



from __future__ import annotations

from typing import TypedDict



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	42
							lowerCamelCase__         =	42






def        A       (      _lowerCamelCase						):





							'''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						):





							'''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."						)

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''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:
														_lowerCAmelCase  :							Tuple				     =					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)."						)

							_lowerCAmelCase  :							Optional[int]				     =					[""] * len(_lowerCamelCase						)
							for _ in range(len(_lowerCamelCase						)						):
														for i in range(len(_lowerCamelCase						)						):
																					_lowerCAmelCase  :							List[str]				     =					bwt_string[i] + ordered_rotations[i]
														ordered_rotations.sort()
							return ordered_rotations[idx_original_string]


if __name__ == "__main__":
						_snake_case	=						"Provide a string that I will generate its BWT transform: "
						_snake_case	=						input(entry_msg).strip()
						_snake_case	=						bwt_transform(s)
						print(
						    f'''Burrows Wheeler transform for string \'{s}\' results '''
						    f'''in \'{result["bwt_string"]}\''''
						)
						_snake_case	=						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}\''''
						)





import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


_snake_case	=						{
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					XLNetConfig.from_json_file(_lowerCamelCase						)

							_lowerCAmelCase  :							Any				     =					finetuning_task.lower() if finetuning_task is not None else ""
							if finetuning_task in GLUE_TASKS_NUM_LABELS:
														print(F"Building PyTorch XLNetForSequenceClassification model from configuration: {config}"						)
														_lowerCAmelCase  :							Any				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					GLUE_TASKS_NUM_LABELS[finetuning_task]
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetForSequenceClassification(_lowerCamelCase						)
							elif "squad" in finetuning_task:
														_lowerCAmelCase  :							Union[str, Any]				     =					finetuning_task
														_lowerCAmelCase  :							Any				     =					XLNetForQuestionAnswering(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							Union[str, Any]				     =					XLNetLMHeadModel(_lowerCamelCase						)

							# Load weights from tf checkpoint
							load_tf_weights_in_xlnet(_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						)

							# Save pytorch-model
							_lowerCAmelCase  :							Optional[int]				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							Dict				     =					os.path.join(_lowerCamelCase       ,				_lowerCamelCase						)
							print(F"Save PyTorch model to {os.path.abspath(_lowerCamelCase						)}"						)
							torch.save(model.state_dict()       ,				_lowerCamelCase						)
							print(F"Save configuration file to {os.path.abspath(_lowerCamelCase						)}"						)
							with open(_lowerCamelCase       ,				"w"       ,				encoding="utf-8"						) as f:
														f.write(config.to_json_string()						)


if __name__ == "__main__":
						_snake_case	=						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(
						    "--xlnet_config_file",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        "The config json file corresponding to the pre-trained XLNet model. \n"
						        "This specifies the model architecture."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the folder to store the PyTorch model or dataset/vocab.",
						)
						parser.add_argument(
						    "--finetuning_task",
						    default=None,
						    type=str,
						    help="Name of a task on which the XLNet TensorFlow model was fine-tuned",
						)
						_snake_case	=						parser.parse_args()
						print(args)

						convert_xlnet_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
						)
