



"""simple docstring"""






from torch import nn

class        __SCREAMING_SNAKE_CASE	(      nn.Module   ):


					'''simple docstring'''





					def __init__(       self							:  List[str],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  List[str]     )->					Union[str, Any]:
								super().__init__()
								lowerCamelCase__		:		Tuple			=class_size
								lowerCamelCase__		:		Dict			=embed_size
								# self.mlp1 = nn.Linear(embed_size, embed_size)
								# self.mlp2 = (nn.Linear(embed_size, class_size))
								lowerCamelCase__		:		List[Any]			=nn.Linear(lowerCamelCase,  lowerCamelCase     )




					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  Optional[Any]     )->					Optional[Any]:
								# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
								# hidden_state = self.mlp2(hidden_state)
								lowerCamelCase__		:		Any			=self.mlp(lowerCamelCase     )
								return logits









"""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      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

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

			# Save pytorch-model
			lowerCamelCase__		:		List[str]			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			lowerCamelCase__		:		int			=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__":
		_lowercase : Optional[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."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




