




import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCAmelCase_		:				str									=     logging.get_logger(__name__)




class 		__A      (			UpperCamelCase__			):

						'''simple docstring'''





						UpperCamelCase     						=					["""input_values""", """attention_mask"""]

						def __init__(		self   :str    , __snake_case   :int = 1    , __snake_case   :int = 1_60_00    , __snake_case   :float = 0.0    , __snake_case   :bool = False    , __snake_case   :int = 80    , __snake_case   :int = 16    , __snake_case   :int = 64    , __snake_case   :str = "hann_window"    , __snake_case   :float = 1.0    , __snake_case   :float = 80    , __snake_case   :float = 76_00    , __snake_case   :float = 1E-10    , __snake_case   :int = 2    , __snake_case   :bool = True    , **__snake_case   :List[Any]    , ):
										'''simple docstring'''

										super().__init__(feature_size=__snake_case    , sampling_rate=__snake_case    , padding_value=__snake_case    , **__snake_case )
										__magic_name__	:	Optional[int]								=do_normalize
										__magic_name__	:	Any								=return_attention_mask

										__magic_name__	:	List[Any]								=num_mel_bins
										__magic_name__	:	str								=hop_length
										__magic_name__	:	str								=win_length
										__magic_name__	:	List[Any]								=win_function
										__magic_name__	:	Optional[int]								=frame_signal_scale
										__magic_name__	:	List[str]								=fmin
										__magic_name__	:	int								=fmax
										__magic_name__	:	Optional[int]								=mel_floor
										__magic_name__	:	Any								=reduction_factor

										__magic_name__	:	int								=win_length * sampling_rate // 10_00
										__magic_name__	:	List[str]								=hop_length * sampling_rate // 10_00
										__magic_name__	:	Tuple								=optimal_fft_length(self.sample_size )
										__magic_name__	:	List[Any]								=(self.n_fft // 2) + 1

										__magic_name__	:	int								=window_function(window_length=self.sample_size    , name=self.win_function    , periodic=__snake_case )

										__magic_name__	:	Union[str, Any]								=mel_filter_bank(
										    num_frequency_bins=self.n_freqs    , num_mel_filters=self.num_mel_bins    , min_frequency=self.fmin    , max_frequency=self.fmax    , sampling_rate=self.sampling_rate    , norm="""slaney"""    , mel_scale="""slaney"""    , )

										if frame_signal_scale != 1.0:
														warnings.warn(
														    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""    , __snake_case    , )
										if reduction_factor != 2.0:
														warnings.warn(
														    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""    , __snake_case    , )

						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def     A__						(		__snake_case   :List[np.ndarray]    , __snake_case   :List[np.ndarray]    , __snake_case   :float = 0.0 ):
										'''simple docstring'''

										if attention_mask is not None:
														__magic_name__	:	Tuple								=np.array(__snake_case    , np.intaa )
														__magic_name__	:	Optional[int]								=[]

														for vector, length in zip(__snake_case    , attention_mask.sum(-1 ) ):
																		__magic_name__	:	Union[str, Any]								=(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
																		if length < normed_slice.shape[0]:
																						__magic_name__	:	str								=padding_value

																		normed_input_values.append(__snake_case )
										else:
														__magic_name__	:	Any								=[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

										return normed_input_values

						def     A__						(		self   :Dict    , __snake_case   :np.ndarray    , ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=spectrogram(
										    __snake_case    , window=self.window    , frame_length=self.sample_size    , hop_length=self.sample_stride    , fft_length=self.n_fft    , mel_filters=self.mel_filters    , mel_floor=self.mel_floor    , log_mel="""log10"""    , )
										return log_mel_spec.T

						def __call__(		self   :int    , __snake_case   :Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None    , __snake_case   :Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None    , __snake_case   :Union[bool, str, PaddingStrategy] = False    , __snake_case   :Optional[int] = None    , __snake_case   :bool = False    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[Union[str, TensorType]] = None    , __snake_case   :Optional[int] = None    , **__snake_case   :Any    , ):
										'''simple docstring'''

										if audio is None and audio_target is None:
														raise ValueError("""You must provide either `audio` or `audio_target` values.""" )

										if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																		raise ValueError(
																		    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
																		    f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"
																		    f" {self.sampling_rate} and not {sampling_rate}." )
										else:
														logger.warning(
														    """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
														    """Failing to do so can result in silent errors that might be hard to debug.""" )

										if audio is not None:
														__magic_name__	:	Union[str, Any]								=self._process_audio(
														    __snake_case    , __snake_case    , __snake_case    , __snake_case    , __snake_case    , __snake_case    , __snake_case    , __snake_case    , **__snake_case    , )
										else:
														__magic_name__	:	List[str]								=None

										if audio_target is not None:
														__magic_name__	:	Any								=self._process_audio(
														    __snake_case    , __snake_case    , __snake_case    , __snake_case    , __snake_case    , __snake_case    , __snake_case    , __snake_case    , **__snake_case    , )

														if inputs is None:
																		return inputs_target
														else:
																		__magic_name__	:	List[str]								=inputs_target["""input_values"""]
																		__magic_name__	:	List[str]								=inputs_target.get("""attention_mask""" )
																		if decoder_attention_mask is not None:
																						__magic_name__	:	str								=decoder_attention_mask

										return inputs

						def     A__						(		self   :List[str]    , __snake_case   :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    , __snake_case   :bool = False    , __snake_case   :Union[bool, str, PaddingStrategy] = False    , __snake_case   :Optional[int] = None    , __snake_case   :bool = False    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[Union[str, TensorType]] = None    , **__snake_case   :Union[str, Any]    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=isinstance(__snake_case    , np.ndarray ) and len(speech.shape ) > 1
										if is_batched_numpy and len(speech.shape ) > 2:
														raise ValueError(f"Only mono-channel audio is supported for input to {self}" )
										__magic_name__	:	Optional[int]								=is_batched_numpy or (
										    isinstance(__snake_case    , (list, tuple) ) and (isinstance(speech[0]    , (np.ndarray, tuple, list) ))
										)

										if is_batched:
														__magic_name__	:	Any								=[np.asarray(__snake_case    , dtype=np.floataa ) for speech in speech]
										elif not is_batched and not isinstance(__snake_case    , np.ndarray ):
														__magic_name__	:	Dict								=np.asarray(__snake_case    , dtype=np.floataa )
										elif isinstance(__snake_case    , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
														__magic_name__	:	List[str]								=speech.astype(np.floataa )

										# always return batch
										if not is_batched:
														__magic_name__	:	int								=[speech]

										# needed to make pad() work on spectrogram inputs
										__magic_name__	:	List[str]								=self.feature_size

										# convert into correct format for padding
										if is_target:
														__magic_name__	:	Optional[int]								=[self._extract_mel_features(__snake_case ) for waveform in speech]
														__magic_name__	:	Optional[int]								=BatchFeature({"""input_values""": features} )
														__magic_name__	:	List[str]								=self.num_mel_bins
										else:
														__magic_name__	:	int								=BatchFeature({"""input_values""": speech} )

										__magic_name__	:	List[str]								=self.pad(
										    __snake_case    , padding=__snake_case    , max_length=__snake_case    , truncation=__snake_case    , pad_to_multiple_of=__snake_case    , return_attention_mask=__snake_case    , **__snake_case    , )

										__magic_name__	:	Optional[int]								=feature_size_hack

										# convert input values to correct format
										__magic_name__	:	Tuple								=padded_inputs["""input_values"""]
										if not isinstance(input_values[0]    , np.ndarray ):
														__magic_name__	:	Optional[Any]								=[np.asarray(__snake_case    , dtype=np.floataa ) for array in input_values]
										elif (
										    not isinstance(__snake_case    , np.ndarray )
										    and isinstance(input_values[0]    , np.ndarray )
										    and input_values[0].dtype is np.dtype(np.floataa )
										):
														__magic_name__	:	Union[str, Any]								=[array.astype(np.floataa ) for array in input_values]
										elif isinstance(__snake_case    , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
														__magic_name__	:	Any								=input_values.astype(np.floataa )

										# convert attention_mask to correct format
										__magic_name__	:	Optional[Any]								=padded_inputs.get("""attention_mask""" )
										if attention_mask is not None:
														__magic_name__	:	str								=[np.asarray(__snake_case    , dtype=np.intaa ) for array in attention_mask]

										# zero-mean and unit-variance normalization
										if not is_target and self.do_normalize:
														__magic_name__	:	Optional[Any]								=(
														    attention_mask
														    if self._get_padding_strategies(__snake_case    , max_length=__snake_case ) is not PaddingStrategy.DO_NOT_PAD
														    else None
														)
														__magic_name__	:	Dict								=self.zero_mean_unit_var_norm(
														    padded_inputs["""input_values"""]    , attention_mask=__snake_case    , padding_value=self.padding_value )

										if return_tensors is not None:
														__magic_name__	:	Any								=padded_inputs.convert_to_tensors(__snake_case )

										return padded_inputs


						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Dict								=super().to_dict()

										# Don't serialize these as they are derived from the other properties.
										__magic_name__	:	Optional[Any]								=["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
										for name in names:
														if name in output:
																		del output[name]

										return output










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 		__A      (			unittest.TestCase			):

						def __init__(		self   :List[str]    , __snake_case   :str    , __snake_case   :List[Any]=7    , __snake_case   :List[str]=3    , __snake_case   :int=30    , __snake_case   :int=4_00    , __snake_case   :List[str]=True    , __snake_case   :List[str]=None    , __snake_case   :Tuple=True    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Any=[0.5, 0.5, 0.5]    , __snake_case   :Union[str, Any]=True    , __snake_case   :str=1 / 2_55    , __snake_case   :int=True    , ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
										__magic_name__	:	str								=parent
										__magic_name__	:	Tuple								=batch_size
										__magic_name__	:	Optional[Any]								=num_channels
										__magic_name__	:	Optional[int]								=min_resolution
										__magic_name__	:	Optional[Any]								=max_resolution
										__magic_name__	:	Optional[Any]								=do_resize
										__magic_name__	:	Any								=size
										__magic_name__	:	str								=do_normalize
										__magic_name__	:	str								=image_mean
										__magic_name__	:	List[Any]								=image_std
										__magic_name__	:	Optional[int]								=do_rescale
										__magic_name__	:	List[str]								=rescale_factor
										__magic_name__	:	Optional[int]								=do_pad

						def     A__						(		self   :Optional[int] ):
										'''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   :List[str]    , __snake_case   :Any    , __snake_case   :List[Any]=False ):
										'''simple docstring'''

										if not batched:
														__magic_name__	:	Any								=image_inputs[0]
														if isinstance(__snake_case    , Image.Image ):
																		__magic_name__				, __magic_name__	:	Dict								=image.size
														else:
																		__magic_name__				, __magic_name__	:	Dict								=image.shape[1], image.shape[2]
														if w < h:
																		__magic_name__	:	Any								=int(self.size["""shortest_edge"""] * h / w )
																		__magic_name__	:	Dict								=self.size["""shortest_edge"""]
														elif w > h:
																		__magic_name__	:	Union[str, Any]								=self.size["""shortest_edge"""]
																		__magic_name__	:	Optional[int]								=int(self.size["""shortest_edge"""] * w / h )
														else:
																		__magic_name__	:	Any								=self.size["""shortest_edge"""]
																		__magic_name__	:	List[str]								=self.size["""shortest_edge"""]

										else:
														__magic_name__	:	List[str]								=[]
														for image in image_inputs:
																		__magic_name__				, __magic_name__	:	Tuple								=self.get_expected_values([image] )
																		expected_values.append((expected_height, expected_width) )
														__magic_name__	:	Optional[int]								=max(__snake_case    , key=lambda __snake_case : item[0] )[0]
														__magic_name__	:	Tuple								=max(__snake_case    , key=lambda __snake_case : item[1] )[1]

										return expected_height, expected_width




@require_torch
@require_vision
class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					DetaImageProcessor if is_vision_available() else None

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	int								=DetaImageProcessingTester(self )

						@property
						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										return self.image_processor_tester.prepare_image_processor_dict()

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(__snake_case    , """image_mean""" ) )
										self.assertTrue(hasattr(__snake_case    , """image_std""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_normalize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_resize""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_rescale""" ) )
										self.assertTrue(hasattr(__snake_case    , """do_pad""" ) )
										self.assertTrue(hasattr(__snake_case    , """size""" ) )

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=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    , __snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										pass

						def     A__						(		self   :str ):
										'''simple docstring'''

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

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

										__magic_name__				, __magic_name__	:	str								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__snake_case )

										__magic_name__	:	Dict								=image_processing(__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   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	int								=self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										__magic_name__	:	Optional[Any]								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , numpify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , np.ndarray )

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

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__	:	List[str]								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Any								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__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   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										__magic_name__	:	Dict								=prepare_image_inputs(self.image_processor_tester    , equal_resolution=__snake_case    , torchify=__snake_case )
										for image in image_inputs:
														self.assertIsInstance(__snake_case    , torch.Tensor )

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

										__magic_name__				, __magic_name__	:	int								=self.image_processor_tester.get_expected_values(__snake_case )

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

										# Test batched
										__magic_name__	:	Dict								=image_processing(__snake_case    , return_tensors="""pt""" ).pixel_values

										__magic_name__				, __magic_name__	:	Tuple								=self.image_processor_tester.get_expected_values(__snake_case    , batched=__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   :str ):
										'''simple docstring'''

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

										__magic_name__	:	str								={"""image_id""": 3_97_69, """annotations""": target}

										# encode them
										__magic_name__	:	Optional[Any]								=DetaImageProcessor()
										__magic_name__	:	Any								=image_processing(images=__snake_case    , annotations=__snake_case    , return_tensors="""pt""" )

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

										__magic_name__	:	Optional[int]								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	Dict								=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Optional[int]								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	int								=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Any								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	Optional[Any]								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[str]								=torch.tensor([75, 75, 63, 65, 17, 17] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify orig_size
										__magic_name__	:	Optional[Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	Tuple								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )


						@slow
						def     A__						(		self   :int ):
										'''simple docstring'''

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

										__magic_name__	:	Optional[Any]								={"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

										__magic_name__	:	List[str]								=pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )

										# encode them
										__magic_name__	:	int								=DetaImageProcessor(format="""coco_panoptic""" )
										__magic_name__	:	Tuple								=image_processing(images=__snake_case    , annotations=__snake_case    , masks_path=__snake_case    , return_tensors="""pt""" )

										# verify pixel values
										__magic_name__	:	Union[str, Any]								=torch.Size([1, 3, 8_00, 10_66] )
										self.assertEqual(encoding["""pixel_values"""].shape    , __snake_case )

										__magic_name__	:	str								=torch.tensor([0.2796, 0.3138, 0.3481] )
										self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]    , __snake_case    , atol=1E-4 ) )

										# verify area
										__magic_name__	:	str								=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]    , __snake_case ) )
										# verify boxes
										__magic_name__	:	Dict								=torch.Size([6, 4] )
										self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape    , __snake_case )
										__magic_name__	:	Optional[Any]								=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]    , __snake_case    , atol=1E-3 ) )
										# verify image_id
										__magic_name__	:	Dict								=torch.tensor([3_97_69] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]    , __snake_case ) )
										# verify is_crowd
										__magic_name__	:	int								=torch.tensor([0, 0, 0, 0, 0, 0] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]    , __snake_case ) )
										# verify class_labels
										__magic_name__	:	List[Any]								=torch.tensor([17, 17, 63, 75, 75, 93] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]    , __snake_case ) )
										# verify masks
										__magic_name__	:	List[Any]								=82_28_73
										self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()    , __snake_case )
										# verify orig_size
										__magic_name__	:	Union[str, Any]								=torch.tensor([4_80, 6_40] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]    , __snake_case ) )
										# verify size
										__magic_name__	:	List[Any]								=torch.tensor([8_00, 10_66] )
										self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]    , __snake_case ) )




