



"""simple docstring"""
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()



def 				lowerCAmelCase_(      lowercase_   :							Dict  ,      lowercase_   :							Any  ,      lowercase_   :							int							)		->				Union[str, Any]:
				# Initialise PyTorch model
				_lowerCamelCase					     = json.load(open(lowercase_  ,      '''r'''							)							)
				_lowerCamelCase					     = GPTNeoConfig(
				    hidden_size=config_json['''n_embd''']  ,      num_layers=config_json['''n_layer''']  ,      num_heads=config_json['''n_head''']  ,      attention_types=config_json['''attention_types''']  ,      max_position_embeddings=config_json['''n_positions''']  ,      resid_dropout=config_json['''res_dropout''']  ,      embed_dropout=config_json['''embed_dropout''']  ,      attention_dropout=config_json['''attn_dropout''']  ,      )
				print(F"""Building PyTorch model from configuration: {config}"""							)
				_lowerCamelCase					     = GPTNeoForCausalLM(lowercase_							)

				# Load weights from tf checkpoint
				load_tf_weights_in_gpt_neo(lowercase_  ,      lowercase_  ,      lowercase_							)

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
				model.save_pretrained(lowercase_							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  Tuple										=					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			__SCREAMING_SNAKE_CASE     :  Optional[int]										=					parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








"""simple docstring"""







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 						lowercase__(A					,		A					,		A						)				->int:




						"""simple docstring"""



						lowercase__				:      List[str]=       RoFormerConfig.from_json_file(A						)
						print(f'''Building PyTorch model from configuration: {config}'''						)
						lowercase__				:      Tuple=       RoFormerForMaskedLM(A						)

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(A					,		A					,		A						)

						# Save pytorch-model
						print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
						torch.save(model.state_dict()					,		A					,		_use_new_zipfile_serialization=A						)


if __name__ == "__main__":
			a :				List[str]      					=		argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
			)
			parser.add_argument(
			    """--bert_config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        """The config json file corresponding to the pre-trained BERT model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			a :				str      					=		parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






