






'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor
class 				lowercase       (							unittest.TestCase      ):


				'''simple docstring'''

				def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     List[str]				,     __lowerCamelCase     :     List[str]=7				,     __lowerCamelCase     :     List[Any]=3				,     __lowerCamelCase     :     List[Any]=30				,     __lowerCamelCase     :     Tuple=400				,     __lowerCamelCase     :     str=True				,     __lowerCamelCase     :     List[Any]=None				,     __lowerCamelCase     :     Optional[int]=True				,     __lowerCamelCase     :     Tuple=[0.5, 0.5, 0.5]				,     __lowerCamelCase     :     List[str]=[0.5, 0.5, 0.5]				,     __lowerCamelCase     :     Dict=True				,     __lowerCamelCase     :     List[str]=1 / 255				,     __lowerCamelCase     :     int=True				,     )							->							Dict:

					'''simple docstring'''




					lowerCamelCase__  =     size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
					lowerCamelCase__  =     parent
					lowerCamelCase__  =     batch_size
					lowerCamelCase__  =     num_channels
					lowerCamelCase__  =     min_resolution
					lowerCamelCase__  =     max_resolution
					lowerCamelCase__  =     do_resize
					lowerCamelCase__  =     size
					lowerCamelCase__  =     do_normalize
					lowerCamelCase__  =     image_mean
					lowerCamelCase__  =     image_std
					lowerCamelCase__  =     do_rescale
					lowerCamelCase__  =     rescale_factor
					lowerCamelCase__  =     do_pad

				def    a__				(					self     :     Any     )							->							int:

					'''simple docstring'''




					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					}




				def    a__				(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     List[str]=False     )							->							str:

					'''simple docstring'''




					if not batched:
						lowerCamelCase__  =     image_inputs[0]
						if isinstance(__lowerCamelCase				,     Image.Image     ):
							lowerCamelCase__  ,	lowerCamelCase__  =     image.size
						else:
							lowerCamelCase__  ,	lowerCamelCase__  =     image.shape[1], image.shape[2]
						if w < h:
							lowerCamelCase__  =     int(self.size["shortest_edge"] * h / w     )
							lowerCamelCase__  =     self.size["shortest_edge"]
						elif w > h:
							lowerCamelCase__  =     self.size["shortest_edge"]
							lowerCamelCase__  =     int(self.size["shortest_edge"] * w / h     )
						else:
							lowerCamelCase__  =     self.size["shortest_edge"]
							lowerCamelCase__  =     self.size["shortest_edge"]

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

					return expected_height, expected_width
@require_torch
@require_vision
class 				lowercase       (							_lowerCamelCase ,       unittest.TestCase      ):


				'''simple docstring'''

				lowerCAmelCase__      				=							ConditionalDetrImageProcessor if is_vision_available() else None

				def    a__				(					self     :     Optional[int]     )							->							List[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     ConditionalDetrImageProcessingTester(self     )

				@property
				def    a__				(					self     :     str     )							->							Any:

					'''simple docstring'''




					return self.image_processor_tester.prepare_image_processor_dict()

				def    a__				(					self     :     Optional[int]     )							->							Dict:

					'''simple docstring'''




					lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
					self.assertTrue(hasattr(__lowerCamelCase				,     "image_mean"     )     )
					self.assertTrue(hasattr(__lowerCamelCase				,     "image_std"     )     )
					self.assertTrue(hasattr(__lowerCamelCase				,     "do_normalize"     )     )
					self.assertTrue(hasattr(__lowerCamelCase				,     "do_resize"     )     )
					self.assertTrue(hasattr(__lowerCamelCase				,     "size"     )     )

				def    a__				(					self     :     Optional[Any]     )							->							Optional[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     self.image_processing_class.from_dict(self.image_processor_dict     )
					self.assertEqual(image_processor.size				,     {"shortest_edge": 18, "longest_edge": 1333}     )
					self.assertEqual(image_processor.do_pad				,     __lowerCamelCase     )

					lowerCamelCase__  =     self.image_processing_class.from_dict(
					    self.image_processor_dict				,     size=42				,     max_size=84				,     pad_and_return_pixel_mask=__lowerCamelCase     )
					self.assertEqual(image_processor.size				,     {"shortest_edge": 42, "longest_edge": 84}     )
					self.assertEqual(image_processor.do_pad				,     __lowerCamelCase     )

				def    a__				(					self     :     Tuple     )							->							Union[str, Any]:

					'''simple docstring'''




					pass

				def    a__				(					self     :     Optional[Any]     )							->							Any:

					'''simple docstring'''




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

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

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

					self.assertEqual(
					    encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

					# Test batched
					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

					lowerCamelCase__  =     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,
					        expected_height,
					        expected_width,
					    )				,     )

				def    a__				(					self     :     int     )							->							Tuple:

					'''simple docstring'''




					lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
					# create random numpy tensors
					lowerCamelCase__  =     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
					lowerCamelCase__  =     image_processing(image_inputs[0]				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

					self.assertEqual(
					    encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

					# Test batched
					lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

					self.assertEqual(
					    encoded_images.shape				,     (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )				,     )

				def    a__				(					self     :     Optional[int]     )							->							List[str]:

					'''simple docstring'''




					lowerCamelCase__  =     self.image_processing_class(**self.image_processor_dict     )
					# create random PyTorch tensors
					lowerCamelCase__  =     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
					lowerCamelCase__  =     image_processing(image_inputs[0]				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase     )

					self.assertEqual(
					    encoded_images.shape				,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,     )

					# Test batched
					lowerCamelCase__  =     image_processing(__lowerCamelCase				,     return_tensors="pt"     ).pixel_values

					lowerCamelCase__  ,	lowerCamelCase__  =     self.image_processor_tester.get_expected_values(__lowerCamelCase				,     batched=__lowerCamelCase     )

					self.assertEqual(
					    encoded_images.shape				,     (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )				,     )

				@slow
				def    a__				(					self     :     Any     )							->							int:

					'''simple docstring'''




					lowerCamelCase__  =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
					with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,     "r"     ) as f:
						lowerCamelCase__  =     json.loads(f.read()     )

					lowerCamelCase__  =     {"image_id": 39769, "annotations": target}

					# encode them
					lowerCamelCase__  =     ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50"     )
					lowerCamelCase__  =     image_processing(images=__lowerCamelCase				,     annotations=__lowerCamelCase				,     return_tensors="pt"     )

					# verify pixel values
					lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
					self.assertEqual(encoding["pixel_values"].shape				,     __lowerCamelCase     )

					lowerCamelCase__  =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,     __lowerCamelCase				,     atol=1E-4     )     )

					# verify area
					lowerCamelCase__  =     torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     __lowerCamelCase     )     )
					# verify boxes
					lowerCamelCase__  =     torch.Size([6, 4]     )
					self.assertEqual(encoding["labels"][0]["boxes"].shape				,     __lowerCamelCase     )
					lowerCamelCase__  =     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,     __lowerCamelCase				,     atol=1E-3     )     )
					# verify image_id
					lowerCamelCase__  =     torch.tensor([39769]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     __lowerCamelCase     )     )
					# verify is_crowd
					lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     __lowerCamelCase     )     )
					# verify class_labels
					lowerCamelCase__  =     torch.tensor([75, 75, 63, 65, 17, 17]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     __lowerCamelCase     )     )
					# verify orig_size
					lowerCamelCase__  =     torch.tensor([480, 640]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     __lowerCamelCase     )     )
					# verify size
					lowerCamelCase__  =     torch.tensor([800, 1066]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     __lowerCamelCase     )     )




				@slow
				def    a__				(					self     :     List[str]     )							->							List[Any]:

					'''simple docstring'''




					lowerCamelCase__  =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
					with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"				,     "r"     ) as f:
						lowerCamelCase__  =     json.loads(f.read()     )

					lowerCamelCase__  =     {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

					lowerCamelCase__  =     pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"     )

					# encode them
					lowerCamelCase__  =     ConditionalDetrImageProcessor(format="coco_panoptic"     )
					lowerCamelCase__  =     image_processing(images=__lowerCamelCase				,     annotations=__lowerCamelCase				,     masks_path=__lowerCamelCase				,     return_tensors="pt"     )

					# verify pixel values
					lowerCamelCase__  =     torch.Size([1, 3, 800, 1066]     )
					self.assertEqual(encoding["pixel_values"].shape				,     __lowerCamelCase     )

					lowerCamelCase__  =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]     )
					self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,     __lowerCamelCase				,     atol=1E-4     )     )

					# verify area
					lowerCamelCase__  =     torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,     __lowerCamelCase     )     )
					# verify boxes
					lowerCamelCase__  =     torch.Size([6, 4]     )
					self.assertEqual(encoding["labels"][0]["boxes"].shape				,     __lowerCamelCase     )
					lowerCamelCase__  =     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,     __lowerCamelCase				,     atol=1E-3     )     )
					# verify image_id
					lowerCamelCase__  =     torch.tensor([39769]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,     __lowerCamelCase     )     )
					# verify is_crowd
					lowerCamelCase__  =     torch.tensor([0, 0, 0, 0, 0, 0]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,     __lowerCamelCase     )     )
					# verify class_labels
					lowerCamelCase__  =     torch.tensor([17, 17, 63, 75, 75, 93]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,     __lowerCamelCase     )     )
					# verify masks
					lowerCamelCase__  =     822873
					self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,     __lowerCamelCase     )
					# verify orig_size
					lowerCamelCase__  =     torch.tensor([480, 640]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,     __lowerCamelCase     )     )
					# verify size
					lowerCamelCase__  =     torch.tensor([800, 1066]     )
					self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,     __lowerCamelCase     )     )









'''simple docstring'''





def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

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

  lowerCamelCase__  =     update_area_of_max_square(lowercase__				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     lowercase__)

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

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






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 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]

  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(lowercase__				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     lowercase__				,     lowercase__)

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

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

 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 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(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(dp_array[row][col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0

 return largest_square_area






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 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(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(current_row[col]				,     lowercase__)
   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]]))

