"""simple docstring"""






from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCAmelCase__  :  Optional[Any]						     =	logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase__  :  List[str]						     =	'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=8       ):
				UpperCAmelCase__             =							height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				UpperCAmelCase__             =							width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def __init__(				self   :	Tuple    ,lowerCamelCase__   :	UNetaDConditionModel    ,lowerCamelCase__   :	DDPMScheduler    ,lowerCamelCase__   :	VQModel    ,):
									super().__init__()

									self.register_modules(
									    unet=lowerCamelCase__    ,scheduler=lowerCamelCase__    ,movq=lowerCamelCase__    ,)
									UpperCAmelCase__             =							2 ** (len(self.movq.config.block_out_channels			) - 1)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Dict    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	int			):
									if latents is None:
													UpperCAmelCase__             =							randn_tensor(lowerCamelCase__    ,generator=lowerCamelCase__    ,device=lowerCamelCase__    ,dtype=lowerCamelCase__			)
									else:
													if latents.shape != shape:
																	raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
													UpperCAmelCase__             =							latents.to(lowerCamelCase__			)

									UpperCAmelCase__             =							latents * scheduler.init_noise_sigma
									return latents







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	List[str]=0			):
									if is_accelerate_available():
													from accelerate import cpu_offload
									else:
													raise ImportError('Please install accelerate via `pip install accelerate`'			)

									UpperCAmelCase__             =							torch.device(f'''cuda:{gpu_id}'''			)

									UpperCAmelCase__             =							[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																	cpu_offload(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	Tuple=0			):
									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=lowerCamelCase__			)
													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, self.movq]:
													UpperCAmelCase__      ,	UpperCAmelCase__             =							cpu_offload_with_hook(lowerCamelCase__    ,lowerCamelCase__    ,prev_module_hook=lowerCamelCase__			)

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







					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									if not hasattr(self.unet    ,'_hf_hook'			):
													return self.device
									for module in self.unet.modules():
													if (
													    hasattr(lowerCamelCase__    ,'_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







					@torch.no_grad()
					@replace_example_docstring(lowerCamelCase__			)
					def __call__(				self   :	str    ,lowerCamelCase__   :	Union[torch.FloatTensor, List[torch.FloatTensor]]    ,lowerCamelCase__   :	Union[torch.FloatTensor, List[torch.FloatTensor]]    ,lowerCamelCase__   :	torch.FloatTensor    ,lowerCamelCase__   :	int = 512    ,lowerCamelCase__   :	int = 512    ,lowerCamelCase__   :	int = 100    ,lowerCamelCase__   :	float = 4.0    ,lowerCamelCase__   :	int = 1    ,lowerCamelCase__   :	Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,lowerCamelCase__   :	Optional[torch.FloatTensor] = None    ,lowerCamelCase__   :	Optional[str] = "pil"    ,lowerCamelCase__   :	bool = True    ,):
									UpperCAmelCase__             =							self._execution_device

									UpperCAmelCase__             =							guidance_scale > 1.0

									if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=0			)
									if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=0			)
									if isinstance(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							torch.cat(lowerCamelCase__    ,dim=0			)

									UpperCAmelCase__             =							image_embeds.shape[0] * num_images_per_prompt

									if do_classifier_free_guidance:
													UpperCAmelCase__             =							image_embeds.repeat_interleave(lowerCamelCase__    ,dim=0			)
													UpperCAmelCase__             =							negative_image_embeds.repeat_interleave(lowerCamelCase__    ,dim=0			)
													UpperCAmelCase__             =							hint.repeat_interleave(lowerCamelCase__    ,dim=0			)

									UpperCAmelCase__             =							torch.cat([negative_image_embeds, image_embeds]    ,dim=0			).to(dtype=self.unet.dtype    ,device=lowerCamelCase__			)
									UpperCAmelCase__             =							torch.cat([hint, hint]    ,dim=0			).to(dtype=self.unet.dtype    ,device=lowerCamelCase__			)
									self.scheduler.set_timesteps(lowerCamelCase__    ,device=lowerCamelCase__			)
									UpperCAmelCase__             =							self.scheduler.timesteps

									UpperCAmelCase__             =							self.movq.config.latent_channels

									UpperCAmelCase__      ,	UpperCAmelCase__             =							downscale_height_and_width(lowerCamelCase__    ,lowerCamelCase__    ,self.movq_scale_factor			)

									# create initial latent
									UpperCAmelCase__             =							self.prepare_latents(
									    (batch_size, num_channels_latents, height, width)    ,image_embeds.dtype    ,lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,self.scheduler    ,)

									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__             =							{'image_embeds': image_embeds, 'hint': hint}
													UpperCAmelCase__             =							self.unet(
													    sample=lowerCamelCase__    ,timestep=lowerCamelCase__    ,encoder_hidden_states=lowerCamelCase__    ,added_cond_kwargs=lowerCamelCase__    ,return_dict=lowerCamelCase__    ,)[0]

													if do_classifier_free_guidance:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							noise_pred.split(latents.shape[1]    ,dim=1			)
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							noise_pred.chunk(2			)
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							variance_pred.chunk(2			)
																	UpperCAmelCase__             =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCAmelCase__             =							torch.cat([noise_pred, variance_pred_text]    ,dim=1			)

													if not (
													    hasattr(self.scheduler.config    ,'variance_type'			)
													    and self.scheduler.config.variance_type in ["learned", "learned_range"]
													):
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							noise_pred.split(latents.shape[1]    ,dim=1			)

													# compute the previous noisy sample x_t -> x_t-1
													UpperCAmelCase__             =							self.scheduler.step(
													    lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__    ,generator=lowerCamelCase__    ,)[0]
									# post-processing
									UpperCAmelCase__             =							self.movq.decode(lowerCamelCase__    ,force_not_quantize=lowerCamelCase__			)['sample']

									if output_type not in ["pt", "np", "pil"]:
													raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''			)

									if output_type in ["np", "pil"]:
													UpperCAmelCase__             =							image * 0.5 + 0.5
													UpperCAmelCase__             =							image.clamp(0    ,1			)
													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 ImagePipelineOutput(images=lowerCamelCase__			)






'''simple docstring'''
import unittest

import numpy as np
import requests

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

     from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
     lowerCamelCase :      int					 =     False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor







class 							A__    (       unittest.TestCase  ):





  def __init__(		self      :	List[str]							,					_a      :	List[Any]							,					_a      :	List[str]=7							,					_a      :	List[str]=3							,					_a      :	Tuple=18							,					_a      :	Tuple=30							,					_a      :	str=400							,					_a      :	Tuple=None							,					_a      :	Union[str, Any]=True							,					_a      :	List[str]=True							,					_a      :	Optional[int]=None							,					)	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =size if size is not None else {'height': 20, 'width': 20}
       _SCREAMING_SNAKE_CASE							    =parent
       _SCREAMING_SNAKE_CASE							    =batch_size
       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =min_resolution
       _SCREAMING_SNAKE_CASE							    =max_resolution
       _SCREAMING_SNAKE_CASE							    =size
       _SCREAMING_SNAKE_CASE							    =do_normalize
       _SCREAMING_SNAKE_CASE							    =do_convert_rgb
       _SCREAMING_SNAKE_CASE							    =[512, 1024, 2048, 4096]
       _SCREAMING_SNAKE_CASE							    =patch_size if patch_size is not None else {'height': 16, 'width': 16}





  def 						A  (		self      :	Any )	->    List[str]:





       '''simple docstring'''






       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





  def 						A  (		self      :	int )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       _SCREAMING_SNAKE_CASE							    =Image.open(requests.get(_a							,					stream=_a ).raw ).convert('RGB' )
       return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,   reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,   )
@require_torch
@require_vision
class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				PixaStructImageProcessor if is_vision_available() else None





  def 						A  (		self      :	Dict )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =PixaStructImageProcessingTester(self )





  @property
  def 						A  (		self      :	Optional[Any] )	->    int:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self      :	Any )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_a							,					'do_normalize' ) )
       self.assertTrue(hasattr(_a							,					'do_convert_rgb' ) )





  def 						A  (		self      :	Any )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processor_tester.prepare_dummy_image()

       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       _SCREAMING_SNAKE_CASE							    =2048

       _SCREAMING_SNAKE_CASE							    =image_processor(_a							,					return_tensors='pt'							,					max_patches=_a )
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,					torch.tensor(0.06_06 )							,					atol=1e-3							,					rtol=1e-3 ) )





  def 						A  (		self      :	Any )	->    Any:





       '''simple docstring'''






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

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





  def 						A  (		self      :	List[str] )	->    Optional[Any]:





       '''simple docstring'''






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

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       _SCREAMING_SNAKE_CASE							    =True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(_a ):
                 _SCREAMING_SNAKE_CASE							    =image_processor(
                     image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches

            _SCREAMING_SNAKE_CASE							    ='Hello'

            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a							,					header_text=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a							,					header_text=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





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





       '''simple docstring'''






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

       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)





  def 						A  (		self      :	Union[str, Any] )	->    Tuple:





       '''simple docstring'''






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

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11     ,   reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'     ,   )
@require_torch
@require_vision
class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				PixaStructImageProcessor if is_vision_available() else None





  def 						A  (		self      :	str )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =PixaStructImageProcessingTester(self							,					num_channels=4 )
       _SCREAMING_SNAKE_CASE							    =3





  @property
  def 						A  (		self      :	List[str] )	->    Optional[Any]:





       '''simple docstring'''






       return self.image_processor_tester.prepare_image_processor_dict()





  def 						A  (		self      :	List[str] )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(_a							,					'do_normalize' ) )
       self.assertTrue(hasattr(_a							,					'do_convert_rgb' ) )





  def 						A  (		self      :	Dict )	->    int:





       '''simple docstring'''






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

       # Test not batched input
       _SCREAMING_SNAKE_CASE							    =(
           (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            _SCREAMING_SNAKE_CASE							    =image_processor(
                image_inputs[0]							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(1, max_patch, expected_hidden_dim)							,					)

            # Test batched
            _SCREAMING_SNAKE_CASE							    =image_processor(
                _a							,					return_tensors='pt'							,					max_patches=_a ).flattened_patches
            self.assertEqual(
                encoded_images.shape							,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,					)
