






'''simple docstring'''

import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


SCREAMING_SNAKE_CASE__     =       '▁'

SCREAMING_SNAKE_CASE__     =       get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class     a_						(      lowerCamelCase     ,	unittest.TestCase     ):
  lowercase							=				BigBirdTokenizer
  lowercase							=				BigBirdTokenizerFast
  lowercase							=				True
  lowercase							=				True

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        super().setUp()

        UpperCamelCase      = self.tokenizer_class(_SCREAMING_SNAKE_CASE      ,		keep_accents=_SCREAMING_SNAKE_CASE				)
        tokenizer.save_pretrained(self.tmpdirname				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = """<s>"""
        UpperCamelCase      = 1

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

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = list(self.get_tokenizer().get_vocab().keys()				)

        self.assertEqual(vocab_keys[0]      ,		"""<unk>"""				)
        self.assertEqual(vocab_keys[1]      ,		"""<s>"""				)
        self.assertEqual(vocab_keys[-1]      ,		"""[MASK]"""				)
        self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		1004				)

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



        """simple docstring"""






        self.assertEqual(self.get_tokenizer().vocab_size      ,		1000				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        if not self.test_rust_tokenizer:
              return

        UpperCamelCase      = self.get_tokenizer()
        UpperCamelCase      = self.get_rust_tokenizer()

        UpperCamelCase      = """I was born in 92000, and this is falsé."""

        UpperCamelCase      = tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = tokenizer.encode(_SCREAMING_SNAKE_CASE      ,		add_special_tokens=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE      ,		add_special_tokens=_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = self.get_rust_tokenizer()
        UpperCamelCase      = tokenizer.encode(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = rust_tokenizer.encode(_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = BigBirdTokenizer(_SCREAMING_SNAKE_CASE      ,		keep_accents=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = tokenizer.tokenize("""This is a test"""				)
        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)      ,		[285, 46, 10, 170, 382]      ,		)

        UpperCamelCase      = tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
        self.assertListEqual(
            _SCREAMING_SNAKE_CASE      ,		[
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """9""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """é""",
                """.""",
            ]      ,		)
        UpperCamelCase      = tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(
            _SCREAMING_SNAKE_CASE      ,		[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,		)

        UpperCamelCase      = tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(
            _SCREAMING_SNAKE_CASE      ,		[
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """<unk>""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """<unk>""",
                """.""",
            ]      ,		)

  @cached_property
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""				)

  @slow
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = """Hello World!"""
        UpperCamelCase      = [65, 18536, 2260, 101, 66]

        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE				)				)

  @slow
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = (
            """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
            """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
        )
        # fmt: off
        UpperCamelCase      = [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
        # fmt: on
        self.assertListEqual(_SCREAMING_SNAKE_CASE      ,		self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE				)				)

  @require_torch
  @slow
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        import torch

        from transformers import BigBirdConfig, BigBirdModel

        # Build sequence
        UpperCamelCase      = list(self.big_tokenizer.get_vocab().keys()				)[:10]
        UpperCamelCase      = """ """.join(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = self.big_tokenizer.encode_plus(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""      ,		return_token_type_ids=_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = self.big_tokenizer.batch_encode_plus(
            [sequence + """ """ + sequence]      ,		return_tensors="""pt"""      ,		return_token_type_ids=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = BigBirdConfig(attention_type="""original_full"""				)
        UpperCamelCase      = BigBirdModel(_SCREAMING_SNAKE_CASE				)

        assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

        with torch.no_grad():
              model(**_SCREAMING_SNAKE_CASE				)
              model(**_SCREAMING_SNAKE_CASE				)

  @slow
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""				)
        UpperCamelCase      = tokenizer.decode(tokenizer("""Paris is the [MASK]."""				).input_ids				)

        self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""				)


  @slow
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = {"""input_ids""": [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 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], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 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=_SCREAMING_SNAKE_CASE      ,		model_name="""google/bigbird-roberta-base"""      ,		revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""      ,		)








'''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 ConditionalDetrImageProcessor






class     a_						(      unittest.TestCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=30      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=1 / 255      ,		_SCREAMING_SNAKE_CASE=True      ,		)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = min_resolution
        UpperCamelCase      = max_resolution
        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = do_normalize
        UpperCamelCase      = image_mean
        UpperCamelCase      = image_std
        UpperCamelCase      = do_rescale
        UpperCamelCase      = rescale_factor
        UpperCamelCase      = do_pad

  def   A__						(				self				)  ->				str:



        """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      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Optional[int]:



        """simple docstring"""






        if not batched:
              UpperCamelCase      = image_inputs[0]
              if isinstance(_SCREAMING_SNAKE_CASE      ,		Image.Image				):
                    UpperCamelCase  ,UpperCamelCase      = image.size
              else:
                    UpperCamelCase  ,UpperCamelCase      = image.shape[1], image.shape[2]
              if w < h:
                    UpperCamelCase      = int(self.size["""shortest_edge"""] * h / w				)
                    UpperCamelCase      = self.size["""shortest_edge"""]
              elif w > h:
                    UpperCamelCase      = self.size["""shortest_edge"""]
                    UpperCamelCase      = int(self.size["""shortest_edge"""] * w / h				)
              else:
                    UpperCamelCase      = self.size["""shortest_edge"""]
                    UpperCamelCase      = self.size["""shortest_edge"""]

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

        return expected_height, expected_width






@require_torch
@require_vision
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				ConditionalDetrImageProcessor if is_vision_available() else None

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



        """simple docstring"""






        UpperCamelCase      = ConditionalDetrImageProcessingTester(self				)

  @property
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_mean"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_std"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_normalize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_resize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""size"""				)				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size      ,		{"""shortest_edge""": 18, """longest_edge""": 1333}				)
        self.assertEqual(image_processor.do_pad      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = self.image_processing_class.from_dict(
            self.image_processor_dict      ,		size=42      ,		max_size=84      ,		pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE				)
        self.assertEqual(image_processor.size      ,		{"""shortest_edge""": 42, """longest_edge""": 84}				)
        self.assertEqual(image_processor.do_pad      ,		_SCREAMING_SNAKE_CASE				)

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



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






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

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = image_processing(_SCREAMING_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				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        UpperCamelCase      = prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE      ,		numpify=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		np.ndarray				)

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_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				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        UpperCamelCase      = prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE      ,		torchify=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		torch.Tensor				)

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

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_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				)  ->				Any:



        """simple docstring"""






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

        UpperCamelCase      = {"""image_id""": 39769, """annotations""": target}

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([75, 75, 63, 65, 17, 17]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)


  @slow
  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






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

        UpperCamelCase      = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

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

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor(format="""coco_panoptic"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		masks_path=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([17, 17, 63, 75, 75, 93]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify masks
        UpperCamelCase      = 822873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,		_SCREAMING_SNAKE_CASE				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
