
'''simple docstring'''


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


snake_case_ :  Tuple     =					logging.get_logger(__name__)  # pylint: disable=invalid-name


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







      super().__init__()

      if safety_checker is None:
         logger.warning(
             f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
             ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
             ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
             ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
             ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
             ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''   )

      self.register_modules(
          speech_model=lowerCamelCase__ ,   speech_processor=lowerCamelCase__ ,   vae=lowerCamelCase__ ,   text_encoder=lowerCamelCase__ ,   tokenizer=lowerCamelCase__ ,   unet=lowerCamelCase__ ,   scheduler=lowerCamelCase__ ,   feature_extractor=lowerCamelCase__ ,   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ = "auto"   ):

      '''simple docstring'''







      if slice_size == "auto":
         UpperCamelCase       =  self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.enable_attention_slicing(lowerCamelCase__   )







   @torch.no_grad()
   def __call__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=1_6_0_0_0 ,   lowerCamelCase__ = 5_1_2 ,   lowerCamelCase__ = 5_1_2 ,   lowerCamelCase__ = 5_0 ,   lowerCamelCase__ = 7.5 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 1 ,   lowerCamelCase__ = 0.0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "pil" ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 1 ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  self.speech_processor.feature_extractor(
          lowerCamelCase__ ,   return_tensors='''pt''' ,   sampling_rate=lowerCamelCase__   ).input_features.to(self.device   )
      UpperCamelCase       =  self.speech_model.generate(lowerCamelCase__ ,   max_length=4_8_0_0_0_0   )

      UpperCamelCase       =  self.speech_processor.tokenizer.batch_decode(lowerCamelCase__ ,   skip_special_tokens=lowerCamelCase__ ,   normalize=lowerCamelCase__   )[
          0
      ]

      if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  1
      elif isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  len(lowerCamelCase__   )
      else:
         raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__   )}'   )

      if height % 8 != 0 or width % 8 != 0:
         raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'   )

      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(lowerCamelCase__ ,   lowerCamelCase__   ) or callback_steps <= 0)
      ):
         raise ValueError(
             f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
             f' {type(lowerCamelCase__   )}.'   )

      # get prompt text embeddings
      UpperCamelCase       =  self.tokenizer(
          lowerCamelCase__ ,   padding='''max_length''' ,   max_length=self.tokenizer.model_max_length ,   return_tensors='''pt''' ,   )
      UpperCamelCase       =  text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         UpperCamelCase       =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
         logger.warning(
             '''The following part of your input was truncated because CLIP can only handle sequences up to'''
             f' {self.tokenizer.model_max_length} tokens: {removed_text}'   )
         UpperCamelCase       =  text_input_ids[:, : self.tokenizer.model_max_length]
      UpperCamelCase       =  self.text_encoder(text_input_ids.to(self.device   )   )[0]

      # duplicate text embeddings for each generation per prompt, using mps friendly method
      UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  text_embeddings.shape
      UpperCamelCase       =  text_embeddings.repeat(1 ,   lowerCamelCase__ ,   1   )
      UpperCamelCase       =  text_embeddings.view(bs_embed * num_images_per_prompt ,   lowerCamelCase__ ,   -1   )

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      UpperCamelCase       =  guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
         UpperCamelCase       =  42
         if negative_prompt is None:
            UpperCamelCase       =  [''''''] * batch_size
         elif type(lowerCamelCase__   ) is not type(lowerCamelCase__   ):
            raise TypeError(
                f'`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase__   )} !='
                f' {type(lowerCamelCase__   )}.'   )
         elif isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
            UpperCamelCase       =  [negative_prompt]
         elif batch_size != len(lowerCamelCase__   ):
            raise ValueError(
                f'`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase__   )}, but `prompt`:'
                f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                ''' the batch size of `prompt`.'''   )
         else:
            UpperCamelCase       =  negative_prompt

         UpperCamelCase       =  text_input_ids.shape[-1]
         UpperCamelCase       =  self.tokenizer(
             lowerCamelCase__ ,   padding='''max_length''' ,   max_length=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt''' ,   )
         UpperCamelCase       =  self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         UpperCamelCase       =  uncond_embeddings.shape[1]
         UpperCamelCase       =  uncond_embeddings.repeat(1 ,   lowerCamelCase__ ,   1   )
         UpperCamelCase       =  uncond_embeddings.view(batch_size * num_images_per_prompt ,   lowerCamelCase__ ,   -1   )

         # For classifier free guidance, we need to do two forward passes.
         # Here we concatenate the unconditional and text embeddings into a single batch
         # to avoid doing two forward passes
         UpperCamelCase       =  torch.cat([uncond_embeddings, text_embeddings]   )

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      UpperCamelCase       =  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      UpperCamelCase       =  text_embeddings.dtype
      if latents is None:
         if self.device.type == "mps":
            # randn does not exist on mps
            UpperCamelCase       =  torch.randn(lowerCamelCase__ ,   generator=lowerCamelCase__ ,   device='''cpu''' ,   dtype=lowerCamelCase__   ).to(
                self.device   )
         else:
            UpperCamelCase       =  torch.randn(lowerCamelCase__ ,   generator=lowerCamelCase__ ,   device=self.device ,   dtype=lowerCamelCase__   )
      else:
         if latents.shape != latents_shape:
            raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'   )
         UpperCamelCase       =  latents.to(self.device   )

      # set timesteps
      self.scheduler.set_timesteps(lowerCamelCase__   )

      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      UpperCamelCase       =  self.scheduler.timesteps.to(self.device   )

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCamelCase       =  latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      UpperCamelCase       =  '''eta''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
      UpperCamelCase       =  {}
      if accepts_eta:
         UpperCamelCase       =  eta

      for i, t in enumerate(self.progress_bar(lowerCamelCase__   )   ):
         # expand the latents if we are doing classifier free guidance
         UpperCamelCase       =  torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
         UpperCamelCase       =  self.scheduler.scale_model_input(lowerCamelCase__ ,   lowerCamelCase__   )

         # predict the noise residual
         UpperCamelCase       =  self.unet(lowerCamelCase__ ,   lowerCamelCase__ ,   encoder_hidden_states=lowerCamelCase__   ).sample

         # perform guidance
         if do_classifier_free_guidance:
            UpperCamelCase     , UpperCamelCase       =  noise_pred.chunk(2   )
            UpperCamelCase       =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         # compute the previous noisy sample x_t -> x_t-1
         UpperCamelCase       =  self.scheduler.step(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   **lowerCamelCase__   ).prev_sample

         # call the callback, if provided
         if callback is not None and i % callback_steps == 0:
            callback(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )

      UpperCamelCase       =  1 / 0.18215 * latents
      UpperCamelCase       =  self.vae.decode(lowerCamelCase__   ).sample

      UpperCamelCase       =  (image / 2 + 0.5).clamp(0 ,   1   )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      UpperCamelCase       =  image.cpu().permute(0 ,   2 ,   3 ,   1   ).float().numpy()

      if output_type == "pil":
         UpperCamelCase       =  self.numpy_to_pil(lowerCamelCase__   )

      if not return_dict:
         return image

      return StableDiffusionPipelineOutput(images=lowerCamelCase__ ,   nsfw_content_detected=lowerCamelCase__   )







'''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 DeformableDetrImageProcessor


class 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__=7 ,   lowerCamelCase__=3 ,   lowerCamelCase__=3_0 ,   lowerCamelCase__=4_0_0 ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=[0.5, 0.5, 0.5] ,   lowerCamelCase__=True ,   lowerCamelCase__=1 / 2_5_5 ,   lowerCamelCase__=True ,   ):

      '''simple docstring'''







      UpperCamelCase       =  size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
      UpperCamelCase       =  parent
      UpperCamelCase       =  batch_size
      UpperCamelCase       =  num_channels
      UpperCamelCase       =  min_resolution
      UpperCamelCase       =  max_resolution
      UpperCamelCase       =  do_resize
      UpperCamelCase       =  size
      UpperCamelCase       =  do_normalize
      UpperCamelCase       =  image_mean
      UpperCamelCase       =  image_std
      UpperCamelCase       =  do_rescale
      UpperCamelCase       =  rescale_factor
      UpperCamelCase       =  do_pad







   def       UpperCAmelCase				(			self   ):

      '''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       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=False   ):

      '''simple docstring'''







      if not batched:
         UpperCamelCase       =  image_inputs[0]
         if isinstance(lowerCamelCase__ ,   Image.Image   ):
            UpperCamelCase     , UpperCamelCase       =  image.size
         else:
            UpperCamelCase     , UpperCamelCase       =  image.shape[1], image.shape[2]
         if w < h:
            UpperCamelCase       =  int(self.size['''shortest_edge'''] * h / w   )
            UpperCamelCase       =  self.size['''shortest_edge''']
         elif w > h:
            UpperCamelCase       =  self.size['''shortest_edge''']
            UpperCamelCase       =  int(self.size['''shortest_edge'''] * w / h   )
         else:
            UpperCamelCase       =  self.size['''shortest_edge''']
            UpperCamelCase       =  self.size['''shortest_edge''']

      else:
         UpperCamelCase       =  []
         for image in image_inputs:
            UpperCamelCase     , UpperCamelCase       =  self.get_expected_values([image]   )
            expected_values.append((expected_height, expected_width)   )
         UpperCamelCase       =  max(lowerCamelCase__ ,   key=lambda lowerCamelCase__   : item[0]   )[0]
         UpperCamelCase       =  max(lowerCamelCase__ ,   key=lambda lowerCamelCase__   : item[1]   )[1]

      return expected_height, expected_width




@require_torch
@require_vision
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							DeformableDetrImageProcessor if is_vision_available() else None







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  DeformableDetrImageProcessingTester(self   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.image_processor_tester.prepare_image_processor_dict()







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}   )
      self.assertEqual(image_processor.do_pad ,   lowerCamelCase__   )

      UpperCamelCase       =  self.image_processing_class.from_dict(
          self.image_processor_dict ,   size=4_2 ,   max_size=8_4 ,   pad_and_return_pixel_mask=lowerCamelCase__   )
      self.assertEqual(image_processor.size ,   {'''shortest_edge''': 4_2, '''longest_edge''': 8_4}   )
      self.assertEqual(image_processor.do_pad ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      pass







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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

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

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__   )

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

      # Test batched
      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )

      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   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       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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

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

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__   )

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

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )

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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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

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

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__   )

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

      # Test batched
      UpperCamelCase       =  image_processing(lowerCamelCase__ ,   return_tensors='''pt'''   ).pixel_values

      UpperCamelCase     , UpperCamelCase       =  self.image_processor_tester.get_expected_values(lowerCamelCase__ ,   batched=lowerCamelCase__   )

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







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
      with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' ,   '''r'''   ) as f:
         UpperCamelCase       =  json.loads(f.read()   )

      UpperCamelCase       =  {'''image_id''': 3_9_7_6_9, '''annotations''': target}

      # encode them
      UpperCamelCase       =  DeformableDetrImageProcessor()
      UpperCamelCase       =  image_processing(images=lowerCamelCase__ ,   annotations=lowerCamelCase__ ,   return_tensors='''pt'''   )

      # verify pixel values
      UpperCamelCase       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
      self.assertEqual(encoding['''pixel_values'''].shape ,   lowerCamelCase__   )

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

      # verify area
      UpperCamelCase       =  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   lowerCamelCase__   )   )
      # verify boxes
      UpperCamelCase       =  torch.Size([6, 4]   )
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowerCamelCase__   )
      UpperCamelCase       =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   lowerCamelCase__ ,   atol=1e-3   )   )
      # verify image_id
      UpperCamelCase       =  torch.tensor([3_9_7_6_9]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowerCamelCase__   )   )
      # verify is_crowd
      UpperCamelCase       =  torch.tensor([0, 0, 0, 0, 0, 0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowerCamelCase__   )   )
      # verify class_labels
      UpperCamelCase       =  torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowerCamelCase__   )   )
      # verify orig_size
      UpperCamelCase       =  torch.tensor([4_8_0, 6_4_0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowerCamelCase__   )   )
      # verify size
      UpperCamelCase       =  torch.tensor([8_0_0, 1_0_6_6]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowerCamelCase__   )   )







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
      with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' ,   '''r'''   ) as f:
         UpperCamelCase       =  json.loads(f.read()   )

      UpperCamelCase       =  {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

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

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

      # verify pixel values
      UpperCamelCase       =  torch.Size([1, 3, 8_0_0, 1_0_6_6]   )
      self.assertEqual(encoding['''pixel_values'''].shape ,   lowerCamelCase__   )

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

      # verify area
      UpperCamelCase       =  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,   lowerCamelCase__   )   )
      # verify boxes
      UpperCamelCase       =  torch.Size([6, 4]   )
      self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,   lowerCamelCase__   )
      UpperCamelCase       =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,   lowerCamelCase__ ,   atol=1e-3   )   )
      # verify image_id
      UpperCamelCase       =  torch.tensor([3_9_7_6_9]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,   lowerCamelCase__   )   )
      # verify is_crowd
      UpperCamelCase       =  torch.tensor([0, 0, 0, 0, 0, 0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,   lowerCamelCase__   )   )
      # verify class_labels
      UpperCamelCase       =  torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,   lowerCamelCase__   )   )
      # verify masks
      UpperCamelCase       =  8_2_2_8_7_3
      self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,   lowerCamelCase__   )
      # verify orig_size
      UpperCamelCase       =  torch.tensor([4_8_0, 6_4_0]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,   lowerCamelCase__   )   )
      # verify size
      UpperCamelCase       =  torch.tensor([8_0_0, 1_0_6_6]   )
      self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,   lowerCamelCase__   )   )





