"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:torch.FloatTensor
    _UpperCAmelCase	:Optional[torch.FloatTensor]						    = None



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase=0.9_9_9       ,    __UpperCAmelCase="cosine"       ,    )  ->  Any:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__UpperCAmelCase ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__UpperCAmelCase ):
         return math.exp(t * -1_2.0 )

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )

   lowercase__:   Dict					=      []
   for i in range(__UpperCAmelCase ):
      lowercase__:   str					=      i / num_diffusion_timesteps
      lowercase__:   int					=      (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase ) / alpha_bar_fn(__UpperCAmelCase )       ,    __UpperCAmelCase ) )
   return torch.tensor(__UpperCAmelCase       ,    dtype=torch.floataa )




class     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Tuple						    = 1






    @register_to_config
    def __init__(   self    ,			_UpperCAmelCase = 1000    ,			_UpperCAmelCase = 0.0_001    ,			_UpperCAmelCase = 0.02    ,			_UpperCAmelCase = "linear"    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = True    ,			_UpperCAmelCase = 0    ,			_UpperCAmelCase = "epsilon"    ,			_UpperCAmelCase = 1.0    ,			**_UpperCAmelCase    ,			):
       if kwargs.get('''set_alpha_to_one'''    ,			_UpperCAmelCase							) is not None:
          lowercase__:   Optional[Any]					=      (
              '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
          )
          deprecate('''set_alpha_to_one'''    ,			'''1.0.0'''    ,			_UpperCAmelCase    ,			standard_warn=_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      kwargs['''set_alpha_to_one''']
       if trained_betas is not None:
          lowercase__:   Any					=      torch.tensor(_UpperCAmelCase    ,			dtype=torch.floataa							)
       elif beta_schedule == "linear":
          lowercase__:   Any					=      torch.linspace(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			dtype=torch.floataa							)
       elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowercase__:   Dict					=      (
              torch.linspace(beta_start**0.5    ,			beta_end**0.5    ,			_UpperCAmelCase    ,			dtype=torch.floataa							) ** 2
          )
       elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          lowercase__:   str					=      betas_for_alpha_bar(_UpperCAmelCase							)
       else:
          raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""							)

       lowercase__:   str					=      1.0 - self.betas
       lowercase__:   Optional[int]					=      torch.cumprod(self.alphas    ,			dim=0							)

       # At every step in inverted ddim, we are looking into the next alphas_cumprod
       # For the final step, there is no next alphas_cumprod, and the index is out of bounds
       # `set_alpha_to_zero` decides whether we set this parameter simply to zero
       # in this case, self.step() just output the predicted noise
       # or whether we use the final alpha of the "non-previous" one.
       lowercase__:   str					=      torch.tensor(0.0							) if set_alpha_to_zero else self.alphas_cumprod[-1]

       # standard deviation of the initial noise distribution
       lowercase__:   Optional[Any]					=      1.0

       # setable values
       lowercase__:   int					=      None
       lowercase__:   Tuple					=      torch.from_numpy(np.arange(0    ,			_UpperCAmelCase							).copy().astype(np.intaa							)							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       return sample






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):

       if num_inference_steps > self.config.num_train_timesteps:
          raise ValueError(
              F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
              F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
              F""" maximal {self.config.num_train_timesteps} timesteps."""							)

       lowercase__:   Tuple					=      num_inference_steps
       lowercase__:   Optional[Any]					=      self.config.num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       lowercase__:   List[Any]					=      (np.arange(0    ,			_UpperCAmelCase							) * step_ratio).round().copy().astype(np.intaa							)
       lowercase__:   Optional[int]					=      torch.from_numpy(_UpperCAmelCase							).to(_UpperCAmelCase							)
       self.timesteps += self.config.steps_offset






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 0.0    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = True    ,			):
       # 1. get previous step value (=t+1)
       lowercase__:   Any					=      timestep + self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       # change original implementation to exactly match noise levels for analogous forward process
       lowercase__:   Any					=      self.alphas_cumprod[timestep]
       lowercase__:   Optional[Any]					=      (
           self.alphas_cumprod[prev_timestep]
           if prev_timestep < self.config.num_train_timesteps
           else self.final_alpha_cumprod
       )

       lowercase__:   int					=      1 - alpha_prod_t

       # 3. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       if self.config.prediction_type == "epsilon":
          lowercase__:   Any					=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          lowercase__:   Dict					=      model_output
       elif self.config.prediction_type == "sample":
          lowercase__:   str					=      model_output
          lowercase__:   List[str]					=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
       elif self.config.prediction_type == "v_prediction":
          lowercase__:   Optional[int]					=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          lowercase__:   Dict					=      (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
       else:
          raise ValueError(
              F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
              ''' `v_prediction`'''							)

       # 4. Clip or threshold "predicted x_0"
       if self.config.clip_sample:
          lowercase__:   List[Any]					=      pred_original_sample.clamp(
              -self.config.clip_sample_range    ,			self.config.clip_sample_range							)

       # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       lowercase__:   str					=      (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

       # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
       lowercase__:   Dict					=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

       if not return_dict:
          return (prev_sample, pred_original_sample)
       return DDIMSchedulerOutput(prev_sample=_UpperCAmelCase    ,			pred_original_sample=_UpperCAmelCase							)






    def __len__(   self							):
       return self.config.num_train_timesteps

"""simple docstring"""






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import YolosImageProcessor


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

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






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

       else:
          lowercase__:   Optional[int]					=      []
          for image in image_inputs:
             lowercase__, lowercase__:   int					=      self.get_expected_values([image]							)
             expected_values.append((expected_height, expected_width)							)
          lowercase__:   Union[str, Any]					=      max(_UpperCAmelCase    ,			key=lambda _UpperCAmelCase							: item[0]							)[0]
          lowercase__:   Dict					=      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	:Optional[int]						    = YolosImageProcessor if is_vision_available() else None






    def _snake_case			(   self							):
       lowercase__:   int					=      YolosImageProcessingTester(self							)






    @property
    def _snake_case			(   self							):
       return self.image_processor_tester.prepare_image_processor_dict()






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      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    ,			'''size'''							)							)






    def _snake_case			(   self							):
       lowercase__:   Any					=      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    ,			_UpperCAmelCase							)

       lowercase__:   Any					=      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 _snake_case			(   self							):
       pass






    def _snake_case			(   self							):
       # Initialize image_processing
       lowercase__:   Union[str, Any]					=      self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       lowercase__:   Dict					=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_UpperCAmelCase							)
       for image in image_inputs:
          self.assertIsInstance(_UpperCAmelCase    ,			Image.Image							)

       # Test not batched input
       lowercase__:   int					=      image_processing(image_inputs[0]    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   Optional[int]					=      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
       lowercase__, lowercase__:   Any					=      self.image_processor_tester.get_expected_values(_UpperCAmelCase    ,			batched=_UpperCAmelCase							)

       lowercase__:   int					=      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 _snake_case			(   self							):
       # Initialize image_processing
       lowercase__:   List[str]					=      self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       lowercase__:   Optional[Any]					=      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
       lowercase__:   List[Any]					=      image_processing(image_inputs[0]    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   str					=      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
       lowercase__:   Dict					=      image_processing(_UpperCAmelCase    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   str					=      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 _snake_case			(   self							):
       # Initialize image_processing
       lowercase__:   Any					=      self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       lowercase__:   Optional[Any]					=      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
       lowercase__:   Optional[int]					=      image_processing(image_inputs[0]    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   int					=      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
       lowercase__:   List[str]					=      image_processing(_UpperCAmelCase    ,			return_tensors='''pt'''							).pixel_values

       lowercase__, lowercase__:   List[Any]					=      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 _snake_case			(   self							):
       # Initialize image_processings
       lowercase__:   Union[str, Any]					=      self.image_processing_class(**self.image_processor_dict							)
       lowercase__:   Optional[Any]					=      self.image_processing_class(do_resize=_UpperCAmelCase    ,			do_normalize=_UpperCAmelCase    ,			do_rescale=_UpperCAmelCase							)
       # create random PyTorch tensors
       lowercase__:   Optional[Any]					=      prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=_UpperCAmelCase    ,			torchify=_UpperCAmelCase							)
       for image in image_inputs:
          self.assertIsInstance(_UpperCAmelCase    ,			torch.Tensor							)

       # Test whether the method "pad" and calling the image processor return the same tensors
       lowercase__:   List[str]					=      image_processing_a.pad(_UpperCAmelCase    ,			return_tensors='''pt'''							)
       lowercase__:   Tuple					=      image_processing_a(_UpperCAmelCase    ,			return_tensors='''pt'''							)

       self.assertTrue(
           torch.allclose(encoded_images_with_method['''pixel_values''']    ,			encoded_images['''pixel_values''']    ,			atol=1e-4							)							)






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

       lowercase__:   Dict					=      {'''image_id''': 39769, '''annotations''': target}

       # encode them
       lowercase__:   Dict					=      YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''							)
       lowercase__:   Any					=      image_processing(images=_UpperCAmelCase    ,			annotations=_UpperCAmelCase    ,			return_tensors='''pt'''							)

       # verify pixel values
       lowercase__:   Optional[Any]					=      torch.Size([1, 3, 800, 1066]							)
       self.assertEqual(encoding['''pixel_values'''].shape    ,			_UpperCAmelCase							)

       lowercase__:   Optional[Any]					=      torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)

       # verify area
       lowercase__:   Tuple					=      torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,			_UpperCAmelCase							)							)
       # verify boxes
       lowercase__:   str					=      torch.Size([6, 4]							)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,			_UpperCAmelCase    ,			atol=1e-3							)							)
       # verify image_id
       lowercase__:   Optional[int]					=      torch.tensor([39769]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,			_UpperCAmelCase							)							)
       # verify is_crowd
       lowercase__:   Optional[int]					=      torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,			_UpperCAmelCase							)							)
       # verify class_labels
       lowercase__:   Dict					=      torch.tensor([75, 75, 63, 65, 17, 17]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,			_UpperCAmelCase							)							)
       # verify orig_size
       lowercase__:   List[Any]					=      torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,			_UpperCAmelCase							)							)
       # verify size
       lowercase__:   List[Any]					=      torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,			_UpperCAmelCase							)							)






    @slow
    def _snake_case			(   self							):
       # prepare image, target and masks_path
       lowercase__:   str					=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    ,			'''r'''							) as f:
          lowercase__:   str					=      json.loads(f.read()							)

       lowercase__:   List[Any]					=      {'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

       lowercase__:   Dict					=      pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''							)

       # encode them
       lowercase__:   Union[str, Any]					=      YolosImageProcessor(format='''coco_panoptic'''							)
       lowercase__:   Optional[Any]					=      image_processing(images=_UpperCAmelCase    ,			annotations=_UpperCAmelCase    ,			masks_path=_UpperCAmelCase    ,			return_tensors='''pt'''							)

       # verify pixel values
       lowercase__:   Optional[int]					=      torch.Size([1, 3, 800, 1066]							)
       self.assertEqual(encoding['''pixel_values'''].shape    ,			_UpperCAmelCase							)

       lowercase__:   Dict					=      torch.tensor([0.2_796, 0.3_138, 0.3_481]							)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)

       # verify area
       lowercase__:   str					=      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']    ,			_UpperCAmelCase							)							)
       # verify boxes
       lowercase__:   List[str]					=      torch.Size([6, 4]							)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    ,			_UpperCAmelCase							)
       lowercase__:   List[Any]					=      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    ,			_UpperCAmelCase    ,			atol=1e-3							)							)
       # verify image_id
       lowercase__:   int					=      torch.tensor([39769]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    ,			_UpperCAmelCase							)							)
       # verify is_crowd
       lowercase__:   int					=      torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    ,			_UpperCAmelCase							)							)
       # verify class_labels
       lowercase__:   Dict					=      torch.tensor([17, 17, 63, 75, 75, 93]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    ,			_UpperCAmelCase							)							)
       # verify masks
       lowercase__:   Union[str, Any]					=      822873
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    ,			_UpperCAmelCase							)
       # verify orig_size
       lowercase__:   List[Any]					=      torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    ,			_UpperCAmelCase							)							)
       # verify size
       lowercase__:   Union[str, Any]					=      torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    ,			_UpperCAmelCase							)							)
