
"""simple docstring"""




from __future__ import annotations

from typing import TypedDict







class   _lowercase						(       UpperCamelCase_				):


     '''simple docstring'''





     _A								=							42
     _A								=							42





def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     if not isinstance(__lowerCAmelCase       ,    __lowerCAmelCase		):
          raise TypeError("The parameter s type must be str."		)

     return [s[i:] + s[:i] for i in range(len(__lowerCAmelCase		)		)]



def 							a__ (				lowerCAmelCase	:       str		):






     '''simple docstring'''
     if not isinstance(__lowerCAmelCase       ,    __lowerCAmelCase		):
          raise TypeError("The parameter s type must be str."		)
     if not s:
          raise ValueError("The parameter s must not be empty."		)

     UpperCAmelCase__							: str 							=						all_rotations(__lowerCAmelCase		)
     rotations.sort()  # sort the list of rotations in alphabetically order
     # make a string composed of the last char of each rotation
     UpperCAmelCase__							: BWTTransformDict 							=						{
         "bwt_string": "".join([word[-1] for word in rotations]		),
         "idx_original_string": rotations.index(__lowerCAmelCase		),
     }
     return response



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     if not isinstance(__lowerCAmelCase       ,    __lowerCAmelCase		):
          raise TypeError("The parameter bwt_string type must be str."		)
     if not bwt_string:
          raise ValueError("The parameter bwt_string must not be empty."		)
     try:
          UpperCAmelCase__							: str 							=						int(__lowerCAmelCase		)
     except ValueError:
          raise TypeError(
              "The parameter idx_original_string type must be int or passive"
              " of cast to int."		)
     if idx_original_string < 0:
          raise ValueError("The parameter idx_original_string must not be lower than 0."		)
     if idx_original_string >= len(__lowerCAmelCase		):
          raise ValueError(
              "The parameter idx_original_string must be lower than" " len(bwt_string)."		)

     UpperCAmelCase__							: List[str] 							=						[""""""] * len(__lowerCAmelCase		)
     for _ in range(len(__lowerCAmelCase		)		):
          for i in range(len(__lowerCAmelCase		)		):
               UpperCAmelCase__							: Any 							=						bwt_string[i] + ordered_rotations[i]
          ordered_rotations.sort()
     return ordered_rotations[idx_original_string]


if __name__ == "__main__":
    A__      :	Union[str, Any]          =    "Provide a string that I will generate its BWT transform: "
    A__      :	List[str]          =    input(entry_msg).strip()
    A__      :	List[str]          =    bwt_transform(s)
    print(
        f"""Burrows Wheeler transform for string \'{s}\' results """
        f"""in \'{result["bwt_string"]}\'"""
    )
    A__      :	Dict          =    reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
    print(
        f"""Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' """
        f"""we get original string \'{original_string}\'"""
    )







"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

     # Load weights from numpy
     load_tf_weights_in_gpta(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # Save pytorch-model
     UpperCAmelCase__							: Optional[int] 							=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__							: Any 							=						pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(F"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,    lowerCAmelCase		)
     print(F"Save configuration file to {pytorch_config_dump_path}"		)
     with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
    A__      :	Optional[int]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



