


"""simple docstring"""




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__magic_name__						: Tuple    =				get_tests_dir('fixtures/test_sentencepiece_no_bos.model')




@require_sentencepiece
@require_tokenizers
class 			__snake_case   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       PegasusTokenizer
							__a					 =       PegasusTokenizerFast
							__a					 =       True
							__a					 =       True




							def   __a   (     self:    int   ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCamelCase          =      PegasusTokenizer(A_   )
										tokenizer.save_pretrained(self.tmpdirname   )




							@cached_property
							def   __a   (     self:    Tuple   ):
										return PegasusTokenizer.from_pretrained("""google/pegasus-large"""   )




							def   __a   (     self:    Union[str, Any]     ,							**A_:    Tuple   ):
										return PegasusTokenizer.from_pretrained(self.tmpdirname     ,							**A_   )




							def   __a   (     self:    Optional[int]     ,							A_:    int   ):
										return ("This is a test", "This is a test")




							def   __a   (     self:    List[str]   ):
										__lowerCamelCase          =      """</s>"""
										__lowerCamelCase          =      1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_   )     ,							A_   )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_   )     ,							A_   )




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      list(self.get_tokenizer().get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]     ,							"""<pad>"""   )
										self.assertEqual(vocab_keys[1]     ,							"""</s>"""   )
										self.assertEqual(vocab_keys[-1]     ,							"""v"""   )
										self.assertEqual(len(A_   )     ,							11_03   )




							def   __a   (     self:    Dict   ):
										self.assertEqual(self.get_tokenizer().vocab_size     ,							11_03   )




							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
										__lowerCamelCase          =      self.tokenizer_class.from_pretrained(self.tmpdirname   )
										__lowerCamelCase          =      (
										    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
										    """ </s> <pad> <pad> <pad>"""
										)
										__lowerCamelCase          =      rust_tokenizer([raw_input_str]     ,							return_tensors=A_     ,							add_special_tokens=A_   ).input_ids[0]
										__lowerCamelCase          =      py_tokenizer([raw_input_str]     ,							return_tensors=A_     ,							add_special_tokens=A_   ).input_ids[0]
										self.assertListEqual(A_     ,							A_   )




							def   __a   (     self:    Optional[int]   ):
										__lowerCamelCase          =      self._large_tokenizer
										# <mask_1> masks whole sentence while <mask_2> masks single word
										__lowerCamelCase          =      """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
										__lowerCamelCase          =      [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
										__lowerCamelCase          =      tokenizer([raw_input_str]     ,							return_tensors=A_   ).input_ids[0]
										self.assertListEqual(A_     ,							A_   )




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      self._large_tokenizer
										# The tracebacks for the following asserts are **better** without messages or self.assertEqual
										assert tokenizer.vocab_size == 9_61_03
										assert tokenizer.pad_token_id == 0
										assert tokenizer.eos_token_id == 1
										assert tokenizer.offset == 1_03
										assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
										assert tokenizer.unk_token == "<unk>"
										assert tokenizer.model_max_length == 10_24
										__lowerCamelCase          =      """To ensure a smooth flow of bank resolutions."""
										__lowerCamelCase          =      [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
										__lowerCamelCase          =      tokenizer([raw_input_str]     ,							return_tensors=A_   ).input_ids[0]
										self.assertListEqual(A_     ,							A_   )
										assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




							@require_torch
							def   __a   (     self:    Union[str, Any]   ):
										__lowerCamelCase          =      ["""This is going to be way too long.""" * 1_50, """short example"""]
										__lowerCamelCase          =      ["""not super long but more than 5 tokens""", """tiny"""]
										__lowerCamelCase          =      self._large_tokenizer(A_     ,							padding=A_     ,							truncation=A_     ,							return_tensors="""pt"""   )
										__lowerCamelCase          =      self._large_tokenizer(
										    text_target=A_     ,							max_length=5     ,							padding=A_     ,							truncation=A_     ,							return_tensors="""pt"""   )

										assert batch.input_ids.shape == (2, 10_24)
										assert batch.attention_mask.shape == (2, 10_24)
										assert targets["input_ids"].shape == (2, 5)
										assert len(A_   ) == 2  # input_ids, attention_mask.




							@slow
							def   __a   (     self:    Any   ):
										# fmt: off
										__lowerCamelCase          =      {"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=A_     ,							model_name="""google/bigbird-pegasus-large-arxiv"""     ,							revision="""ba85d0851d708441f91440d509690f1ab6353415"""     ,							)




@require_sentencepiece
@require_tokenizers
class 			__snake_case   (lowerCamelCase			,		unittest.TestCase  ):
							__a					 =       PegasusTokenizer
							__a					 =       PegasusTokenizerFast
							__a					 =       True
							__a					 =       True




							def   __a   (     self:    int   ):
										super().setUp()

										# We have a SentencePiece fixture for testing
										__lowerCamelCase          =      PegasusTokenizer(A_     ,							offset=0     ,							mask_token_sent=A_     ,							mask_token="""[MASK]"""   )
										tokenizer.save_pretrained(self.tmpdirname   )




							@cached_property
							def   __a   (     self:    Optional[Any]   ):
										return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""   )




							def   __a   (     self:    Any     ,							**A_:    Optional[Any]   ):
										return PegasusTokenizer.from_pretrained(self.tmpdirname     ,							**A_   )




							def   __a   (     self:    Optional[int]     ,							A_:    str   ):
										return ("This is a test", "This is a test")




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
										__lowerCamelCase          =      self.tokenizer_class.from_pretrained(self.tmpdirname   )
										__lowerCamelCase          =      (
										    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
										    """ <pad> <pad> <pad>"""
										)
										__lowerCamelCase          =      rust_tokenizer([raw_input_str]     ,							return_tensors=A_     ,							add_special_tokens=A_   ).input_ids[0]
										__lowerCamelCase          =      py_tokenizer([raw_input_str]     ,							return_tensors=A_     ,							add_special_tokens=A_   ).input_ids[0]
										self.assertListEqual(A_     ,							A_   )




							@require_torch
							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      ["""This is going to be way too long.""" * 10_00, """short example"""]
										__lowerCamelCase          =      ["""not super long but more than 5 tokens""", """tiny"""]
										__lowerCamelCase          =      self._large_tokenizer(A_     ,							padding=A_     ,							truncation=A_     ,							return_tensors="""pt"""   )
										__lowerCamelCase          =      self._large_tokenizer(
										    text_target=A_     ,							max_length=5     ,							padding=A_     ,							truncation=A_     ,							return_tensors="""pt"""   )

										assert batch.input_ids.shape == (2, 40_96)
										assert batch.attention_mask.shape == (2, 40_96)
										assert targets["input_ids"].shape == (2, 5)
										assert len(A_   ) == 2  # input_ids, attention_mask.




							def   __a   (     self:    str   ):
										__lowerCamelCase          =      (
										    """This is an example string that is used to test the original TF implementation against the HF"""
										    """ implementation"""
										)

										__lowerCamelCase          =      self._large_tokenizer(A_   ).input_ids

										self.assertListEqual(
										    A_     ,							[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]     ,							)








"""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_   )   )




