

import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


_lowerCAmelCase					:			Optional[int]  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  *__snake_case					,  **__snake_case					)   -> None:




						'''simple docstring'''
						warnings.warn(
						    'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
						    ' Please use ImageGPTImageProcessor instead.'					,  __snake_case					,  )
						super().__init__(*__snake_case					,  **__snake_case					)









import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def        UpperCamelCase_(							_snake_case   :  Dict       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  str	):




			"""simple docstring"""






			if openai_config_file == "":
						__a        =OpenAIGPTConfig()
			else:
						__a        =OpenAIGPTConfig.from_json_file(_snake_case	)
			__a        =OpenAIGPTModel(_snake_case	)

			# Load weights from numpy
			load_tf_weights_in_openai_gpt(_snake_case       ,     _snake_case       ,     _snake_case	)

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


if __name__ == "__main__":
						_lowerCAmelCase					:			Tuple  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--openai_checkpoint_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the TensorFlow checkpoint path.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--openai_config_file",
						    default="",
						    type=str,
						    help=(
						        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
						        "This specifies the model architecture."
						    ),
						)
						_lowerCAmelCase					:			int  				=			parser.parse_args()
						convert_openai_checkpoint_to_pytorch(
						    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
						)






