

from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache









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
						)






