




import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		)
		from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  List[Any]      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Optional[int]=13      ,   __lowerCamelCase       :  Optional[int]=7      ,   __lowerCamelCase       :  List[str]=True      ,   __lowerCamelCase       :  Any=True      ,   __lowerCamelCase       :  str=True      ,   __lowerCamelCase       :  Union[str, Any]=True      ,   __lowerCamelCase       :  Any=99      ,   __lowerCamelCase       :  Optional[Any]=24      ,   __lowerCamelCase       :  Optional[int]=2      ,   __lowerCamelCase       :  Union[str, Any]=6      ,   __lowerCamelCase       :  Any=37      ,   __lowerCamelCase       :  Union[str, Any]="gelu"      ,   __lowerCamelCase       :  List[str]=0.1      ,   __lowerCamelCase       :  Tuple=0.1      ,   __lowerCamelCase       :  Union[str, Any]=5_12      ,   __lowerCamelCase       :  List[Any]=16      ,   __lowerCamelCase       :  List[Any]=2      ,   __lowerCamelCase       :  str=0.02      ,   __lowerCamelCase       :  str=3      ,   __lowerCamelCase       :  str=None      ,   __lowerCamelCase       :  Tuple=10_00      ,   )      ->  List[Any]:
							a					=    parent
							a					=    batch_size
							a					=    seq_length
							a					=    is_training
							a					=    use_input_mask
							a					=    use_token_type_ids
							a					=    use_labels
							a					=    vocab_size
							a					=    hidden_size
							a					=    num_hidden_layers
							a					=    num_attention_heads
							a					=    intermediate_size
							a					=    hidden_act
							a					=    hidden_dropout_prob
							a					=    attention_probs_dropout_prob
							a					=    max_position_embeddings
							a					=    type_vocab_size
							a					=    type_sequence_label_size
							a					=    initializer_range
							a					=    num_labels
							a					=    scope
							a					=    range_bbox





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  List[Any]:
							a					=    ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size							)

							a					=    ids_tensor([self.batch_size, self.seq_length, 4]      ,   self.range_bbox							)
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]							):
										for j in range(bbox.shape[1]							):
													if bbox[i, j, 3] < bbox[i, j, 1]:
																a					=    bbox[i, j, 3]
																a					=    bbox[i, j, 1]
																a					=    t
													if bbox[i, j, 2] < bbox[i, j, 0]:
																a					=    bbox[i, j, 2]
																a					=    bbox[i, j, 0]
																a					=    t

							a					=    None
							if self.use_input_mask:
										a					=    ids_tensor([self.batch_size, self.seq_length]      ,   vocab_size=2							)

							a					=    None
							if self.use_token_type_ids:
										a					=    ids_tensor([self.batch_size, self.seq_length]      ,   self.type_vocab_size							)

							a					=    None
							a					=    None
							if self.use_labels:
										a					=    ids_tensor([self.batch_size]      ,   self.type_sequence_label_size							)
										a					=    ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels							)

							a					=    self.get_config()

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Dict:
							return LiltConfig(
							    vocab_size=self.vocab_size      ,   hidden_size=self.hidden_size      ,   num_hidden_layers=self.num_hidden_layers      ,   num_attention_heads=self.num_attention_heads      ,   intermediate_size=self.intermediate_size      ,   hidden_act=self.hidden_act      ,   hidden_dropout_prob=self.hidden_dropout_prob      ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,   max_position_embeddings=self.max_position_embeddings      ,   type_vocab_size=self.type_vocab_size      ,   initializer_range=self.initializer_range      ,   )





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Union[str, Any]      ,   )      ->  Dict:
							a					=    LiltModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   bbox=__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase							)
							a					=    model(__lowerCamelCase      ,   bbox=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase							)
							a					=    model(__lowerCamelCase      ,   bbox=__lowerCamelCase							)

							self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)							)
							self.parent.assertEqual(result.pooler_output.shape      ,   (self.batch_size, self.hidden_size)							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Optional[Any]      ,   )      ->  List[Any]:
							a					=    self.num_labels
							a					=    LiltForTokenClassification(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(
							    __lowerCamelCase      ,   bbox=__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   )      ->  Tuple:
							a					=    LiltForQuestionAnswering(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(
							    __lowerCamelCase      ,   bbox=__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   start_positions=__lowerCamelCase      ,   end_positions=__lowerCamelCase      ,   )
							self.parent.assertEqual(result.start_logits.shape      ,   (self.batch_size, self.seq_length)							)
							self.parent.assertEqual(result.end_logits.shape      ,   (self.batch_size, self.seq_length)							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  str:
							a					=    self.prepare_config_and_inputs()
							(
							    (
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	(
							    a
							)					,	
							)					=    config_and_inputs
							a					=    {
							    "input_ids": input_ids,
							    "bbox": bbox,
							    "token_type_ids": token_type_ids,
							    "attention_mask": input_mask,
							}
							return config, inputs_dict




@require_torch
class 					snake_case__							(_UpperCamelCase					, _UpperCamelCase					, _UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[Any]											=      (
				    (
				        LiltModel,
				        LiltForSequenceClassification,
				        LiltForTokenClassification,
				        LiltForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_ :							str											=      (
				    {
				        """feature-extraction""": LiltModel,
				        """question-answering""": LiltForQuestionAnswering,
				        """text-classification""": LiltForSequenceClassification,
				        """token-classification""": LiltForTokenClassification,
				        """zero-shot""": LiltForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_ :							int											=      False
				SCREAMING_SNAKE_CASE_ :							Any											=      False





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Tuple							)      ->  List[str]:
							return True





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Union[str, Any]:
							a					=    LiltModelTester(self							)
							a					=    ConfigTester(self      ,   config_class=__lowerCamelCase      ,   hidden_size=37							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Union[str, Any]:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Optional[int]:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Tuple:
							a					=    self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
										a					=    type
										self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Dict:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Any:
							for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a					=    LiltModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




@require_torch
@slow
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Tuple:
							a					=    LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"							).to(__lowerCamelCase							)

							a					=    torch.tensor([[1, 2]]      ,   device=__lowerCamelCase							)
							a					=    torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]      ,   device=__lowerCamelCase							)

							# forward pass
							with torch.no_grad():
										a					=    model(input_ids=__lowerCamelCase      ,   bbox=__lowerCamelCase							)

							a					=    torch.Size([1, 2, 7_68]							)
							a					=    torch.tensor(
							    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]      ,   device=__lowerCamelCase      ,   )

							self.assertTrue(outputs.last_hidden_state.shape      ,   __lowerCamelCase							)
							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]      ,   __lowerCamelCase      ,   atol=1e-3							)							)












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       :  Dict      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Any=7      ,   __lowerCamelCase       :  List[Any]=3      ,   __lowerCamelCase       :  int=30      ,   __lowerCamelCase       :  int=4_00      ,   __lowerCamelCase       :  Dict=True      ,   __lowerCamelCase       :  Tuple=None      ,   __lowerCamelCase       :  Optional[int]=True      ,   __lowerCamelCase       :  Optional[Any]=[0.5, 0.5, 0.5]      ,   __lowerCamelCase       :  Optional[Any]=[0.5, 0.5, 0.5]      ,   __lowerCamelCase       :  Dict=True      ,   __lowerCamelCase       :  List[str]=1 / 2_55      ,   __lowerCamelCase       :  Optional[int]=True      ,   )      ->  str:
							# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
							a					=    size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
							a					=    parent
							a					=    batch_size
							a					=    num_channels
							a					=    min_resolution
							a					=    max_resolution
							a					=    do_resize
							a					=    size
							a					=    do_normalize
							a					=    image_mean
							a					=    image_std
							a					=    do_rescale
							a					=    rescale_factor
							a					=    do_pad





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							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       :  Union[str, Any]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  str=False							)      ->  List[str]:
							if not batched:
										a					=    image_inputs[0]
										if isinstance(__lowerCamelCase      ,   Image.Image							):
													a					,	a					=    image.size
										else:
													a					,	a					=    image.shape[1], image.shape[2]
										if w < h:
													a					=    int(self.size["shortest_edge"] * h / w							)
													a					=    self.size["shortest_edge"]
										elif w > h:
													a					=    self.size["shortest_edge"]
													a					=    int(self.size["shortest_edge"] * w / h							)
										else:
													a					=    self.size["shortest_edge"]
													a					=    self.size["shortest_edge"]

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

							return expected_height, expected_width




@require_torch
@require_vision
class 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Tuple											=      DetaImageProcessor if is_vision_available() else None





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					=    DetaImageProcessingTester(self							)





				@property
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Optional[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Tuple:
							a					=    self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "image_mean"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "image_std"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_normalize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_resize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_rescale"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_pad"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "size"							)							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Union[str, Any]:
							a					=    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      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  int:
							pass





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Any:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   Image.Image							)

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

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

							# Test batched
							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							a					=    image_processing(__lowerCamelCase      ,   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       :  Union[str, Any]							)      ->  Optional[Any]:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase      ,   numpify=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   np.ndarray							)

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

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							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							)      ->  List[str]:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase      ,   torchify=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   torch.Tensor							)

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

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase							)

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

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values

							a					,	a					=    self.image_processor_tester.get_expected_values(__lowerCamelCase      ,   batched=__lowerCamelCase							)

							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							)      ->  List[Any]:
							# prepare image and target
							a					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,   "r"							) as f:
										a					=    json.loads(f.read()							)

							a					=    {"image_id": 3_97_69, "annotations": target}

							# encode them
							a					=    DetaImageProcessor()
							a					=    image_processing(images=__lowerCamelCase      ,   annotations=__lowerCamelCase      ,   return_tensors="pt"							)

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

							a					=    torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   __lowerCamelCase      ,   atol=1e-4							)							)

							# verify area
							a					=    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   __lowerCamelCase							)							)
							# verify boxes
							a					=    torch.Size([6, 4]							)
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   __lowerCamelCase							)
							a					=    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   __lowerCamelCase      ,   atol=1e-3							)							)
							# verify image_id
							a					=    torch.tensor([3_97_69]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   __lowerCamelCase							)							)
							# verify is_crowd
							a					=    torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   __lowerCamelCase							)							)
							# verify class_labels
							a					=    torch.tensor([75, 75, 63, 65, 17, 17]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   __lowerCamelCase							)							)
							# verify orig_size
							a					=    torch.tensor([4_80, 6_40]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   __lowerCamelCase							)							)
							# verify size
							a					=    torch.tensor([8_00, 10_66]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   __lowerCamelCase							)							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Union[str, Any]:
							# prepare image, target and masks_path
							a					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"      ,   "r"							) as f:
										a					=    json.loads(f.read()							)

							a					=    {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

							a					=    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"							)

							# encode them
							a					=    DetaImageProcessor(format="coco_panoptic"							)
							a					=    image_processing(images=__lowerCamelCase      ,   annotations=__lowerCamelCase      ,   masks_path=__lowerCamelCase      ,   return_tensors="pt"							)

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

							a					=    torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,   __lowerCamelCase      ,   atol=1e-4							)							)

							# verify area
							a					=    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,   __lowerCamelCase							)							)
							# verify boxes
							a					=    torch.Size([6, 4]							)
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,   __lowerCamelCase							)
							a					=    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,   __lowerCamelCase      ,   atol=1e-3							)							)
							# verify image_id
							a					=    torch.tensor([3_97_69]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,   __lowerCamelCase							)							)
							# verify is_crowd
							a					=    torch.tensor([0, 0, 0, 0, 0, 0]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,   __lowerCamelCase							)							)
							# verify class_labels
							a					=    torch.tensor([17, 17, 63, 75, 75, 93]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,   __lowerCamelCase							)							)
							# verify masks
							a					=    82_28_73
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,   __lowerCamelCase							)
							# verify orig_size
							a					=    torch.tensor([4_80, 6_40]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,   __lowerCamelCase							)							)
							# verify size
							a					=    torch.tensor([8_00, 10_66]							)
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,   __lowerCamelCase							)							)






