"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'Wav2Vec2FeatureExtractor'
      snake_case__     :  Optional[Any]				    =      'AutoTokenizer'

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__(__lowerCAmelCase						,			__lowerCAmelCase		)
            lowercase								=      self.feature_extractor
            lowercase								=      False

      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            try:
                  return super().from_pretrained(__lowerCAmelCase						,			**__lowerCAmelCase		)
            except OSError:
                  warnings.warn(
                      f'Loading a tokenizer inside {cls.__name__} from a config that does not'
                      """ include a `tokenizer_class` attribute is deprecated and will be """
                      """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
                      """ attribute to either your `config.json` or `tokenizer_config.json` """
                      """file to suppress this warning: """						,			__lowerCAmelCase						,			)

                  lowercase								=      WavaVecaFeatureExtractor.from_pretrained(__lowerCAmelCase						,			**__lowerCAmelCase		)
                  lowercase								=      WavaVecaCTCTokenizer.from_pretrained(__lowerCAmelCase						,			**__lowerCAmelCase		)

                  return cls(feature_extractor=__lowerCAmelCase						,			tokenizer=__lowerCAmelCase		)

      def __call__(		self						,			*__lowerCAmelCase						,			**__lowerCAmelCase		):







            """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."""		)
                  lowercase								=      kwargs.pop("""raw_speech"""		)
            else:
                  lowercase								=      kwargs.pop("""audio"""						,			__lowerCAmelCase		)
            lowercase								=      kwargs.pop("""sampling_rate"""						,			__lowerCAmelCase		)
            lowercase								=      kwargs.pop("""text"""						,			__lowerCAmelCase		)
            if len(__lowerCAmelCase		) > 0:
                  lowercase								=      args[0]
                  lowercase								=      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:
                  lowercase								=      self.feature_extractor(__lowerCAmelCase						,			*__lowerCAmelCase						,			sampling_rate=__lowerCAmelCase						,			**__lowerCAmelCase		)
            if text is not None:
                  lowercase								=      self.tokenizer(__lowerCAmelCase						,			**__lowerCAmelCase		)

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

      def 							A__			(		self						,			*__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            if self._in_target_context_manager:
                  return self.current_processor.pad(*__lowerCAmelCase						,			**__lowerCAmelCase		)

            lowercase								=      kwargs.pop("""input_features"""						,			__lowerCAmelCase		)
            lowercase								=      kwargs.pop("""labels"""						,			__lowerCAmelCase		)
            if len(__lowerCAmelCase		) > 0:
                  lowercase								=      args[0]
                  lowercase								=      args[1:]

            if input_features is not None:
                  lowercase								=      self.feature_extractor.pad(__lowerCAmelCase						,			*__lowerCAmelCase						,			**__lowerCAmelCase		)
            if labels is not None:
                  lowercase								=      self.tokenizer.pad(__lowerCAmelCase						,			**__lowerCAmelCase		)

            if labels is None:
                  return input_features
            elif input_features is None:
                  return labels
            else:
                  lowercase								=      labels["""input_ids"""]
                  return input_features

      def 							A__			(		self						,			*__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



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

      def 							A__			(		self						,			*__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



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







      @contextmanager
      def 							A__			(		self		):







            """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."""		)
            lowercase								=      True
            lowercase								=      self.tokenizer
            yield
            lowercase								=      self.feature_extractor
            lowercase								=      False




"""simple docstring"""






def      UpperCAmelCase__			(							lowerCAmelCase__	:int = 1_0**9						)					->       int:
      '''simple docstring'''


      lowercase								=      1
      lowercase								=      2

      lowercase								=      0
      lowercase								=      0
      lowercase								=      0
      while perimeter <= max_perimeter:
            perimeters_sum += perimeter

            prev_value += 2 * value
            value += prev_value

            lowercase								=      2 * value + 2 if i % 2 == 0 else 2 * value - 2
            i += 1

      return perimeters_sum


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



