



'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Optional[Any]			    =		'''Speech2TextFeatureExtractor'''
			_A      :		Any			    =		'''Speech2TextTokenizer'''

			def __init__(							self     : str							,     lowerCAmelCase__     : List[str]							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__							,     lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		int           =		self.feature_extractor
					__SCREAMING_SNAKE_CASE       :		Any           =		False

			def __call__(							self     : Any							,     *lowerCAmelCase__     : Optional[Any]							,     **lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






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

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

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

			def UpperCamelCase__					(							self     : Dict							,     *lowerCAmelCase__     : int							,     **lowerCAmelCase__     : List[Any]						):





					"""simple docstring"""






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

			def UpperCamelCase__					(							self     : Any							,     *lowerCAmelCase__     : List[Any]							,     **lowerCAmelCase__     : Dict						):





					"""simple docstring"""






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



			@contextmanager
			def UpperCamelCase__					(							self     : Optional[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."""						)
					__SCREAMING_SNAKE_CASE       :		Any           =		True
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.tokenizer
					yield
					__SCREAMING_SNAKE_CASE       :		int           =		self.feature_extractor
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		False




'''simple docstring'''




from math import ceil, sqrt


def 				lowerCAmelCase_   (	_lowerCamelCase:   int = 1_00_00_00					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0

		for outer_width in range(3       ,      (limit // 4) + 2					):
				if outer_width**2 > limit:
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		max(ceil(sqrt(outer_width**2 - limit					)					)       ,      1					)
				else:
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		1
				if (outer_width - hole_width_lower_bound) % 2:
						hole_width_lower_bound += 1

				answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

		return answer


if __name__ == "__main__":
						print(f"{solution() = }")