

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						["""image_processor""", """tokenizer"""]
 UpperCAmelCase							    =						"""CLIPImageProcessor"""
 UpperCAmelCase							    =						("""CLIPTokenizer""", """CLIPTokenizerFast""")


 def __init__(						self		: int			,						lowerCAmelCase__		: Optional[Any]=None			,						lowerCAmelCase__		: List[str]=None			,						**lowerCAmelCase__		: Optional[Any]    )    ->      str:
   UpperCAmelCase					      =  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					      =  kwargs.pop("feature_extractor"    )

   UpperCAmelCase					      =  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__    )


 def __call__(						self		: List[str]			,						lowerCAmelCase__		: List[str]=None			,						lowerCAmelCase__		: int=None			,						lowerCAmelCase__		: Dict=None			,						**lowerCAmelCase__		: Tuple    )    ->      Tuple:

   if text is None and images is None:
     raise ValueError("You have to specify either text or images. Both cannot be none."    )

   if text is not None:
     UpperCAmelCase					      =  self.tokenizer(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__    )

   if images is not None:
     UpperCAmelCase					      =  self.image_processor(lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__    )

   if text is not None and images is not None:
     UpperCAmelCase					      =  image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCAmelCase__    )			,						tensor_type=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Optional[Any]			,						*lowerCAmelCase__		: Optional[int]			,						**lowerCAmelCase__		: str    )    ->      Dict:
   return self.tokenizer.batch_decode(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict			,						*lowerCAmelCase__		: str			,						**lowerCAmelCase__		: List[str]    )    ->      int:
   return self.tokenizer.decode(*lowerCAmelCase__			,						**lowerCAmelCase__    )


 @property
 def _UpperCamelCase	(						self		: Optional[int]    )    ->      str:
   UpperCAmelCase					      =  self.tokenizer.model_input_names
   UpperCAmelCase					      =  self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      int:
   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 _UpperCamelCase	(						self		: Tuple    )    ->      Optional[int]:
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,						lowerCAmelCase__			,						)
   return self.image_processor






import unittest

import numpy as np
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A = None   ,       ):
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)

  if shape_a[0] != shape_b[0]:
    UpperCAmelCase					      =  (
        "Expected the same number of rows for A and B. "
        F"Instead found A of size {shape_a} and B of size {shape_b}"
    )
    raise ValueError(__A			)

  if shape_b[1] != shape_c[1]:
    UpperCAmelCase					      =  (
        "Expected the same number of columns for B and C. "
        F"Instead found B of size {shape_b} and C of size {shape_c}"
    )
    raise ValueError(__A			)

  UpperCAmelCase					      =  pseudo_inv
  if a_inv is None:
    try:
      UpperCAmelCase					      =  np.linalg.inv(__A			)
    except np.linalg.LinAlgError:
      raise ValueError(
          "Input matrix A is not invertible. Cannot compute Schur complement."			)

  return mat_c - mat_b.T @ a_inv @ mat_b






class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[str]    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   UpperCAmelCase					      =  schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  np.block([[a, b], [b.T, c]]    )

   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )

   self.assertAlmostEqual(lowerCAmelCase__			,						det_a * det_s    )


 def _UpperCamelCase	(						self		: str    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1, 3], [6, 3, 5]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()



