





from __future__ import annotations

from typing import Any
class        SCREAMING_SNAKE_CASE     :


   def __init__(					self    :       List[str]    ,  a    :       int    ,  a    :       int    ,  a    :       float = 0 )->     None:





     """simple docstring"""







     lowercase__    ,					lowercase__		    =						row, column
     lowercase__		    =						[[default_value for c in range(a )] for r in range(a )]


   def __str__(					self    :       int )->     str:





     """simple docstring"""







     lowercase__		    =						f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

     # Make string identifier
     lowercase__		    =						0
     for row_vector in self.array:
       for obj in row_vector:
         lowercase__		    =						max(a    ,  len(str(a ) ) )
     lowercase__		    =						f"""%{max_element_length}s"""

     # Make string and return
     def single_line(a    :       list[float] ) -> str:
       nonlocal string_format_identifier
       lowercase__		    =						'['
       line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
       line += "]"
       return line

     s += "\n".join(single_line(a ) for row_vector in self.array )
     return s


   def __repr__(					self    :       List[str] )->     str:





     """simple docstring"""







     return str(self )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict    ,  a    :       tuple[int, int] )->     bool:





     """simple docstring"""







     if not (isinstance(a    ,  (list, tuple) ) and len(a ) == 2):
       return False
     elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
       return False
     else:
       return True


   def __getitem__(					self    :       List[Any]    ,  a    :       tuple[int, int] )->     Any:





     """simple docstring"""







     assert self.validate_indicies(a )
     return self.array[loc[0]][loc[1]]


   def __setitem__(					self    :       int    ,  a    :       tuple[int, int]    ,  a    :       float )->     None:





     """simple docstring"""







     assert self.validate_indicies(a )
     lowercase__		    =						value


   def __add__(					self    :       List[Any]    ,  a    :       Matrix )->     Matrix:





     """simple docstring"""







     assert isinstance(a    ,  a )
     assert self.row == another.row and self.column == another.column

     # Add
     lowercase__		    =						Matrix(self.row    ,  self.column )
     for r in range(self.row ):
       for c in range(self.column ):
         lowercase__		    =						self[r, c] + another[r, c]
     return result


   def __neg__(					self    :       str )->     Matrix:





     """simple docstring"""







     lowercase__		    =						Matrix(self.row    ,  self.column )
     for r in range(self.row ):
       for c in range(self.column ):
         lowercase__		    =						-self[r, c]
     return result


   def __sub__(					self    :       int    ,  a    :       Matrix )->     Matrix:





     """simple docstring"""







     return self + (-another)


   def __mul__(					self    :       Any    ,  a    :       int | float | Matrix )->     Matrix:





     """simple docstring"""







     if isinstance(a    ,  (int, float) ):  # Scalar multiplication
       lowercase__		    =						Matrix(self.row    ,  self.column )
       for r in range(self.row ):
         for c in range(self.column ):
           lowercase__		    =						self[r, c] * another
       return result
     elif isinstance(a    ,  a ):  # Matrix multiplication
       assert self.column == another.row
       lowercase__		    =						Matrix(self.row    ,  another.column )
       for r in range(self.row ):
         for c in range(another.column ):
           for i in range(self.column ):
             result[r, c] += self[r, i] * another[i, c]
       return result
     else:
       lowercase__		    =						f"""Unsupported type given for another ({type(a )})"""
       raise TypeError(a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Matrix:





     """simple docstring"""







     lowercase__		    =						Matrix(self.column    ,  self.row )
     for r in range(self.row ):
       for c in range(self.column ):
         lowercase__		    =						self[r, c]
     return result


   def  SCREAMING_SNAKE_CASE_							(					self    :       int    ,  a    :       Matrix    ,  a    :       Matrix )->     Any:





     """simple docstring"""







     assert isinstance(a    ,  a ) and isinstance(a    ,  a )
     assert self.row == self.column == u.row == v.row  # u, v should be column vector
     assert u.column == v.column == 1  # u, v should be column vector

     # Calculate
     lowercase__		    =						v.transpose()
     lowercase__		    =						(v_t * self * u)[0, 0] + 1
     if numerator_factor == 0:
       return None  # It's not invertable
     return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




      def     __UpperCamelCase						()       ->     None:
        # a^(-1)
        lowercase__		    =						Matrix(3	,		3	,		0	)
        for i in range(3	):
          lowercase__		    =						1
        print(F"""a^(-1) is {ainv}"""	)
        # u, v
        lowercase__		    =						Matrix(3	,		1	,		0	)
        lowercase__    ,					lowercase__    ,					lowercase__		    =						1, 2, -3
        lowercase__		    =						Matrix(3	,		1	,		0	)
        lowercase__    ,					lowercase__    ,					lowercase__		    =						4, -2, 5
        print(F"""u is {u}"""	)
        print(F"""v is {v}"""	)
        print(F"""uv^T is {u * v.transpose()}"""	)
        # Sherman Morrison
        print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)}"""	)




      def     __UpperCamelCase						()       ->     None:
        import doctest

        doctest.testmod()


      testa()









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DonutImageProcessor
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def __init__(					self    :       Any    ,  a    :       str    ,  a    :       List[Any]=7    ,  a    :       int=3    ,  a    :       int=18    ,  a    :       Optional[Any]=30    ,  a    :       Optional[int]=400    ,  a    :       int=True    ,  a    :       Tuple=None    ,  a    :       Optional[Any]=True    ,  a    :       str=False    ,  a    :       str=True    ,  a    :       int=True    ,  a    :       Tuple=[0.5, 0.5, 0.5]    ,  a    :       Any=[0.5, 0.5, 0.5]    ,  )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						num_channels
     lowercase__			    =						image_size
     lowercase__			    =						min_resolution
     lowercase__			    =						max_resolution
     lowercase__			    =						do_resize
     lowercase__			    =						size if size is not None else {'height': 18, 'width': 20}
     lowercase__			    =						do_thumbnail
     lowercase__			    =						do_align_axis
     lowercase__			    =						do_pad
     lowercase__			    =						do_normalize
     lowercase__			    =						image_mean
     lowercase__			    =						image_std


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[Any]				      = DonutImageProcessor if is_vision_available() else None


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						DonutImageProcessingTester(self )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     int:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(a    ,  'do_resize' ) )
     self.assertTrue(hasattr(a    ,  'size' ) )
     self.assertTrue(hasattr(a    ,  'do_thumbnail' ) )
     self.assertTrue(hasattr(a    ,  'do_align_long_axis' ) )
     self.assertTrue(hasattr(a    ,  'do_pad' ) )
     self.assertTrue(hasattr(a    ,  'do_normalize' ) )
     self.assertTrue(hasattr(a    ,  'image_mean' ) )
     self.assertTrue(hasattr(a    ,  'image_std' ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size    ,  {'height': 18, 'width': 20} )

     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42 )
     self.assertEqual(image_processor.size    ,  {'height': 42, 'width': 42} )

     # Previous config had dimensions in (width, height) order
     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=(42, 84) )
     self.assertEqual(image_processor.size    ,  {'height': 84, 'width': 42} )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     pass


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  Image.Image )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  numpify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  np.ndarray )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  torchify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  torch.Tensor )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


