





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


A       :   Any					= get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')


@require_sentencepiece
@require_tokenizers
class     A     (_A  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			Any							     =       PegasusTokenizer
			__lowerCamelCase      :			Any							     =       PegasusTokenizerFast
			__lowerCamelCase      :			int							     =       True
			__lowerCamelCase      :			Union[str, Any]							     =       True






			def   a_			(     self  :       Tuple  )      ->      List[Any]:







					"""simple docstring"""






					super().setUp()

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






			@cached_property
			def   a_			(     self  :       Union[str, Any]  )      ->      int:







					"""simple docstring"""






					return PegasusTokenizer.from_pretrained("""google/pegasus-large"""  )






			def   a_			(     self  :       Union[str, Any]				,      **__lowerCAmelCase  :       Any  )      ->      PegasusTokenizer:







					"""simple docstring"""






					return PegasusTokenizer.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       str				,      __lowerCAmelCase  :       Optional[Any]  )      ->      List[Any]:







					"""simple docstring"""






					return ("This is a test", "This is a test")






			def   a_			(     self  :       Dict  )      ->      Dict:







					"""simple docstring"""






					A__     =  """</s>"""
					A__     =  1

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






			def   a_			(     self  :       Optional[Any]  )      ->      List[str]:







					"""simple docstring"""






					A__     =  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(__lowerCAmelCase  )				,      11_03  )






			def   a_			(     self  :       Optional[int]  )      ->      Union[str, Any]:







					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size				,      11_03  )






			def   a_			(     self  :       Any  )      ->      int:







					"""simple docstring"""






					A__     =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
					A__     =  self.tokenizer_class.from_pretrained(self.tmpdirname  )
					A__     =  (
					    """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>"""
					)
					A__     =  rust_tokenizer([raw_input_str]				,      return_tensors=__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  ).input_ids[0]
					A__     =  py_tokenizer([raw_input_str]				,      return_tensors=__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  ).input_ids[0]
					self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )






			def   a_			(     self  :       List[str]  )      ->      Optional[Any]:







					"""simple docstring"""






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






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







					"""simple docstring"""






					A__     =  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
					A__     =  """To ensure a smooth flow of bank resolutions."""
					A__     =  [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
					A__     =  tokenizer([raw_input_str]				,      return_tensors=__lowerCAmelCase  ).input_ids[0]
					self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )
					assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






			@require_torch
			def   a_			(     self  :       Dict  )      ->      Any:







					"""simple docstring"""






					A__     =  ["""This is going to be way too long.""" * 1_50, """short example"""]
					A__     =  ["""not super long but more than 5 tokens""", """tiny"""]
					A__     =  self._large_tokenizer(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      truncation=__lowerCAmelCase				,      return_tensors="""pt"""  )
					A__     =  self._large_tokenizer(
					    text_target=__lowerCAmelCase				,      max_length=5				,      padding=__lowerCAmelCase				,      truncation=__lowerCAmelCase				,      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(__lowerCAmelCase  ) == 2  # input_ids, attention_mask.







			@slow
			def   a_			(     self  :       Union[str, Any]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  {"""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=__lowerCAmelCase				,      model_name="""google/bigbird-pegasus-large-arxiv"""				,      revision="""ba85d0851d708441f91440d509690f1ab6353415"""				,      )


@require_sentencepiece
@require_tokenizers
class     A     (_A  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			List[str]							     =       PegasusTokenizer
			__lowerCamelCase      :			Dict							     =       PegasusTokenizerFast
			__lowerCamelCase      :			Optional[int]							     =       True
			__lowerCamelCase      :			Any							     =       True






			def   a_			(     self  :       int  )      ->      Any:







					"""simple docstring"""






					super().setUp()

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






			@cached_property
			def   a_			(     self  :       Dict  )      ->      str:







					"""simple docstring"""






					return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""  )






			def   a_			(     self  :       Any				,      **__lowerCAmelCase  :       Tuple  )      ->      PegasusTokenizer:







					"""simple docstring"""






					return PegasusTokenizer.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Union[str, Any]  )      ->      Optional[int]:







					"""simple docstring"""






					return ("This is a test", "This is a test")






			def   a_			(     self  :       Any  )      ->      Optional[Any]:







					"""simple docstring"""






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






			@require_torch
			def   a_			(     self  :       List[Any]  )      ->      str:







					"""simple docstring"""






					A__     =  ["""This is going to be way too long.""" * 10_00, """short example"""]
					A__     =  ["""not super long but more than 5 tokens""", """tiny"""]
					A__     =  self._large_tokenizer(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      truncation=__lowerCAmelCase				,      return_tensors="""pt"""  )
					A__     =  self._large_tokenizer(
					    text_target=__lowerCAmelCase				,      max_length=5				,      padding=__lowerCAmelCase				,      truncation=__lowerCAmelCase				,      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(__lowerCAmelCase  ) == 2  # input_ids, attention_mask.







			def   a_			(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  (
					    """This is an example string that is used to test the original TF implementation against the HF"""
					    """ implementation"""
					)

					A__     =  self._large_tokenizer(__lowerCAmelCase  ).input_ids

					self.assertListEqual(
					    __lowerCAmelCase				,      [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]				,      )








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 DeformableDetrImageProcessor


class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Dict=7			,						__lowerCAmelCase					:					Tuple=3			,						__lowerCAmelCase					:					int=30			,						__lowerCAmelCase					:					Dict=4_00			,						__lowerCAmelCase					:					Optional[Any]=True			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Optional[Any]=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					Dict=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					List[str]=True			,						__lowerCAmelCase					:					List[str]=1 / 2_55			,						__lowerCAmelCase					:					int=True			,						)   ->	List[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     snake_case_      (    self					:					Optional[int]      )   ->	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     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					str=False      )   ->	Dict:
    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 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Any =  DeformableDetrImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					Optional[int]      )   ->	Any:
    _A      =       DeformableDetrImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    _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     snake_case_      (    self					:					List[str]      )   ->	int:
    _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      )

    _A      =       self.image_processing_class.from_dict(
        self.image_processor_dict			,						size=42			,						max_size=84			,						pad_and_return_pixel_mask=__lowerCAmelCase      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 42, '''longest_edge''': 84}      )
    self.assertEqual(image_processor.do_pad			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[int]:
    # 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     snake_case_      (    self					:					Tuple      )   ->	int:
    # 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     snake_case_      (    self					:					Optional[Any]      )   ->	int:
    # 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     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    # 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      =       DeformableDetrImageProcessor()
    _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([0.2796, 0.3138, 0.3481]      )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )

    # verify area
    _A      =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
    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([0.5503, 0.2765, 0.0604, 0.2215]      )
    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     snake_case_      (    self					:					List[str]      )   ->	List[str]:
    # 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      =       DeformableDetrImageProcessor(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([0.2796, 0.3138, 0.3481]      )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )

    # verify area
    _A      =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]      )
    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([0.2625, 0.5437, 0.4688, 0.8625]      )
    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      )      )



