




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	list[list[str]]								=[[] for _ in range(lowerCamelCase   )]
				__magic_name__	:	Any								=key - 1

				if key <= 0:
								raise ValueError("""Height of grid can't be 0 or negative"""   )
				if key == 1 or len(lowerCamelCase   ) <= key:
								return input_string

				for position, character in enumerate(lowerCamelCase   ):
								__magic_name__	:	Dict								=position % (lowest * 2)  # puts it in bounds
								__magic_name__	:	int								=min(lowerCamelCase	,     lowest * 2 - num   )  # creates zigzag pattern
								temp_grid[num].append(lowerCamelCase   )
				__magic_name__	:	Union[str, Any]								=["""""".join(lowerCamelCase   ) for row in temp_grid]
				__magic_name__	:	Optional[int]								="""""".join(lowerCamelCase   )

				return output_string




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Optional[Any]								=[]
				__magic_name__	:	List[str]								=key - 1

				if key <= 0:
								raise ValueError("""Height of grid can't be 0 or negative"""   )
				if key == 1:
								return input_string

				__magic_name__	:	list[list[str]]								=[[] for _ in range(lowerCamelCase   )]  # generates template
				for position in range(len(lowerCamelCase   )   ):
								__magic_name__	:	str								=position % (lowest * 2)  # puts it in bounds
								__magic_name__	:	Optional[int]								=min(lowerCamelCase	,     lowest * 2 - num   )  # creates zigzag pattern
								temp_grid[num].append("""*"""   )

				__magic_name__	:	Optional[Any]								=0
				for row in temp_grid:  # fills in the characters
								__magic_name__	:	str								=input_string[counter : counter + len(lowerCamelCase   )]
								grid.append(list(lowerCamelCase   )   )
								counter += len(lowerCamelCase   )

				__magic_name__	:	str								=""""""  # reads as zigzag
				for position in range(len(lowerCamelCase   )   ):
								__magic_name__	:	int								=position % (lowest * 2)  # puts it in bounds
								__magic_name__	:	Any								=min(lowerCamelCase	,     lowest * 2 - num   )  # creates zigzag pattern
								output_string += grid[num][0]
								grid[num].pop(0   )
				return output_string




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Any								={}
				for key_guess in range(1	,     len(lowerCamelCase   )   ):  # tries every key
								__magic_name__	:	Optional[Any]								=decrypt(lowerCamelCase	,     lowerCamelCase   )
				return results


if __name__ == "__main__":
							import doctest

							doctest.testmod()










import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""Speech2TextFeatureExtractor"""
						UpperCamelCase     						=					"""Speech2TextTokenizer"""

						def __init__(		self   :Any    , __snake_case   :int    , __snake_case   :Any ):
										'''simple docstring'''

										super().__init__(__snake_case    , __snake_case )
										__magic_name__	:	Optional[int]								=self.feature_extractor
										__magic_name__	:	Any								=False

						def __call__(		self   :Tuple    , *__snake_case   :List[str]    , **__snake_case   :List[str] ):
										'''simple docstring'''

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

										if "raw_speech" in kwargs:
														warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
														__magic_name__	:	Tuple								=kwargs.pop("""raw_speech""" )
										else:
														__magic_name__	:	List[Any]								=kwargs.pop("""audio"""    , __snake_case )
										__magic_name__	:	Union[str, Any]								=kwargs.pop("""sampling_rate"""    , __snake_case )
										__magic_name__	:	Dict								=kwargs.pop("""text"""    , __snake_case )
										if len(__snake_case ) > 0:
														__magic_name__	:	Union[str, Any]								=args[0]
														__magic_name__	:	int								=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 audio is not None:
														__magic_name__	:	List[str]								=self.feature_extractor(__snake_case    , *__snake_case    , sampling_rate=__snake_case    , **__snake_case )
										if text is not None:
														__magic_name__	:	Any								=self.tokenizer(__snake_case    , **__snake_case )

										if text is None:
														return inputs
										elif audio is None:
														return encodings
										else:
														__magic_name__	:	List[str]								=encodings["""input_ids"""]
														return inputs

						def     A__						(		self   :List[Any]    , *__snake_case   :str    , **__snake_case   :List[str] ):
										'''simple docstring'''

										return self.tokenizer.batch_decode(*__snake_case    , **__snake_case )

						def     A__						(		self   :int    , *__snake_case   :int    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

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


						@contextmanager
						def     A__						(		self   :int ):
										'''simple docstring'''

										warnings.warn(
										    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
										    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
										    """your audio inputs, or in a separate call.""" )
										__magic_name__	:	int								=True
										__magic_name__	:	List[Any]								=self.tokenizer
										yield
										__magic_name__	:	List[Any]								=self.feature_extractor
										__magic_name__	:	str								=False




