'''simple docstring'''




import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor

__SCREAMING_SNAKE_CASE       :     Tuple     =  random.Random()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[str, Any]			,	_UpperCAmelCase  :      Any=1.0			,	_UpperCAmelCase  :      Optional[Any]=None			,	_UpperCAmelCase  :      List[str]=None    )    ->    str:


       """simple docstring"""







       if rng is None:
              _UpperCAmelCase     :  List[Any]							      =    global_rng

       _UpperCAmelCase     :  Optional[Any]							      =    []
       for batch_idx in range(shape[0]    ):
              values.append([]    )
              for _ in range(shape[1]    ):
                     values[-1].append(rng.random() * scale    )

       return values

@require_torch
@require_torchaudio
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				int						,					A						:				str						,					A						:				Dict=7						,					A						:				List[Any]=400						,					A						:				Union[str, Any]=2000						,					A						:				str=24						,					A						:				Optional[Any]=24						,					A						:				Optional[Any]=0.0						,					A						:				Optional[int]=16000						,					A						:				str=True						,					A						:				Optional[Any]=True						,					):
             _UpperCAmelCase     :  Optional[int]							      =    parent
             _UpperCAmelCase     :  int							      =    batch_size
             _UpperCAmelCase     :  Tuple							      =    min_seq_length
             _UpperCAmelCase     :  List[str]							      =    max_seq_length
             _UpperCAmelCase     :  Tuple							      =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             _UpperCAmelCase     :  Dict							      =    feature_size
             _UpperCAmelCase     :  List[Any]							      =    num_mel_bins
             _UpperCAmelCase     :  Union[str, Any]							      =    padding_value
             _UpperCAmelCase     :  Optional[Any]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    return_attention_mask
             _UpperCAmelCase     :  Tuple							      =    do_normalize




      def      _A	(       self						:				Union[str, Any]       ):
             return {
                 "feature_size": self.feature_size,
                 "num_mel_bins": self.num_mel_bins,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
                 "do_normalize": self.do_normalize,
             }





      def      _A	(       self						:				str						,					A						:				Tuple=False						,					A						:				Any=False       ):
             def _flatten(A						:				Optional[Any]       ):
                    return list(itertools.chain(*A       )       )

             if equal_length:
                    _UpperCAmelCase     :  List[Any]							      =    [floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
             else:
                    # make sure that inputs increase in size
                    _UpperCAmelCase     :  Dict							      =    [
                        floats_list((x, self.feature_size)       )
                        for x in range(self.min_seq_length						,					self.max_seq_length						,					self.seq_length_diff       )
                    ]
             if numpify:
                    _UpperCAmelCase     :  Optional[int]							      =    [np.asarray(A       ) for x in speech_inputs]
             return speech_inputs

@require_torch
@require_torchaudio
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						SpeechaTextFeatureExtractor if is_speech_available() else None




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Optional[Any]							      =    SpeechaTextFeatureExtractionTester(self       )




      def      _A	(       self						:				Optional[Any]						,					A						:				Any       ):
             self.assertTrue(np.all(np.mean(A						,					axis=0       ) < 1E-3       )       )
             self.assertTrue(np.all(np.abs(np.var(A						,					axis=0       ) - 1       ) < 1E-3       )       )




      def      _A	(       self						:				Any       ):
             # Tests that all call wrap to encode_plus and batch_encode_plus
             _UpperCAmelCase     :  str							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             # create three inputs of length 800, 1000, and 1200
             _UpperCAmelCase     :  Optional[Any]							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]
             _UpperCAmelCase     :  int							      =    [np.asarray(A       ) for speech_input in speech_inputs]

             # Test feature size
             _UpperCAmelCase     :  Tuple							      =    feature_extractor(A						,					padding=A						,					return_tensors="np"       ).input_features
             self.assertTrue(input_features.ndim == 3       )
             self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[Any]							      =    feature_extractor(speech_inputs[0]						,					return_tensors="np"       ).input_features
             _UpperCAmelCase     :  str							      =    feature_extractor(np_speech_inputs[0]						,					return_tensors="np"       ).input_features
             self.assertTrue(np.allclose(A						,					A						,					atol=1E-3       )       )

             # Test batched
             _UpperCAmelCase     :  List[str]							      =    feature_extractor(A						,					return_tensors="np"       ).input_features
             _UpperCAmelCase     :  List[str]							      =    feature_extractor(A						,					return_tensors="np"       ).input_features
             for enc_seq_a, enc_seq_a in zip(A						,					A       ):
                    self.assertTrue(np.allclose(A						,					A						,					atol=1E-3       )       )

             # Test 2-D numpy arrays are batched.
             _UpperCAmelCase     :  str							      =    [floats_list((1, x)       )[0] for x in (800, 800, 800)]
             _UpperCAmelCase     :  Tuple							      =    np.asarray(A       )
             _UpperCAmelCase     :  Union[str, Any]							      =    feature_extractor(A						,					return_tensors="np"       ).input_features
             _UpperCAmelCase     :  Any							      =    feature_extractor(A						,					return_tensors="np"       ).input_features
             for enc_seq_a, enc_seq_a in zip(A						,					A       ):
                    self.assertTrue(np.allclose(A						,					A						,					atol=1E-3       )       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  str							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]

             _UpperCAmelCase     :  Tuple							      =    ["longest", "max_length", "do_not_pad"]
             _UpperCAmelCase     :  int							      =    [None, 16, None]
             for max_length, padding in zip(A						,					A       ):
                    _UpperCAmelCase     :  str							      =    feature_extractor(
                        A						,					padding=A						,					max_length=A						,					return_attention_mask=A       )
                    _UpperCAmelCase     :  int							      =    inputs.input_features
                    _UpperCAmelCase     :  Any							      =    inputs.attention_mask
                    _UpperCAmelCase     :  Dict							      =    [np.sum(A       ) for x in attention_mask]

                    self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]       )
                    self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]       )
                    self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  Dict							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]

             _UpperCAmelCase     :  Optional[int]							      =    ["longest", "max_length", "do_not_pad"]
             _UpperCAmelCase     :  Optional[int]							      =    [None, 16, None]
             for max_length, padding in zip(A						,					A       ):
                    _UpperCAmelCase     :  List[Any]							      =    feature_extractor(
                        A						,					max_length=A						,					padding=A						,					return_tensors="np"						,					return_attention_mask=A       )
                    _UpperCAmelCase     :  Dict							      =    inputs.input_features
                    _UpperCAmelCase     :  Dict							      =    inputs.attention_mask
                    _UpperCAmelCase     :  str							      =    [np.sum(A       ) for x in attention_mask]

                    self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]       )
                    self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6       )
                    self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]       )
                    self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6       )
                    self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  int							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  Any							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]
             _UpperCAmelCase     :  Optional[int]							      =    feature_extractor(
                 A						,					padding="max_length"						,					max_length=4						,					truncation=A						,					return_tensors="np"						,					return_attention_mask=A						,					)
             _UpperCAmelCase     :  List[Any]							      =    inputs.input_features
             _UpperCAmelCase     :  List[Any]							      =    inputs.attention_mask
             _UpperCAmelCase     :  List[str]							      =    np.sum(attention_mask == 1						,					axis=1       )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
             self._check_zero_mean_unit_variance(input_features[1]       )
             self._check_zero_mean_unit_variance(input_features[2]       )




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  int							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  Union[str, Any]							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]
             _UpperCAmelCase     :  Optional[int]							      =    feature_extractor(
                 A						,					padding="longest"						,					max_length=4						,					truncation=A						,					return_tensors="np"						,					return_attention_mask=A						,					)
             _UpperCAmelCase     :  Dict							      =    inputs.input_features
             _UpperCAmelCase     :  Tuple							      =    inputs.attention_mask
             _UpperCAmelCase     :  List[Any]							      =    np.sum(attention_mask == 1						,					axis=1       )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
             self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]       )
             self._check_zero_mean_unit_variance(input_features[2]       )

             # make sure that if max_length < longest -> then pad to max_length
             self.assertEqual(input_features.shape						,					(3, 4, 24)       )

             _UpperCAmelCase     :  Tuple							      =    [floats_list((1, x)       )[0] for x in range(800						,					1400						,					200       )]
             _UpperCAmelCase     :  Dict							      =    feature_extractor(
                 A						,					padding="longest"						,					max_length=16						,					truncation=A						,					return_tensors="np"						,					return_attention_mask=A						,					)
             _UpperCAmelCase     :  int							      =    inputs.input_features
             _UpperCAmelCase     :  Tuple							      =    inputs.attention_mask
             _UpperCAmelCase     :  Any							      =    np.sum(attention_mask == 1						,					axis=1       )

             self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]       )
             self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]       )
             self._check_zero_mean_unit_variance(input_features[2]       )

             # make sure that if max_length < longest -> then pad to max_length
             self.assertEqual(input_features.shape						,					(3, 6, 24)       )




      def      _A	(       self						:				Optional[int]       ):
             import torch

             _UpperCAmelCase     :  List[str]							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  str							      =    np.random.rand(100						,					32       ).astype(np.floataa       )
             _UpperCAmelCase     :  Dict							      =    np_speech_inputs.tolist()

             for inputs in [py_speech_inputs, np_speech_inputs]:
                    _UpperCAmelCase     :  Tuple							      =    feature_extractor.pad([{"input_features": inputs}]						,					return_tensors="np"       )
                    self.assertTrue(np_processed.input_features.dtype == np.floataa       )
                    _UpperCAmelCase     :  List[str]							      =    feature_extractor.pad([{"input_features": inputs}]						,					return_tensors="pt"       )
                    self.assertTrue(pt_processed.input_features.dtype == torch.floataa       )




      def      _A	(       self						:				Dict						,					A						:				List[str]       ):
             from datasets import load_dataset

             _UpperCAmelCase     :  List[str]							      =    load_dataset("hf-internal-testing/librispeech_asr_dummy"						,					"clean"						,					split="validation"       )
             # automatic decoding with librispeech
             _UpperCAmelCase     :  Dict							      =    ds.sort("id"       ).select(range(A       )       )[:num_samples]["audio"]

             return [x["array"] for x in speech_samples]





      def      _A	(       self						:				str       ):
             # fmt: off
             _UpperCAmelCase     :  str							      =    np.array([
                 -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
                 -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
                 -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
             ]       )
             # fmt: on

             _UpperCAmelCase     :  int							      =    self._load_datasamples(1       )
             _UpperCAmelCase     :  Tuple							      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
             _UpperCAmelCase     :  Optional[int]							      =    feature_extractor(A						,					return_tensors="pt"       ).input_features
             self.assertEquals(input_features.shape						,					(1, 584, 24)       )
             self.assertTrue(np.allclose(input_features[0, 0, :30]						,					A						,					atol=1E-4       )       )







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 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def __init__(			self      :	List[str]					, UpperCAmelCase__      :	Optional[int]					, UpperCAmelCase__      :	int=7					, UpperCAmelCase__      :	Dict=3					, UpperCAmelCase__      :	List[Any]=30					, UpperCAmelCase__      :	Any=400					, UpperCAmelCase__      :	Optional[Any]=True					, UpperCAmelCase__      :	List[str]=None					, UpperCAmelCase__      :	Any=True					, UpperCAmelCase__      :	Optional[Any]=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	Any=[0.5, 0.5, 0.5]					, UpperCAmelCase__      :	List[str]=True					, UpperCAmelCase__      :	Optional[int]=1 / 255					, UpperCAmelCase__      :	Optional[Any]=True					, )						->str:





           '''simple docstring'''

           A__       =    size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           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        SCREAMING_SNAKE_CASE							(			self      :	Any)						->List[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        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	List[str]					, UpperCAmelCase__      :	int=False)						->Optional[Any]:





           '''simple docstring'''

           if not batched:
                  A__       =    image_inputs[0]
                  if isinstance(UpperCAmelCase__					, 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(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[0])[0]
                  A__       =    max(UpperCAmelCase__					, key=lambda UpperCAmelCase__: item[1])[1]

           return expected_height, expected_width


@require_torch
@require_vision
class 				UpperCamelCase_    ( UpperCAmelCase__		,					unittest.TestCase     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      DeformableDetrImageProcessor if is_vision_available() else None


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->Tuple:





           '''simple docstring'''

           A__       =    DeformableDetrImageProcessingTester(self)


    @property
    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Any:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str])						->List[str]:





           '''simple docstring'''

           A__       =    self.image_processing_class(**self.image_processor_dict)
           self.assertTrue(hasattr(UpperCAmelCase__					, '''image_mean'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''image_std'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_normalize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_resize'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_rescale'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''do_pad'''))
           self.assertTrue(hasattr(UpperCAmelCase__					, '''size'''))


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





           '''simple docstring'''

           A__       =    self.image_processing_class.from_dict(self.image_processor_dict)
           self.assertEqual(image_processor.size					, {'''shortest_edge''': 18, '''longest_edge''': 1_333})
           self.assertEqual(image_processor.do_pad					, UpperCAmelCase__)

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


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





           '''simple docstring'''

           pass


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->Dict:





           '''simple docstring'''

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

           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(UpperCAmelCase__					, batched=UpperCAmelCase__)

           A__       =    image_processing(UpperCAmelCase__					, 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        SCREAMING_SNAKE_CASE							(			self      :	int)						->Optional[int]:





           '''simple docstring'''

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

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

           # Test batched
           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__					, batched=UpperCAmelCase__)

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


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Tuple:





           '''simple docstring'''

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

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

           # Test batched
           A__       =    image_processing(UpperCAmelCase__					, return_tensors='''pt''').pixel_values

           A__ ,							A__       =    self.image_processor_tester.get_expected_values(UpperCAmelCase__					, batched=UpperCAmelCase__)

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


    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->List[str]:





           '''simple docstring'''

           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''': 39_769, '''annotations''': target}

           # encode them
           A__       =    DeformableDetrImageProcessor()
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

           A__       =    torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					, UpperCAmelCase__					, atol=1e-4))

           # verify area
           A__       =    torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))





    @slow
    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->Optional[int]:





           '''simple docstring'''

           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''': 39_769, '''segments_info''': target}

           A__       =    pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

           # encode them
           A__       =    DeformableDetrImageProcessor(format='''coco_panoptic''')
           A__       =    image_processing(images=UpperCAmelCase__					, annotations=UpperCAmelCase__					, masks_path=UpperCAmelCase__					, return_tensors='''pt''')

           # verify pixel values
           A__       =    torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape					, UpperCAmelCase__)

           A__       =    torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]					, UpperCAmelCase__					, atol=1e-4))

           # verify area
           A__       =    torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']					, UpperCAmelCase__))
           # verify boxes
           A__       =    torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape					, UpperCAmelCase__)
           A__       =    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]					, UpperCAmelCase__					, atol=1e-3))
           # verify image_id
           A__       =    torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']					, UpperCAmelCase__))
           # verify is_crowd
           A__       =    torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']					, UpperCAmelCase__))
           # verify class_labels
           A__       =    torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']					, UpperCAmelCase__))
           # verify masks
           A__       =    822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()					, UpperCAmelCase__)
           # verify orig_size
           A__       =    torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']					, UpperCAmelCase__))
           # verify size
           A__       =    torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']					, UpperCAmelCase__))


