
"""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						:							Dict							,     lowerCAmelCase						:							Tuple							,     lowerCAmelCase						:							int		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__				:			Any							=    json.load(open(lowerCAmelCase							,     "r"		)		)
     UpperCAmelCase__				:			Any							=    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}"		)
     UpperCAmelCase__				:			Optional[int]							=    GPTNeoForCausalLM(lowerCAmelCase		)

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

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


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





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    str(lowerCAmelCase		).strip()
     if not oct_string:
          raise ValueError("Empty string was passed to the function"		)
     UpperCAmelCase__				:			Tuple							=    oct_string[0] == "-"
     if is_negative:
          UpperCAmelCase__				:			List[Any]							=    oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCAmelCase		) <= 7 for char in oct_string		):
          raise ValueError("Non-octal value was passed to the function"		)
     UpperCAmelCase__				:			Optional[int]							=    0
     for char in oct_string:
          UpperCAmelCase__				:			Tuple							=    8 * decimal_number + int(lowerCAmelCase		)
     if is_negative:
          UpperCAmelCase__				:			int							=    -decimal_number
     return decimal_number


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

    testmod()



