




import warnings
from contextlib import contextmanager

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




class    __lowerCAmelCase       (							lowerCAmelCase):
       _a 					=						'Wav2Vec2FeatureExtractor'
       _a 					=						'AutoTokenizer'






       def __init__(   self:					str       ,					_lowerCAmelCase:					Dict       ,					_lowerCAmelCase:					List[str]				):
         super().__init__(_lowerCAmelCase       ,					_lowerCAmelCase				)
         lowercase    :List[str]      	=						self.feature_extractor
         lowercase    :int      	=						False






       @classmethod
       def 					SCREAMING_SNAKE_CASE    (   cls:					int       ,					_lowerCAmelCase:					List[Any]       ,					**_lowerCAmelCase:					int				):







         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    :Optional[Any]      	=						WavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase       ,					**_lowerCAmelCase				)
           lowercase    :str      	=						WavaVecaCTCTokenizer.from_pretrained(_lowerCAmelCase       ,					**_lowerCAmelCase				)

           return cls(feature_extractor=_lowerCAmelCase       ,					tokenizer=_lowerCAmelCase				)






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







         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    :Union[str, Any]      	=						kwargs.pop("raw_speech"				)
         else:
           lowercase    :Optional[int]      	=						kwargs.pop("audio"       ,					_lowerCAmelCase				)
         lowercase    :Tuple      	=						kwargs.pop("sampling_rate"       ,					_lowerCAmelCase				)
         lowercase    :Any      	=						kwargs.pop("text"       ,					_lowerCAmelCase				)
         if len(_lowerCAmelCase				) > 0:
           lowercase    :Any      	=						args[0]
           lowercase    :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:
           lowercase    :Any      	=						self.feature_extractor(_lowerCAmelCase       ,					*_lowerCAmelCase       ,					sampling_rate=_lowerCAmelCase       ,					**_lowerCAmelCase				)
         if text is not None:
           lowercase    :List[Any]      	=						self.tokenizer(_lowerCAmelCase       ,					**_lowerCAmelCase				)

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					*_lowerCAmelCase:					Tuple       ,					**_lowerCAmelCase:					Any				):







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

         lowercase    :Optional[Any]      	=						kwargs.pop("input_features"       ,					_lowerCAmelCase				)
         lowercase    :Any      	=						kwargs.pop("labels"       ,					_lowerCAmelCase				)
         if len(_lowerCAmelCase				) > 0:
           lowercase    :List[str]      	=						args[0]
           lowercase    :Dict      	=						args[1:]

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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Any       ,					*_lowerCAmelCase:					Optional[Any]       ,					**_lowerCAmelCase:					Optional[Any]				):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,					**_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]       ,					*_lowerCAmelCase:					str       ,					**_lowerCAmelCase:					str				):
         return self.tokenizer.decode(*_lowerCAmelCase       ,					**_lowerCAmelCase				)






       @contextmanager
       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         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    :Optional[Any]      	=						True
         lowercase    :Optional[Any]      	=						self.tokenizer
         yield
         lowercase    :Any      	=						self.feature_extractor
         lowercase    :Any      	=						False









from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst



def       UpperCAmelCase__  (     ):
  lowercase	,    lowercase    :Union[str, Any]      	=						9, 14  # noqa: F841
  lowercase    :Optional[Any]      	=						[
      [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],
  ]

  lowercase    :Optional[Any]      	=						defaultdict(lowerCamelCase   )
  for nodea, nodea, cost in edges:
    adjancency[nodea].append([nodea, cost]   )
    adjancency[nodea].append([nodea, cost]   )

  lowercase    :Optional[int]      	=						mst(lowerCamelCase   )

  lowercase    :Any      	=						[
      [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:
    lowercase    :Any      	=						tuple(answer[:2]   )
    lowercase    :str      	=						tuple(edge[::-1]   )
    assert edge in result or reverse in result



