




"""simple docstring"""




def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					int:

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

						lowerCamelCase			      =					update_area_of_max_square(snake_case__       , col + 1				)
						lowerCamelCase			      =					update_area_of_max_square(row + 1       , col + 1				)
						lowerCamelCase			      =					update_area_of_max_square(row + 1       , snake_case__				)

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

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



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					int:

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

						lowerCamelCase			      =					update_area_of_max_square_using_dp_array(snake_case__       , col + 1       , snake_case__				)
						lowerCamelCase			      =					update_area_of_max_square_using_dp_array(row + 1       , col + 1       , snake_case__				)
						lowerCamelCase			      =					update_area_of_max_square_using_dp_array(row + 1       , snake_case__       , snake_case__				)

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

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

			return largest_square_area[0]



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					int:
			lowerCamelCase			      =					[[0] * (cols + 1) for _ in range(rows + 1				)]
			lowerCamelCase			      =					0
			for row in range(rows - 1       , -1       , -1				):
						for col in range(cols - 1       , -1       , -1				):
									lowerCamelCase			      =					dp_array[row][col + 1]
									lowerCamelCase			      =					dp_array[row + 1][col + 1]
									lowerCamelCase			      =					dp_array[row + 1][col]

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

			return largest_square_area



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					int:
			lowerCamelCase			      =					[0] * (cols + 1)
			lowerCamelCase			      =					[0] * (cols + 1)
			lowerCamelCase			      =					0
			for row in range(rows - 1       , -1       , -1				):
						for col in range(cols - 1       , -1       , -1				):
									lowerCamelCase			      =					current_row[col + 1]
									lowerCamelCase			      =					next_row[col + 1]
									lowerCamelCase			      =					next_row[col]

									if mat[row][col] == 1:
												lowerCamelCase			      =					1 + min(snake_case__       , snake_case__       , snake_case__				)
												lowerCamelCase			      =					max(current_row[col]       , snake_case__				)
									else:
												lowerCamelCase			      =					0
						lowerCamelCase			      =					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 json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					tempfile.mkdtemp()

							# fmt: off
							lowerCamelCase			      =					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
							# fmt: on
							lowerCamelCase			      =					os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES["""vocab_file"""] )
							with open(self.vocab_file		, """w"""		, encoding="""utf-8""" ) as vocab_writer:
										vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

							lowerCamelCase			      =					{
							    """do_resize""": True,
							    """size""": {"""height""": 18, """width""": 18},
							    """do_normalize""": True,
							    """image_mean""": [0.5, 0.5, 0.5],
							    """image_std""": [0.5, 0.5, 0.5],
							}
							lowerCamelCase			      =					os.path.join(self.tmpdirname		, _a )
							with open(self.image_processor_file		, """w"""		, encoding="""utf-8""" ) as fp:
										json.dump(_a		, _a )





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							return BertTokenizer.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							return ViTImageProcessor.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							shutil.rmtree(self.tmpdirname )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					[np.random.randint(255		, size=(3, 30, 400)		, dtype=np.uinta )]

							lowerCamelCase			      =					[Image.fromarray(np.moveaxis(_a		, 0		, -1 ) ) for x in image_inputs]

							return image_inputs





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_tokenizer()
							lowerCamelCase			      =					self.get_image_processor()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							processor.save_pretrained(self.tmpdirname )
							lowerCamelCase			      =					VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

							self.assertEqual(processor.tokenizer.get_vocab()		, tokenizer.get_vocab() )
							self.assertIsInstance(processor.tokenizer		, (BertTokenizer, BertTokenizerFast) )

							self.assertEqual(processor.image_processor.to_json_string()		, image_processor.to_json_string() )
							self.assertIsInstance(processor.image_processor		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					VisionTextDualEncoderProcessor(
							    tokenizer=self.get_tokenizer()		, image_processor=self.get_image_processor() )
							processor.save_pretrained(self.tmpdirname )

							lowerCamelCase			      =					self.get_tokenizer(bos_token="""(BOS)"""		, eos_token="""(EOS)""" )
							lowerCamelCase			      =					self.get_image_processor(do_normalize=_a		, padding_value=1.0 )

							lowerCamelCase			      =					VisionTextDualEncoderProcessor.from_pretrained(
							    self.tmpdirname		, bos_token="""(BOS)"""		, eos_token="""(EOS)"""		, do_normalize=_a		, padding_value=1.0 )

							self.assertEqual(processor.tokenizer.get_vocab()		, tokenizer_add_kwargs.get_vocab() )
							self.assertIsInstance(processor.tokenizer		, (BertTokenizer, BertTokenizerFast) )

							self.assertEqual(processor.image_processor.to_json_string()		, image_processor_add_kwargs.to_json_string() )
							self.assertIsInstance(processor.image_processor		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					image_processor(_a		, return_tensors="""np""" )
							lowerCamelCase			      =					processor(images=_a		, return_tensors="""np""" )

							for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()		, input_processor[key].sum()		, delta=1e-2 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""

							lowerCamelCase			      =					processor(text=_a )

							lowerCamelCase			      =					tokenizer(_a )

							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]		, encoded_processor[key] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""
							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					processor(text=_a		, images=_a )

							self.assertListEqual(list(inputs.keys() )		, ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )

							# test if it raises when no input is passed
							with self.assertRaises(_a ):
										processor()





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCamelCase			      =					processor.batch_decode(_a )
							lowerCamelCase			      =					tokenizer.batch_decode(_a )

							self.assertListEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""
							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					processor(text=_a		, images=_a )

							self.assertListEqual(list(inputs.keys() )		, processor.model_input_names )



