






"""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 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      __UpperCamelCase							=						"Wav2Vec2FeatureExtractor"
      __UpperCamelCase							=						"AutoTokenizer"
      def __init__(		self  :						Tuple		,			lowercase_  :						Dict		,			lowercase_  :						Any):




          '''simple docstring'''


          super().__init__(lowercase_		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						Dict							=	self.feature_extractor
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	False
      @classmethod
      def _SCREAMING_SNAKE_CASE				(		cls  :						int		,			lowercase_  :						List[str]		,			**lowercase_  :						Optional[Any]):




          '''simple docstring'''


          try:
              return super().from_pretrained(lowercase_		,			**lowercase_)
          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: '''		,			lowercase_		,			)

              SCREAMING_SNAKE_CASE_   :						str							=	WavaVecaFeatureExtractor.from_pretrained(lowercase_		,			**lowercase_)
              SCREAMING_SNAKE_CASE_   :						int							=	WavaVecaCTCTokenizer.from_pretrained(lowercase_		,			**lowercase_)

              return cls(feature_extractor=lowercase_		,			tokenizer=lowercase_)
      def __call__(		self  :						Dict		,			*lowercase_  :						List[Any]		,			**lowercase_  :						str):




          '''simple docstring'''


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

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

          if text is None:
              return inputs
          elif audio is None:
              return encodings
          else:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	encodings['''input_ids''']
              return inputs
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]		,			*lowercase_  :						str		,			**lowercase_  :						str):




          '''simple docstring'''


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

          SCREAMING_SNAKE_CASE_   :						int							=	kwargs.pop('''input_features'''		,			lowercase_)
          SCREAMING_SNAKE_CASE_   :						str							=	kwargs.pop('''labels'''		,			lowercase_)
          if len(lowercase_) > 0:
              SCREAMING_SNAKE_CASE_   :						List[Any]							=	args[0]
              SCREAMING_SNAKE_CASE_   :						List[str]							=	args[1:]

          if input_features is not None:
              SCREAMING_SNAKE_CASE_   :						Optional[int]							=	self.feature_extractor.pad(lowercase_		,			*lowercase_		,			**lowercase_)
          if labels is not None:
              SCREAMING_SNAKE_CASE_   :						Tuple							=	self.tokenizer.pad(lowercase_		,			**lowercase_)

          if labels is None:
              return input_features
          elif input_features is None:
              return labels
          else:
              SCREAMING_SNAKE_CASE_   :						int							=	labels['''input_ids''']
              return input_features
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			*lowercase_  :						Dict		,			**lowercase_  :						Optional[int]):




          '''simple docstring'''


          return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			*lowercase_  :						Optional[Any]		,			**lowercase_  :						Optional[Any]):




          '''simple docstring'''


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



      @contextmanager
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''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_   :						List[Any]							=	True
          SCREAMING_SNAKE_CASE_   :						List[str]							=	self.tokenizer
          yield
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.feature_extractor
          SCREAMING_SNAKE_CASE_   :						List[str]							=	False










"""simple docstring"""






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       _A    ()				-> Union[str, Any]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Tuple							=	9, 14  # noqa: F841
    SCREAMING_SNAKE_CASE_   :						Tuple							=	[
        [0, 1, 4],
        [0, 7, 8],
        [1, 2, 8],
        [7, 8, 7],
        [7, 6, 1],
        [2, 8, 2],
        [8, 6, 6],
        [2, 3, 7],
        [2, 5, 4],
        [6, 5, 2],
        [3, 5, 14],
        [3, 4, 9],
        [5, 4, 10],
        [1, 7, 11],
    ]

    SCREAMING_SNAKE_CASE_   :						Dict							=	defaultdict(__a							)
    for nodea, nodea, cost in edges:
        adjancency[nodea].append([nodea, cost]							)
        adjancency[nodea].append([nodea, cost]							)

    SCREAMING_SNAKE_CASE_   :						Any							=	mst(__a							)

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[
        [7, 6, 1],
        [2, 8, 2],
        [6, 5, 2],
        [0, 1, 4],
        [2, 5, 4],
        [2, 3, 7],
        [0, 7, 8],
        [3, 4, 9],
    ]

    for answer in expected:
        SCREAMING_SNAKE_CASE_   :						Dict							=	tuple(answer[:2]							)
        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	tuple(edge[::-1]							)
        assert edge in result or reverse in result


