
from __future__ import annotations

from typing import TypedDict





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     42
					SCREAMING_SNAKE_CASE           =     42



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     list[str]:
							if not isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
														raise TypeError("""The parameter s type must be str."""							)

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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     BWTTransformDict:
							if not isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
														raise TypeError("""The parameter s type must be str."""							)
							if not s:
														raise ValueError("""The parameter s must not be empty."""							)

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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   int							)	->     str:
							if not isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
														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_					=       int(UpperCAmelCase__							)
							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(UpperCAmelCase__							):
														raise ValueError(
														    """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""							)

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


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

lowercase   =	os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase   =	[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]



@add_start_docstrings(AutoConfig.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return AutoConfig.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Any:
							return AutoTokenizer.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModel.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Union[str, Any]:
							return AutoModel.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Optional[Any]:
							return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     int:
							return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Dict:
							return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Any      ,					**UpperCAmelCase__					:   List[str]							)	->     Union[str, Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)





