"""simple docstring"""






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__A			   = 8



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase=BITS )  ->  List[str]:
   lowercase__:   Optional[int]					=      x.device

   lowercase__:   int					=      (x * 2_5_5).int().clamp(0       ,    2_5_5 )

   lowercase__:   Dict					=      2 ** torch.arange(bits - 1       ,    -1       ,    -1       ,    device=__UpperCAmelCase )
   lowercase__:   List[Any]					=      rearrange(__UpperCAmelCase       ,    '''d -> d 1 1''' )
   lowercase__:   str					=      rearrange(__UpperCAmelCase       ,    '''b c h w -> b c 1 h w''' )

   lowercase__:   List[str]					=      ((x & mask) != 0).float()
   lowercase__:   Optional[Any]					=      rearrange(__UpperCAmelCase       ,    '''b c d h w -> b (c d) h w''' )
   lowercase__:   List[str]					=      bits * 2 - 1
   return bits



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase=BITS )  ->  Any:
   lowercase__:   Union[str, Any]					=      x.device

   lowercase__:   List[Any]					=      (x > 0).int()
   lowercase__:   Optional[int]					=      2 ** torch.arange(bits - 1       ,    -1       ,    -1       ,    device=__UpperCAmelCase       ,    dtype=torch.intaa )

   lowercase__:   Tuple					=      rearrange(__UpperCAmelCase       ,    '''d -> d 1 1''' )
   lowercase__:   Union[str, Any]					=      rearrange(__UpperCAmelCase       ,    '''b (c d) h w -> b c d h w'''       ,    d=8 )
   lowercase__:   List[Any]					=      reduce(x * mask       ,    '''b c d h w -> b c h w'''       ,    '''sum''' )
   return (dec / 2_5_5).clamp(0.0       ,    1.0 )



def SCREAMING_SNAKE_CASE__			(   self       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase = 0.0       ,    __UpperCAmelCase = True       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase = True       ,    )  ->  Union[DDIMSchedulerOutput, Tuple]:
   if self.num_inference_steps is None:
      raise ValueError(
          '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler''' )

   # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
   # Ideally, read DDIM paper in-detail understanding

   # Notation (<variable name> -> <name in paper>
   # - pred_noise_t -> e_theta(x_t, t)
   # - pred_original_sample -> f_theta(x_t, t) or x_0
   # - std_dev_t -> sigma_t
   # - eta -> η
   # - pred_sample_direction -> "direction pointing to x_t"
   # - pred_prev_sample -> "x_t-1"

   # 1. get previous step value (=t-1)
   lowercase__:   List[Any]					=      timestep - self.config.num_train_timesteps // self.num_inference_steps

   # 2. compute alphas, betas
   lowercase__:   int					=      self.alphas_cumprod[timestep]
   lowercase__:   int					=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

   lowercase__:   Optional[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
   lowercase__:   str					=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

   # 4. Clip "predicted x_0"
   lowercase__:   Dict					=      self.bit_scale
   if self.config.clip_sample:
      lowercase__:   int					=      torch.clamp(__UpperCAmelCase       ,    -scale       ,    __UpperCAmelCase )

   # 5. compute variance: "sigma_t(η)" -> see formula (16)
   # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
   lowercase__:   Tuple					=      self._get_variance(__UpperCAmelCase       ,    __UpperCAmelCase )
   lowercase__:   List[str]					=      eta * variance ** 0.5

   if use_clipped_model_output:
      # the model_output is always re-derived from the clipped x_0 in Glide
      lowercase__:   List[Any]					=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

   # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   lowercase__:   Union[str, Any]					=      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

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

   if eta > 0:
      # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
      lowercase__:   List[str]					=      model_output.device if torch.is_tensor(__UpperCAmelCase ) else '''cpu'''
      lowercase__:   Optional[int]					=      torch.randn(model_output.shape       ,    dtype=model_output.dtype       ,    generator=__UpperCAmelCase ).to(__UpperCAmelCase )
      lowercase__:   List[Any]					=      self._get_variance(__UpperCAmelCase       ,    __UpperCAmelCase ) ** 0.5 * eta * noise

      lowercase__:   Union[str, Any]					=      prev_sample + variance

   if not return_dict:
      return (prev_sample,)

   return DDIMSchedulerOutput(prev_sample=__UpperCAmelCase       ,    pred_original_sample=__UpperCAmelCase )



def SCREAMING_SNAKE_CASE__			(   self       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase="epsilon"       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase = True       ,    )  ->  Union[DDPMSchedulerOutput, Tuple]:
   lowercase__:   Tuple					=      timestep

   if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
      lowercase__, lowercase__:   Optional[Any]					=      torch.split(__UpperCAmelCase       ,    sample.shape[1]       ,    dim=1 )
   else:
      lowercase__:   Optional[int]					=      None

   # 1. compute alphas, betas
   lowercase__:   Optional[int]					=      self.alphas_cumprod[t]
   lowercase__:   int					=      self.alphas_cumprod[t - 1] if t > 0 else self.one
   lowercase__:   List[str]					=      1 - alpha_prod_t
   lowercase__:   int					=      1 - alpha_prod_t_prev

   # 2. compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
   if prediction_type == "epsilon":
      lowercase__:   Dict					=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
   elif prediction_type == "sample":
      lowercase__:   List[Any]					=      model_output
   else:
      raise ValueError(F"""Unsupported prediction_type {prediction_type}.""" )

   # 3. Clip "predicted x_0"
   lowercase__:   int					=      self.bit_scale
   if self.config.clip_sample:
      lowercase__:   Optional[int]					=      torch.clamp(__UpperCAmelCase       ,    -scale       ,    __UpperCAmelCase )

   # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   lowercase__:   Tuple					=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
   lowercase__:   int					=      self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

   # 5. Compute predicted previous sample µ_t
   # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
   lowercase__:   Union[str, Any]					=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

   # 6. Add noise
   lowercase__:   Optional[int]					=      0
   if t > 0:
      lowercase__:   Any					=      torch.randn(
          model_output.size()       ,    dtype=model_output.dtype       ,    layout=model_output.layout       ,    generator=__UpperCAmelCase ).to(model_output.device )
      lowercase__:   List[str]					=      (self._get_variance(__UpperCAmelCase       ,    predicted_variance=__UpperCAmelCase ) ** 0.5) * noise

   lowercase__:   Optional[Any]					=      pred_prev_sample + variance

   if not return_dict:
      return (pred_prev_sample,)

   return DDPMSchedulerOutput(prev_sample=__UpperCAmelCase       ,    pred_original_sample=__UpperCAmelCase )




class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 1.0    ,			):
       super().__init__()
       lowercase__:   Optional[Any]					=      bit_scale
       lowercase__:   Any					=      (
           ddim_bit_scheduler_step if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							) else ddpm_bit_scheduler_step
       )

       self.register_modules(unet=_UpperCAmelCase    ,			scheduler=_UpperCAmelCase							)






    @torch.no_grad()
    def __call__(   self    ,			_UpperCAmelCase = 256    ,			_UpperCAmelCase = 256    ,			_UpperCAmelCase = 50    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = "pil"    ,			_UpperCAmelCase = True    ,			**_UpperCAmelCase    ,			):
       lowercase__:   Union[str, Any]					=      torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)    ,			generator=_UpperCAmelCase    ,			)
       lowercase__:   Tuple					=      decimal_to_bits(_UpperCAmelCase							) * self.bit_scale
       lowercase__:   Union[str, Any]					=      latents.to(self.device							)

       self.scheduler.set_timesteps(_UpperCAmelCase							)

       for t in self.progress_bar(self.scheduler.timesteps							):
          # predict the noise residual
          lowercase__:   Any					=      self.unet(_UpperCAmelCase    ,			_UpperCAmelCase							).sample

          # compute the previous noisy sample x_t -> x_t-1
          lowercase__:   Dict					=      self.scheduler.step(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							).prev_sample

       lowercase__:   Optional[int]					=      bits_to_decimal(_UpperCAmelCase							)

       if output_type == "pil":
          lowercase__:   Tuple					=      self.numpy_to_pil(_UpperCAmelCase							)

       if not return_dict:
          return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase							)

"""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							)							)
