"""simple docstring"""


def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""

  def update_area_of_max_square(lowercase      ,lowercase						) -> int:
    # BASE CASE
    if row >= rows or col >= cols:
      return 0

    _UpperCAmelCase         =					update_area_of_max_square(lowercase      ,col + 1						)
    _UpperCAmelCase         =					update_area_of_max_square(row + 1      ,col + 1						)
    _UpperCAmelCase         =					update_area_of_max_square(row + 1      ,lowercase						)

    if mat[row][col]:
      _UpperCAmelCase         =					1 + min([right, diagonal, down]						)
      _UpperCAmelCase         =					max(largest_square_area[0]      ,lowercase						)
      return sub_problem_sol
    else:
      return 0

  _UpperCAmelCase         =					[0]
  update_area_of_max_square(0      ,0						)
  return largest_square_area[0]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""

  def update_area_of_max_square_using_dp_array(
      lowercase      ,lowercase      ,lowercase						) -> int:
    if row >= rows or col >= cols:
      return 0
    if dp_array[row][col] != -1:
      return dp_array[row][col]

    _UpperCAmelCase         =					update_area_of_max_square_using_dp_array(lowercase      ,col + 1      ,lowercase						)
    _UpperCAmelCase         =					update_area_of_max_square_using_dp_array(row + 1      ,col + 1      ,lowercase						)
    _UpperCAmelCase         =					update_area_of_max_square_using_dp_array(row + 1      ,lowercase      ,lowercase						)

    if mat[row][col]:
      _UpperCAmelCase         =					1 + min([right, diagonal, down]						)
      _UpperCAmelCase         =					max(largest_square_area[0]      ,lowercase						)
      _UpperCAmelCase         =					sub_problem_sol
      return sub_problem_sol
    else:
      return 0

  _UpperCAmelCase         =					[0]
  _UpperCAmelCase         =					[[-1] * cols for _ in range(lowercase						)]
  update_area_of_max_square_using_dp_array(0      ,0      ,lowercase						)

  return largest_square_area[0]






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[[0] * (cols + 1) for _ in range(rows + 1						)]
  _UpperCAmelCase         =					0
  for row in range(rows - 1      ,-1      ,-1						):
    for col in range(cols - 1      ,-1      ,-1						):
      _UpperCAmelCase         =					dp_array[row][col + 1]
      _UpperCAmelCase         =					dp_array[row + 1][col + 1]
      _UpperCAmelCase         =					dp_array[row + 1][col]

      if mat[row][col] == 1:
        _UpperCAmelCase         =					1 + min(lowercase      ,lowercase      ,lowercase						)
        _UpperCAmelCase         =					max(dp_array[row][col]      ,lowercase						)
      else:
        _UpperCAmelCase         =					0

  return largest_square_area






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[0] * (cols + 1)
  _UpperCAmelCase         =					[0] * (cols + 1)
  _UpperCAmelCase         =					0
  for row in range(rows - 1      ,-1      ,-1						):
    for col in range(cols - 1      ,-1      ,-1						):
      _UpperCAmelCase         =					current_row[col + 1]
      _UpperCAmelCase         =					next_row[col + 1]
      _UpperCAmelCase         =					next_row[col]

      if mat[row][col] == 1:
        _UpperCAmelCase         =					1 + min(lowercase      ,lowercase      ,lowercase						)
        _UpperCAmelCase         =					max(current_row[col]      ,lowercase						)
      else:
        _UpperCAmelCase         =					0
    _UpperCAmelCase         =					current_row

  return largest_square_area


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

"""simple docstring"""


import unittest

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 MobileViTImageProcessor


class 				a (						unittest.TestCase      ):


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Optional[Any]=7      , __lowerCAmelCase		:     Optional[Any]=3      , __lowerCAmelCase		:     Optional[Any]=18      , __lowerCAmelCase		:     str=30      , __lowerCAmelCase		:     List[str]=400      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     str=None      , __lowerCAmelCase		:     Optional[int]=True      , __lowerCAmelCase		:     int=None      , __lowerCAmelCase		:     List[str]=True      , ):
      _UpperCAmelCase         =					size if size is not None else {"""shortest_edge""": 20}
      _UpperCAmelCase         =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					min_resolution
      _UpperCAmelCase         =					max_resolution
      _UpperCAmelCase         =					do_resize
      _UpperCAmelCase         =					size
      _UpperCAmelCase         =					do_center_crop
      _UpperCAmelCase         =					crop_size
      _UpperCAmelCase         =					do_flip_channel_order


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }



@require_torch
@require_vision
class 				a (						lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[int]   							=							MobileViTImageProcessor if is_vision_available() else None


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      _UpperCAmelCase         =					MobileViTImageProcessingTester(self    )


    @property
    def    lowerCAmelCase_     (    self		:     Tuple    ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_resize"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """size"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """center_crop"""    )    )
      self.assertTrue(hasattr(__lowerCAmelCase      , """do_flip_channel_order"""    )    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 20}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 18, """width""": 18}    )

      _UpperCAmelCase         =					self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84    )
      self.assertEqual(image_processor.size      , {"""shortest_edge""": 42}    )
      self.assertEqual(image_processor.crop_size      , {"""height""": 84, """width""": 84}    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Dict    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PIL images
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , Image.Image    )

      # Test not batched input
      _UpperCAmelCase         =					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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     str    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random numpy tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , numpify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , np.ndarray    )

      # Test not batched input
      _UpperCAmelCase         =					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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # Initialize image_processing
      _UpperCAmelCase         =					self.image_processing_class(**self.image_processor_dict    )
      # create random PyTorch tensors
      _UpperCAmelCase         =					prepare_image_inputs(self.image_processor_tester      , equal_resolution=__lowerCAmelCase      , torchify=__lowerCAmelCase    )
      for image in image_inputs:
        self.assertIsInstance(__lowerCAmelCase      , torch.Tensor    )

      # Test not batched input
      _UpperCAmelCase         =					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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )

      # Test batched
      _UpperCAmelCase         =					image_processing(__lowerCAmelCase      , 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.crop_size["""height"""],
              self.image_processor_tester.crop_size["""width"""],
          )      , )
