






'''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 a_    (    UpperCamelCase_							):
			_snake_case        =							["""image_processor""", """tokenizer"""]
			_snake_case        =							"""LayoutLMv2ImageProcessor"""
			_snake_case        =							("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")







			def __init__(self					,   __a=None					,   __a=None					,   **__a)			->      Tuple:
						"""simple docstring"""






						if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'					,   __a					,   )
									__snake_case				:      List[str]     							= kwargs.pop('feature_extractor')

						__snake_case				:      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__(__a					,   __a)







			def __call__(self					,   __a					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = True					,   __a = False					,   __a = None					,   __a = None					,   __a = 0					,   __a = None					,   __a = None					,   __a = None					,   __a = False					,   __a = False					,   __a = False					,   __a = False					,   __a = True					,   __a = None					,   **__a					,   )			->      BatchEncoding:
						"""simple docstring"""






						if self.image_processor.apply_ocr and (boxes is not None):
									raise ValueError(
									    'You cannot provide bounding boxes '
									    'if you initialized the image processor with apply_ocr set to True.')

						if self.image_processor.apply_ocr and (word_labels is not None):
									raise ValueError(
									    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.')

						if return_overflowing_tokens is True and return_offsets_mapping is False:
									raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.')

						# first, apply the image processor
						__snake_case				:      str     							= self.image_processor(images=__a					,   return_tensors=__a)

						# second, apply the tokenizer
						if text is not None and self.image_processor.apply_ocr and text_pair is None:
									if isinstance(__a					,   __a):
												__snake_case				:      Optional[int]     							= [text]  # add batch dimension (as the image processor always adds a batch dimension)
									__snake_case				:      str     							= features['words']

						__snake_case				:      Union[str, Any]     							= self.tokenizer(
						    text=text if text is not None else features['words']					,   text_pair=text_pair if text_pair is not None else None					,   boxes=boxes if boxes is not None else features['boxes']					,   word_labels=__a					,   add_special_tokens=__a					,   padding=__a					,   truncation=__a					,   max_length=__a					,   stride=__a					,   pad_to_multiple_of=__a					,   return_token_type_ids=__a					,   return_attention_mask=__a					,   return_overflowing_tokens=__a					,   return_special_tokens_mask=__a					,   return_offsets_mapping=__a					,   return_length=__a					,   verbose=__a					,   return_tensors=__a					,   **__a					,   )

						# add pixel values
						__snake_case				:      Any     							= features.pop('pixel_values')
						if return_overflowing_tokens is True:
									__snake_case				:      str     							= self.get_overflowing_images(__a					,   encoded_inputs['overflow_to_sample_mapping'])
						__snake_case				:      str     							= images

						return encoded_inputs







			def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      str:
						"""simple docstring"""






						__snake_case				:      Union[str, Any]     							= []
						for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx])

						if len(__a) != len(__a):
									raise ValueError(
									    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
									    F""" {len(__a)} and {len(__a)}""")

						return images_with_overflow







			def     SCREAMING_SNAKE_CASE__					(self					,   *__a					,   **__a)			->      List[str]:
						"""simple docstring"""






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







			def     SCREAMING_SNAKE_CASE__					(self					,   *__a					,   **__a)			->      Any:
						"""simple docstring"""






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







			@property
			def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
						"""simple docstring"""






						return ["input_ids", "bbox", "attention_mask", "image"]







			@property
			def     SCREAMING_SNAKE_CASE__					(self)			->      str:
						"""simple docstring"""






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







			@property
			def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
						"""simple docstring"""






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







'''simple docstring'''






from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload




class a_    :







   def __init__(self					,   __a = None)			->      None:
      """simple docstring"""






      if components is None:
         __snake_case				:      List[str]     							= []
      __snake_case				:      Optional[int]     							= list(__a)







   def __len__(self)			->      int:
      """simple docstring"""






      return len(self.__components)







   def __str__(self)			->      str:
      """simple docstring"""






      return "(" + ",".join(map(__a					,   self.__components)) + ")"







   def __add__(self					,   __a)			->      Vector:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= len(self)
      if size == len(__a):
         __snake_case				:      Optional[int]     							= [self.__components[i] + other.component(__a) for i in range(__a)]
         return Vector(__a)
      else:
         raise Exception('must have the same size')







   def __sub__(self					,   __a)			->      Vector:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= len(self)
      if size == len(__a):
         __snake_case				:      Optional[int]     							= [self.__components[i] - other.component(__a) for i in range(__a)]
         return Vector(__a)
      else:  # error case
         raise Exception('must have the same size')







   @overload
   def __mul__(self					,   __a)			->      Vector:
      """simple docstring"""






      ...







   @overload
   def __mul__(self					,   __a)			->      float:
      """simple docstring"""






      ...







   def __mul__(self					,   __a)			->      float | Vector:
      """simple docstring"""






      if isinstance(__a					,   (float, int)):
         __snake_case				:      str     							= [c * other for c in self.__components]
         return Vector(__a)
      elif isinstance(__a					,   __a) and len(self) == len(__a):
         __snake_case				:      List[Any]     							= len(self)
         __snake_case				:      Dict     							= [self.__components[i] * other.component(__a) for i in range(__a)]
         return sum(__a)
      else:  # error case
         raise Exception('invalid operand!')







   def     SCREAMING_SNAKE_CASE__					(self)			->      Vector:
      """simple docstring"""






      return Vector(self.__components)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      float:
      """simple docstring"""






      if isinstance(__a					,   __a) and -len(self.__components) <= i < len(self.__components):
         return self.__components[i]
      else:
         raise Exception('index out of range')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      None:
      """simple docstring"""






      assert -len(self.__components) <= pos < len(self.__components)
      __snake_case				:      int     							= value







   def     SCREAMING_SNAKE_CASE__					(self)			->      float:
      """simple docstring"""






      if len(self.__components) == 0:
         raise Exception('Vector is empty')
      __snake_case				:      Tuple     							= [c**2 for c in self.__components]
      return math.sqrt(sum(__a))







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = False)			->      float:
      """simple docstring"""






      __snake_case				:      Tuple     							= self * other
      __snake_case				:      Optional[int]     							= self.euclidean_length() * other.euclidean_length()
      if deg:
         return math.degrees(math.acos(num / den))
      else:
         return math.acos(num / den)




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Vector:







   """simple docstring"""
   assert isinstance(A  ,		A  )
   return Vector([0] * dimension  )




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  )    -> Vector:







   """simple docstring"""
   assert isinstance(A  ,		A  ) and (isinstance(A  ,		A  ))
   __snake_case				:      Any     							= [0] * dimension
   __snake_case				:      int     							= 1
   return Vector(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       float  ,		A  :       Vector  ,		A  :       Vector  )    -> Vector:







   """simple docstring"""
   assert (
       isinstance(A  ,		A  )
       and isinstance(A  ,		A  )
       and (isinstance(A  ,		(int, float)  ))
   )
   return x * scalar + y




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  ,		A  :       int  )    -> Vector:







   """simple docstring"""
   random.seed(A  )
   __snake_case				:      List[Any]     							= [random.randint(A  ,		A  ) for _ in range(A  )]
   return Vector(A  )




class a_    :







   def __init__(self					,   __a					,   __a					,   __a)			->      None:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= matrix
      __snake_case				:      int     							= w
      __snake_case				:      str     							= h







   def __str__(self)			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= ''
      for i in range(self.__height):
         ans += "|"
         for j in range(self.__width):
            if j < self.__width - 1:
               ans += str(self.__matrix[i][j]) + ","
            else:
               ans += str(self.__matrix[i][j]) + "|\n"
      return ans







   def __add__(self					,   __a)			->      Matrix:
      """simple docstring"""






      if self.__width == other.width() and self.__height == other.height():
         __snake_case				:      Tuple     							= []
         for i in range(self.__height):
            __snake_case				:      List[Any]     							= [
                self.__matrix[i][j] + other.component(__a					,   __a)
                for j in range(self.__width)
            ]
            matrix.append(__a)
         return Matrix(__a					,   self.__width					,   self.__height)
      else:
         raise Exception('matrix must have the same dimension!')







   def __sub__(self					,   __a)			->      Matrix:
      """simple docstring"""






      if self.__width == other.width() and self.__height == other.height():
         __snake_case				:      str     							= []
         for i in range(self.__height):
            __snake_case				:      List[str]     							= [
                self.__matrix[i][j] - other.component(__a					,   __a)
                for j in range(self.__width)
            ]
            matrix.append(__a)
         return Matrix(__a					,   self.__width					,   self.__height)
      else:
         raise Exception('matrices must have the same dimension!')







   @overload
   def __mul__(self					,   __a)			->      Matrix:
      """simple docstring"""






      ...







   @overload
   def __mul__(self					,   __a)			->      Vector:
      """simple docstring"""






      ...







   def __mul__(self					,   __a)			->      Vector | Matrix:
      """simple docstring"""






      if isinstance(__a					,   __a):  # matrix-vector
         if len(__a) == self.__width:
            __snake_case				:      Tuple     							= zero_vector(self.__height)
            for i in range(self.__height):
               __snake_case				:      Union[str, Any]     							= [
                   self.__matrix[i][j] * other.component(__a)
                   for j in range(self.__width)
               ]
               ans.change_component(__a					,   sum(__a))
            return ans
         else:
            raise Exception(
                'vector must have the same size as the '
                'number of columns of the matrix!')
      elif isinstance(__a					,   (int, float)):  # matrix-scalar
         __snake_case				:      str     							= [
             [self.__matrix[i][j] * other for j in range(self.__width)]
             for i in range(self.__height)
         ]
         return Matrix(__a					,   self.__width					,   self.__height)
      return None







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      return self.__height







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      return self.__width







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if 0 <= x < self.__height and 0 <= y < self.__width:
         return self.__matrix[x][y]
      else:
         raise Exception('change_component: indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a)			->      None:
      """simple docstring"""






      if 0 <= x < self.__height and 0 <= y < self.__width:
         __snake_case				:      List[Any]     							= value
      else:
         raise Exception('change_component: indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      __snake_case				:      List[Any]     							= self.__matrix[:x] + self.__matrix[x + 1 :]
      for i in range(len(__a)):
         __snake_case				:      Tuple     							= minor[i][:y] + minor[i][y + 1 :]
      return Matrix(__a					,   self.__width - 1					,   self.__height - 1).determinant()







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      if 0 <= x < self.__height and 0 <= y < self.__width:
         return (-1) ** (x + y) * self.minor(__a					,   __a)
      else:
         raise Exception('Indices out of bounds')







   def     SCREAMING_SNAKE_CASE__					(self)			->      float:
      """simple docstring"""






      if self.__height != self.__width:
         raise Exception('Matrix is not square')
      if self.__height < 1:
         raise Exception('Matrix has no element')
      elif self.__height == 1:
         return self.__matrix[0][0]
      elif self.__height == 2:
         return (
             self.__matrix[0][0] * self.__matrix[1][1]
             - self.__matrix[0][1] * self.__matrix[1][0]
         )
      else:
         __snake_case				:      Any     							= [
             self.__matrix[0][y] * self.cofactor(0					,   __a) for y in range(self.__width)
         ]
         return sum(__a)




def        _SCREAMING_SNAKE_CASE  (				A  :       int  )    -> Matrix:







   """simple docstring"""
   __snake_case				:      list[list[float]]     							= [[0] * n for _ in range(A  )]
   return Matrix(A  ,		A  ,		A  )





def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       int  ,		A  :       int  ,		A  :       int  )    -> Matrix:







   """simple docstring"""
   random.seed(A  )
   __snake_case				:      list[list[float]]     							= [
       [random.randint(A  ,		A  ) for _ in range(A  )] for _ in range(A  )
   ]
   return Matrix(A  ,		A  ,		A  )