

'''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_					(	snake_case ,    snake_case ,    snake_case	):
  # Initialise PyTorch model
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   json.load(open(snake_case ,    "r"	)	)
  SCREAMING_SNAKE_CASE:int   =   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}'''	)
  SCREAMING_SNAKE_CASE:Any   =   GPTNeoForCausalLM(snake_case	)

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

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


if __name__ == "__main__":
    A_      =							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_      =							parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''

def 				A_					(	snake_case	):
  return "".join(chr(ord(snake_case	) + 32	) if "A" <= char <= "Z" else char for char in word	)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






