


from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowerCAmelCase__  :Optional[Any]             =							logging.get_logger(__name__)

lowerCAmelCase__  :Dict             =							{
    '''Visual-Attention-Network/van-base''': (
        '''https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'''
    ),
}

class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[int]           =     'van'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=224      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=[7, 3, 3, 3]      ,   _SCREAMING_SNAKE_CASE=[4, 2, 2, 2]      ,   _SCREAMING_SNAKE_CASE=[64, 128, 320, 512]      ,   _SCREAMING_SNAKE_CASE=[3, 3, 12, 3]      ,   _SCREAMING_SNAKE_CASE=[8, 8, 4, 4]      ,   _SCREAMING_SNAKE_CASE="gelu"      ,   _SCREAMING_SNAKE_CASE=0.02      ,   _SCREAMING_SNAKE_CASE=1e-6      ,   _SCREAMING_SNAKE_CASE=1e-2      ,   _SCREAMING_SNAKE_CASE=0.0      ,   _SCREAMING_SNAKE_CASE=0.0      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Dict:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      image_size
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      patch_sizes
									_UpperCAmelCase					=      strides
									_UpperCAmelCase					=      hidden_sizes
									_UpperCAmelCase					=      depths
									_UpperCAmelCase					=      mlp_ratios
									_UpperCAmelCase					=      hidden_act
									_UpperCAmelCase					=      initializer_range
									_UpperCAmelCase					=      layer_norm_eps
									_UpperCAmelCase					=      layer_scale_init_value
									_UpperCAmelCase					=      drop_path_rate
									_UpperCAmelCase					=      dropout_rate




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      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=30      ,   _SCREAMING_SNAKE_CASE=400      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=1 / 255      ,   _SCREAMING_SNAKE_CASE=True      ,   )			->	str:




									"""simple docstring"""





									_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						)			->	Union[str, Any]:




									"""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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False						)			->	Optional[Any]:




									"""simple docstring"""





									if not batched:
																_UpperCAmelCase					=      image_inputs[0]
																if isinstance(_SCREAMING_SNAKE_CASE      ,   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(_SCREAMING_SNAKE_CASE      ,   key=lambda _SCREAMING_SNAKE_CASE						: item[0]						)[0]
																_UpperCAmelCase					=      max(_SCREAMING_SNAKE_CASE      ,   key=lambda _SCREAMING_SNAKE_CASE						: item[1]						)[1]

									return expected_height, expected_width



@require_torch
@require_vision
class 		__a				(    UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Tuple           =     ConditionalDetrImageProcessor if is_vision_available() else None
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      ConditionalDetrImageProcessingTester(self						)
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_mean'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_std'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_normalize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_resize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'size'						)						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_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      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.image_processing_class.from_dict(
									    self.image_processor_dict      ,   size=42      ,   max_size=84      ,   pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE						)
									self.assertEqual(image_processor.size      ,   {'shortest_edge': 42, 'longest_edge': 84}						)
									self.assertEqual(image_processor.do_pad      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




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

									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(_SCREAMING_SNAKE_CASE      ,   batched=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   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:




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

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

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

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

									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:




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

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

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

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

									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						)			->	Optional[Any]:




									"""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': 39769, 'annotations': target}

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

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

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

									# verify area
									_UpperCAmelCase					=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify boxes
									_UpperCAmelCase					=      torch.Size([6, 4]						)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)
									# verify image_id
									_UpperCAmelCase					=      torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify is_crowd
									_UpperCAmelCase					=      torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify class_labels
									_UpperCAmelCase					=      torch.tensor([75, 75, 63, 65, 17, 17]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify orig_size
									_UpperCAmelCase					=      torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify size
									_UpperCAmelCase					=      torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,   _SCREAMING_SNAKE_CASE						)						)






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




									"""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': 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=_SCREAMING_SNAKE_CASE      ,   annotations=_SCREAMING_SNAKE_CASE      ,   masks_path=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						)

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

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

									# verify area
									_UpperCAmelCase					=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify boxes
									_UpperCAmelCase					=      torch.Size([6, 4]						)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)
									# verify image_id
									_UpperCAmelCase					=      torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify is_crowd
									_UpperCAmelCase					=      torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify class_labels
									_UpperCAmelCase					=      torch.tensor([17, 17, 63, 75, 75, 93]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify masks
									_UpperCAmelCase					=      822873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,   _SCREAMING_SNAKE_CASE						)
									# verify orig_size
									_UpperCAmelCase					=      torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify size
									_UpperCAmelCase					=      torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,   _SCREAMING_SNAKE_CASE						)						)
