


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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


_lowerCamelCase :       Union[str, Any]     					=    logging.get_logger(__name__)




class      SCREAMING_SNAKE_CASE__  (			A__      ):





	'''simple docstring'''



	_UpperCAmelCase :	List[Any]					     =      ['''pixel_values''']



	def __init__(						self			:					List[str]    ,						lowercase			:					bool = True    ,						lowercase			:					Dict[str, int] = None    ,						lowercase			:					bool = True    ,						lowercase			:					Dict[str, int] = None    ,						lowercase			:					PILImageResampling = PILImageResampling.BICUBIC    ,						lowercase			:					bool = True    ,						lowercase			:					Union[int, float] = 1 / 255    ,						lowercase			:					bool = True    ,						lowercase			:					Optional[Union[float, List[float]]] = None    ,						lowercase			:					Optional[Union[float, List[float]]] = None    ,						**lowercase			:					Optional[int]    ,						):


				'''simple docstring'''


				super().__init__(**lowercase			)
				_snake_case						=    crop_size if crop_size is not None else {"height": 256, "width": 256}
				_snake_case						=    get_size_dict(lowercase    ,						param_name='crop_size'			)
				_snake_case						=    size if size is not None else {"height": 224, "width": 224}
				_snake_case						=    get_size_dict(lowercase			)

				_snake_case						=    do_center_crop
				_snake_case						=    crop_size
				_snake_case						=    do_resize
				_snake_case						=    size
				_snake_case						=    resample
				_snake_case						=    do_rescale
				_snake_case						=    rescale_factor
				_snake_case						=    do_normalize
				_snake_case						=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				_snake_case						=    image_std if image_std is not None else IMAGENET_DEFAULT_STD



	def 				A							(						self			:					str    ,						lowercase			:					np.ndarray    ,						lowercase			:					Dict[str, int]    ,						lowercase			:					Optional[int] = None    ,						lowercase			:					Optional[Union[str, ChannelDimension]] = None    ,						**lowercase			:					Optional[Any]    ,						):


				'''simple docstring'''


				_snake_case						=    self.size if size is None else size
				_snake_case						=    get_size_dict(lowercase			)
				_snake_case						=    get_size_dict(lowercase    ,						param_name='crop_size'			)

				_snake_case						=    get_image_size(lowercase			)
				_snake_case						=    min(lowercase    ,						lowercase			)
				_snake_case						=    (size["height"] / crop_size["height"]) * min_dim
				_snake_case						=    (size["width"] / crop_size["width"]) * min_dim
				return center_crop(lowercase    ,						size=(cropped_height, cropped_width)    ,						data_format=lowercase    ,						**lowercase			)



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


				'''simple docstring'''


				_snake_case						=    get_size_dict(lowercase			)
				if "height" not in size or "width" not in size:
							raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''			)
				return resize(
				    lowercase    ,						size=(size['height'], size['width'])    ,						resample=lowercase    ,						data_format=lowercase    ,						**lowercase			)



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


				'''simple docstring'''


				return rescale(lowercase    ,						scale=lowercase    ,						data_format=lowercase    ,						**lowercase			)



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


				'''simple docstring'''


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





	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					ImageInput    ,						lowercase			:					Optional[bool] = None    ,						lowercase			:					Optional[Dict[str, int]] = None    ,						lowercase			:					Optional[bool] = None    ,						lowercase			:					Optional[Dict[str, int]] = None    ,						lowercase			:					PILImageResampling = None    ,						lowercase			:					Optional[bool] = None    ,						lowercase			:					Optional[float] = None    ,						lowercase			:					Optional[bool] = None    ,						lowercase			:					Optional[Union[float, List[float]]] = None    ,						lowercase			:					Optional[Union[float, List[float]]] = None    ,						lowercase			:					Optional[Union[str, TensorType]] = None    ,						lowercase			:					ChannelDimension = ChannelDimension.FIRST    ,						**lowercase			:					str    ,						):


				'''simple docstring'''


				_snake_case						=    do_center_crop if do_center_crop is not None else self.do_center_crop
				_snake_case						=    crop_size if crop_size is not None else self.crop_size
				_snake_case						=    get_size_dict(lowercase    ,						param_name='crop_size'			)
				_snake_case						=    do_resize if do_resize is not None else self.do_resize
				_snake_case						=    size if size is not None else self.size
				_snake_case						=    get_size_dict(lowercase			)
				_snake_case						=    resample if resample is not None else self.resample
				_snake_case						=    do_rescale if do_rescale is not None else self.do_rescale
				_snake_case						=    rescale_factor if rescale_factor is not None else self.rescale_factor
				_snake_case						=    do_normalize if do_normalize is not None else self.do_normalize
				_snake_case						=    image_mean if image_mean is not None else self.image_mean
				_snake_case						=    image_std if image_std is not None else self.image_std

				_snake_case						=    make_list_of_images(lowercase			)

				if not valid_images(lowercase			):
							raise ValueError(
							    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
							    'torch.Tensor, tf.Tensor or jax.ndarray.'			)

				if do_center_crop and crop_size is None:
							raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'			)

				if do_resize and size is None:
							raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'			)

				# All transformations expect numpy arrays.
				_snake_case						=    [to_numpy_array(lowercase			) for image in images]

				if do_center_crop:
							_snake_case						=    [self.center_crop(lowercase    ,						lowercase    ,						size=lowercase			) for image in images]

				if do_resize:
							_snake_case						=    [self.resize(image=lowercase    ,						size=lowercase    ,						resample=lowercase			) for image in images]

				if do_rescale:
							_snake_case						=    [self.rescale(image=lowercase    ,						scale=lowercase			) for image in images]

				if do_normalize:
							_snake_case						=    [self.normalize(image=lowercase    ,						mean=lowercase    ,						std=lowercase			) for image in images]

				_snake_case						=    [to_channel_dimension_format(lowercase    ,						lowercase			) for image in images]

				_snake_case						=    {"pixel_values": images}
				return BatchFeature(data=lowercase    ,						tensor_type=lowercase			)

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  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(__lowercase ,      __lowercase ,      __lowercase				)

			# Save pytorch-model
			_snake_case						=    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_snake_case						=    pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''				)
			torch.save(model.state_dict() ,      __lowercase				)
			print(f'''Save configuration file to {pytorch_config_dump_path}'''				)
			with open(__lowercase ,      'w' ,      encoding='utf-8'				) as f:
						f.write(config.to_json_string()				)


if __name__ == "__main__":
			_lowerCamelCase :       List[Any]     					=    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[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)