





"""simple docstring"""






from __future__ import annotations

from decimal import Decimal

from numpy import array

def       __A (     a_  :list[list[float]])						->						list[list[float]]:
				__a	:  List[Any]         =					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
								__a	:  Union[str, Any]         =					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
								__a	:  Optional[int]         =					[[0.0, 0.0], [0.0, 0.0]]
								__a    ,  __a	:  str         =					matrix[1][1], matrix[0][0]
								__a    ,  __a	:  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
								__a	:  Optional[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
								__a	:  Tuple         =					[
								    [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)],
								]
								__a	:  Dict         =					(d(matrix[1][1]) * d(matrix[2][2])) - (
								    d(matrix[1][2]) * d(matrix[2][1])
								)
								__a	:  str         =					-(
								    (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
								)
								__a	:  int         =					(d(matrix[1][0]) * d(matrix[2][1])) - (
								    d(matrix[1][1]) * d(matrix[2][0])
								)
								__a	:  str         =					-(
								    (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
								)
								__a	:  int         =					(d(matrix[0][0]) * d(matrix[2][2])) - (
								    d(matrix[0][2]) * d(matrix[2][0])
								)
								__a	:  Tuple         =					-(
								    (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
								)
								__a	:  Tuple         =					(d(matrix[0][1]) * d(matrix[1][2])) - (
								    d(matrix[0][2]) * d(matrix[1][1])
								)
								__a	:  Union[str, Any]         =					-(
								    (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
								)
								__a	:  List[str]         =					(d(matrix[0][0]) * d(matrix[1][1])) - (
								    d(matrix[0][1]) * d(matrix[1][0])
								)

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

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
								__a	:  Optional[int]         =					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     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					['''image_processor''', '''tokenizer''']
					__lowerCAmelCase					=					'''CLIPImageProcessor'''
					__lowerCAmelCase					=					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')







					def __init__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):
									__a	:  Optional[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		,     )
													__a	:  Any         =					kwargs.pop('''feature_extractor'''       )

									__a	:  Optional[Any]         =					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       )







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

									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	:  Any         =					self.tokenizer(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if images is not None:
													__a	:  List[str]         =					self.image_processor(_UpperCAmelCase		,     return_tensors=_UpperCAmelCase		,     **_UpperCAmelCase       )

									if text is not None and images is not None:
													__a	:  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       ):
									return self.tokenizer.batch_decode(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.decode(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Union[str, Any]         =					self.tokenizer.model_input_names
									__a	:  Dict         =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )