"""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 DeformableDetrImageProcessor






class 				snake_case__     (   unittest.TestCase   ):


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase=7  ,						lowerCamelCase=3  ,						lowerCamelCase=30  ,						lowerCamelCase=400  ,						lowerCamelCase=True  ,						lowerCamelCase=None  ,						lowerCamelCase=True  ,						lowerCamelCase=[0.5, 0.5, 0.5]  ,						lowerCamelCase=[0.5, 0.5, 0.5]  ,						lowerCamelCase=True  ,						lowerCamelCase=1 / 255  ,						lowerCamelCase=True  ,						):
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        __a     				=  size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
        __a     				=  parent
        __a     				=  batch_size
        __a     				=  num_channels
        __a     				=  min_resolution
        __a     				=  max_resolution
        __a     				=  do_resize
        __a     				=  size
        __a     				=  do_normalize
        __a     				=  image_mean
        __a     				=  image_std
        __a     				=  do_rescale
        __a     				=  rescale_factor
        __a     				=  do_pad


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

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

        return expected_height, expected_width






@require_torch
@require_vision
class 				snake_case__     (   snake_case_,		unittest.TestCase   ):
 _snake_case   :			int           =		DeformableDetrImageProcessor if is_vision_available() else None


 def 		a__ (				self		):
        __a     				=  DeformableDetrImageProcessingTester(self		)


 @property
 def 		a__ (				self		):
        return self.image_processor_tester.prepare_image_processor_dict()


 def 		a__ (				self		):
        __a     				=  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  ,						"do_rescale"		)		)
        self.assertTrue(hasattr(lowerCamelCase  ,						"do_pad"		)		)
        self.assertTrue(hasattr(lowerCamelCase  ,						"size"		)		)


 def 		a__ (				self		):
        __a     				=  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		)

        __a     				=  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		):
        pass


 def 		a__ (				self		):
        # Initialize image_processing
        __a     				=  self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        __a     				=  prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=lowerCamelCase		)
        for image in image_inputs:
               self.assertIsInstance(lowerCamelCase  ,						Image.Image		)

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

        __a				, __a     				=  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
        __a				, __a     				=  self.image_processor_tester.get_expected_values(lowerCamelCase  ,						batched=lowerCamelCase		)

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

        __a				, __a     				=  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
        __a     				=  image_processing(lowerCamelCase  ,						return_tensors="pt"		).pixel_values

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

        __a				, __a     				=  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
        __a     				=  image_processing(lowerCamelCase  ,						return_tensors="pt"		).pixel_values

        __a				, __a     				=  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		):
        # prepare image and target
        __a     				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"  ,						"r"		) as f:
               __a     				=  json.loads(f.read()		)

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

        # encode them
        __a     				=  DeformableDetrImageProcessor()
        __a     				=  image_processing(images=lowerCamelCase  ,						annotations=lowerCamelCase  ,						return_tensors="pt"		)

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

        __a     				=  torch.tensor([0.2796, 0.3138, 0.3481]		)
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,						lowerCamelCase  ,						atol=1E-4		)		)

        # verify area
        __a     				=  torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]  ,						lowerCamelCase		)		)
        # verify boxes
        __a     				=  torch.Size([6, 4]		)
        self.assertEqual(encoding["labels"][0]["boxes"].shape  ,						lowerCamelCase		)
        __a     				=  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,						lowerCamelCase  ,						atol=1E-3		)		)
        # verify image_id
        __a     				=  torch.tensor([39769]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,						lowerCamelCase		)		)
        # verify is_crowd
        __a     				=  torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,						lowerCamelCase		)		)
        # verify class_labels
        __a     				=  torch.tensor([75, 75, 63, 65, 17, 17]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,						lowerCamelCase		)		)
        # verify orig_size
        __a     				=  torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,						lowerCamelCase		)		)
        # verify size
        __a     				=  torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,						lowerCamelCase		)		)






 @slow
 def 		a__ (				self		):
        # prepare image, target and masks_path
        __a     				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"  ,						"r"		) as f:
               __a     				=  json.loads(f.read()		)

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

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

        # encode them
        __a     				=  DeformableDetrImageProcessor(format="coco_panoptic"		)
        __a     				=  image_processing(images=lowerCamelCase  ,						annotations=lowerCamelCase  ,						masks_path=lowerCamelCase  ,						return_tensors="pt"		)

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

        __a     				=  torch.tensor([0.2796, 0.3138, 0.3481]		)
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,						lowerCamelCase  ,						atol=1E-4		)		)

        # verify area
        __a     				=  torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]  ,						lowerCamelCase		)		)
        # verify boxes
        __a     				=  torch.Size([6, 4]		)
        self.assertEqual(encoding["labels"][0]["boxes"].shape  ,						lowerCamelCase		)
        __a     				=  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,						lowerCamelCase  ,						atol=1E-3		)		)
        # verify image_id
        __a     				=  torch.tensor([39769]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,						lowerCamelCase		)		)
        # verify is_crowd
        __a     				=  torch.tensor([0, 0, 0, 0, 0, 0]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,						lowerCamelCase		)		)
        # verify class_labels
        __a     				=  torch.tensor([17, 17, 63, 75, 75, 93]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,						lowerCamelCase		)		)
        # verify masks
        __a     				=  822873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()  ,						lowerCamelCase		)
        # verify orig_size
        __a     				=  torch.tensor([480, 640]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,						lowerCamelCase		)		)
        # verify size
        __a     				=  torch.tensor([800, 1066]		)
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,						lowerCamelCase		)		)

"""simple docstring"""



def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     __a     				=  mf_knapsack(i - 1 ,  a ,  a ,  a							)
              else:
                     __a     				=  max(
                         mf_knapsack(i - 1 ,  a ,  a ,  a							) ,  mf_knapsack(i - 1 ,  a ,  a ,  j - wt[i - 1]							) + val[i - 1] ,  )
              __a     				=  val
       return f[i][j]



def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  [[0] * (w + 1) for _ in range(n + 1							)]

       for i in range(1 ,  n + 1							):
              for w_ in range(1 ,  w + 1							):
                     if wt[i - 1] <= w_:
                            __a     				=  max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,  dp[i - 1][w_]							)
                     else:
                            __a     				=  dp[i - 1][w_]

       return dp[n][w_], dp



def 					_lowerCamelCase(					a ,  a ,  a							):
       if not (isinstance(a ,  (list, tuple)							) and isinstance(a ,  (list, tuple)							)):
              raise ValueError(
                  "Both the weights and values vectors must be either lists or tuples"							)

       __a     				=  len(a							)
       if num_items != len(a							):
              __a     				=  (
                  "The number of weights must be the same as the number of values.\n"
                  F"But got {num_items} weights and {len(a							)} values"
              )
              raise ValueError(a							)
       for i in range(a							):
              if not isinstance(wt[i] ,  a							):
                     __a     				=  (
                         "All weights must be integers but got weight of "
                         F"type {type(wt[i]							)} at index {i}"
                     )
                     raise TypeError(a							)

       __a				, __a     				=  knapsack(a ,  a ,  a ,  a							)
       __a     				=  set()
       _construct_solution(a ,  a ,  a ,  a ,  a							)

       return optimal_val, example_optional_set



def 					_lowerCamelCase(					a ,  a ,  a ,  a ,  a							):
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(a ,  a ,  i - 1 ,  a ,  a							)
              else:
                     optimal_set.add(a							)
                     _construct_solution(a ,  a ,  i - 1 ,  j - wt[i - 1] ,  a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Tuple			=	[3, 2, 4, 4]
   SCREAMING_SNAKE_CASE__:List[str]			=	[4, 3, 2, 3]
   SCREAMING_SNAKE_CASE__:List[str]			=	4
   SCREAMING_SNAKE_CASE__:List[str]			=	6
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[int]			=	knapsack(w, wt, val, n)
   print(optimal_solution)
   print(mf_knapsack(n, wt, val, w))  # switched the n and w

   # testing the dynamic programming problem with example
   # the optimal subset for the above example are items 3 and 4
   SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__:Optional[Any]			=	knapsack_with_example_solution(w, wt, val)
   assert optimal_solution == 8
   assert optimal_subset == {3, 4}
   print("""optimal_value = """, optimal_solution)
   print("""An optimal subset corresponding to the optimal value""", optimal_subset)
