




"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__				):



 '''simple docstring'''





 __UpperCAmelCase		   =							["""image_processor""", """tokenizer"""]
 __UpperCAmelCase		   =							"""AutoImageProcessor"""
 __UpperCAmelCase		   =							"""AutoTokenizer"""






 def __init__(self					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  None
  if "feature_extractor" in kwargs:
   warnings.warn(
       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
       " instead."					,     lowerCAmelCase__					,     )
   _UpperCamelCase      :	Union[str, Any]					      =  kwargs.pop("feature_extractor"   )

  _UpperCamelCase      :	List[str]					      =  image_processor if image_processor is not None else feature_extractor
  if image_processor is None:
   raise ValueError("You need to specify an `image_processor`."   )
  if tokenizer is None:
   raise ValueError("You need to specify a `tokenizer`."   )

  super().__init__(lowerCAmelCase__					,     lowerCAmelCase__   )
  _UpperCamelCase      :	List[Any]					      =  self.image_processor
  _UpperCamelCase      :	Optional[int]					      =  False






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

  '''simple docstring'''







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

  _UpperCamelCase      :	int					      =  kwargs.pop("images"					,     lowerCAmelCase__   )
  _UpperCamelCase      :	Optional[Any]					      =  kwargs.pop("text"					,     lowerCAmelCase__   )
  if len(lowerCAmelCase__   ) > 0:
   _UpperCamelCase      :	Dict					      =  args[0]
   _UpperCamelCase      :	Any					      =  args[1:]

  if images is None and text is None:
   raise ValueError("You need to specify either an `images` or `text` input to process."   )

  if images is not None:
   _UpperCamelCase      :	str					      =  self.image_processor(lowerCAmelCase__					,     *lowerCAmelCase__					,     **lowerCAmelCase__   )
  if text is not None:
   _UpperCamelCase      :	Tuple					      =  self.tokenizer(lowerCAmelCase__					,     **lowerCAmelCase__   )

  if text is None:
   return inputs
  elif images is None:
   return encodings
  else:
   _UpperCamelCase      :	Optional[int]					      =  encodings["input_ids"]
   return inputs






 def lowercase_      (self					,     *lowerCAmelCase__					,     **lowerCAmelCase__   ):

  '''simple docstring'''







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






 def lowercase_      (self					,     *lowerCAmelCase__					,     **lowerCAmelCase__   ):

  '''simple docstring'''







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






 @contextmanager
 def lowercase_      (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 images inputs, or in a separate call."   )
  _UpperCamelCase      :	str					      =  True
  _UpperCamelCase      :	str					      =  self.tokenizer
  yield
  _UpperCamelCase      :	List[str]					      =  self.image_processor
  _UpperCamelCase      :	Optional[int]					      =  False






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  warnings.warn(
      "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,     lowerCAmelCase__					,     )
  return self.image_processor_class






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  warnings.warn(
      "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,     lowerCAmelCase__					,     )
  return self.image_processor









"""simple docstring"""
import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)

 _UpperCamelCase      :	NDArray[floataa]					      =  np.zeros((rows, 1)  ,	dtype=__lowerCAmelCase						)
 for row in reversed(range(__lowerCAmelCase						)						):
  _UpperCamelCase      :	List[str]					      =  0
  for col in range(row + 1  ,	__lowerCAmelCase						):
   total += coefficients[row, col] * x[col]

  _UpperCamelCase      :	Dict					      =  (vector[row] - total) / coefficients[row, row]

 return x






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		NDArray[floataa]  ,	__lowerCAmelCase			:		NDArray[floataa]						)    ->       NDArray[floataa]:
 _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.shape(__lowerCAmelCase						)
 if rows != columns:
  return np.array(()  ,	dtype=__lowerCAmelCase						)

 # augmented matrix
 _UpperCamelCase      :	NDArray[floataa]					      =  np.concatenate((coefficients, vector)  ,	axis=1						)
 _UpperCamelCase      :	List[str]					      =  augmented_mat.astype("float64"						)

 # scale the matrix leaving it triangular
 for row in range(rows - 1						):
  _UpperCamelCase      :	Optional[Any]					      =  augmented_mat[row, row]
  for col in range(row + 1  ,	__lowerCAmelCase						):
   _UpperCamelCase      :	Union[str, Any]					      =  augmented_mat[col, row] / pivot
   augmented_mat[col, :] -= factor * augmented_mat[row, :]

 _UpperCamelCase      :	Any					      =  retroactive_resolution(
     augmented_mat[:, 0:columns]  ,	augmented_mat[:, columns : columns + 1]						)

 return x


if __name__ == "__main__":
    import doctest

    doctest.testmod()



