


'''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 						_A					(lowerCAmelCase__	:Union[str, Any]					,    lowerCAmelCase__	:Dict					,    lowerCAmelCase__	:List[str]		) ->	Union[str, Any]:




     '''simple docstring'''



     _a         =				json.load(open(_UpperCAmelCase					,    'r'		)		)
     _a         =				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}'		)
     _a         =				GPTNeoForCausalLM(_UpperCAmelCase		)

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

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'		)
     model.save_pretrained(_UpperCAmelCase		)


if __name__ == "__main__":
 a_       :     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."
 )
 a_       :     Optional[int] 							=     parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








"""simple docstring"""


def 				a__      (	_UpperCAmelCase						)     ->	int:

 '''simple docstring'''






 snake_case__     				=					0
 for plain_chr in plain_text:
  snake_case__     				=					(
      ord(_UpperCAmelCase						) + (hash_value << 6) + (hash_value << 1_6) - hash_value
  )
 return hash_value

