


'''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 DetaImageProcessor

class 		snake_case				(    unittest.TestCase						):

				"""simple docstring"""




				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       ,    )    ->      Optional[Any]:







					"""simple docstring"""






					snake_case__			:			Union[str, Any]             =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
					snake_case__			:			Dict             =		parent
					snake_case__			:			Optional[Any]             =		batch_size
					snake_case__			:			List[str]             =		num_channels
					snake_case__			:			List[Any]             =		min_resolution
					snake_case__			:			Optional[int]             =		max_resolution
					snake_case__			:			Optional[Any]             =		do_resize
					snake_case__			:			Optional[Any]             =		size
					snake_case__			:			Dict             =		do_normalize
					snake_case__			:			Optional[Any]             =		image_mean
					snake_case__			:			Tuple             =		image_std
					snake_case__			:			Dict             =		do_rescale
					snake_case__			:			Dict             =		rescale_factor
					snake_case__			:			Union[str, Any]             =		do_pad





				def    lowercase__							(	self )    ->      int:







					"""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    lowercase__							(	self       ,    lowerCamelCase       ,    lowerCamelCase=False )    ->      Any:







					"""simple docstring"""






					if not batched:
						snake_case__			:			Optional[Any]             =		image_inputs[0]
						if isinstance(__a       ,    Image.Image ):
							snake_case__			:			List[str]             =		image.size
						else:
							snake_case__			:			Dict             =		image.shape[1], image.shape[2]
						if w < h:
							snake_case__			:			List[Any]             =		int(self.size['''shortest_edge'''] * h / w )
							snake_case__			:			Any             =		self.size["""shortest_edge"""]
						elif w > h:
							snake_case__			:			List[Any]             =		self.size["""shortest_edge"""]
							snake_case__			:			Optional[int]             =		int(self.size['''shortest_edge'''] * w / h )
						else:
							snake_case__			:			Tuple             =		self.size["""shortest_edge"""]
							snake_case__			:			Optional[Any]             =		self.size["""shortest_edge"""]

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

					return expected_height, expected_width



@require_torch
@require_vision
class 		snake_case				(    a__				,    unittest.TestCase						):

				"""simple docstring"""




				_lowerCAmelCase           =    DetaImageProcessor if is_vision_available() else None





				def    lowercase__							(	self )    ->      Union[str, Any]:







					"""simple docstring"""






					snake_case__			:			List[Any]             =		DetaImageProcessingTester(self )





				@property
				def    lowercase__							(	self )    ->      str:







					"""simple docstring"""






					return self.image_processor_tester.prepare_image_processor_dict()





				def    lowercase__							(	self )    ->      Dict:







					"""simple docstring"""






					snake_case__			:			Union[str, Any]             =		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_resize''' ) )
					self.assertTrue(hasattr(__a       ,    '''do_rescale''' ) )
					self.assertTrue(hasattr(__a       ,    '''do_pad''' ) )
					self.assertTrue(hasattr(__a       ,    '''size''' ) )





				def    lowercase__							(	self )    ->      Optional[Any]:







					"""simple docstring"""






					snake_case__			:			List[Any]             =		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       ,    __a )





				def    lowercase__							(	self )    ->      int:







					"""simple docstring"""






					pass





				def    lowercase__							(	self )    ->      Dict:







					"""simple docstring"""






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

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

					snake_case__			:			int             =		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
					snake_case__			:			str             =		self.image_processor_tester.get_expected_values(__a       ,    batched=__a )

					snake_case__			:			Union[str, Any]             =		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    lowercase__							(	self )    ->      str:







					"""simple docstring"""






					snake_case__			:			str             =		self.image_processing_class(**self.image_processor_dict )
					# create random numpy tensors
					snake_case__			:			Optional[Any]             =		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
					snake_case__			:			Tuple             =		image_processing(image_inputs[0]       ,    return_tensors='''pt''' ).pixel_values

					snake_case__			:			Tuple             =		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
					snake_case__			:			Dict             =		image_processing(__a       ,    return_tensors='''pt''' ).pixel_values

					snake_case__			:			Optional[Any]             =		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    lowercase__							(	self )    ->      Any:







					"""simple docstring"""






					snake_case__			:			Optional[int]             =		self.image_processing_class(**self.image_processor_dict )
					# create random PyTorch tensors
					snake_case__			:			int             =		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
					snake_case__			:			int             =		image_processing(image_inputs[0]       ,    return_tensors='''pt''' ).pixel_values

					snake_case__			:			str             =		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
					snake_case__			:			str             =		image_processing(__a       ,    return_tensors='''pt''' ).pixel_values

					snake_case__			:			Tuple             =		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    lowercase__							(	self )    ->      Any:







					"""simple docstring"""






					snake_case__			:			Dict             =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
					with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,    '''r''' ) as f:
						snake_case__			:			Optional[Any]             =		json.loads(f.read() )

					snake_case__			:			Optional[Any]             =		{"""image_id""": 39769, """annotations""": target}

					# encode them
					snake_case__			:			str             =		DetaImageProcessor()
					snake_case__			:			List[Any]             =		image_processing(images=__a       ,    annotations=__a       ,    return_tensors='''pt''' )

					# verify pixel values
					snake_case__			:			Any             =		torch.Size([1, 3, 800, 1066] )
					self.assertEqual(encoding['''pixel_values'''].shape       ,    __a )

					snake_case__			:			Dict             =		torch.tensor([0.2_796, 0.3_138, 0.3_481] )
					self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,    __a       ,    atol=1E-4 ) )

					# verify area
					snake_case__			:			int             =		torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,    __a ) )
					# verify boxes
					snake_case__			:			Any             =		torch.Size([6, 4] )
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,    __a )
					snake_case__			:			Dict             =		torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,    __a       ,    atol=1E-3 ) )
					# verify image_id
					snake_case__			:			Dict             =		torch.tensor([39769] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,    __a ) )
					# verify is_crowd
					snake_case__			:			Union[str, Any]             =		torch.tensor([0, 0, 0, 0, 0, 0] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,    __a ) )
					# verify class_labels
					snake_case__			:			Optional[int]             =		torch.tensor([75, 75, 63, 65, 17, 17] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,    __a ) )
					# verify orig_size
					snake_case__			:			Union[str, Any]             =		torch.tensor([480, 640] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,    __a ) )
					# verify size
					snake_case__			:			Optional[Any]             =		torch.tensor([800, 1066] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,    __a ) )





				@slow
				def    lowercase__							(	self )    ->      int:







					"""simple docstring"""






					snake_case__			:			Union[str, Any]             =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
					with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,    '''r''' ) as f:
						snake_case__			:			Optional[int]             =		json.loads(f.read() )

					snake_case__			:			Optional[int]             =		{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

					snake_case__			:			Optional[Any]             =		pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )

					# encode them
					snake_case__			:			Optional[int]             =		DetaImageProcessor(format='''coco_panoptic''' )
					snake_case__			:			Dict             =		image_processing(images=__a       ,    annotations=__a       ,    masks_path=__a       ,    return_tensors='''pt''' )

					# verify pixel values
					snake_case__			:			Union[str, Any]             =		torch.Size([1, 3, 800, 1066] )
					self.assertEqual(encoding['''pixel_values'''].shape       ,    __a )

					snake_case__			:			str             =		torch.tensor([0.2_796, 0.3_138, 0.3_481] )
					self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,    __a       ,    atol=1E-4 ) )

					# verify area
					snake_case__			:			List[str]             =		torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,    __a ) )
					# verify boxes
					snake_case__			:			Union[str, Any]             =		torch.Size([6, 4] )
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,    __a )
					snake_case__			:			List[str]             =		torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,    __a       ,    atol=1E-3 ) )
					# verify image_id
					snake_case__			:			List[Any]             =		torch.tensor([39769] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,    __a ) )
					# verify is_crowd
					snake_case__			:			int             =		torch.tensor([0, 0, 0, 0, 0, 0] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,    __a ) )
					# verify class_labels
					snake_case__			:			str             =		torch.tensor([17, 17, 63, 75, 75, 93] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,    __a ) )
					# verify masks
					snake_case__			:			int             =		822873
					self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,    __a )
					# verify orig_size
					snake_case__			:			Any             =		torch.tensor([480, 640] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,    __a ) )
					# verify size
					snake_case__			:			Tuple             =		torch.tensor([800, 1066] )
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,    __a ) )











'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase      :   Tuple      =       logging.get_logger(__name__)

lowercase      :   Dict      =       {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     str          =					"""deta"""
     lowerCAmelCase_  :     Any          =					{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(				self							,__a=None							,__a=900							,__a=2_048							,__a=6							,__a=2_048							,__a=8							,__a=6							,__a=1_024							,__a=8							,__a=0.0							,__a=True							,__a="relu"							,__a=256							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.02							,__a=1.0							,__a=True							,__a=False							,__a="sine"							,__a=5							,__a=4							,__a=4							,__a=True							,__a=300							,__a=True							,__a=True							,__a=1							,__a=5							,__a=2							,__a=1							,__a=1							,__a=5							,__a=2							,__a=0.1							,__a=0.25							,**__a							,)      ->				int:
          if backbone_config is None:
               logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
               snake_case			:      int        =			CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
          else:
               if isinstance(__a							,__a							):
                    snake_case			:      str        =			backbone_config.pop("""model_type"""							)
                    snake_case			:      Optional[Any]        =			CONFIG_MAPPING[backbone_model_type]
                    snake_case			:      List[Any]        =			config_class.from_dict(__a							)

          snake_case			:      Any        =			backbone_config
          snake_case			:      Optional[Any]        =			num_queries
          snake_case			:      Dict        =			max_position_embeddings
          snake_case			:      int        =			d_model
          snake_case			:      str        =			encoder_ffn_dim
          snake_case			:      Dict        =			encoder_layers
          snake_case			:      List[Any]        =			encoder_attention_heads
          snake_case			:      int        =			decoder_ffn_dim
          snake_case			:      List[Any]        =			decoder_layers
          snake_case			:      Dict        =			decoder_attention_heads
          snake_case			:      Dict        =			dropout
          snake_case			:      Dict        =			attention_dropout
          snake_case			:      List[Any]        =			activation_dropout
          snake_case			:      Dict        =			activation_function
          snake_case			:      List[Any]        =			init_std
          snake_case			:      Any        =			init_xavier_std
          snake_case			:      List[Any]        =			encoder_layerdrop
          snake_case			:      Optional[Any]        =			auxiliary_loss
          snake_case			:      Optional[int]        =			position_embedding_type
          # deformable attributes
          snake_case			:      Tuple        =			num_feature_levels
          snake_case			:      List[Any]        =			encoder_n_points
          snake_case			:      Dict        =			decoder_n_points
          snake_case			:      int        =			two_stage
          snake_case			:      Any        =			two_stage_num_proposals
          snake_case			:      List[str]        =			with_box_refine
          snake_case			:      Tuple        =			assign_first_stage
          if two_stage is True and with_box_refine is False:
               raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
          # Hungarian matcher
          snake_case			:      Optional[int]        =			class_cost
          snake_case			:      int        =			bbox_cost
          snake_case			:      Optional[int]        =			giou_cost
          # Loss coefficients
          snake_case			:      str        =			mask_loss_coefficient
          snake_case			:      Any        =			dice_loss_coefficient
          snake_case			:      List[Any]        =			bbox_loss_coefficient
          snake_case			:      List[Any]        =			giou_loss_coefficient
          snake_case			:      List[str]        =			eos_coefficient
          snake_case			:      Optional[Any]        =			focal_alpha
          super().__init__(is_encoder_decoder=__a							,**__a							)







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.encoder_attention_heads







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.d_model







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      List[str]        =			copy.deepcopy(self.__dict__							)
          snake_case			:      List[str]        =			self.backbone_config.to_dict()
          snake_case			:      List[Any]        =			self.__class__.model_type
          return output



