



'''simple docstring'''





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""MCTCTFeatureExtractor"""
  a_				   ="""AutoTokenizer"""




  def __init__(					self :      str     ,      _a :      Optional[Any]     ,      _a :      Dict  )   ->   Tuple:
    super().__init__(_a     ,      _a  )
    __lowerCamelCase			:			Optional[Any]       			=					self.feature_extractor
    __lowerCamelCase			:			Optional[int]       			=					False




  def __call__(					self :      List[str]     ,      *_a :      int     ,      **_a :      Optional[int]  )   ->   Optional[Any]:
    # For backward compatibility
    if self._in_target_context_manager:
      return self.current_processor(*_a     ,      **_a  )

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

    if text is None:
      return inputs
    elif audio is None:
      return encodings
    else:
      __lowerCamelCase			:			Dict       			=					encodings['input_ids']
      return inputs




  def 		_lowercase	(					self :      Union[str, Any]     ,      *_a :      str     ,      **_a :      Union[str, Any]  )   ->   int:
    return self.tokenizer.batch_decode(*_a     ,      **_a  )




  def 		_lowercase	(					self :      Tuple     ,      *_a :      Tuple     ,      **_a :      Any  )   ->   Optional[int]:
    # For backward compatibility
    if self._in_target_context_manager:
      return self.current_processor.pad(*_a     ,      **_a  )

    __lowerCamelCase			:			List[Any]       			=					kwargs.pop('input_features'     ,      _a  )
    __lowerCamelCase			:			List[str]       			=					kwargs.pop('labels'     ,      _a  )
    if len(_a  ) > 0:
      __lowerCamelCase			:			List[str]       			=					args[0]
      __lowerCamelCase			:			Tuple       			=					args[1:]

    if input_features is not None:
      __lowerCamelCase			:			Optional[Any]       			=					self.feature_extractor.pad(_a     ,      *_a     ,      **_a  )
    if labels is not None:
      __lowerCamelCase			:			List[Any]       			=					self.tokenizer.pad(_a     ,      **_a  )

    if labels is None:
      return input_features
    elif input_features is None:
      return labels
    else:
      __lowerCamelCase			:			Optional[int]       			=					labels['input_ids']
      return input_features




  def 		_lowercase	(					self :      Any     ,      *_a :      Any     ,      **_a :      str  )   ->   str:
    return self.tokenizer.decode(*_a     ,      **_a  )




  @contextmanager
  def 		_lowercase	(					self :      List[str]  )   ->   int:
    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.'  )
    __lowerCamelCase			:			Union[str, Any]       			=					True
    __lowerCamelCase			:			int       			=					self.tokenizer
    yield
    __lowerCamelCase			:			Union[str, Any]       			=					self.feature_extractor
    __lowerCamelCase			:			str       			=					False











'''simple docstring'''





def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> tuple[float, float]:

  # Check if the input is valid
  if not len(_lowerCAmelCase			) == len(_lowerCAmelCase			) == 3:
    raise ValueError('Please enter a valid equation.'			)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError('Both a & b of two equations can\'t be zero.'			)

  # Extract the coefficients
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					equationa
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Any       			=					equationa

  # Calculate the determinants of the matrices
  __lowerCamelCase			:			Union[str, Any]       			=					aa * ba - aa * ba
  __lowerCamelCase			:			Dict       			=					ca * ba - ca * ba
  __lowerCamelCase			:			List[str]       			=					aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError('Infinite solutions. (Consistent system)'			)
    else:
      raise ValueError('No solution. (Inconsistent system)'			)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      __lowerCamelCase			:			Tuple       			=					determinant_x / determinant
      __lowerCamelCase			:			Union[str, Any]       			=					determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)






