




import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image




class 		__A      :

						def __init__(		self   :List[Any]    , __snake_case   :List[str]    , __snake_case   :Dict=3    , __snake_case   :Optional[Any]=32    , __snake_case   :List[str]=3    , __snake_case   :str=10    , __snake_case   :Tuple=[8, 16, 32, 64]    , __snake_case   :Any=[1, 1, 2, 1]    , __snake_case   :List[str]=True    , __snake_case   :int=True    , __snake_case   :List[str]="relu"    , __snake_case   :Tuple=3    , __snake_case   :Dict=None    , __snake_case   :Any=["stage2", "stage3", "stage4"]    , __snake_case   :Tuple=[2, 3, 4]    , __snake_case   :Any=1    , ):
										'''simple docstring'''

										__magic_name__	:	int								=parent
										__magic_name__	:	List[str]								=batch_size
										__magic_name__	:	Tuple								=image_size
										__magic_name__	:	Tuple								=num_channels
										__magic_name__	:	Any								=embeddings_size
										__magic_name__	:	Dict								=hidden_sizes
										__magic_name__	:	Optional[int]								=depths
										__magic_name__	:	Union[str, Any]								=is_training
										__magic_name__	:	Optional[int]								=use_labels
										__magic_name__	:	Optional[Any]								=hidden_act
										__magic_name__	:	Tuple								=num_labels
										__magic_name__	:	Any								=scope
										__magic_name__	:	Optional[int]								=len(__snake_case )
										__magic_name__	:	Optional[int]								=out_features
										__magic_name__	:	List[str]								=out_indices
										__magic_name__	:	Optional[int]								=num_groups

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	str								=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

										__magic_name__	:	Dict								=None
										if self.use_labels:
														__magic_name__	:	int								=ids_tensor([self.batch_size]    , self.num_labels )

										__magic_name__	:	Optional[Any]								=self.get_config()

										return config, pixel_values, labels

						def     A__						(		self   :str ):
										'''simple docstring'''

										return BitConfig(
										    num_channels=self.num_channels    , embeddings_size=self.embeddings_size    , hidden_sizes=self.hidden_sizes    , depths=self.depths    , hidden_act=self.hidden_act    , num_labels=self.num_labels    , out_features=self.out_features    , out_indices=self.out_indices    , num_groups=self.num_groups    , )

						def     A__						(		self   :List[str]    , __snake_case   :str    , __snake_case   :str    , __snake_case   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=BitModel(config=__snake_case )
										model.to(__snake_case )
										model.eval()
										__magic_name__	:	Tuple								=model(__snake_case )
										self.parent.assertEqual(
										    result.last_hidden_state.shape    , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    , )

						def     A__						(		self   :Dict    , __snake_case   :Optional[int]    , __snake_case   :Any    , __snake_case   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.num_labels
										__magic_name__	:	Tuple								=BitForImageClassification(__snake_case )
										model.to(__snake_case )
										model.eval()
										__magic_name__	:	Union[str, Any]								=model(__snake_case    , labels=__snake_case )
										self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels) )

						def     A__						(		self   :List[str]    , __snake_case   :Optional[Any]    , __snake_case   :Tuple    , __snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	int								=BitBackbone(config=__snake_case )
										model.to(__snake_case )
										model.eval()
										__magic_name__	:	int								=model(__snake_case )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps )    , len(config.out_features ) )
										self.parent.assertListEqual(list(result.feature_maps[0].shape )    , [self.batch_size, self.hidden_sizes[1], 4, 4] )

										# verify channels
										self.parent.assertEqual(len(model.channels )    , len(config.out_features ) )
										self.parent.assertListEqual(model.channels    , config.hidden_sizes[1:] )

										# verify backbone works with out_features=None
										__magic_name__	:	Tuple								=None
										__magic_name__	:	Union[str, Any]								=BitBackbone(config=__snake_case )
										model.to(__snake_case )
										model.eval()
										__magic_name__	:	int								=model(__snake_case )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps )    , 1 )
										self.parent.assertListEqual(list(result.feature_maps[0].shape )    , [self.batch_size, self.hidden_sizes[-1], 1, 1] )

										# verify channels
										self.parent.assertEqual(len(model.channels )    , 1 )
										self.parent.assertListEqual(model.channels    , [config.hidden_sizes[-1]] )


						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.prepare_config_and_inputs()
										__magic_name__				, __magic_name__				, __magic_name__	:	str								=config_and_inputs
										__magic_name__	:	str								={"""pixel_values""": pixel_values}
										return config, inputs_dict




@require_torch
class 		__A      (			UpperCamelCase__					,						UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						UpperCamelCase     						=					(
						    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						UpperCamelCase     						=					False
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False
						UpperCamelCase     						=					False

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=BitModelTester(self )
										__magic_name__	:	List[Any]								=ConfigTester(self    , config_class=__snake_case    , has_text_modality=__snake_case )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										return

						@unittest.skip(reason="""Bit does not output attentions""" )
						def     A__						(		self   :Dict ):
										'''simple docstring'''

										pass

						@unittest.skip(reason="""Bit does not use inputs_embeds""" )
						def     A__						(		self   :int ):
										'''simple docstring'''

										pass

						@unittest.skip(reason="""Bit does not support input and output embeddings""" )
						def     A__						(		self   :Dict ):
										'''simple docstring'''

										pass

						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__				, __magic_name__	:	Dict								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														__magic_name__	:	List[str]								=model_class(__snake_case )
														__magic_name__	:	Dict								=inspect.signature(model.forward )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														__magic_name__	:	str								=[*signature.parameters.keys()]

														__magic_name__	:	Dict								=["""pixel_values"""]
														self.assertListEqual(arg_names[:1]    , __snake_case )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__snake_case )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Dict								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*__snake_case )

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__				, __magic_name__	:	List[str]								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														__magic_name__	:	List[Any]								=model_class(config=__snake_case )
														for name, module in model.named_modules():
																		if isinstance(__snake_case    , (nn.BatchNormad, nn.GroupNorm) ):
																						self.assertTrue(
																						    torch.all(module.weight == 1 )    , msg=f"Parameter {name} of model {model_class} seems not properly initialized"    , )
																						self.assertTrue(
																						    torch.all(module.bias == 0 )    , msg=f"Parameter {name} of model {model_class} seems not properly initialized"    , )

						def     A__						(		self   :str ):
										'''simple docstring'''

										def check_hidden_states_output(__snake_case   :List[str]    , __snake_case   :List[Any]    , __snake_case   :Union[str, Any] ):
														__magic_name__	:	str								=model_class(__snake_case )
														model.to(__snake_case )
														model.eval()

														with torch.no_grad():
																		__magic_name__	:	Tuple								=model(**self._prepare_for_class(__snake_case    , __snake_case ) )

														__magic_name__	:	Dict								=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														__magic_name__	:	str								=self.model_tester.num_stages
														self.assertEqual(len(__snake_case )    , expected_num_stages + 1 )

														# Bit's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:] )    , [self.model_tester.image_size // 4, self.model_tester.image_size // 4]    , )

										__magic_name__				, __magic_name__	:	int								=self.model_tester.prepare_config_and_inputs_for_common()
										__magic_name__	:	Dict								=["""preactivation""", """bottleneck"""]
										for model_class in self.all_model_classes:
														for layer_type in layers_type:
																		__magic_name__	:	Tuple								=layer_type
																		__magic_name__	:	Dict								=True
																		check_hidden_states_output(__snake_case    , __snake_case    , __snake_case )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		__magic_name__	:	Tuple								=True

																		check_hidden_states_output(__snake_case    , __snake_case    , __snake_case )

						@unittest.skip(reason="""Bit does not use feedforward chunking""" )
						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										pass

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__snake_case )


						@slow
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__magic_name__	:	Any								=BitModel.from_pretrained(__snake_case )
														self.assertIsNotNone(__snake_case )




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Dict								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
				return image




@require_torch
@require_vision
class 		__A      (			unittest.TestCase			):

						@cached_property
						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										return (
										    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
										)


						@slow
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								=BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__snake_case )

										__magic_name__	:	int								=self.default_image_processor
										__magic_name__	:	List[str]								=prepare_img()
										__magic_name__	:	List[Any]								=image_processor(images=__snake_case    , return_tensors="""pt""" ).to(__snake_case )

										# forward pass
										with torch.no_grad():
														__magic_name__	:	Optional[Any]								=model(**__snake_case )

										# verify the logits
										__magic_name__	:	Tuple								=torch.Size((1, 10_00) )
										self.assertEqual(outputs.logits.shape    , __snake_case )

										__magic_name__	:	Dict								=torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(__snake_case )

										self.assertTrue(torch.allclose(outputs.logits[0, :3]    , __snake_case    , atol=1E-4 ) )




@require_torch
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					(BitBackbone,) if is_torch_available() else ()
						UpperCamelCase     						=					BitConfig

						UpperCamelCase     						=					False

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=BitModelTester(self )










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 		__A      (			unittest.TestCase			):

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

										__magic_name__	:	Optional[int]								=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
										__magic_name__	:	str								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=num_channels
										__magic_name__	:	Optional[int]								=min_resolution
										__magic_name__	:	Optional[Any]								=max_resolution
										__magic_name__	:	Optional[Any]								=do_resize
										__magic_name__	:	Any								=size
										__magic_name__	:	str								=do_normalize
										__magic_name__	:	str								=image_mean
										__magic_name__	:	List[Any]								=image_std
										__magic_name__	:	Optional[int]								=do_rescale
										__magic_name__	:	List[str]								=rescale_factor
										__magic_name__	:	Optional[int]								=do_pad

						def     A__						(		self   :Optional[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     A__						(		self   :List[str]    , __snake_case   :Any    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

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

										else:
														__magic_name__	:	List[str]								=[]
														for image in image_inputs:
																		__magic_name__				, __magic_name__	:	Tuple								=self.get_expected_values([image] )
																		expected_values.append((expected_height, expected_width) )
														__magic_name__	:	Optional[int]								=max(__snake_case    , key=lambda __snake_case : item[0] )[0]
														__magic_name__	:	Tuple								=max(__snake_case    , key=lambda __snake_case : item[1] )[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					DetaImageProcessor if is_vision_available() else None

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	int								=DetaImageProcessingTester(self )

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(__snake_case    , """image_mean""" ) )
										self.assertTrue(hasattr(__snake_case    , """image_std""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_normalize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_resize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_rescale""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_pad""" ) )
										self.assertTrue(hasattr(__snake_case    , """size""" ) )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=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    , __snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										pass

						def     A__						(		self   :str ):
										'''simple docstring'''

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

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

										__magic_name__				, __magic_name__	:	str								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										__magic_name__	:	Dict								=image_processing(__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     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	int								=self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										__magic_name__	:	Optional[Any]								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , numpify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , np.ndarray )

										# Test not batched input
										__magic_name__	:	str								=image_processing(image_inputs[0]    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__	:	List[str]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Any								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										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[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										__magic_name__	:	Dict								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , torchify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , torch.Tensor )

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

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Tuple								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__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     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
										with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""    , """r""" ) as f:
														__magic_name__	:	Optional[int]								=json.loads(f.read() )

										__magic_name__	:	str								={"""image_id""": 3_97_69, """annotations""": target}

										# encode them
										__magic_name__	:	Optional[Any]								=DetaImageProcessor()
										__magic_name__	:	Any								=image_processing(images=__snake_case    , annotations=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Tuple								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	Optional[int]								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	Dict								=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Optional[int]								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	int								=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Any								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	Optional[Any]								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[str]								=torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify orig_size
										__magic_name__	:	Optional[Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	Tuple								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )


						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""    , """r""" ) as f:
														__magic_name__	:	str								=json.loads(f.read() )

										__magic_name__	:	Optional[Any]								={"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

										__magic_name__	:	List[str]								=pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

										# encode them
										__magic_name__	:	int								=DetaImageProcessor(format="""coco_panoptic""" )
										__magic_name__	:	Tuple								=image_processing(images=__snake_case    , annotations=__snake_case    , masks_path=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Union[str, Any]								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	str								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	str								=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Dict								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	Optional[Any]								=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Dict								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	int								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[Any]								=torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify masks
										__magic_name__	:	List[Any]								=82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    , __snake_case )
										# verify orig_size
										__magic_name__	:	Union[str, Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	List[Any]								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )




