
def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Dict:







				"""simple docstring"""



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

								__A       =					update_area_of_max_square(_a						,  col + 1		)
								__A       =					update_area_of_max_square(row + 1						,  col + 1		)
								__A       =					update_area_of_max_square(row + 1						,  _a		)

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

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


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Optional[Any]:







				"""simple docstring"""



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

								__A       =					update_area_of_max_square_using_dp_array(_a						,  col + 1						,  _a		)
								__A       =					update_area_of_max_square_using_dp_array(row + 1						,  col + 1						,  _a		)
								__A       =					update_area_of_max_square_using_dp_array(row + 1						,  _a						,  _a		)

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

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

				return largest_square_area[0]


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      List[Any]:







				"""simple docstring"""



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

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

				return largest_square_area


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



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

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



import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:






			class _UpperCamelCase				:
						'''simple docstring'''







						@staticmethod
						def 				__UpperCamelCase			(      *a				:			str   ,   **a				:			int						)				->							str:

							"""simple docstring"""

							pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =MODEL_FOR_OBJECT_DETECTION_MAPPING
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			str   ,   a				:			Optional[Any]   ,   a				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ObjectDetectionPipeline(model=a   ,   image_processor=a						)
				return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			Optional[int]   ,   a				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"   ,   threshold=0.0						)

				self.assertGreater(len(a						)   ,   0						)
				for detected_object in outputs:
					self.assertEqual(
					    a   ,   {
					        "score": ANY(a						),
					        "label": ANY(a						),
					        "box": {"xmin": ANY(a						), "ymin": ANY(a						), "xmax": ANY(a						), "ymax": ANY(a						)},
					    }   ,   )

				import datasets

				SCREAMING_SNAKE_CASE :   Any					  =							datasets.load_dataset("hf-internal-testing/fixtures_image_utils"   ,   "image"   ,   split="test"						)

				SCREAMING_SNAKE_CASE :   Dict					  =							[
				    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						),
				    "http://images.cocodataset.org/val2017/000000039769.jpg",
				    # RGBA
				    dataset[0]["file"],
				    # LA
				    dataset[1]["file"],
				    # L
				    dataset[2]["file"],
				]
				SCREAMING_SNAKE_CASE :   Tuple					  =							object_detector(a   ,   threshold=0.0						)

				self.assertEqual(len(a						)   ,   len(a						)						)
				for outputs in batch_outputs:
					self.assertGreater(len(a						)   ,   0						)
					for detected_object in outputs:
						self.assertEqual(
						    a   ,   {
						        "score": ANY(a						),
						        "label": ANY(a						),
						        "box": {"xmin": ANY(a						), "ymin": ANY(a						), "xmax": ANY(a						), "ymax": ANY(a						)},
						    }   ,   )
			@require_tf
			@unittest.skip("Object detection not implemented in TF"						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							str:

				"""simple docstring"""

				pass
			@require_torch
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							"hf-internal-testing/tiny-detr-mobilenetsv3"

				SCREAMING_SNAKE_CASE :   Dict					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

				SCREAMING_SNAKE_CASE :   int					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"   ,   threshold=0.0						)

				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   Dict					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]   ,   threshold=0.0   ,   )

				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        [
				            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				        ],
				        [
				            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
				        ],
				    ]   ,   )
			@require_torch
			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   int					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   int					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        [
				            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				        ],
				        [
				            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				        ],
				    ]   ,   )
			@require_torch
			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Tuple					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        [
				            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				        ],
				        [
				            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
				            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
				            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
				            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				        ],
				    ]   ,   )
			@require_torch
			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							0.9985
				SCREAMING_SNAKE_CASE :   int					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   List[str]					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   str					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"   ,   threshold=a						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
				        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
				    ]   ,   )






			@require_torch
			@require_pytesseract
			@slow
			def 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							"Narsil/layoutlmv3-finetuned-funsd"
				SCREAMING_SNAKE_CASE :   Dict					  =							0.9993

				SCREAMING_SNAKE_CASE :   str					  =							pipeline("object-detection"   ,   model=a   ,   threshold=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							object_detector(
				    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"						)
				self.assertEqual(
				    nested_simplify(a   ,   decimals=4						)   ,   [
				        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
				        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
				    ]   ,   )