


"""simple docstring"""




import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    SqueezeBertForMaskedLM,
					    SqueezeBertForMultipleChoice,
					    SqueezeBertForQuestionAnswering,
					    SqueezeBertForSequenceClassification,
					    SqueezeBertForTokenClassification,
					    SqueezeBertModel,
					)




class 			__snake_case   (lowerCamelCase  ):




							def __init__(     self:    str     ,							A_:    Optional[int]     ,							A_:    str=13     ,							A_:    Optional[int]=7     ,							A_:    Any=True     ,							A_:    Tuple=True     ,							A_:    int=False     ,							A_:    List[str]=True     ,							A_:    Optional[int]=99     ,							A_:    Dict=32     ,							A_:    List[Any]=5     ,							A_:    Optional[Any]=4     ,							A_:    Optional[Any]=64     ,							A_:    Union[str, Any]="gelu"     ,							A_:    Optional[Any]=0.1     ,							A_:    Union[str, Any]=0.1     ,							A_:    str=5_12     ,							A_:    Optional[int]=16     ,							A_:    int=2     ,							A_:    str=0.02     ,							A_:    int=3     ,							A_:    Dict=4     ,							A_:    str=None     ,							A_:    Optional[int]=2     ,							A_:    Optional[Any]=2     ,							A_:    Any=2     ,							A_:    Tuple=2     ,							A_:    Union[str, Any]=4     ,							A_:    List[Any]=1     ,							):
										__lowerCamelCase          =      parent
										__lowerCamelCase          =      batch_size
										__lowerCamelCase          =      seq_length
										__lowerCamelCase          =      is_training
										__lowerCamelCase          =      use_input_mask
										__lowerCamelCase          =      use_token_type_ids
										__lowerCamelCase          =      use_labels
										__lowerCamelCase          =      vocab_size
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_hidden_layers
										__lowerCamelCase          =      num_attention_heads
										__lowerCamelCase          =      intermediate_size
										__lowerCamelCase          =      hidden_act
										__lowerCamelCase          =      hidden_dropout_prob
										__lowerCamelCase          =      attention_probs_dropout_prob
										__lowerCamelCase          =      max_position_embeddings
										__lowerCamelCase          =      type_vocab_size
										__lowerCamelCase          =      type_sequence_label_size
										__lowerCamelCase          =      initializer_range
										__lowerCamelCase          =      num_labels
										__lowerCamelCase          =      num_choices
										__lowerCamelCase          =      scope
										__lowerCamelCase          =      q_groups
										__lowerCamelCase          =      k_groups
										__lowerCamelCase          =      v_groups
										__lowerCamelCase          =      post_attention_groups
										__lowerCamelCase          =      intermediate_groups
										__lowerCamelCase          =      output_groups




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size   )

										__lowerCamelCase          =      None
										if self.use_input_mask:
													__lowerCamelCase          =      random_attention_mask([self.batch_size, self.seq_length]   )

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

										__lowerCamelCase          =      self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




							def   __a   (     self:    Any   ):
										return SqueezeBertConfig(
										    embedding_size=self.hidden_size     ,							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     ,							attention_probs_dropout_prob=self.hidden_dropout_prob     ,							attention_dropout=self.attention_probs_dropout_prob     ,							max_position_embeddings=self.max_position_embeddings     ,							initializer_range=self.initializer_range     ,							q_groups=self.q_groups     ,							k_groups=self.k_groups     ,							v_groups=self.v_groups     ,							post_attention_groups=self.post_attention_groups     ,							intermediate_groups=self.intermediate_groups     ,							output_groups=self.output_groups     ,							)




							def   __a   (     self:    str     ,							A_:    Dict     ,							A_:    str     ,							A_:    Optional[Any]     ,							A_:    List[Any]     ,							A_:    List[Any]     ,							A_:    Tuple   ):
										__lowerCamelCase          =      SqueezeBertModel(config=A_   )
										model.to(A_   )
										model.eval()
										__lowerCamelCase          =      model(A_     ,							A_   )
										__lowerCamelCase          =      model(A_   )
										self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)   )




							def   __a   (     self:    Union[str, Any]     ,							A_:    List[Any]     ,							A_:    int     ,							A_:    Union[str, Any]     ,							A_:    Any     ,							A_:    Optional[Any]     ,							A_:    int   ):
										__lowerCamelCase          =      SqueezeBertForMaskedLM(config=A_   )
										model.to(A_   )
										model.eval()
										__lowerCamelCase          =      model(A_     ,							attention_mask=A_     ,							labels=A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)   )




							def   __a   (     self:    Optional[int]     ,							A_:    Optional[int]     ,							A_:    Union[str, Any]     ,							A_:    Optional[Any]     ,							A_:    Dict     ,							A_:    List[str]     ,							A_:    Optional[Any]   ):
										__lowerCamelCase          =      SqueezeBertForQuestionAnswering(config=A_   )
										model.to(A_   )
										model.eval()
										__lowerCamelCase          =      model(
										    A_     ,							attention_mask=A_     ,							start_positions=A_     ,							end_positions=A_   )
										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   __a   (     self:    Dict     ,							A_:    Dict     ,							A_:    Optional[Any]     ,							A_:    Dict     ,							A_:    int     ,							A_:    Optional[int]     ,							A_:    Any   ):
										__lowerCamelCase          =      self.num_labels
										__lowerCamelCase          =      SqueezeBertForSequenceClassification(A_   )
										model.to(A_   )
										model.eval()
										__lowerCamelCase          =      model(A_     ,							attention_mask=A_     ,							labels=A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)   )




							def   __a   (     self:    Optional[Any]     ,							A_:    int     ,							A_:    Dict     ,							A_:    Optional[int]     ,							A_:    List[str]     ,							A_:    Dict     ,							A_:    Any   ):
										__lowerCamelCase          =      self.num_labels
										__lowerCamelCase          =      SqueezeBertForTokenClassification(config=A_   )
										model.to(A_   )
										model.eval()

										__lowerCamelCase          =      model(A_     ,							attention_mask=A_     ,							labels=A_   )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.num_labels)   )




							def   __a   (     self:    List[str]     ,							A_:    Dict     ,							A_:    Optional[int]     ,							A_:    Tuple     ,							A_:    List[Any]     ,							A_:    Optional[Any]     ,							A_:    Any   ):
										__lowerCamelCase          =      self.num_choices
										__lowerCamelCase          =      SqueezeBertForMultipleChoice(config=A_   )
										model.to(A_   )
										model.eval()
										__lowerCamelCase          =      input_ids.unsqueeze(1   ).expand(-1     ,							self.num_choices     ,							-1   ).contiguous()
										__lowerCamelCase          =      input_mask.unsqueeze(1   ).expand(-1     ,							self.num_choices     ,							-1   ).contiguous()
										__lowerCamelCase          =      model(
										    A_     ,							attention_mask=A_     ,							labels=A_     ,							)
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_choices)   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      self.prepare_config_and_inputs()
										((__lowerCamelCase)     ,(__lowerCamelCase)     ,(__lowerCamelCase)     ,(__lowerCamelCase)     ,(__lowerCamelCase)     ,(__lowerCamelCase))          =      config_and_inputs
										__lowerCamelCase          =      {"""input_ids""": input_ids, """attention_mask""": input_mask}
										return config, inputs_dict




@require_torch
class 			__snake_case   (lowerCamelCase			,		lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       (
							    (
							        SqueezeBertModel,
							        SqueezeBertForMaskedLM,
							        SqueezeBertForMultipleChoice,
							        SqueezeBertForQuestionAnswering,
							        SqueezeBertForSequenceClassification,
							        SqueezeBertForTokenClassification,
							    )
							    if is_torch_available()
							    else None
							)
							__a					 =       (
							    {
							        '''feature-extraction''': SqueezeBertModel,
							        '''fill-mask''': SqueezeBertForMaskedLM,
							        '''question-answering''': SqueezeBertForQuestionAnswering,
							        '''text-classification''': SqueezeBertForSequenceClassification,
							        '''token-classification''': SqueezeBertForTokenClassification,
							        '''zero-shot''': SqueezeBertForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__a					 =       False
							__a					 =       True
							__a					 =       False




							def   __a   (     self:    Dict   ):
										__lowerCamelCase          =      SqueezeBertModelTester(self   )
										__lowerCamelCase          =      ConfigTester(self     ,							config_class=A_     ,							dim=37   )




							def   __a   (     self:    int   ):
										self.config_tester.run_common_tests()




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_model(*A_   )




							def   __a   (     self:    str   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_masked_lm(*A_   )




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_question_answering(*A_   )




							def   __a   (     self:    List[str]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A_   )




							def   __a   (     self:    Any   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_token_classification(*A_   )




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A_   )




							@slow
							def   __a   (     self:    str   ):
										for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__lowerCamelCase          =      SqueezeBertModel.from_pretrained(A_   )
													self.assertIsNotNone(A_   )




@require_sentencepiece
@require_tokenizers
@require_torch
class 			__snake_case   (unittest.TestCase  ):




							@slow
							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""   )

										__lowerCamelCase          =      torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]   )
										__lowerCamelCase          =      model(A_   )[0]
										__lowerCamelCase          =      torch.Size((1, 3)   )
										self.assertEqual(output.shape     ,							A_   )
										__lowerCamelCase          =      torch.tensor([[0.6_401, -0.0_349, -0.6_041]]   )
										self.assertTrue(torch.allclose(A_     ,							A_     ,							atol=1E-4   )   )








"""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  ):




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




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

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

										return expected_height, expected_width




@require_torch
@require_vision
class 			__snake_case   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       DetaImageProcessor if is_vision_available() else None




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      DetaImageProcessingTester(self   )




							@property
							def   __a   (     self:    Optional[int]   ):
										return self.image_processor_tester.prepare_image_processor_dict()




							def   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      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   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      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     ,							A_   )




							def   __a   (     self:    Any   ):
										pass




							def   __a   (     self:    Optional[Any]   ):
										# Initialize image_processing
										__lowerCamelCase          =      self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										__lowerCamelCase          =      prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=A_   )
										for image in image_inputs:
													self.assertIsInstance(A_     ,							Image.Image   )

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

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

										__lowerCamelCase          =      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   __a   (     self:    int   ):
										# Initialize image_processing
										__lowerCamelCase          =      self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										__lowerCamelCase          =      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
										__lowerCamelCase          =      image_processing(image_inputs[0]     ,							return_tensors="""pt"""   ).pixel_values

										__lowerCamelCase     ,__lowerCamelCase          =      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
										__lowerCamelCase          =      image_processing(A_     ,							return_tensors="""pt"""   ).pixel_values

										__lowerCamelCase     ,__lowerCamelCase          =      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   __a   (     self:    Dict   ):
										# Initialize image_processing
										__lowerCamelCase          =      self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										__lowerCamelCase          =      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
										__lowerCamelCase          =      image_processing(image_inputs[0]     ,							return_tensors="""pt"""   ).pixel_values

										__lowerCamelCase     ,__lowerCamelCase          =      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
										__lowerCamelCase          =      image_processing(A_     ,							return_tensors="""pt"""   ).pixel_values

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

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

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

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

										__lowerCamelCase          =      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
										__lowerCamelCase          =      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"""]     ,							A_   )   )
										# verify boxes
										__lowerCamelCase          =      torch.Size([6, 4]   )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape     ,							A_   )
										__lowerCamelCase          =      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
										__lowerCamelCase          =      torch.tensor([3_97_69]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]     ,							A_   )   )
										# verify is_crowd
										__lowerCamelCase          =      torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]     ,							A_   )   )
										# verify class_labels
										__lowerCamelCase          =      torch.tensor([75, 75, 63, 65, 17, 17]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]     ,							A_   )   )
										# verify orig_size
										__lowerCamelCase          =      torch.tensor([4_80, 6_40]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]     ,							A_   )   )
										# verify size
										__lowerCamelCase          =      torch.tensor([8_00, 10_66]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]     ,							A_   )   )




							@slow
							def   __a   (     self:    Tuple   ):
										# prepare image, target and masks_path
										__lowerCamelCase          =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
										with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""     ,							"""r"""   ) as f:
													__lowerCamelCase          =      json.loads(f.read()   )

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

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

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

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

										__lowerCamelCase          =      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
										__lowerCamelCase          =      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"""]     ,							A_   )   )
										# verify boxes
										__lowerCamelCase          =      torch.Size([6, 4]   )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape     ,							A_   )
										__lowerCamelCase          =      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
										__lowerCamelCase          =      torch.tensor([3_97_69]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]     ,							A_   )   )
										# verify is_crowd
										__lowerCamelCase          =      torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]     ,							A_   )   )
										# verify class_labels
										__lowerCamelCase          =      torch.tensor([17, 17, 63, 75, 75, 93]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]     ,							A_   )   )
										# verify masks
										__lowerCamelCase          =      82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()     ,							A_   )
										# verify orig_size
										__lowerCamelCase          =      torch.tensor([4_80, 6_40]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]     ,							A_   )   )
										# verify size
										__lowerCamelCase          =      torch.tensor([8_00, 10_66]   )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]     ,							A_   )   )




