



'''simple docstring'''


import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor




class      __UpperCamelCase       (unittest.TestCase			):







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=7 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=18 ,				_lowerCAmelCase=30 ,				_lowerCAmelCase=400 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=None ,				_lowerCAmelCase=True ,				_lowerCAmelCase=None ,				_lowerCAmelCase=True ,				_lowerCAmelCase=[0.5, 0.5, 0.5] ,				_lowerCAmelCase=[0.5, 0.5, 0.5] ,				_lowerCAmelCase=False ,				)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							size if size is not None else {"""height""": 20, """width""": 20}
							lowercase        =							crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							num_channels
							lowercase        =							image_size
							lowercase        =							min_resolution
							lowercase        =							max_resolution
							lowercase        =							do_resize
							lowercase        =							size
							lowercase        =							do_center_crop
							lowercase        =							crop_size
							lowercase        =							do_normalize
							lowercase        =							image_mean
							lowercase        =							image_std
							lowercase        =							do_reduce_labels







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							load_dataset("""hf-internal-testing/fixtures_ade20k""" ,  split="""test"""						)

						lowercase        =							Image.open(dataset[0]["""file"""]						)
						lowercase        =							Image.open(dataset[1]["""file"""]						)

						return image, map







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							load_dataset("""hf-internal-testing/fixtures_ade20k""" ,  split="""test"""						)

						lowercase        =							Image.open(ds[0]["""file"""]						)
						lowercase        =							Image.open(ds[1]["""file"""]						)
						lowercase        =							Image.open(ds[2]["""file"""]						)
						lowercase        =							Image.open(ds[3]["""file"""]						)

						return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class      __UpperCamelCase       (_UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					BeitImageProcessor if is_vision_available() else None







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							BeitImageProcessingTester(self						)







	@property
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							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_normalize"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""image_mean"""						)						)
							self.assertTrue(hasattr(_lowerCAmelCase ,				"""image_std"""						)						)







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size ,				{"""height""": 20, """width""": 20}						)
							self.assertEqual(image_processor.crop_size ,				{"""height""": 18, """width""": 18}						)
							self.assertEqual(image_processor.do_reduce_labels ,				_lowerCAmelCase						)

							lowercase        =							self.image_processing_class.from_dict(
							    self.image_processor_dict ,				size=42 ,				crop_size=84 ,				reduce_labels=_lowerCAmelCase						)
							self.assertEqual(image_processor.size ,				{"""height""": 42, """width""": 42}						)
							self.assertEqual(image_processor.crop_size ,				{"""height""": 84, """width""": 84}						)
							self.assertEqual(image_processor.do_reduce_labels ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Tuple:
							'''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=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)

							# Test batched
							lowercase        =							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   _a   (    self						)    ->    List[Any]:
							'''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=_lowerCAmelCase ,				numpify=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)

							# Test batched
							lowercase        =							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   _a   (    self						)    ->    List[Any]:
							'''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=_lowerCAmelCase ,				torchify=_lowerCAmelCase						)
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				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.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)

							# Test batched
							lowercase        =							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   _a   (    self						)    ->    List[str]:
							'''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=_lowerCAmelCase ,				torchify=_lowerCAmelCase						)
							lowercase        =							[]
							for image in image_inputs:
													self.assertIsInstance(_lowerCAmelCase ,				torch.Tensor						)
													maps.append(torch.zeros(image.shape[-2:]						).long()						)

							# Test not batched input
							lowercase        =							image_processing(image_inputs[0] ,				maps[0] ,				return_tensors="""pt"""						)
							self.assertEqual(
							    encoding["""pixel_values"""].shape ,				(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)
							self.assertEqual(
							    encoding["""labels"""].shape ,				(
							        1,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)
							self.assertEqual(encoding["""labels"""].dtype ,				torch.long						)
							self.assertTrue(encoding["""labels"""].min().item() >= 0						)
							self.assertTrue(encoding["""labels"""].max().item() <= 255						)

							# Test batched
							lowercase        =							image_processing(_lowerCAmelCase ,				_lowerCAmelCase ,				return_tensors="""pt"""						)
							self.assertEqual(
							    encoding["""pixel_values"""].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"""],
							    ) ,				)
							self.assertEqual(
							    encoding["""labels"""].shape ,				(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)
							self.assertEqual(encoding["""labels"""].dtype ,				torch.long						)
							self.assertTrue(encoding["""labels"""].min().item() >= 0						)
							self.assertTrue(encoding["""labels"""].max().item() <= 255						)

							# Test not batched input (PIL images)
							lowercase     ,   lowercase        =							prepare_semantic_single_inputs()

							lowercase        =							image_processing(_lowerCAmelCase ,				_lowerCAmelCase ,				return_tensors="""pt"""						)
							self.assertEqual(
							    encoding["""pixel_values"""].shape ,				(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)
							self.assertEqual(
							    encoding["""labels"""].shape ,				(
							        1,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)
							self.assertEqual(encoding["""labels"""].dtype ,				torch.long						)
							self.assertTrue(encoding["""labels"""].min().item() >= 0						)
							self.assertTrue(encoding["""labels"""].max().item() <= 255						)

							# Test batched input (PIL images)
							lowercase     ,   lowercase        =							prepare_semantic_batch_inputs()

							lowercase        =							image_processing(_lowerCAmelCase ,				_lowerCAmelCase ,				return_tensors="""pt"""						)
							self.assertEqual(
							    encoding["""pixel_values"""].shape ,				(
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)
							self.assertEqual(
							    encoding["""labels"""].shape ,				(
							        2,
							        self.image_processor_tester.crop_size["""height"""],
							        self.image_processor_tester.crop_size["""width"""],
							    ) ,				)
							self.assertEqual(encoding["""labels"""].dtype ,				torch.long						)
							self.assertTrue(encoding["""labels"""].min().item() >= 0						)
							self.assertTrue(encoding["""labels"""].max().item() <= 255						)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							self.image_processing_class(**self.image_processor_dict						)

							# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
							lowercase     ,   lowercase        =							prepare_semantic_single_inputs()
							lowercase        =							image_processing(_lowerCAmelCase ,				_lowerCAmelCase ,				return_tensors="""pt"""						)
							self.assertTrue(encoding["""labels"""].min().item() >= 0						)
							self.assertTrue(encoding["""labels"""].max().item() <= 150						)

							lowercase        =							True
							lowercase        =							image_processing(_lowerCAmelCase ,				_lowerCAmelCase ,				return_tensors="""pt"""						)
							self.assertTrue(encoding["""labels"""].min().item() >= 0						)
							self.assertTrue(encoding["""labels"""].max().item() <= 255						)







'''simple docstring'''


def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						):

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

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

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

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







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						):

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

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

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

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

						return largest_square_area[0]







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						):
						lowercase        =							[[0] * (cols + 1) for _ in range(rows + 1						)]
						lowercase        =							0
						for row in range(rows - 1 ,  -1 ,  -1						):
												for col in range(cols - 1 ,  -1 ,  -1						):
																		lowercase        =							dp_array[row][col + 1]
																		lowercase        =							dp_array[row + 1][col + 1]
																		lowercase        =							dp_array[row + 1][col]

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

						return largest_square_area







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int ,  lowercase_    :      int ,  lowercase_    :      list[list[int]]						):
						lowercase        =							[0] * (cols + 1)
						lowercase        =							[0] * (cols + 1)
						lowercase        =							0
						for row in range(rows - 1 ,  -1 ,  -1						):
												for col in range(cols - 1 ,  -1 ,  -1						):
																		lowercase        =							current_row[col + 1]
																		lowercase        =							next_row[col + 1]
																		lowercase        =							next_row[col]

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


