

import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
			import transformers
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int       )					->				Union[str, Any]:
	random.seed(a__       )
	np.random.seed(a__       )
	torch.manual_seed(a__       )
	torch.cuda.manual_seed_all(a__       )
	# ^^ safe to call this function even if cuda is not available







class      lowerCamelCase_      :




	def __init__(     self						:     Optional[int]					,      __A						:     Iterable[torch.nn.Parameter]					,      __A						:     float = 0.9_9_9_9					,      __A						:     float = 0.0					,      __A						:     int = 0					,      __A						:     bool = False					,      __A						:     Union[float, int] = 1.0					,      __A						:     Union[float, int] = 2 / 3					,      __A						:     Optional[Any] = None					,      __A						:     Dict[str, Any] = None					,      **__A						:     Union[str, Any]					,      ):

		if isinstance(__A					,      torch.nn.Module							):
			__A :			Optional[Any]		    =			(
			    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
			    """Please pass the parameters of the module instead."""
			)
			deprecate(
			    """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""					,      """1.0.0"""					,      __A					,      standard_warn=__A					,      )
			__A :			List[Any]		    =			parameters.parameters()

			# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
			__A :			List[Any]		    =			True

		if kwargs.get("""max_value"""					,      __A							) is not None:
			__A :			List[Any]		    =			"""The `max_value` argument is deprecated. Please use `decay` instead."""
			deprecate("""max_value"""					,      """1.0.0"""					,      __A					,      standard_warn=__A							)
			__A :			Any		    =			kwargs["""max_value"""]

		if kwargs.get("""min_value"""					,      __A							) is not None:
			__A :			List[str]		    =			"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
			deprecate("""min_value"""					,      """1.0.0"""					,      __A					,      standard_warn=__A							)
			__A :			List[Any]		    =			kwargs["""min_value"""]

		__A :			Optional[int]		    =			list(__A							)
		__A :			Optional[int]		    =			[p.clone().detach() for p in parameters]

		if kwargs.get("""device"""					,      __A							) is not None:
			__A :			str		    =			"""The `device` argument is deprecated. Please use `to` instead."""
			deprecate("""device"""					,      """1.0.0"""					,      __A					,      standard_warn=__A							)
			self.to(device=kwargs["""device"""]							)

		__A :			Tuple		    =			None

		__A :			str		    =			decay
		__A :			List[str]		    =			min_decay
		__A :			Tuple		    =			update_after_step
		__A :			Dict		    =			use_ema_warmup
		__A :			Optional[Any]		    =			inv_gamma
		__A :			Any		    =			power
		__A :			Optional[int]		    =			0
		__A :			Union[str, Any]		    =			None  # set in `step()`

		__A :			Optional[int]		    =			model_cls
		__A :			str		    =			model_config




	@classmethod
	def        lowerCAmelCase_       (     cls						:     Tuple					,      __A						:     List[Any]					,      __A						:     Optional[Any]							):
		__A    ,				__A :			str		    =			model_cls.load_config(__A					,      return_unused_kwargs=__A							)
		__A :			Optional[Any]		    =			model_cls.from_pretrained(__A							)

		__A :			Optional[Any]		    =			cls(model.parameters()					,      model_cls=__A					,      model_config=model.config							)

		ema_model.load_state_dict(__A							)
		return ema_model




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     Tuple							):
		if self.model_cls is None:
			raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""							)

		if self.model_config is None:
			raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""							)

		__A :			List[str]		    =			self.model_cls.from_config(self.model_config							)
		__A :			List[str]		    =			self.state_dict()
		state_dict.pop("""shadow_params"""					,      __A							)

		model.register_to_config(**__A							)
		self.copy_to(model.parameters()							)
		model.save_pretrained(__A							)




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     int							):
		__A :			Tuple		    =			max(0					,      optimization_step - self.update_after_step - 1							)

		if step <= 0:
			return 0.0

		if self.use_ema_warmup:
			__A :			Any		    =			1 - (1 + step / self.inv_gamma) ** -self.power
		else:
			__A :			str		    =			(1 + step) / (10 + step)

		__A :			Dict		    =			min(__A					,      self.decay							)
		# make sure decay is not smaller than min_decay
		__A :			Dict		    =			max(__A					,      self.min_decay							)
		return cur_decay_value




	@torch.no_grad()
	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     Iterable[torch.nn.Parameter]							):
		if isinstance(__A					,      torch.nn.Module							):
			__A :			Tuple		    =			(
			    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
			    """Please pass the parameters of the module instead."""
			)
			deprecate(
			    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""					,      """1.0.0"""					,      __A					,      standard_warn=__A					,      )
			__A :			Union[str, Any]		    =			parameters.parameters()

		__A :			Any		    =			list(__A							)

		self.optimization_step += 1

		# Compute the decay factor for the exponential moving average.
		__A :			List[Any]		    =			self.get_decay(self.optimization_step							)
		__A :			Optional[int]		    =			decay
		__A :			Union[str, Any]		    =			1 - decay

		__A :			int		    =			contextlib.nullcontext
		if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
			import deepspeed

		for s_param, param in zip(self.shadow_params					,      __A							):
			if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
				__A :			List[str]		    =			deepspeed.zero.GatheredParameters(__A					,      modifier_rank=__A							)

			with context_manager():
				if param.requires_grad:
					s_param.sub_(one_minus_decay * (s_param - param)							)
				else:
					s_param.copy_(__A							)




	def        lowerCAmelCase_       (     self						:     str					,      __A						:     Iterable[torch.nn.Parameter]							):
		__A :			Union[str, Any]		    =			list(__A							)
		for s_param, param in zip(self.shadow_params					,      __A							):
			param.data.copy_(s_param.to(param.device							).data							)




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     List[Any]=None					,      __A						:     List[Any]=None							):
		__A :			Tuple		    =			[
		    p.to(device=__A					,      dtype=__A							) if p.is_floating_point() else p.to(device=__A							)
		    for p in self.shadow_params
		]




	def        lowerCAmelCase_       (     self						:     Tuple							):
		return {
		    "decay": self.decay,
		    "min_decay": self.min_decay,
		    "optimization_step": self.optimization_step,
		    "update_after_step": self.update_after_step,
		    "use_ema_warmup": self.use_ema_warmup,
		    "inv_gamma": self.inv_gamma,
		    "power": self.power,
		    "shadow_params": self.shadow_params,
		}




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      __A						:     Iterable[torch.nn.Parameter]							):
		__A :			List[str]		    =			[param.detach().cpu().clone() for param in parameters]




	def        lowerCAmelCase_       (     self						:     Optional[Any]					,      __A						:     Iterable[torch.nn.Parameter]							):
		if self.temp_stored_params is None:
			raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""							)
		for c_param, param in zip(self.temp_stored_params					,      __A							):
			param.data.copy_(c_param.data							)

		# Better memory-wise.
		__A :			Any		    =			None




	def        lowerCAmelCase_       (     self						:     Any					,      __A						:     dict							):
		__A :			Optional[Any]		    =			copy.deepcopy(__A							)

		__A :			Optional[Any]		    =			state_dict.get("""decay"""					,      self.decay							)
		if self.decay < 0.0 or self.decay > 1.0:
			raise ValueError("""Decay must be between 0 and 1"""							)

		__A :			int		    =			state_dict.get("""min_decay"""					,      self.min_decay							)
		if not isinstance(self.min_decay					,      __A							):
			raise ValueError("""Invalid min_decay"""							)

		__A :			List[Any]		    =			state_dict.get("""optimization_step"""					,      self.optimization_step							)
		if not isinstance(self.optimization_step					,      __A							):
			raise ValueError("""Invalid optimization_step"""							)

		__A :			List[Any]		    =			state_dict.get("""update_after_step"""					,      self.update_after_step							)
		if not isinstance(self.update_after_step					,      __A							):
			raise ValueError("""Invalid update_after_step"""							)

		__A :			int		    =			state_dict.get("""use_ema_warmup"""					,      self.use_ema_warmup							)
		if not isinstance(self.use_ema_warmup					,      __A							):
			raise ValueError("""Invalid use_ema_warmup"""							)

		__A :			Any		    =			state_dict.get("""inv_gamma"""					,      self.inv_gamma							)
		if not isinstance(self.inv_gamma					,      (float, int)							):
			raise ValueError("""Invalid inv_gamma"""							)

		__A :			List[Any]		    =			state_dict.get("""power"""					,      self.power							)
		if not isinstance(self.power					,      (float, int)							):
			raise ValueError("""Invalid power"""							)

		__A :			Dict		    =			state_dict.get("""shadow_params"""					,      __A							)
		if shadow_params is not None:
			__A :			Union[str, Any]		    =			shadow_params
			if not isinstance(self.shadow_params					,      __A							):
				raise ValueError("""shadow_params must be a list"""							)
			if not all(isinstance(__A					,      torch.Tensor							) for p in self.shadow_params							):
				raise ValueError("""shadow_params must all be Tensors"""							)








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      lowerCamelCase_      (	unittest.TestCase	):




	def __init__(     self						:     Optional[int]					,      __A						:     Union[str, Any]					,      __A						:     int=7					,      __A						:     int=3					,      __A						:     int=30					,      __A						:     Dict=400					,      __A						:     str=True					,      __A						:     str=None					,      __A						:     str=True					,      __A						:     Optional[int]=[0.5, 0.5, 0.5]					,      __A						:     List[str]=[0.5, 0.5, 0.5]					,      __A						:     Optional[Any]=True					,      __A						:     int=1 / 255					,      __A						:     List[Any]=True					,      ):
		# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
		__A :			Union[str, Any]		    =			parent
		__A :			Union[str, Any]		    =			batch_size
		__A :			Union[str, Any]		    =			num_channels
		__A :			Optional[Any]		    =			min_resolution
		__A :			Union[str, Any]		    =			max_resolution
		__A :			Any		    =			do_resize
		__A :			Union[str, Any]		    =			size
		__A :			Optional[int]		    =			do_normalize
		__A :			Dict		    =			image_mean
		__A :			Optional[int]		    =			image_std
		__A :			Tuple		    =			do_rescale
		__A :			Optional[Any]		    =			rescale_factor
		__A :			Tuple		    =			do_pad




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

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

		return expected_height, expected_width






@require_torch
@require_vision
class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				DetaImageProcessor if is_vision_available() else None




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Tuple		    =			DetaImageProcessingTester(self							)




	@property
	def        lowerCAmelCase_       (     self						:     List[str]							):
		return self.image_processor_tester.prepare_image_processor_dict()




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




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Dict		    =			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					,      __A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		pass




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# Initialize image_processing
		__A :			Tuple		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		__A :			List[Any]		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      Image.Image							)

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

		__A    ,				__A :			int		    =			self.image_processor_tester.get_expected_values(__A							)

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

		# Test batched
		__A    ,				__A :			List[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

		__A :			List[str]		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values
		self.assertEqual(
		    encoded_images.shape					,      (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )					,      )




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# Initialize image_processing
		__A :			Tuple		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random numpy tensors
		__A :			List[str]		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A					,      numpify=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      np.ndarray							)

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

		__A    ,				__A :			Optional[int]		    =			self.image_processor_tester.get_expected_values(__A							)

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

		# Test batched
		__A :			int		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			Optional[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

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




	def        lowerCAmelCase_       (     self						:     Dict							):
		# Initialize image_processing
		__A :			int		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		__A :			Dict		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A					,      torchify=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      torch.Tensor							)

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

		__A    ,				__A :			Optional[int]		    =			self.image_processor_tester.get_expected_values(__A							)

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

		# Test batched
		__A :			Tuple		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values

		__A    ,				__A :			Any		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

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




	@slow
	def        lowerCAmelCase_       (     self						:     Tuple							):
		# prepare image and target
		__A :			Tuple		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""					,      """r"""							) as f:
			__A :			Any		    =			json.loads(f.read()							)

		__A :			int		    =			{"""image_id""": 3_9769, """annotations""": target}

		# encode them
		__A :			List[str]		    =			DetaImageProcessor()
		__A :			List[str]		    =			image_processing(images=__A					,      annotations=__A					,      return_tensors="""pt"""							)

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

		__A :			Union[str, Any]		    =			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]					,      __A					,      atol=1e-4							)							)

		# verify area
		__A :			Tuple		    =			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"""]					,      __A							)							)
		# verify boxes
		__A :			Tuple		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			List[str]		    =			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]					,      __A					,      atol=1e-3							)							)
		# verify image_id
		__A :			List[str]		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Dict		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			List[Any]		    =			torch.tensor([75, 75, 63, 65, 17, 17]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify orig_size
		__A :			str		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)




	@slow
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# prepare image, target and masks_path
		__A :			Optional[int]		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""					,      """r"""							) as f:
			__A :			Tuple		    =			json.loads(f.read()							)

		__A :			Optional[int]		    =			{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

		__A :			Any		    =			pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

		# encode them
		__A :			Any		    =			DetaImageProcessor(format="""coco_panoptic"""							)
		__A :			int		    =			image_processing(images=__A					,      annotations=__A					,      masks_path=__A					,      return_tensors="""pt"""							)

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

		__A :			Optional[Any]		    =			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]					,      __A					,      atol=1e-4							)							)

		# verify area
		__A :			Union[str, Any]		    =			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"""]					,      __A							)							)
		# verify boxes
		__A :			Union[str, Any]		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			Union[str, Any]		    =			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]					,      __A					,      atol=1e-3							)							)
		# verify image_id
		__A :			Any		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Any		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			Optional[Any]		    =			torch.tensor([17, 17, 63, 75, 75, 93]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify masks
		__A :			List[str]		    =			82_2873
		self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,      __A							)
		# verify orig_size
		__A :			Tuple		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)





