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 DetrImageProcessor




class    a__				(       unittest.TestCase			):






     def __init__(			self		:		Union[str, Any]  ,    a		:		Union[str, Any]  ,    a		:		int=7  ,    a		:		Tuple=3  ,    a		:		Union[str, Any]=30  ,    a		:		int=4_00  ,    a		:		Tuple=True  ,    a		:		List[Any]=None  ,    a		:		Any=True  ,    a		:		str=1 / 2_55  ,    a		:		Optional[Any]=True  ,    a		:		Optional[Any]=[0.5, 0.5, 0.5]  ,    a		:		List[str]=[0.5, 0.5, 0.5]  ,    a		:		List[Any]=True  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
         __lowerCamelCase          =  parent
         __lowerCamelCase          =  batch_size
         __lowerCamelCase          =  num_channels
         __lowerCamelCase          =  min_resolution
         __lowerCamelCase          =  max_resolution
         __lowerCamelCase          =  do_resize
         __lowerCamelCase          =  size
         __lowerCamelCase          =  do_rescale
         __lowerCamelCase          =  rescale_factor
         __lowerCamelCase          =  do_normalize
         __lowerCamelCase          =  image_mean
         __lowerCamelCase          =  image_std
         __lowerCamelCase          =  do_pad






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		List[str]  ,    a		:		Optional[int]=False   ):





         """simple docstring"""



         if not batched:
             __lowerCamelCase          =  image_inputs[0]
             if isinstance(A__  ,    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(A__  ,    key=lambda a   : item[0]   )[0]
             __lowerCamelCase          =  max(A__  ,    key=lambda a   : item[1]   )[1]

         return expected_height, expected_width




@require_torch
@require_vision
class    a__				(       SCREAMING_SNAKE_CASE__							,       unittest.TestCase			):
     lowerCamelCase  :					Optional[int]							=DetrImageProcessor if is_vision_available() else None






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  DetrImageProcessingTester(self   )






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(A__  ,    '''image_mean'''   )   )
         self.assertTrue(hasattr(A__  ,    '''image_std'''   )   )
         self.assertTrue(hasattr(A__  ,    '''do_normalize'''   )   )
         self.assertTrue(hasattr(A__  ,    '''do_rescale'''   )   )
         self.assertTrue(hasattr(A__  ,    '''rescale_factor'''   )   )
         self.assertTrue(hasattr(A__  ,    '''do_resize'''   )   )
         self.assertTrue(hasattr(A__  ,    '''size'''   )   )
         self.assertTrue(hasattr(A__  ,    '''do_pad'''   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size  ,    {'''shortest_edge''': 18, '''longest_edge''': 13_33}   )
         self.assertEqual(image_processor.do_pad  ,    A__   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         pass






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """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=A__   )
         for image in image_inputs:
             self.assertIsInstance(A__  ,    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(A__   )

         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(A__  ,    batched=A__   )

         __lowerCamelCase          =  image_processing(A__  ,    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        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """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=A__  ,    numpify=A__   )
         for image in image_inputs:
             self.assertIsInstance(A__  ,    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(A__   )

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

         # Test batched
         __lowerCamelCase          =  image_processing(A__  ,    return_tensors='''pt'''   ).pixel_values

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

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """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=A__  ,    torchify=A__   )
         for image in image_inputs:
             self.assertIsInstance(A__  ,    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(A__   )

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

         # Test batched
         __lowerCamelCase          =  image_processing(A__  ,    return_tensors='''pt'''   ).pixel_values

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

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






     @slow
     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """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''': 3_97_69, '''annotations''': target}

         # encode them
         __lowerCamelCase          =  DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''   )
         __lowerCamelCase          =  image_processing(images=A__  ,    annotations=A__  ,    return_tensors='''pt'''   )

         # verify pixel values
         __lowerCamelCase          =  torch.Size([1, 3, 8_00, 10_66]   )
         self.assertEqual(encoding['''pixel_values'''].shape  ,    A__   )

         __lowerCamelCase          =  torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,    A__  ,    atol=1e-4   )   )

         # verify area
         __lowerCamelCase          =  torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']  ,    A__   )   )
         # verify boxes
         __lowerCamelCase          =  torch.Size([6, 4]   )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,    A__   )
         __lowerCamelCase          =  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,    A__  ,    atol=1e-3   )   )
         # verify image_id
         __lowerCamelCase          =  torch.tensor([3_97_69]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,    A__   )   )
         # verify is_crowd
         __lowerCamelCase          =  torch.tensor([0, 0, 0, 0, 0, 0]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,    A__   )   )
         # verify class_labels
         __lowerCamelCase          =  torch.tensor([75, 75, 63, 65, 17, 17]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,    A__   )   )
         # verify orig_size
         __lowerCamelCase          =  torch.tensor([4_80, 6_40]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,    A__   )   )
         # verify size
         __lowerCamelCase          =  torch.tensor([8_00, 10_66]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,    A__   )   )






     @slow
     def        SCREAMING_SNAKE_CASE__       (			self		:		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''': 3_97_69, '''segments_info''': target}

         __lowerCamelCase          =  pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''   )

         # encode them
         __lowerCamelCase          =  DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''   )
         __lowerCamelCase          =  image_processing(images=A__  ,    annotations=A__  ,    masks_path=A__  ,    return_tensors='''pt'''   )

         # verify pixel values
         __lowerCamelCase          =  torch.Size([1, 3, 8_00, 10_66]   )
         self.assertEqual(encoding['''pixel_values'''].shape  ,    A__   )

         __lowerCamelCase          =  torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,    A__  ,    atol=1e-4   )   )

         # verify area
         __lowerCamelCase          =  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']  ,    A__   )   )
         # verify boxes
         __lowerCamelCase          =  torch.Size([6, 4]   )
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,    A__   )
         __lowerCamelCase          =  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,    A__  ,    atol=1e-3   )   )
         # verify image_id
         __lowerCamelCase          =  torch.tensor([3_97_69]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,    A__   )   )
         # verify is_crowd
         __lowerCamelCase          =  torch.tensor([0, 0, 0, 0, 0, 0]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,    A__   )   )
         # verify class_labels
         __lowerCamelCase          =  torch.tensor([17, 17, 63, 75, 75, 93]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,    A__   )   )
         # verify masks
         __lowerCamelCase          =  82_28_73
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()  ,    A__   )
         # verify orig_size
         __lowerCamelCase          =  torch.tensor([4_80, 6_40]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,    A__   )   )
         # verify size
         __lowerCamelCase          =  torch.tensor([8_00, 10_66]   )
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,    A__   )   )

'''simple docstring'''




from bisect import bisect
from itertools import accumulate






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   str:
    __lowerCamelCase          =  sorted(zip(UpperCamelCase__    ,  UpperCamelCase__    )    ,  key=lambda UpperCamelCase__    : x[0] / x[1]    ,  reverse=UpperCamelCase__    )
    __lowerCamelCase     ,  __lowerCamelCase          =  [i[0] for i in r], [i[1] for i in r]
    __lowerCamelCase          =  list(accumulate(UpperCamelCase__    )    )
    __lowerCamelCase          =  bisect(UpperCamelCase__    ,  UpperCamelCase__    )
    return (
        0
        if k == 0
        else sum(vl[:k]    ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
        if k != n
        else sum(vl[:k]    )
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
