


"""simple docstring"""

from __future__ import annotations

from typing import Any





class  lowerCamelCase__    :
      def __init__(		self						:						Tuple   ,							A_						:						int   ,							A_						:						int   ,							A_						:						float = 0				):






       '''simple docstring'''


       __lowercase				,		__lowercase						=						row, column
       __lowercase						=						[[default_value for c in range(_lowercase				)] for r in range(_lowercase				)]
      def __str__(		self						:						Dict				):






       '''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(_lowercase   ,							len(str(_lowercase				)				)				)
       __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(_lowercase				) for row_vector in self.array				)
       return s
      def __repr__(		self						:						Union[str, Any]				):






       '''simple docstring'''


       return str(self				)
      def SCREAMING_SNAKE_CASE_						(		self						:						str   ,							A_						:						tuple[int, int]				):






       '''simple docstring'''


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






       '''simple docstring'''


       assert self.validate_indicies(_lowercase				)
       return self.array[loc[0]][loc[1]]
      def __setitem__(		self						:						str   ,							A_						:						tuple[int, int]   ,							A_						:						float				):






       '''simple docstring'''


       assert self.validate_indicies(_lowercase				)
       __lowercase						=						value
      def __add__(		self						:						Any   ,							A_						:						Matrix				):






       '''simple docstring'''


       assert isinstance(_lowercase   ,							_lowercase				)
       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						:						Optional[Any]				):






       '''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				):






       '''simple docstring'''


       return self + (-another)
      def __mul__(		self						:						Tuple   ,							A_						:						int | float | Matrix				):






       '''simple docstring'''


       if isinstance(_lowercase   ,							(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(_lowercase   ,							_lowercase				):  # 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(_lowercase				)})'''
        raise TypeError(_lowercase				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''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						:						Optional[int]   ,							A_						:						Matrix   ,							A_						:						Matrix				):






       '''simple docstring'''


       assert isinstance(_lowercase   ,							_lowercase				) and isinstance(_lowercase   ,							_lowercase				)
       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 lowerCAmelCase_   (					):






        """simple docstring"""







        __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 lowerCAmelCase_   (					):






        """simple docstring"""







        import doctest

        doctest.testmod()

       testa()






'''simple docstring'''
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   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					def __init__(	self     :						int      ,       _lowercase     :						Any      ,       _lowercase     :						Tuple=7      ,       _lowercase     :						Tuple=3      ,       _lowercase     :						str=18      ,       _lowercase     :						Union[str, Any]=30      ,       _lowercase     :						Dict=400      ,       _lowercase     :						int=True      ,       _lowercase     :						List[Any]=None      ,       _lowercase     :						int=True      ,       _lowercase     :						Optional[int]=False      ,       _lowercase     :						str=True      ,       _lowercase     :						Union[str, Any]=True      ,       _lowercase     :						Any=[0.5, 0.5, 0.5]      ,       _lowercase     :						str=[0.5, 0.5, 0.5]      ,       )    ->							Optional[Any]:
								A_									= parent
								A_									= batch_size
								A_									= num_channels
								A_									= image_size
								A_									= min_resolution
								A_									= max_resolution
								A_									= do_resize
								A_									= size if size is not None else {'height': 18, 'width': 20}
								A_									= do_thumbnail
								A_									= do_align_axis
								A_									= do_pad
								A_									= do_normalize
								A_									= image_mean
								A_									= image_std



					def     __snake_case						(	self     :						int)    ->							int:
								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   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		DonutImageProcessor if is_vision_available() else None



					def     __snake_case						(	self     :						Optional[int])    ->							Union[str, Any]:
								A_									= DonutImageProcessingTester(self)



					@property
					def     __snake_case						(	self     :						int)    ->							int:
								return self.image_processor_tester.prepare_image_processor_dict()



					def     __snake_case						(	self     :						Any)    ->							Tuple:
								A_									= self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_lowercase      ,       'do_resize'))
								self.assertTrue(hasattr(_lowercase      ,       'size'))
								self.assertTrue(hasattr(_lowercase      ,       'do_thumbnail'))
								self.assertTrue(hasattr(_lowercase      ,       'do_align_long_axis'))
								self.assertTrue(hasattr(_lowercase      ,       'do_pad'))
								self.assertTrue(hasattr(_lowercase      ,       'do_normalize'))
								self.assertTrue(hasattr(_lowercase      ,       'image_mean'))
								self.assertTrue(hasattr(_lowercase      ,       'image_std'))



					def     __snake_case						(	self     :						int)    ->							Optional[int]:
								A_									= self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size      ,       {'height': 18, 'width': 20})

								A_									= 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
								A_									= self.image_processing_class.from_dict(self.image_processor_dict      ,       size=(42, 84))
								self.assertEqual(image_processor.size      ,       {'height': 84, 'width': 42})



					def     __snake_case						(	self     :						Optional[Any])    ->							Optional[Any]:
								pass



					@is_flaky()
					def     __snake_case						(	self     :						List[str])    ->							int:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       Image.Image)

								# Test not batched input
								A_									= 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
								A_									= image_processing(_lowercase      ,       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     __snake_case						(	self     :						List[Any])    ->							List[Any]:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase      ,       numpify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       np.ndarray)

								# Test not batched input
								A_									= 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
								A_									= image_processing(_lowercase      ,       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     __snake_case						(	self     :						Dict)    ->							int:
								# Initialize image_processing
								A_									= self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								A_									= prepare_image_inputs(self.image_processor_tester      ,       equal_resolution=_lowercase      ,       torchify=_lowercase)
								for image in image_inputs:
											self.assertIsInstance(_lowercase      ,       torch.Tensor)

								# Test not batched input
								A_									= 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
								A_									= image_processing(_lowercase      ,       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'],
								    )      ,       )




