
"""simple docstring"""

from __future__ import annotations

import math






def 							A		(       snake_case   :list       ,      snake_case   :list	)   ->			list:
 if len(snake_case	) != 2 or len(a[0]	) != 2 or len(snake_case	) != 2 or len(b[0]	) != 2:
  raise Exception('Matrices are not 2x2'	)
 __UpperCamelCase       						= [
     [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
     [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
 ]
 return new_matrix






def 							A		(       snake_case   :list       ,      snake_case   :list	)   ->			Tuple:
 return [
     [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]	)	)]
     for row in range(len(snake_case	)	)
 ]






def 							A		(       snake_case   :list       ,      snake_case   :list	)   ->			Union[str, Any]:
 return [
     [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]	)	)]
     for row in range(len(snake_case	)	)
 ]






def 							A		(       snake_case   :list	)   ->			tuple[list, list, list, list]:
 if len(snake_case	) % 2 != 0 or len(a[0]	) % 2 != 0:
  raise Exception('Odd matrices are not supported!'	)

 __UpperCamelCase       						= len(snake_case	)
 __UpperCamelCase       						= matrix_length // 2

 __UpperCamelCase       						= [[a[i][j] for j in range(snake_case       ,      snake_case	)] for i in range(snake_case	)]
 __UpperCamelCase       						= [
     [a[i][j] for j in range(snake_case       ,      snake_case	)] for i in range(snake_case       ,      snake_case	)
 ]

 __UpperCamelCase       						= [[a[i][j] for j in range(snake_case	)] for i in range(snake_case	)]
 __UpperCamelCase       						= [[a[i][j] for j in range(snake_case	)] for i in range(snake_case       ,      snake_case	)]

 return top_left, top_right, bot_left, bot_right






def 							A		(       snake_case   :list	)   ->			tuple[int, int]:
 return len(snake_case	), len(matrix[0]	)






def 							A		(       snake_case   :list	)   ->			None:
 print('\n'.join(str(snake_case	) for line in matrix	)	)






def 							A		(       snake_case   :list       ,      snake_case   :list	)   ->			list:
 if matrix_dimensions(snake_case	) == (2, 2):
  return default_matrix_multiplication(snake_case       ,      snake_case	)

 __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= split_matrix(snake_case	)
 __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= split_matrix(snake_case	)

 __UpperCamelCase       						= actual_strassen(snake_case       ,      matrix_subtraction(snake_case       ,      snake_case	)	)
 __UpperCamelCase       						= actual_strassen(matrix_addition(snake_case       ,      snake_case	)       ,      snake_case	)
 __UpperCamelCase       						= actual_strassen(matrix_addition(snake_case       ,      snake_case	)       ,      snake_case	)
 __UpperCamelCase       						= actual_strassen(snake_case       ,      matrix_subtraction(snake_case       ,      snake_case	)	)
 __UpperCamelCase       						= actual_strassen(matrix_addition(snake_case       ,      snake_case	)       ,      matrix_addition(snake_case       ,      snake_case	)	)
 __UpperCamelCase       						= actual_strassen(matrix_subtraction(snake_case       ,      snake_case	)       ,      matrix_addition(snake_case       ,      snake_case	)	)
 __UpperCamelCase       						= actual_strassen(matrix_subtraction(snake_case       ,      snake_case	)       ,      matrix_addition(snake_case       ,      snake_case	)	)

 __UpperCamelCase       						= matrix_addition(matrix_subtraction(matrix_addition(snake_case       ,      snake_case	)       ,      snake_case	)       ,      snake_case	)
 __UpperCamelCase       						= matrix_addition(snake_case       ,      snake_case	)
 __UpperCamelCase       						= matrix_addition(snake_case       ,      snake_case	)
 __UpperCamelCase       						= matrix_subtraction(matrix_subtraction(matrix_addition(snake_case       ,      snake_case	)       ,      snake_case	)       ,      snake_case	)

 # construct the new matrix from our 4 quadrants
 __UpperCamelCase       						= []
 for i in range(len(snake_case	)	):
  new_matrix.append(top_left[i] + top_right[i]	)
 for i in range(len(snake_case	)	):
  new_matrix.append(bot_left[i] + bot_right[i]	)
 return new_matrix






def 							A		(       snake_case   :list       ,      snake_case   :list	)   ->			list:
 if matrix_dimensions(snake_case	)[1] != matrix_dimensions(snake_case	)[0]:
  __UpperCamelCase       						= (
      'Unable to multiply these matrices, please check the dimensions.\n'
      f'Matrix A: {matrixa}\n'
      f'Matrix B: {matrixa}'
  )
  raise Exception(snake_case	)
 __UpperCamelCase       						= matrix_dimensions(snake_case	)
 __UpperCamelCase       						= matrix_dimensions(snake_case	)

 if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
  return [matrixa, matrixa]

 __UpperCamelCase       						= max(*snake_case       ,      *snake_case	)
 __UpperCamelCase       						= int(math.pow(2       ,      math.ceil(math.loga(snake_case	)	)	)	)
 __UpperCamelCase       						= matrixa
 __UpperCamelCase       						= matrixa

 # Adding zeros to the matrices so that the arrays dimensions are the same and also
 # power of 2
 for i in range(0       ,      snake_case	):
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]       ,      snake_case	):
    new_matrixa[i].append(0	)
  else:
   new_matrixa.append([0] * maxim	)
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]       ,      snake_case	):
    new_matrixa[i].append(0	)
  else:
   new_matrixa.append([0] * maxim	)

 __UpperCamelCase       						= actual_strassen(snake_case       ,      snake_case	)

 # Removing the additional zeros
 for i in range(0       ,      snake_case	):
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]       ,      snake_case	):
    final_matrix[i].pop()
  else:
   final_matrix.pop()
 return final_matrix


if __name__ == "__main__":
      UpperCamelCase			:       Any									=				[
          [2, 3, 4, 5],
          [6, 4, 3, 1],
          [2, 3, 6, 7],
          [3, 1, 2, 4],
          [2, 3, 4, 5],
          [6, 4, 3, 1],
          [2, 3, 6, 7],
          [3, 1, 2, 4],
          [2, 3, 4, 5],
          [6, 2, 3, 1],
      ]
      UpperCamelCase			:       List[str]									=				[[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
      print(strassen(matrixa, matrixa))





"""simple docstring"""

import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import 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 BridgeTowerImageProcessor

class __lowerCAmelCase	(   unittest.TestCase    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = True			,  __UpperCAmelCase = None			,  __UpperCAmelCase = 32			,  __UpperCAmelCase = True			,  __UpperCAmelCase = 1 / 255			,  __UpperCAmelCase = True			,  __UpperCAmelCase = True			,  __UpperCAmelCase = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]			,  __UpperCAmelCase = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]			,  __UpperCAmelCase = True			,  __UpperCAmelCase=7			,  __UpperCAmelCase=30			,  __UpperCAmelCase=400			,  __UpperCAmelCase=3			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= do_resize
      __UpperCamelCase       						= size if size is not None else {'shortest_edge': 288}
      __UpperCamelCase       						= size_divisor
      __UpperCamelCase       						= do_rescale
      __UpperCamelCase       						= rescale_factor
      __UpperCamelCase       						= do_normalize
      __UpperCamelCase       						= do_center_crop
      __UpperCamelCase       						= image_mean
      __UpperCamelCase       						= image_std
      __UpperCamelCase       						= do_pad
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= min_resolution
      __UpperCamelCase       						= max_resolution





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "size_divisor": self.size_divisor,
      }







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=False   ):

      '''simple docstring'''




      if not batched:
       __UpperCamelCase       						= self.size['shortest_edge']
       __UpperCamelCase       						= image_inputs[0]
       if isinstance(__UpperCAmelCase			,  Image.Image   ):
        __UpperCamelCase						,			__UpperCamelCase       						= image.size
       else:
        __UpperCamelCase						,			__UpperCamelCase       						= image.shape[1], image.shape[2]
       __UpperCamelCase       						= size / min(__UpperCAmelCase			,  __UpperCAmelCase   )
       if h < w:
        __UpperCamelCase						,			__UpperCamelCase       						= size, scale * w
       else:
        __UpperCamelCase						,			__UpperCamelCase       						= scale * h, size

       __UpperCamelCase       						= int((1333 / 800) * size   )
       if max(__UpperCAmelCase			,  __UpperCAmelCase   ) > max_size:
        __UpperCamelCase       						= max_size / max(__UpperCAmelCase			,  __UpperCAmelCase   )
        __UpperCamelCase       						= newh * scale
        __UpperCamelCase       						= neww * scale

       __UpperCamelCase						,			__UpperCamelCase       						= int(newh + 0.5   ), int(neww + 0.5   )
       __UpperCamelCase						,			__UpperCamelCase       						= (
           newh // self.size_divisor * self.size_divisor,
           neww // self.size_divisor * self.size_divisor,
       )

      else:
       __UpperCamelCase       						= []
       for image in image_inputs:
        __UpperCamelCase						,			__UpperCamelCase       						= self.get_expected_values([image]   )
        expected_values.append((expected_height, expected_width)   )
       __UpperCamelCase       						= max(__UpperCAmelCase			,  key=lambda __UpperCAmelCase   : item[0]   )[0]
       __UpperCamelCase       						= max(__UpperCAmelCase			,  key=lambda __UpperCAmelCase   : item[1]   )[1]

      return expected_height, expected_width


@require_torch
@require_vision
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		BridgeTowerImageProcessor if is_vision_available() else None





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= BridgeTowerImageProcessingTester(self   )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'image_mean'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'image_std'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'do_normalize'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'do_resize'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'size'   )   )
      self.assertTrue(hasattr(__UpperCAmelCase			,  'size_divisor'   )   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      # Test not batched input
      __UpperCamelCase       						= image_processing(image_inputs[0]			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,  )

      # Test batched
      __UpperCamelCase       						= image_processing(__UpperCAmelCase			,  return_tensors='pt'   ).pixel_values

      __UpperCamelCase						,			__UpperCamelCase       						= self.image_processor_tester.get_expected_values(__UpperCAmelCase			,  batched=__UpperCAmelCase   )
      self.assertEqual(
          encoded_images.shape			,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )			,  )



