






import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

UpperCAmelCase__            =	[[12, 10], [3, 9]]
UpperCAmelCase__            =	[[3, 4], [7, 4]]
UpperCAmelCase__            =	[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
UpperCAmelCase__            =	[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
UpperCAmelCase__            =	[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
UpperCAmelCase__            =	[1]
UpperCAmelCase__            =	[2]

UpperCAmelCase__            =	logging.getLogger()
UpperCAmelCase__            =	logging.DEBUG
UpperCAmelCase__            =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')			,							[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]				)
def 		_a					(   a				:str			,							a				:int				)							->    Any:
 if (np.array(a				)).shape < (2, 2) or (np.array(a				)).shape < (2, 2):
  with pytest.raises(a				):
   logger.info(F"""\n\t{test_addition.__name__} returned integer"""				)
   matop.add(a			,							a				)
 elif (np.array(a				)).shape == (np.array(a				)).shape:
  logger.info(F"""\n\t{test_addition.__name__} with same matrix dims"""				)
  a							=   (np.array(a				) + np.array(a				)).tolist()
  a							=   matop.add(a			,							a				)
  assert theo == act
 else:
  with pytest.raises(a				):
   logger.info(F"""\n\t{test_addition.__name__} with different matrix dims"""				)
   matop.add(a			,							a				)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')			,							[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]				)
def 		_a					(   a				:List[str]			,							a				:Any				)							->    Optional[Any]:
 if (np.array(a				)).shape < (2, 2) or (np.array(a				)).shape < (2, 2):
  with pytest.raises(a				):
   logger.info(F"""\n\t{test_subtraction.__name__} returned integer"""				)
   matop.subtract(a			,							a				)
 elif (np.array(a				)).shape == (np.array(a				)).shape:
  logger.info(F"""\n\t{test_subtraction.__name__} with same matrix dims"""				)
  a							=   (np.array(a				) - np.array(a				)).tolist()
  a							=   matop.subtract(a			,							a				)
  assert theo == act
 else:
  with pytest.raises(a				):
   logger.info(F"""\n\t{test_subtraction.__name__} with different matrix dims"""				)
   assert matop.subtract(a			,							a				)
@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2''')			,							[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]				)
def 		_a					(   a				:int			,							a				:int				)							->    Tuple:
 if (np.array(a				)).shape < (2, 2) or (np.array(a				)).shape < (2, 2):
  logger.info(F"""\n\t{test_multiplication.__name__} returned integer"""				)
  with pytest.raises(a				):
   matop.add(a			,							a				)
 elif (np.array(a				)).shape == (np.array(a				)).shape:
  logger.info(F"""\n\t{test_multiplication.__name__} meets dim requirements"""				)
  a							=   (np.matmul(a			,							a				)).tolist()
  a							=   matop.multiply(a			,							a				)
  assert theo == act
 else:
  with pytest.raises(a				):
   logger.info(
       F"""\n\t{test_multiplication.__name__} does not meet dim requirements"""				)
   assert matop.subtract(a			,							a				)
@pytest.mark.mat_ops
def 		_a					(   )							->    Any:
 a							=   (3.5 * np.array(a				)).tolist()
 a							=   matop.scalar_multiply(a			,							3.5				)
 assert theo == act
@pytest.mark.mat_ops
def 		_a					(   )							->    Any:
 a							=   (np.identity(5				)).tolist()
 a							=   matop.identity(5				)
 assert theo == act

@pytest.mark.mat_ops
@pytest.mark.parametrize('''mat'''			,							[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]				)
def 		_a					(   a				:Union[str, Any]				)							->    Any:
 if (np.array(a				)).shape < (2, 2):
  with pytest.raises(a				):
   logger.info(F"""\n\t{test_transpose.__name__} returned integer"""				)
   matop.transpose(a				)
 else:
  a							=   (np.transpose(a				)).tolist()
  a							=   matop.transpose(a			,							return_map=a				)
  assert theo == act










import warnings

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






class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      __snake_case  						=						['''image_processor''', '''tokenizer''']
      __snake_case  						=						'''AutoImageProcessor'''
      __snake_case  						=						'''AutoTokenizer'''







      def __init__(   self   :		Any				,      __UpperCAmelCase   :		Union[str, Any]=None				,      __UpperCAmelCase   :		int=None				,      **__UpperCAmelCase   :		Union[str, Any]						)					->Dict:




       """simple docstring"""


       a							=   None
       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.'''				,      __UpperCAmelCase				,      )
        a							=   kwargs.pop('''feature_extractor'''						)

       a							=   image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError('''You have to specify an image_processor.'''						)
       if tokenizer is None:
        raise ValueError('''You have to specify a tokenizer.'''						)

       super().__init__(__UpperCAmelCase				,      __UpperCAmelCase						)
       a							=   self.image_processor







      def __call__(   self   :		List[Any]				,      __UpperCAmelCase   :		Optional[Any]=None				,      __UpperCAmelCase   :		Optional[int]=None				,      __UpperCAmelCase   :		str=None				,      **__UpperCAmelCase   :		List[Any]						)					->Dict:




       """simple docstring"""


       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:
        a							=   self.tokenizer(__UpperCAmelCase				,      return_tensors=__UpperCAmelCase				,      **__UpperCAmelCase						)

       if images is not None:
        a							=   self.image_processor(__UpperCAmelCase				,      return_tensors=__UpperCAmelCase				,      **__UpperCAmelCase						)

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







      def    __lowerCAmelCase      (   self   :		List[Any]				,      *__UpperCAmelCase   :		Optional[Any]				,      **__UpperCAmelCase   :		List[str]						)					->Optional[Any]:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		str				,      *__UpperCAmelCase   :		List[Any]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Union[str, Any]:




       """simple docstring"""


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







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->str:




       """simple docstring"""


       a							=   self.tokenizer.model_input_names
       a							=   self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







      @property
      def    __lowerCAmelCase      (   self   :		str						)					->Optional[Any]:




       """simple docstring"""


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







      @property
      def    __lowerCAmelCase      (   self   :		int						)					->Optional[Any]:




       """simple docstring"""


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


