from __future__ import annotations

from decimal import Decimal

from numpy import array
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							int       =	Decimal

   # Check if the provided matrix has 2 rows and 2 columns
   # since this implementation only works for 2x2 matrices
   if len(__a					) == 2 and len(matrix[0]					) == 2 and len(matrix[1]					) == 2:
      # Calculate the determinant of the matrix
      lowerCAmelCase_:							str       =	float(
          d(matrix[0][0]					) * d(matrix[1][1]					) - d(matrix[1][0]					) * d(matrix[0][1]					)					)
      if determinant == 0:
         raise ValueError("This matrix has no inverse."					)

      # Creates a copy of the matrix with swapped positions of the elements
      lowerCAmelCase_:							List[Any]       =	[[0.0, 0.0], [0.0, 0.0]]
      lowerCAmelCase_:							Union[str, Any]       =	matrix[1][1], matrix[0][0]
      lowerCAmelCase_:							Dict       =	-matrix[1][0], -matrix[0][1]

      # Calculate the inverse of the matrix
      return [
          [(float(d(__a					)					) / determinant) or 0.0 for n in row] for row in swapped_matrix
      ]
   elif (
       len(__a					) == 3
       and len(matrix[0]					) == 3
       and len(matrix[1]					) == 3
       and len(matrix[2]					) == 3
   ):
      # Calculate the determinant of the matrix using Sarrus rule
      lowerCAmelCase_:							int       =	float(
          (
              (d(matrix[0][0]					) * d(matrix[1][1]					) * d(matrix[2][2]					))
              + (d(matrix[0][1]					) * d(matrix[1][2]					) * d(matrix[2][0]					))
              + (d(matrix[0][2]					) * d(matrix[1][0]					) * d(matrix[2][1]					))
          )
          - (
              (d(matrix[0][2]					) * d(matrix[1][1]					) * d(matrix[2][0]					))
              + (d(matrix[0][1]					) * d(matrix[1][0]					) * d(matrix[2][2]					))
              + (d(matrix[0][0]					) * d(matrix[1][2]					) * d(matrix[2][1]					))
          )					)
      if determinant == 0:
         raise ValueError("This matrix has no inverse."					)

      # Creating cofactor matrix
      lowerCAmelCase_:							List[Any]       =	[
          [d(0.0					), d(0.0					), d(0.0					)],
          [d(0.0					), d(0.0					), d(0.0					)],
          [d(0.0					), d(0.0					), d(0.0					)],
      ]
      lowerCAmelCase_:							Tuple       =	(d(matrix[1][1]					) * d(matrix[2][2]					)) - (
          d(matrix[1][2]					) * d(matrix[2][1]					)
      )
      lowerCAmelCase_:							List[str]       =	-(
          (d(matrix[1][0]					) * d(matrix[2][2]					)) - (d(matrix[1][2]					) * d(matrix[2][0]					))
      )
      lowerCAmelCase_:							Union[str, Any]       =	(d(matrix[1][0]					) * d(matrix[2][1]					)) - (
          d(matrix[1][1]					) * d(matrix[2][0]					)
      )
      lowerCAmelCase_:							List[Any]       =	-(
          (d(matrix[0][1]					) * d(matrix[2][2]					)) - (d(matrix[0][2]					) * d(matrix[2][1]					))
      )
      lowerCAmelCase_:							Optional[int]       =	(d(matrix[0][0]					) * d(matrix[2][2]					)) - (
          d(matrix[0][2]					) * d(matrix[2][0]					)
      )
      lowerCAmelCase_:							List[Any]       =	-(
          (d(matrix[0][0]					) * d(matrix[2][1]					)) - (d(matrix[0][1]					) * d(matrix[2][0]					))
      )
      lowerCAmelCase_:							List[Any]       =	(d(matrix[0][1]					) * d(matrix[1][2]					)) - (
          d(matrix[0][2]					) * d(matrix[1][1]					)
      )
      lowerCAmelCase_:							List[str]       =	-(
          (d(matrix[0][0]					) * d(matrix[1][2]					)) - (d(matrix[0][2]					) * d(matrix[1][0]					))
      )
      lowerCAmelCase_:							Dict       =	(d(matrix[0][0]					) * d(matrix[1][1]					)) - (
          d(matrix[0][1]					) * d(matrix[1][0]					)
      )

      # Transpose the cofactor matrix (Adjoint matrix)
      lowerCAmelCase_:							Optional[Any]       =	array(__a					)
      for i in range(3					):
         for j in range(3					):
            lowerCAmelCase_:							Optional[Any]       =	cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
      lowerCAmelCase_:							Any       =	array(__a					)
      for i in range(3					):
         for j in range(3					):
            inverse_matrix[i][j] /= d(__a					)

        # Calculate the inverse of the matrix
      return [[float(d(__a					)					) or 0.0 for n in row] for row in inverse_matrix]
   raise ValueError("Please provide a matrix of size 2x2 or 3x3."					)



'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




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

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					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_       ,  )
												UpperCamelCase__  :List[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__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''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:
												UpperCamelCase__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if images is not None:
												UpperCamelCase__  :Any				   =					self.image_processor(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					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       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


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




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

							'''simple docstring'''


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




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Any				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''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