





from __future__ import annotations

from typing import TypedDict
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     str
						__magic_name__:     int


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	if not isinstance(__a				,	__a				):
		raise TypeError('The parameter s type must be str.'				)

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


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	if not isinstance(__a				,	__a				):
		raise TypeError('The parameter s type must be str.'				)
	if not s:
		raise ValueError('The parameter s must not be empty.'				)

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


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	if not isinstance(__a				,	__a				):
		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:
		snake_case_						:     str     =		int(__a				)
	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(__a				):
		raise ValueError(
		    'The parameter idx_original_string must be lower than' ' len(bwt_string).'				)

	snake_case_						:     Union[str, Any]     =		[''] * len(__a				)
	for _ in range(len(__a				)				):
		for i in range(len(__a				)				):
			snake_case_						:     Dict     =		bwt_string[i] + ordered_rotations[i]
		ordered_rotations.sort()
	return ordered_rotations[idx_original_string]


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """XLMForMultipleChoice""",
						    """XLMForQuestionAnswering""",
						    """XLMForQuestionAnsweringSimple""",
						    """XLMForSequenceClassification""",
						    """XLMForTokenClassification""",
						    """XLMModel""",
						    """XLMPreTrainedModel""",
						    """XLMWithLMHeadModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFXLMForMultipleChoice""",
						    """TFXLMForQuestionAnsweringSimple""",
						    """TFXLMForSequenceClassification""",
						    """TFXLMForTokenClassification""",
						    """TFXLMMainLayer""",
						    """TFXLMModel""",
						    """TFXLMPreTrainedModel""",
						    """TFXLMWithLMHeadModel""",
						]


if TYPE_CHECKING:
						from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
						from .tokenization_xlm import XLMTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm import (
												    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMForMultipleChoice,
												    XLMForQuestionAnswering,
												    XLMForQuestionAnsweringSimple,
												    XLMForSequenceClassification,
												    XLMForTokenClassification,
												    XLMModel,
												    XLMPreTrainedModel,
												    XLMWithLMHeadModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm import (
												    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMForMultipleChoice,
												    TFXLMForQuestionAnsweringSimple,
												    TFXLMForSequenceClassification,
												    TFXLMForTokenClassification,
												    TFXLMMainLayer,
												    TFXLMModel,
												    TFXLMPreTrainedModel,
												    TFXLMWithLMHeadModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

