




"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(		snake_case			:					Optional[int]      ,       snake_case			:					str      ,       snake_case			:					str    ):
					# Construct model
					if gpta_config_file == "":
										_lowerCAmelCase:Dict									=  GPTaConfig()
					else:
										_lowerCAmelCase:Dict									=  GPTaConfig.from_json_file(snake_case    )
					_lowerCAmelCase:List[Any]									=  GPTaModel(snake_case    )

					# Load weights from numpy
					load_tf_weights_in_gpta(snake_case      ,       snake_case      ,       snake_case    )

					# Save pytorch-model
					_lowerCAmelCase:Optional[int]									=  pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
					_lowerCAmelCase:Optional[Any]									=  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__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--gpt2_checkpoint_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(
				    '''--gpt2_config_file''',
				    default='''''',
				    type=str,
				    help=(
				        '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
				        '''This specifies the model architecture.'''
				    ),
				)
				UpperCamelCase__						=			parser.parse_args()
				convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)












"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__						=			logging.get_logger(__name__)




class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	Dict     ,*a__      :	List[str]     ,**a__      :	str)						->       None:




											"""simple docstring"""







											warnings.warn(
											    '''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
											    ''' Please use PoolFormerImageProcessor instead.'''     ,a__     ,)
											super().__init__(*a__     ,**a__)






