



"""simple docstring"""







from __future__ import annotations

from typing import TypedDict






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	str
		lowerCAmelCase_       :	int



def   lowerCAmelCase__			(     lowerCamelCase__			) ->							list[str]:
							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   lowerCAmelCase__			(     lowerCamelCase__			) ->							BWTTransformDict:
							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.'			)

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

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							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:
														A         =					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).'			)

							A         =					[''] * len(lowerCamelCase__			)
							for _ in range(len(lowerCamelCase__			)			):
														for i in range(len(lowerCamelCase__			)			):
																					A         =					bwt_string[i] + ordered_rotations[i]
														ordered_rotations.sort()
							return ordered_rotations[idx_original_string]


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







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A    =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							A    =			_LazyModule(__name__, globals()['__file__'], _import_structure)



