]   =						rescaled_image.astype(np.floataa  )
							else:
										lowerCAmelCase__ : Any   =						rescale(a       ,	scale=a       ,	data_format=a       ,	**a  )
							return rescaled_image






				def 				_lowerCamelCase						(		self   : Optional[Any]       ,	a   : np.ndarray       ,	a   : Union[float, List[float]]       ,	a   : Union[float, List[float]]       ,	a   : Optional[Union[str, ChannelDimension]] = None       ,	**a   : Optional[int]       ,	):






							'''simple docstring'''







							return normalize(a       ,	mean=a       ,	std=a       ,	data_format=a       ,	**a  )






				def 				_lowerCamelCase						(		self   : Any       ,	a   : ImageInput       ,	a   : bool = None       ,	a   : Dict[str, int] = None       ,	a   : Optional[int]=None       ,	a   : bool = None       ,	a   : Dict[str, int] = None       ,	a   : bool = None       ,	a   : float = None       ,	a   : bool = 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   : ChannelDimension = ChannelDimension.FIRST       ,	**a   : int       ,	):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						do_resize if do_resize is not None else self.do_resize
							lowerCAmelCase__ : Any   =						resample if resample is not None else self.resample
							lowerCAmelCase__ : str   =						do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCAmelCase__ : str   =						do_rescale if do_rescale is not None else self.do_rescale
							lowerCAmelCase__ : Any   =						rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCAmelCase__ : Optional[int]   =						rescale_offset if rescale_offset is not None else self.rescale_offset
							lowerCAmelCase__ : List[str]   =						do_normalize if do_normalize is not None else self.do_normalize
							lowerCAmelCase__ : List[str]   =						image_mean if image_mean is not None else self.image_mean
							lowerCAmelCase__ : Tuple   =						image_std if image_std is not None else self.image_std
							lowerCAmelCase__ : Union[str, Any]   =						include_top if include_top is not None else self.include_top

							lowerCAmelCase__ : Any   =						size if size is not None else self.size
							lowerCAmelCase__ : int   =						get_size_dict(a  )
							lowerCAmelCase__ : str   =						crop_size if crop_size is not None else self.crop_size
							lowerCAmelCase__ : Union[str, Any]   =						get_size_dict(a       ,	param_name='crop_size'  )

							lowerCAmelCase__ : str   =						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 or resample is None:
										raise ValueError('Size and resample 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.'  )

							# All transformations expect numpy arrays.
							lowerCAmelCase__ : Union[str, Any]   =						[to_numpy_array(a  ) for image in images]

							if do_resize:
										lowerCAmelCase__ : Optional[Any]   =						[self.resize(image=a       ,	size=a       ,	resample=a  ) for image in images]

							if do_center_crop:
										lowerCAmelCase__ : Any   =						[self.center_crop(image=a       ,	size=a  ) for image in images]

							if do_rescale:
										lowerCAmelCase__ : Any   =						[self.rescale(image=a       ,	scale=a       ,	offset=a  ) for image in images]

							if do_normalize:
										lowerCAmelCase__ : List[str]   =						[self.normalize(image=a       ,	mean=a       ,	std=a  ) for image in images]

							if include_top:
										lowerCAmelCase__ : Dict   =						[self.normalize(image=a       ,	mean=[0, 0, 0]       ,	std=a  ) for image in images]

							lowerCAmelCase__ : Optional[int]   =						[to_channel_dimension_format(a       ,	a  ) for image in images]

							lowerCAmelCase__ : Tuple   =						{'pixel_values': images}
							return BatchFeature(data=a       ,	tensor_type=a  )

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 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[str]:
			# Construct configuration depending on size
			lowerCAmelCase__ : Tuple   =						{'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						MODELS[model_size]  # set model hyperparameters
			lowerCAmelCase__ : Optional[int]   =						ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE_			,				n_layer=SCREAMING_SNAKE_CASE_			,				n_head=SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : List[Any]   =						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
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			lowerCAmelCase__ : Optional[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__        =						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__        =						parser.parse_args()
					convert_imagegpt_checkpoint_to_pytorch(
					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
					)