


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 		snake_case_   (				lowercase__     :    Union[str, Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''simple docstring'''






				_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	:							Optional[int]           =  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	:							Union[str, Any]           =  parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





def 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=53_81
				for x in s:
								_lowerCAmelCase  						=((hash_value << 5) + hash_value) + ord(lowercase__     )
				return hash_value & 0Xffff_ffff

