

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 a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	# Construct model
	if openai_config_file == "":
		snake_case_      		= OpenAIGPTConfig()
	else:
		snake_case_      		= OpenAIGPTConfig.from_json_file(lowercase__	)
	snake_case_      		= OpenAIGPTModel(lowercase__	)

	# Load weights from numpy
	load_tf_weights_in_openai_gpt(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__":
		A											=     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.'
		    ),
		)
		A											=     parser.parse_args()
		convert_openai_checkpoint_to_pytorch(
		    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
		)



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A											=     logging.get_logger(__name__)

A											=     {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  """bit"""
			__A						  =  ["""preactivation""", """bottleneck"""]
			__A						  =  ["""SAME""", """VALID"""]


			def __init__(    self ,				__UpperCamelCase=3 ,				__UpperCamelCase=64 ,				__UpperCamelCase=[2_56, 5_12, 10_24, 20_48] ,				__UpperCamelCase=[3, 4, 6, 3] ,				__UpperCamelCase="preactivation" ,				__UpperCamelCase="relu" ,				__UpperCamelCase=None ,				__UpperCamelCase=32 ,				__UpperCamelCase=0.0 ,				__UpperCamelCase=False ,				__UpperCamelCase=32 ,				__UpperCamelCase=1 ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase							)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types							)}"""							)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_      		= global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""							)
				snake_case_      		= num_channels
				snake_case_      		= embedding_size
				snake_case_      		= hidden_sizes
				snake_case_      		= depths
				snake_case_      		= layer_type
				snake_case_      		= hidden_act
				snake_case_      		= global_padding
				snake_case_      		= num_groups
				snake_case_      		= drop_path_rate
				snake_case_      		= embedding_dynamic_padding
				snake_case_      		= output_stride
				snake_case_      		= width_factor

				snake_case_      		= ['stem'] + [f"""stage{idx}""" for idx in range(1 ,				len(__UpperCamelCase							) + 1							)]
				snake_case_		,       snake_case_      		= get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase ,				out_indices=__UpperCamelCase ,				stage_names=self.stage_names							)
