






"""simple docstring"""



from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'EncodecFeatureExtractor'
							snake_case_     =							('T5Tokenizer', 'T5TokenizerFast')


							def __init__(			self      :						Dict			,    snake_case      :						Optional[int]			,    snake_case      :						int							):
													'''simple docstring'''





													super().__init__(snake_case			,    snake_case							)
													A__						:  Any		  =  self.feature_extractor
													A__						:  List[Any]		  =  False


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Optional[int]=None			,    snake_case      :						Any=None			,    snake_case      :						Tuple=True							):
													'''simple docstring'''





													return self.tokenizer.get_decoder_prompt_ids(task=snake_case			,    language=snake_case			,    no_timestamps=snake_case							)


							def __call__(			self      :						Optional[int]			,    *snake_case      :						int			,    **snake_case      :						Optional[Any]							):
													'''simple docstring'''





													if self._in_target_context_manager:
																			return self.current_processor(*snake_case			,    **snake_case							)

													A__						:  Dict		  =  kwargs.pop("""audio"""			,    snake_case							)
													A__						:  Tuple		  =  kwargs.pop("""sampling_rate"""			,    snake_case							)
													A__						:  int		  =  kwargs.pop("""text"""			,    snake_case							)
													if len(snake_case							) > 0:
																			A__						:  Optional[Any]		  =  args[0]
																			A__						:  str		  =  args[1:]

													if audio is None and text is None:
																			raise ValueError("""You need to specify either an `audio` or `text` input to process."""							)

													if text is not None:
																			A__						:  Union[str, Any]		  =  self.tokenizer(snake_case			,    **snake_case							)

													if audio is not None:
																			A__						:  List[Any]		  =  self.feature_extractor(snake_case			,    *snake_case			,    sampling_rate=snake_case			,    **snake_case							)

													if audio is None:
																			return inputs

													elif text is None:
																			return audio_inputs

													else:
																			A__						:  Optional[int]		  =  audio_inputs["""input_values"""]
																			if "padding_mask" in audio_inputs:
																									A__						:  List[str]		  =  audio_inputs["""padding_mask"""]
																			return inputs


							def 						_UpperCamelCase       (			self      :						Tuple			,    *snake_case      :						List[str]			,    **snake_case      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  kwargs.pop("""audio"""			,    snake_case							)
													A__						:  Optional[Any]		  =  kwargs.pop("""padding_mask"""			,    snake_case							)

													if len(snake_case							) > 0:
																			A__						:  List[str]		  =  args[0]
																			A__						:  Tuple		  =  args[1:]

													if audio_values is not None:
																			return self._decode_audio(snake_case			,    padding_mask=snake_case							)
													else:
																			return self.tokenizer.batch_decode(*snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Any			,    *snake_case      :						List[str]			,    **snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return self.tokenizer.decode(*snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Any			,    snake_case      :						Optional = None							):
													'''simple docstring'''





													A__						:  Tuple		  =  to_numpy(snake_case							)
													A__						:  Any		  =  audio_values.shape

													if padding_mask is None:
																			return list(snake_case							)

													A__						:  List[Any]		  =  to_numpy(snake_case							)

													# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
													# token (so that the generated audio values are **not** treated as padded tokens)
													A__						:  Union[str, Any]		  =  seq_len - padding_mask.shape[-1]
													A__						:  int		  =  1 - self.feature_extractor.padding_value
													A__						:  str		  =  np.pad(snake_case			,    ((0, 0), (0, difference))			,    """constant"""			,    constant_values=snake_case							)

													A__						:  List[str]		  =  audio_values.tolist()
													for i in range(snake_case							):
																			A__						:  Any		  =  np.asarray(audio_values[i]							)[
																			    padding_mask[i][None, :] != self.feature_extractor.padding_value
																			]
																			A__						:  Tuple		  =  sliced_audio.reshape(snake_case			,    -1							)

													return audio_values














"""simple docstring"""



from __future__ import annotations






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													A__  ,  A__						:  List[str]		  =  text, pattern
													A__  ,  A__						:  List[str]		  =  len(snake_case							), len(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if char == self.pattern[i]:
																									return i
													return -1


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						int							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if self.pattern[i] != self.text[current_pos + i]:
																									return current_pos + i
													return -1


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  []
													for i in range(self.textLen - self.patLen + 1							):
																			A__						:  List[Any]		  =  self.mismatch_in_text(snake_case							)
																			if mismatch_index == -1:
																									positions.append(snake_case							)
																			else:
																									A__						:  Dict		  =  self.match_in_pattern(self.text[mismatch_index]							)
																									A__						:  Union[str, Any]		  =  (
																									    mismatch_index - match_index
																									)  # shifting index lgtm [py/multiple-definition]
													return positions




A_    =							'''ABAABA'''
A_    =							'''AB'''
A_    =							BoyerMooreSearch(text, pattern)
A_    =							bms.bad_character_heuristic()

if len(positions) == 0:
						print('''No match found''')
else:
						print('''Pattern found in following positions: ''')
						print(positions)






