


"""simple docstring"""



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 					__magic_name__		(			__UpperCAmelCase     ):
							__A			:      Dict        =   ["image_processor", "tokenizer"]
							__A			:      List[str]        =   "ViltImageProcessor"
							__A			:      str        =   ("BertTokenizer", "BertTokenizerFast")






							def __init__(   self  :    List[str]  ,     snake_case__  :    Optional[int]=None  ,     snake_case__  :    Tuple=None  ,     **snake_case__  :    Union[str, Any]				):

														'''simple docstring'''






														lowercase		: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.'''  ,     snake_case__  ,     )
																					lowercase		:Dict       							=				kwargs.pop('''feature_extractor'''				)

														lowercase		:Tuple       							=				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__(snake_case__  ,     snake_case__				)
														lowercase		:Tuple       							=				self.image_processor






							def __call__(   self  :    Optional[int]  ,     snake_case__  :    List[Any]  ,     snake_case__  :    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,     snake_case__  :    bool = True  ,     snake_case__  :    Union[bool, str, PaddingStrategy] = False  ,     snake_case__  :    Union[bool, str, TruncationStrategy] = None  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    int = 0  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    Optional[bool] = None  ,     snake_case__  :    Optional[bool] = None  ,     snake_case__  :    bool = False  ,     snake_case__  :    bool = False  ,     snake_case__  :    bool = False  ,     snake_case__  :    bool = False  ,     snake_case__  :    bool = True  ,     snake_case__  :    Optional[Union[str, TensorType]] = None  ,     **snake_case__  :    Dict  ,     ):

														'''simple docstring'''






														lowercase		:Any       							=				self.tokenizer(
														    text=snake_case__  ,     add_special_tokens=snake_case__  ,     padding=snake_case__  ,     truncation=snake_case__  ,     max_length=snake_case__  ,     stride=snake_case__  ,     pad_to_multiple_of=snake_case__  ,     return_token_type_ids=snake_case__  ,     return_attention_mask=snake_case__  ,     return_overflowing_tokens=snake_case__  ,     return_special_tokens_mask=snake_case__  ,     return_offsets_mapping=snake_case__  ,     return_length=snake_case__  ,     verbose=snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__  ,     )
														# add pixel_values + pixel_mask
														lowercase		:str       							=				self.image_processor(snake_case__  ,     return_tensors=snake_case__				)
														encoding.update(snake_case__				)

														return encoding






							def  __snake_case						(   self  :    Tuple  ,     *snake_case__  :    List[str]  ,     **snake_case__  :    Tuple				):

														'''simple docstring'''






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






							def  __snake_case						(   self  :    str  ,     *snake_case__  :    Any  ,     **snake_case__  :    Dict				):

														'''simple docstring'''






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






							@property
							def  __snake_case						(   self  :    Any				):

														'''simple docstring'''






														lowercase		:Dict       							=				self.tokenizer.model_input_names
														lowercase		:Optional[int]       							=				self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






							@property
							def  __snake_case						(   self  :    Dict				):

														'''simple docstring'''






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







							@property
							def  __snake_case						(   self  :    Optional[Any]				):

														'''simple docstring'''






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





"""simple docstring"""



from __future__ import annotations

from decimal import Decimal

from numpy import array






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

              # Transpose the cofactor matrix (Adjoint matrix)
              lowercase		:Union[str, Any]       							=				array(a_)
              for i in range(3):
                     for j in range(3):
                            lowercase		:Any       							=				cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
              lowercase		:str       							=				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.''')

