

from __future__ import annotations

from typing import TypedDict



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							str
			_UpperCAmelCase       :							int







def       A_				(   _UpperCAmelCase   ):
						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       A_				(   _UpperCAmelCase   ):
						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."   )

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







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						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:
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	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)."   )

						SCREAMING_SNAKE_CASE_:			Tuple  		=	[""] * len(_UpperCAmelCase   )
						for _ in range(len(_UpperCAmelCase   )   ):
												for i in range(len(_UpperCAmelCase   )   ):
																		SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	bwt_string[i] + ordered_rotations[i]
												ordered_rotations.sort()
						return ordered_rotations[idx_original_string]


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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]            =						{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Optional[int]            =						[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						lowerCAmelCase							:  Any            =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






