

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


_UpperCamelCase     =							'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'



class 					lowercase		( lowerCAmelCase_      ):



 '''simple docstring'''


 def __init__(self , __a , __a				)				->      str:






  """simple docstring"""





  super().__init__()

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

  UpperCAmelCase__           =       None




 def 						UpperCamelCase__							(self , __a=0				)				->      Union[str, Any]:






  """simple docstring"""





  if is_accelerate_available() and is_accelerate_version('>=' , '0.14.0'				):
   from accelerate import cpu_offload
  else:
   raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'				)

  UpperCAmelCase__           =       torch.device(F"cuda:{gpu_id}"				)

  if self.device.type != "cpu":
   self.to('cpu' , silence_dtype_warnings=__a				)
   torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

  for cpu_offloaded_model in [self.unet]:
   cpu_offload(__a , __a				)

  if self.safety_checker is not None:
   cpu_offload(self.safety_checker , execution_device=__a , offload_buffers=__a				)




 def 						UpperCamelCase__							(self , __a=0				)				->      Any:






  """simple docstring"""





  if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0'				):
   from accelerate import cpu_offload_with_hook
  else:
   raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'				)

  UpperCAmelCase__           =       torch.device(F"cuda:{gpu_id}"				)

  if self.device.type != "cpu":
   self.to('cpu' , silence_dtype_warnings=__a				)
   torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

  UpperCAmelCase__           =       None
  for cpu_offloaded_model in [self.unet]:
   UpperCAmelCase__           =       cpu_offload_with_hook(__a , __a , prev_module_hook=__a				)

  if self.safety_checker is not None:
   UpperCAmelCase__           =       cpu_offload_with_hook(self.safety_checker , __a , prev_module_hook=__a				)

  # We'll offload the last model manually.
  UpperCAmelCase__           =       hook




 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  if not hasattr(self.unet , '_hf_hook'				):
   return self.device
  for module in self.unet.modules():
   if (
       hasattr(__a , '_hf_hook'				)
       and hasattr(module._hf_hook , 'execution_device'				)
       and module._hf_hook.execution_device is not None
   ):
    return torch.device(module._hf_hook.execution_device				)
  return self.device




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a , __a , __a=None				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       (batch_size, num_channels, height, width)
  if isinstance(__a , __a				) and len(__a				) != batch_size:
   raise ValueError(
       F"You have passed a list of generators of length {len(__a				)}, but requested an effective batch"
       F" size of {batch_size}. Make sure the batch size matches the length of the generators."				)

  if latents is None:
   UpperCAmelCase__           =       randn_tensor(__a , generator=__a , device=__a , dtype=__a				)
  else:
   UpperCAmelCase__           =       latents.to(device=__a , dtype=__a				)

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




 def 						UpperCamelCase__							(self , __a , __a = "pil"				)				->      List[str]:






  """simple docstring"""





  if output_type not in ["pt", "np", "pil"]:
   raise ValueError(
       F"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"				)

  # Equivalent to diffusers.VaeImageProcessor.denormalize
  UpperCAmelCase__           =       (sample / 2 + 0.5).clamp(0 , 1				)
  if output_type == "pt":
   return sample

  # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
  UpperCAmelCase__           =       sample.cpu().permute(0 , 2 , 3 , 1				).numpy()
  if output_type == "np":
   return sample

  # Output_type must be 'pil'
  UpperCAmelCase__           =       self.numpy_to_pil(__a				)
  return sample




 def 						UpperCamelCase__							(self , __a , __a , __a=None				)				->      Optional[int]:






  """simple docstring"""





  if self.unet.config.num_class_embeds is not None:
   if isinstance(__a , __a				):
    UpperCAmelCase__           =       torch.tensor(__a , dtype=torch.int				)
   elif isinstance(__a , __a				):
    assert batch_size == 1, "Batch size must be 1 if classes is an int"
    UpperCAmelCase__           =       torch.tensor([class_labels] , dtype=torch.int				)
   elif class_labels is None:
    # Randomly generate batch_size class labels
    # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
    UpperCAmelCase__           =       torch.randint(0 , self.unet.config.num_class_embeds , size=(batch_size,)				)
   UpperCAmelCase__           =       class_labels.to(__a				)
  else:
   UpperCAmelCase__           =       None
  return class_labels




 def 						UpperCamelCase__							(self , __a , __a , __a , __a , __a , __a				)				->      str:






  """simple docstring"""





  if num_inference_steps is None and timesteps is None:
   raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'				)

  if num_inference_steps is not None and timesteps is not None:
   logger.warning(
       F"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
       ' `timesteps` will be used over `num_inference_steps`.'				)

  if latents is not None:
   UpperCAmelCase__           =       (batch_size, 3, img_size, img_size)
   if latents.shape != expected_shape:
    raise ValueError(F"The shape of latents is {latents.shape} but is expected to be {expected_shape}."				)

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




 @torch.no_grad()
 @replace_example_docstring(__a				)
 def __call__(self , __a = 1 , __a = None , __a = 1 , __a = None , __a = None , __a = None , __a = "pil" , __a = True , __a = None , __a = 1 , )				->      List[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.unet.config.sample_size
  UpperCAmelCase__           =       self._execution_device

  # 1. Check inputs
  self.check_inputs(__a , __a , __a , __a , __a , __a				)

  # 2. Prepare image latents
  # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
  UpperCAmelCase__           =       self.prepare_latents(
      batch_size=__a , num_channels=self.unet.config.in_channels , height=__a , width=__a , dtype=self.unet.dtype , device=__a , generator=__a , latents=__a , )

  # 3. Handle class_labels for class-conditional models
  UpperCAmelCase__           =       self.prepare_class_labels(__a , __a , class_labels=__a				)

  # 4. Prepare timesteps
  if timesteps is not None:
   self.scheduler.set_timesteps(timesteps=__a , device=__a				)
   UpperCAmelCase__           =       self.scheduler.timesteps
   UpperCAmelCase__           =       len(__a				)
  else:
   self.scheduler.set_timesteps(__a				)
   UpperCAmelCase__           =       self.scheduler.timesteps

  # 5. Denoising loop
  # Multistep sampling: implements Algorithm 1 in the paper
  with self.progress_bar(total=__a				) as progress_bar:
   for i, t in enumerate(__a				):
    UpperCAmelCase__           =       self.scheduler.scale_model_input(__a , __a				)
    UpperCAmelCase__           =       self.unet(__a , __a , class_labels=__a , return_dict=__a				)[0]

    UpperCAmelCase__           =       self.scheduler.step(__a , __a , __a , generator=__a				)[0]

    # call the callback, if provided
    progress_bar.update()
    if callback is not None and i % callback_steps == 0:
     callback(__a , __a , __a				)

        # 6. Post-process image sample
  UpperCAmelCase__           =       self.postprocess_image(__a , output_type=__a				)

  # Offload last model to CPU
  if hasattr(self , 'final_offload_hook'				) and self.final_offload_hook is not None:
   self.final_offload_hook.offload()

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=__a				)






import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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():
 import PIL

 from transformers import FlavaImageProcessor
 from transformers.image_utils import PILImageResampling
 from transformers.models.flava.image_processing_flava import (
     FLAVA_CODEBOOK_MEAN,
     FLAVA_CODEBOOK_STD,
     FLAVA_IMAGE_MEAN,
     FLAVA_IMAGE_STD,
 )
else:
 _UpperCamelCase     =    _UpperCamelCase     =    _UpperCamelCase     =    _UpperCamelCase     =    None



class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 def __init__(self , __a , __a=7 , __a=3 , __a=30 , __a=400 , __a=True , __a=None , __a=True , __a=None , __a=None , __a=True , __a=1 / 255 , __a=True , __a=FLAVA_IMAGE_MEAN , __a=FLAVA_IMAGE_STD , __a=14 , __a=75 , __a=None , __a=16 , __a=0.3 , __a=None , __a=True , __a=None , __a=None , __a=True , __a=None , __a=True , __a=True , __a=FLAVA_CODEBOOK_MEAN , __a=FLAVA_CODEBOOK_STD , )				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       size if size is not None else {'height': 224, 'width': 224}
  UpperCAmelCase__           =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
  UpperCAmelCase__           =       codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
  UpperCAmelCase__           =       codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

  UpperCAmelCase__           =       parent
  UpperCAmelCase__           =       batch_size
  UpperCAmelCase__           =       num_channels
  UpperCAmelCase__           =       do_resize
  UpperCAmelCase__           =       do_rescale
  UpperCAmelCase__           =       rescale_factor
  UpperCAmelCase__           =       min_resolution
  UpperCAmelCase__           =       max_resolution
  UpperCAmelCase__           =       size
  UpperCAmelCase__           =       resample if resample is not None else PILImageResampling.BICUBIC
  UpperCAmelCase__           =       do_normalize
  UpperCAmelCase__           =       image_mean
  UpperCAmelCase__           =       image_std
  UpperCAmelCase__           =       do_center_crop
  UpperCAmelCase__           =       crop_size

  UpperCAmelCase__           =       input_size_patches
  UpperCAmelCase__           =       total_mask_patches
  UpperCAmelCase__           =       mask_group_max_patches
  UpperCAmelCase__           =       mask_group_min_patches
  UpperCAmelCase__           =       mask_group_min_aspect_ratio
  UpperCAmelCase__           =       mask_group_max_aspect_ratio

  UpperCAmelCase__           =       codebook_do_resize
  UpperCAmelCase__           =       codebook_size
  UpperCAmelCase__           =       codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
  UpperCAmelCase__           =       codebook_do_center_crop
  UpperCAmelCase__           =       codebook_crop_size
  UpperCAmelCase__           =       codebook_do_map_pixels
  UpperCAmelCase__           =       codebook_do_normalize
  UpperCAmelCase__           =       codebook_image_mean
  UpperCAmelCase__           =       codebook_image_std




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  return {
      "image_mean": self.image_mean,
      "image_std": self.image_std,
      "do_normalize": self.do_normalize,
      "do_resize": self.do_resize,
      "size": self.size,
      "resample": self.resample,
      "do_rescale": self.do_rescale,
      "rescale_factor": self.rescale_factor,
      "do_center_crop": self.do_center_crop,
      "crop_size": self.crop_size,
      "input_size_patches": self.input_size_patches,
      "total_mask_patches": self.total_mask_patches,
      "mask_group_max_patches": self.mask_group_max_patches,
      "mask_group_min_patches": self.mask_group_min_patches,
      "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
      "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
      "codebook_do_resize": self.codebook_do_resize,
      "codebook_size": self.codebook_size,
      "codebook_resample": self.codebook_resample,
      "codebook_do_center_crop": self.codebook_do_center_crop,
      "codebook_crop_size": self.codebook_crop_size,
      "codebook_do_map_pixels": self.codebook_do_map_pixels,
      "codebook_do_normalize": self.codebook_do_normalize,
      "codebook_image_mean": self.codebook_image_mean,
      "codebook_image_std": self.codebook_image_std,
  }




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return (self.size["height"], self.size["width"])




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (
      (self.input_size_patches, self.input_size_patches)
      if not isinstance(self.input_size_patches , __a				)
      else self.input_size_patches
  )




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class 					lowercase		( _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       FlavaImageProcessor if is_vision_available() else None
 __SCREAMING_SNAKE_CASE      							=       None




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       FlavaImageProcessingTester(self				)




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       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 , 'resample'				)				)
  self.assertTrue(hasattr(__a , 'crop_size'				)				)
  self.assertTrue(hasattr(__a , 'do_center_crop'				)				)
  self.assertTrue(hasattr(__a , 'do_rescale'				)				)
  self.assertTrue(hasattr(__a , 'rescale_factor'				)				)
  self.assertTrue(hasattr(__a , 'masking_generator'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_resize'				)				)
  self.assertTrue(hasattr(__a , 'codebook_size'				)				)
  self.assertTrue(hasattr(__a , 'codebook_resample'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_center_crop'				)				)
  self.assertTrue(hasattr(__a , 'codebook_crop_size'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_map_pixels'				)				)
  self.assertTrue(hasattr(__a , 'codebook_do_normalize'				)				)
  self.assertTrue(hasattr(__a , 'codebook_image_mean'				)				)
  self.assertTrue(hasattr(__a , 'codebook_image_std'				)				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class.from_dict(self.image_processor_dict				)
  self.assertEqual(image_processor.size , {'height': 224, 'width': 224}				)
  self.assertEqual(image_processor.crop_size , {'height': 224, 'width': 224}				)
  self.assertEqual(image_processor.codebook_size , {'height': 112, 'width': 112}				)
  self.assertEqual(image_processor.codebook_crop_size , {'height': 112, 'width': 112}				)

  UpperCAmelCase__           =       self.image_processing_class.from_dict(
      self.image_processor_dict , size=42 , crop_size=84 , codebook_size=33 , codebook_crop_size=66				)
  self.assertEqual(image_processor.size , {'height': 42, 'width': 42}				)
  self.assertEqual(image_processor.crop_size , {'height': 84, 'width': 84}				)
  self.assertEqual(image_processor.codebook_size , {'height': 33, 'width': 33}				)
  self.assertEqual(image_processor.codebook_crop_size , {'height': 66, 'width': 66}				)




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """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=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , PIL.Image.Image				)

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

  # Test no bool masked pos
  self.assertFalse('bool_masked_pos' in encoded_images				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()

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

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()

  # Test no bool masked pos
  self.assertFalse('bool_masked_pos' in encoded_images				)

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




 def 						UpperCamelCase__							(self , __a , __a				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  # create random tensors
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , **__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , __a				)

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

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  UpperCAmelCase__           =       image_processing(__a , return_image_mask=__a , return_tensors='pt'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_mask_size()
  self.assertEqual(
      encoded_images.bool_masked_pos.shape , (
          self.image_processor_tester.batch_size,
          expected_height,
          expected_width,
      ) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_tensors='pt'				).pixel_values

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

  # Test masking
  UpperCAmelCase__           =       image_processing(__a , return_image_mask=__a , return_tensors='pt'				)

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_image_size()
  self.assertEqual(
      encoded_images.pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )

  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_mask_size()
  self.assertEqual(
      encoded_images.bool_masked_pos.shape , (
          self.image_processor_tester.batch_size,
          expected_height,
          expected_width,
      ) , )




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  self._test_call_framework(np.ndarray , prepare_kwargs={'numpify': True}				)




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  self._test_call_framework(torch.Tensor , prepare_kwargs={'torchify': True}				)




 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  random.seed(1234				)
  UpperCAmelCase__           =       self.image_processing_class(**self.image_processor_dict				)
  UpperCAmelCase__           =       prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_image_mask=__a , return_tensors='pt'				)
  self.assertEqual(encoded_images.bool_masked_pos.sum().item() , 75				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """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=__a				)
  for image in image_inputs:
   self.assertIsInstance(__a , PIL.Image.Image				)

  # Test not batched input
  UpperCAmelCase__           =       image_processing(image_inputs[0] , return_codebook_pixels=__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_codebook_image_size()
  self.assertEqual(
      encoded_images.codebook_pixel_values.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )

  # Test batched
  UpperCAmelCase__           =       image_processing(__a , return_codebook_pixels=__a , return_tensors='pt'				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.image_processor_tester.get_expected_codebook_image_size()
  self.assertEqual(
      encoded_images.codebook_pixel_values.shape , (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_channels,
          expected_height,
          expected_width,
      ) , )



