






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     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=7		,		_UpperCAmelCase=3		,		_UpperCAmelCase=30		,		_UpperCAmelCase=400		,		_UpperCAmelCase=True		,		_UpperCAmelCase=None		,		_UpperCAmelCase=True		,		_UpperCAmelCase=[0.5, 0.5, 0.5]		,		_UpperCAmelCase=[0.5, 0.5, 0.5]		,		_UpperCAmelCase=True		,		_UpperCAmelCase=1 / 255		,		_UpperCAmelCase=True		,		)      ->			str:
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     UpperCamelCase_						=  size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
     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  _UpperCAmelCase  (  self			)      ->			int:
     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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase=False			)      ->			Tuple:
     if not batched:
        UpperCamelCase_						=  image_inputs[0]
        if isinstance(_UpperCAmelCase		,		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(_UpperCAmelCase		,		key=lambda _UpperCAmelCase			: item[0]			)[0]
        UpperCamelCase_						=  max(_UpperCAmelCase		,		key=lambda _UpperCAmelCase			: item[1]			)[1]

     return expected_height, expected_width




@require_torch
@require_vision
class     _a							(	UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = ConditionalDetrImageProcessor if is_vision_available() else None




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  ConditionalDetrImageProcessingTester(self			)




  @property
  def  _UpperCAmelCase  (  self			)      ->			int:
     return self.image_processor_tester.prepare_image_processor_dict()




  def  _UpperCAmelCase  (  self			)      ->			List[str]:
     UpperCamelCase_						=  self.image_processing_class(**self.image_processor_dict			)
     self.assertTrue(hasattr(_UpperCAmelCase		,		'image_mean'			)			)
     self.assertTrue(hasattr(_UpperCAmelCase		,		'image_std'			)			)
     self.assertTrue(hasattr(_UpperCAmelCase		,		'do_normalize'			)			)
     self.assertTrue(hasattr(_UpperCAmelCase		,		'do_resize'			)			)
     self.assertTrue(hasattr(_UpperCAmelCase		,		'size'			)			)




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  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		,		_UpperCAmelCase			)

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




  def  _UpperCAmelCase  (  self			)      ->			Dict:
     pass




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     # Initialize image_processing
     UpperCamelCase_						=  self.image_processing_class(**self.image_processor_dict			)
     # create random PIL images
     UpperCamelCase_						=  prepare_image_inputs(self.image_processor_tester		,		equal_resolution=_UpperCAmelCase			)
     for image in image_inputs:
        self.assertIsInstance(_UpperCAmelCase		,		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(_UpperCAmelCase			)

     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(_UpperCAmelCase		,		batched=_UpperCAmelCase			)

     UpperCamelCase_						=  image_processing(_UpperCAmelCase		,		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  _UpperCAmelCase  (  self			)      ->			int:
     # Initialize image_processing
     UpperCamelCase_						=  self.image_processing_class(**self.image_processor_dict			)
     # create random numpy tensors
     UpperCamelCase_						=  prepare_image_inputs(self.image_processor_tester		,		equal_resolution=_UpperCAmelCase		,		numpify=_UpperCAmelCase			)
     for image in image_inputs:
        self.assertIsInstance(_UpperCAmelCase		,		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(_UpperCAmelCase			)

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

     # Test batched
     UpperCamelCase_						=  image_processing(_UpperCAmelCase		,		return_tensors='pt'			).pixel_values

     UpperCamelCase_		,   UpperCamelCase_						=  self.image_processor_tester.get_expected_values(_UpperCAmelCase		,		batched=_UpperCAmelCase			)

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




  def  _UpperCAmelCase  (  self			)      ->			Any:
     # Initialize image_processing
     UpperCamelCase_						=  self.image_processing_class(**self.image_processor_dict			)
     # create random PyTorch tensors
     UpperCamelCase_						=  prepare_image_inputs(self.image_processor_tester		,		equal_resolution=_UpperCAmelCase		,		torchify=_UpperCAmelCase			)
     for image in image_inputs:
        self.assertIsInstance(_UpperCAmelCase		,		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(_UpperCAmelCase			)

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

     # Test batched
     UpperCamelCase_						=  image_processing(_UpperCAmelCase		,		return_tensors='pt'			).pixel_values

     UpperCamelCase_		,   UpperCamelCase_						=  self.image_processor_tester.get_expected_values(_UpperCAmelCase		,		batched=_UpperCAmelCase			)

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




  @slow
  def  _UpperCAmelCase  (  self			)      ->			Any:
     # prepare image and target
     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': 39769, 'annotations': target}

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

     # verify pixel values
     UpperCamelCase_						=  torch.Size([1, 3, 800, 1066]			)
     self.assertEqual(encoding['pixel_values'].shape		,		_UpperCAmelCase			)

     UpperCamelCase_						=  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]		,		_UpperCAmelCase		,		atol=1e-4			)			)

     # verify area
     UpperCamelCase_						=  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,		_UpperCAmelCase			)			)
     # verify boxes
     UpperCamelCase_						=  torch.Size([6, 4]			)
     self.assertEqual(encoding['labels'][0]['boxes'].shape		,		_UpperCAmelCase			)
     UpperCamelCase_						=  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]		,		_UpperCAmelCase		,		atol=1e-3			)			)
     # verify image_id
     UpperCamelCase_						=  torch.tensor([39769]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,		_UpperCAmelCase			)			)
     # verify is_crowd
     UpperCamelCase_						=  torch.tensor([0, 0, 0, 0, 0, 0]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,		_UpperCAmelCase			)			)
     # verify class_labels
     UpperCamelCase_						=  torch.tensor([75, 75, 63, 65, 17, 17]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,		_UpperCAmelCase			)			)
     # verify orig_size
     UpperCamelCase_						=  torch.tensor([480, 640]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,		_UpperCAmelCase			)			)
     # verify size
     UpperCamelCase_						=  torch.tensor([800, 1066]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,		_UpperCAmelCase			)			)




  @slow
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     # prepare image, target and masks_path
     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': 39769, 'segments_info': target}

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

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

     # verify pixel values
     UpperCamelCase_						=  torch.Size([1, 3, 800, 1066]			)
     self.assertEqual(encoding['pixel_values'].shape		,		_UpperCAmelCase			)

     UpperCamelCase_						=  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]		,		_UpperCAmelCase		,		atol=1e-4			)			)

     # verify area
     UpperCamelCase_						=  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,		_UpperCAmelCase			)			)
     # verify boxes
     UpperCamelCase_						=  torch.Size([6, 4]			)
     self.assertEqual(encoding['labels'][0]['boxes'].shape		,		_UpperCAmelCase			)
     UpperCamelCase_						=  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]		,		_UpperCAmelCase		,		atol=1e-3			)			)
     # verify image_id
     UpperCamelCase_						=  torch.tensor([39769]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,		_UpperCAmelCase			)			)
     # verify is_crowd
     UpperCamelCase_						=  torch.tensor([0, 0, 0, 0, 0, 0]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,		_UpperCAmelCase			)			)
     # verify class_labels
     UpperCamelCase_						=  torch.tensor([17, 17, 63, 75, 75, 93]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,		_UpperCAmelCase			)			)
     # verify masks
     UpperCamelCase_						=  822873
     self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,		_UpperCAmelCase			)
     # verify orig_size
     UpperCamelCase_						=  torch.tensor([480, 640]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,		_UpperCAmelCase			)			)
     # verify size
     UpperCamelCase_						=  torch.tensor([800, 1066]			)
     self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,		_UpperCAmelCase			)			)










"""simple docstring"""







def     UpperCAmelCase_					(				__a   :			float      ,							__a   :			float						):




			'''simple docstring'''



			if density <= 0:
						raise ValueError('Impossible fluid density'						)
			if bulk_modulus <= 0:
						raise ValueError('Impossible bulk modulus'						)

			return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
	import doctest

	doctest.testmod()





