





"""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__			(  _UpperCAmelCase	,     _UpperCAmelCase	,     _UpperCAmelCase	)    ->  Any:

 '''simple docstring'''






 snake_case__    							=				json.load(open(_UpperCAmelCase	,     "r"	)	)
 snake_case__    							=				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}"	)
 snake_case__    							=				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__					:     List[Any]							=				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__					:     List[Any]							=				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'''






 return int(_UpperCAmelCase	) if x - int(_UpperCAmelCase	) <= 0 else int(_UpperCAmelCase	) + 1


if __name__ == "__main__":
   import doctest

   doctest.testmod()

