





"""simple docstring"""




from __future__ import annotations

from typing import TypedDict


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      str
							lowerCamelCase     :      int






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->list[str]:






	'''simple docstring'''






	if not isinstance(_lowercase					, _lowercase      ):
		raise TypeError("The parameter s type must be str."      )

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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->BWTTransformDict:






	'''simple docstring'''






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

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






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str					, _lowercase		:					int      )				->str:






	'''simple docstring'''






	if not isinstance(_lowercase					, _lowercase      ):
		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   :	Tuple			=       int(_lowercase      )
	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(_lowercase      ):
		raise ValueError(
		    "The parameter idx_original_string must be lower than" " len(bwt_string)."      )

	a   :	str			=       [""] * len(_lowercase      )
	for _ in range(len(_lowercase      )      ):
		for i in range(len(_lowercase      )      ):
			a   :	int			=       bwt_string[i] + ordered_rotations[i]
		ordered_rotations.sort()
	return ordered_rotations[idx_original_string]


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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a		:				List[Any]  			=    TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a		:				Union[str, Any]  			=    TaTokenizerFast

a		:				Union[str, Any]  			=    {'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				str  			=    [
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				List[str]  			=    ['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:				Optional[Any]  			=    ['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a		:				List[Any]  			=    _LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)


