




'''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 			_a  (	unittest.TestCase ):
       def __init__(	self						:	int   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	Dict=7   ,				lowercase						:	Union[str, Any]=3   ,				lowercase						:	int=30   ,				lowercase						:	List[str]=400   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	Any=[0.5, 0.5, 0.5]   ,				lowercase						:	int=[0.5, 0.5, 0.5]   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Tuple=1 / 255   ,				lowercase						:	List[str]=True   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		min_resolution
           UpperCAmelCase			       =		max_resolution
           UpperCAmelCase			       =		do_resize
           UpperCAmelCase			       =		size
           UpperCAmelCase			       =		do_normalize
           UpperCAmelCase			       =		image_mean
           UpperCAmelCase			       =		image_std
           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_pad
       def     A      (	self						:	List[str]				):



           '''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]   ,				lowercase						:	Dict   ,				lowercase						:	Optional[Any]=False				):



           '''simple docstring'''

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

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

           return expected_height, expected_width






@require_torch
@require_vision
class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     List[Any]   				=					ConditionalDetrImageProcessor if is_vision_available() else None
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		ConditionalDetrImageProcessingTester(self				)
       @property
       def     A      (	self						:	int				):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(lowercase   ,				'''image_mean'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''image_std'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''do_normalize'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''do_resize'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''size'''				)				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 18, '''longest_edge''': 1_333}				)
           self.assertEqual(image_processor.do_pad   ,				lowercase				)

           UpperCAmelCase			       =		self.image_processing_class.from_dict(
               self.image_processor_dict   ,				size=42   ,				max_size=84   ,				pad_and_return_pixel_mask=lowercase				)
           self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 42, '''longest_edge''': 84}				)
           self.assertEqual(image_processor.do_pad   ,				lowercase				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

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

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

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase				)

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

           # Test batched
           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase   ,				batched=lowercase				)

           UpperCAmelCase			       =		image_processing(lowercase   ,				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						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random numpy tensors
           UpperCAmelCase			       =		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowercase   ,				numpify=lowercase				)
           for image in image_inputs:
               self.assertIsInstance(lowercase   ,				np.ndarray				)

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

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase				)

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

           # Test batched
           UpperCAmelCase			       =		image_processing(lowercase   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase   ,				batched=lowercase				)

           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]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random PyTorch tensors
           UpperCAmelCase			       =		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowercase   ,				torchify=lowercase				)
           for image in image_inputs:
               self.assertIsInstance(lowercase   ,				torch.Tensor				)

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

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase				)

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

           # Test batched
           UpperCAmelCase			       =		image_processing(lowercase   ,				return_tensors='''pt'''				).pixel_values

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.image_processor_tester.get_expected_values(lowercase   ,				batched=lowercase				)

           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						:	Dict				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		{'''image_id''': 39_769, '''annotations''': target}

           # encode them
           UpperCAmelCase			       =		ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''				)
           UpperCAmelCase			       =		image_processing(images=lowercase   ,				annotations=lowercase   ,				return_tensors='''pt'''				)

           # verify pixel values
           UpperCAmelCase			       =		torch.Size([1, 3, 800, 1_066]				)
           self.assertEqual(encoding['''pixel_values'''].shape   ,				lowercase				)

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

           # verify area
           UpperCAmelCase			       =		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''']   ,				lowercase				)				)
           # verify boxes
           UpperCAmelCase			       =		torch.Size([6, 4]				)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				lowercase				)
           UpperCAmelCase			       =		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				lowercase   ,				atol=1E-3				)				)
           # verify image_id
           UpperCAmelCase			       =		torch.tensor([39_769]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				lowercase				)				)
           # verify is_crowd
           UpperCAmelCase			       =		torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				lowercase				)				)
           # verify class_labels
           UpperCAmelCase			       =		torch.tensor([75, 75, 63, 65, 17, 17]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				lowercase				)				)
           # verify orig_size
           UpperCAmelCase			       =		torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				lowercase				)				)
           # verify size
           UpperCAmelCase			       =		torch.tensor([800, 1_066]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				lowercase				)				)







       @slow
       def     A      (	self						:	str				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

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

           # encode them
           UpperCAmelCase			       =		ConditionalDetrImageProcessor(format='''coco_panoptic'''				)
           UpperCAmelCase			       =		image_processing(images=lowercase   ,				annotations=lowercase   ,				masks_path=lowercase   ,				return_tensors='''pt'''				)

           # verify pixel values
           UpperCAmelCase			       =		torch.Size([1, 3, 800, 1_066]				)
           self.assertEqual(encoding['''pixel_values'''].shape   ,				lowercase				)

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

           # verify area
           UpperCAmelCase			       =		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''']   ,				lowercase				)				)
           # verify boxes
           UpperCAmelCase			       =		torch.Size([6, 4]				)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape   ,				lowercase				)
           UpperCAmelCase			       =		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]   ,				lowercase   ,				atol=1E-3				)				)
           # verify image_id
           UpperCAmelCase			       =		torch.tensor([39_769]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']   ,				lowercase				)				)
           # verify is_crowd
           UpperCAmelCase			       =		torch.tensor([0, 0, 0, 0, 0, 0]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']   ,				lowercase				)				)
           # verify class_labels
           UpperCAmelCase			       =		torch.tensor([17, 17, 63, 75, 75, 93]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']   ,				lowercase				)				)
           # verify masks
           UpperCAmelCase			       =		822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()   ,				lowercase				)
           # verify orig_size
           UpperCAmelCase			       =		torch.tensor([480, 640]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']   ,				lowercase				)				)
           # verify size
           UpperCAmelCase			       =		torch.tensor([800, 1_066]				)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']   ,				lowercase				)				)










'''simple docstring'''







from __future__ import annotations





def 						snake_case_							(_a	:							Dict    ,   _a	:							str    ,   _a	:							Optional[Any]    ,   _a	:							List[str]     ):  # noqa: E741
    while r - l > 1:
        UpperCAmelCase			       =		(l + r) // 2
        if v[m] >= key:
            UpperCAmelCase			       =		m
        else:
            UpperCAmelCase			       =		m  # noqa: E741
    return r





def 						snake_case_							(_a	:							list[int]     ):
    if len(_a     ) == 0:
        return 0

    UpperCAmelCase			       =		[0] * len(_a     )
    UpperCAmelCase			       =		1

    UpperCAmelCase			       =		v[0]

    for i in range(1    ,   len(_a     )     ):
        if v[i] < tail[0]:
            UpperCAmelCase			       =		v[i]
        elif v[i] > tail[length - 1]:
            UpperCAmelCase			       =		v[i]
            length += 1
        else:
            UpperCAmelCase			       =		v[i]

    return length


if __name__ == "__main__":
   import doctest

   doctest.testmod()




