



"""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     (		A	:						Optional[int]     ,				A	:						List[str]     ,				A	:						int       ):

		'''simple docstring'''


		if gpta_config_file == "":
				_UpperCAmelCase							 =		GPTaConfig()
		else:
				_UpperCAmelCase							 =		GPTaConfig.from_json_file(A       )
		_UpperCAmelCase							 =		GPTaModel(A       )

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

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


if __name__ == "__main__":
					lowercase  							=					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.'''
					    ),
					)
					lowercase  							=					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_yolos import YolosImageProcessor


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       *snake_case	,       **snake_case						)			->							None:
									warnings.warn(
									    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
									    ' use YolosImageProcessor instead.'	,       snake_case	,       )
									super().__init__(*snake_case	,       **snake_case						)


