


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""Speech2TextFeatureExtractor"""
					snake_case_		:				Dict										=	"""Speech2TextTokenizer"""


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str						)			->	str:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.feature_extractor
											__snake_case				=		False


					def __call__(						self	:		Tuple     ,    *SCREAMING_SNAKE_CASE	:		Optional[int]     ,    **SCREAMING_SNAKE_CASE	:		Dict						)			->	Any:




											'''simple docstring'''

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

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

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    *SCREAMING_SNAKE_CASE	:		Any     ,    **SCREAMING_SNAKE_CASE	:		Dict						)			->	Optional[int]:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    *SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		Any						)			->	Tuple:




											'''simple docstring'''

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






					@contextmanager
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Tuple:




											'''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."						)
											__snake_case				=		True
											__snake_case				=		self.tokenizer
											yield
											__snake_case				=		self.feature_extractor
											__snake_case				=		False









def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		set()

						# Replace all the whitespace in our sentence
						__snake_case				=		input_str.replace(" "      ,	"" )
						for alpha in input_str:
												if "a" <= alpha.lower() <= "z":
																		frequency.add(alpha.lower() )
						return len(_lowerCAmelCase ) == 26





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		[False] * 26
						for char in input_str:
												if char.islower():
																		__snake_case				=		True
												elif char.isupper():
																		__snake_case				=		True
						return all(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						return len({char for char in input_str.lower() if char.isalpha()} ) == 26





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from timeit import timeit

						__snake_case				=		"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
						print(timeit("is_pangram()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_faster()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_fastest()"      ,	setup=_lowerCAmelCase ) )
						# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
						# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





