


'''simple docstring'''

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCAmelCase      :Any    	=			logging.get_logger(__name__)
class _lowerCamelCase       (						lowercase__							):



		'''simple docstring'''







		A_	:			Dict          =				['input_values', 'padding_mask']







		def __init__(					self		:						Union[str, Any]  ,     _A		:						List[str] = 1  ,     _A		:						List[Any] = 24000  ,     _A		:						int = 0.0  ,     _A		:						Optional[int] = None  ,     _A		:						Any = None  ,     **_A		:						Optional[int]  ,     )  -> int:
			super().__init__(feature_size=_A  ,     sampling_rate=_A  ,     padding_value=_A  ,     **_A			)
			__magic_name__       :    Union[str, Any]     	=     chunk_length_s
			__magic_name__       :    Union[str, Any]     	=     overlap







		@property
		def    __lowerCAmelCase		(					self		:						Union[str, Any]			)  -> Optional[int]:



			if self.chunk_length_s is None:
				return None
			else:
				return int(self.chunk_length_s * self.sampling_rate			)







		@property
		def    __lowerCAmelCase		(					self		:						int			)  -> Optional[int]:



			if self.chunk_length_s is None or self.overlap is None:
				return None
			else:
				return max(1  ,     int((1.0 - self.overlap) * self.chunk_length			)			)







		def __call__(					self		:						Optional[int]  ,     _A		:						Optional[Any]  ,     _A		:						Union[str, Any] = None  ,     _A		:						int = False  ,     _A		:						List[str] = None  ,     _A		:						Optional[int] = None  ,     _A		:						str = None  ,     )  -> BatchFeature:



			if sampling_rate is not None:
				if sampling_rate != self.sampling_rate:
					raise ValueError(
					    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
					    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
					    F' {self.sampling_rate} and not {sampling_rate}.'			)
			else:
				logger.warning(
				    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
				    'Failing to do so can result in silent errors that might be hard to debug.'			)

			if padding and truncation:
				raise ValueError('Both padding and truncation were set. Make sure you only set one.'			)
			elif padding is None:
				# by default let's pad the inputs
				__magic_name__       :    str     	=     True

			__magic_name__       :    Optional[Any]     	=     bool(
			    isinstance(_A  ,     (list, tuple)			) and (isinstance(raw_audio[0]  ,     (np.ndarray, tuple, list)			))			)

			if is_batched:
				__magic_name__       :    int     	=     [np.asarray(_A  ,     dtype=np.floataa			).T for audio in raw_audio]
			elif not is_batched and not isinstance(_A  ,     np.ndarray			):
				__magic_name__       :    int     	=     np.asarray(_A  ,     dtype=np.floataa			)
			elif isinstance(_A  ,     np.ndarray			) and raw_audio.dtype is np.dtype(np.floataa			):
				__magic_name__       :    List[str]     	=     raw_audio.astype(np.floataa			)

			# always return batch
			if not is_batched:
				__magic_name__       :    Tuple     	=     [np.asarray(_A			).T]

			# verify inputs are valid
			for idx, example in enumerate(_A			):
				if example.ndim > 2:
					raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'			)
				if self.feature_size == 1 and example.ndim != 1:
					raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'			)
				if self.feature_size == 2 and example.shape[-1] != 2:
					raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'			)

			__magic_name__       :    Union[str, Any]     	=     None
			__magic_name__       :    List[Any]     	=     BatchFeature({'input_values': raw_audio}			)
			if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
				if truncation:
					__magic_name__       :    Optional[Any]     	=     min(array.shape[0] for array in raw_audio			)
					__magic_name__       :    Dict     	=     int(np.floor(max_length / self.chunk_stride			)			)
					__magic_name__       :    int     	=     (nb_step - 1) * self.chunk_stride + self.chunk_length
				elif padding:
					__magic_name__       :    Tuple     	=     max(array.shape[0] for array in raw_audio			)
					__magic_name__       :    Any     	=     int(np.ceil(max_length / self.chunk_stride			)			)
					__magic_name__       :    str     	=     (nb_step - 1) * self.chunk_stride + self.chunk_length
					__magic_name__       :    List[Any]     	=     'max_length'
				else:
					__magic_name__       :    str     	=     input_values

        # normal padding on batch
			if padded_inputs is None:
				__magic_name__       :    str     	=     self.pad(
				    _A  ,     max_length=_A  ,     truncation=_A  ,     padding=_A  ,     return_attention_mask=_A  ,     )
				if padding:
					__magic_name__       :    int     	=     padded_inputs.pop('attention_mask'			)

			__magic_name__       :    Dict     	=     []
			for example in padded_inputs.pop('input_values'			):
				if self.feature_size == 1:
					__magic_name__       :    Any     	=     example[..., None]
				input_values.append(example.T			)

			__magic_name__       :    List[str]     	=     input_values
			if return_tensors is not None:
				__magic_name__       :    List[str]     	=     padded_inputs.convert_to_tensors(_A			)

			return padded_inputs




'''simple docstring'''





from __future__ import annotations

from typing import TypedDict






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str
 __lowerCamelCase    :       int





def lowerCAmelCase  (__A):



     """simple docstring"""



     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 lowerCAmelCase  (__A):



     """simple docstring"""



     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.''')

     _a      =  all_rotations(__A)
     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(__A),
     }
     return response




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     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:
          _a      =  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).''')

     _a      =  [''''''] * len(__A)
     for _ in range(len(__A)):
          for i in range(len(__A)):
               _a      =  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}'"""
  )



