
'''simple docstring'''







from ...configuration_utils import PretrainedConfig






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     '''bert-generation'''






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=5_03_58				,							__SCREAMING_SNAKE_CASE=10_24				,							__SCREAMING_SNAKE_CASE=24				,							__SCREAMING_SNAKE_CASE=16				,							__SCREAMING_SNAKE_CASE=40_96				,							__SCREAMING_SNAKE_CASE="gelu"				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=0.1				,							__SCREAMING_SNAKE_CASE=5_12				,							__SCREAMING_SNAKE_CASE=0.02				,							__SCREAMING_SNAKE_CASE=1E-1_2				,							__SCREAMING_SNAKE_CASE=0				,							__SCREAMING_SNAKE_CASE=2				,							__SCREAMING_SNAKE_CASE=1				,							__SCREAMING_SNAKE_CASE="absolute"				,							__SCREAMING_SNAKE_CASE=True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





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

											lowercase_	:     Optional[int]													=       vocab_size
											lowercase_	:     Optional[int]													=       hidden_size
											lowercase_	:     str													=       num_hidden_layers
											lowercase_	:     int													=       num_attention_heads
											lowercase_	:     str													=       hidden_act
											lowercase_	:     str													=       intermediate_size
											lowercase_	:     str													=       hidden_dropout_prob
											lowercase_	:     Optional[int]													=       attention_probs_dropout_prob
											lowercase_	:     List[str]													=       max_position_embeddings
											lowercase_	:     Optional[int]													=       initializer_range
											lowercase_	:     Optional[int]													=       layer_norm_eps
											lowercase_	:     str													=       position_embedding_type
											lowercase_	:     List[str]													=       use_cache







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 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				int   ):
							# Construct model
							if gpta_config_file == "":
														a__             =     GPTaConfig()
							else:
														a__             =     GPTaConfig.from_json_file(__lowerCAmelCase   )
							a__             =     GPTaModel(__lowerCAmelCase   )

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

							# 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()					,    __lowerCAmelCase   )
							print(F'Save configuration file to {pytorch_config_dump_path}'   )
							with open(__lowerCAmelCase					,    'w'					,    encoding='utf-8'   ) as f:
														f.write(config.to_json_string()   )


if __name__ == "__main__":
						snake_case							:       List[Any]     			=      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.'''
						    ),
						)
						snake_case							:       Any     			=      parser.parse_args()
						convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




