


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


A				:   Union[str, Any]		= logging.get_logger(__name__)  # pylint: disable=invalid-name

A				:   Dict		= "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\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\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        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\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 				lowercase_		(		_A      :   Any   , _A      :   Tuple   , _A      :   Union[str, Any]=8						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[int]   =  height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      lowerCamelCase__		:    Dict   =  width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor

def 				lowercase_		(		_A      :   Optional[int]   , _A      :   Union[str, Any]=512   , _A      :   str=512						):

      """simple docstring"""




      lowerCamelCase__		:    Union[str, Any]   =  pil_image.resize((w, h)   , resample=Image.BICUBIC   , reducing_gap=1						)
      lowerCamelCase__		:    Any   =  np.array(pil_image.convert("RGB"						)						)
      lowerCamelCase__		:    List[str]   =  arr.astype(np.floataa						) / 127.5 - 1
      lowerCamelCase__		:    Dict   =  np.transpose(_A   , [2, 0, 1]						)
      lowerCamelCase__		:    Union[str, Any]   =  torch.from_numpy(_A						).unsqueeze(0						)
      return image







class       _lowercase			(							lowercase__):


       """simple docstring"""




       def __init__(       self							:      Optional[int] ,				__lowerCamelCase							:      UNetaDConditionModel ,				__lowerCamelCase							:      DDPMScheduler ,				__lowerCamelCase							:      VQModel ,				):


             '''simple docstring'''



             super().__init__()

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





       def 						lowerCAmelCase					(       self							:      Any ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  min(int(num_inference_steps * strength	) ,				__lowerCamelCase	)

             lowerCamelCase__		:    str   =  max(num_inference_steps - init_timestep ,				0	)
             lowerCamelCase__		:    int   =  self.scheduler.timesteps[t_start:]

             return timesteps, num_inference_steps - t_start





       def 						lowerCAmelCase					(       self							:      Union[str, Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Dict=None	):


             '''simple docstring'''



             if not isinstance(__lowerCamelCase ,				(torch.Tensor, PIL.Image.Image, list)	):
                   raise ValueError(
                       f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowerCamelCase	)}"	)

             lowerCamelCase__		:    List[Any]   =  image.to(device=__lowerCamelCase ,				dtype=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  batch_size * num_images_per_prompt

             if image.shape[1] == 4:
                   lowerCamelCase__		:    int   =  image

             else:
                   if isinstance(__lowerCamelCase ,				__lowerCamelCase	) and len(__lowerCamelCase	) != batch_size:
                         raise ValueError(
                             f"You have passed a list of generators of length {len(__lowerCamelCase	)}, but requested an effective batch"
                             f" size of {batch_size}. Make sure the batch size matches the length of the generators."	)

                   elif isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                         lowerCamelCase__		:    Optional[Any]   =  [
                             self.movq.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(__lowerCamelCase	)
                         ]
                         lowerCamelCase__		:    int   =  torch.cat(__lowerCamelCase ,				dim=0	)
                   else:
                         lowerCamelCase__		:    List[str]   =  self.movq.encode(__lowerCamelCase	).latent_dist.sample(__lowerCamelCase	)

                   lowerCamelCase__		:    List[str]   =  self.movq.config.scaling_factor * init_latents

             lowerCamelCase__		:    int   =  torch.cat([init_latents] ,				dim=0	)

             lowerCamelCase__		:    Optional[int]   =  init_latents.shape
             lowerCamelCase__		:    Any   =  randn_tensor(__lowerCamelCase ,				generator=__lowerCamelCase ,				device=__lowerCamelCase ,				dtype=__lowerCamelCase	)

             # get latents
             lowerCamelCase__		:    Optional[int]   =  self.scheduler.add_noise(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)

             lowerCamelCase__		:    Tuple   =  init_latents

             return latents





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      str=0	):


             '''simple docstring'''



             if is_accelerate_available():
                   from accelerate import cpu_offload
             else:
                   raise ImportError("Please install accelerate via `pip install accelerate`"	)

             lowerCamelCase__		:    Tuple   =  torch.device(f"cuda:{gpu_id}"	)

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





       def 						lowerCAmelCase					(       self							:      int ,				__lowerCamelCase							:      str=0	):


             '''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."	)

             lowerCamelCase__		:    Union[str, Any]   =  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)

             lowerCamelCase__		:    Union[str, Any]   =  None
             for cpu_offloaded_model in [self.unet, self.movq]:
                   lowerCamelCase__   ,       lowerCamelCase__		:    Union[str, Any]   =  cpu_offload_with_hook(__lowerCamelCase ,				__lowerCamelCase ,				prev_module_hook=__lowerCamelCase	)

             # We'll offload the last model manually.
             lowerCamelCase__		:    Any   =  hook





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


             '''simple docstring'''



             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							:      Union[str, Any] ,				__lowerCamelCase							:      Union[torch.FloatTensor, List[torch.FloatTensor]] ,				__lowerCamelCase							:      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] ,				__lowerCamelCase							:      Union[torch.FloatTensor, List[torch.FloatTensor]] ,				__lowerCamelCase							:      torch.FloatTensor ,				__lowerCamelCase							:      int = 512 ,				__lowerCamelCase							:      int = 512 ,				__lowerCamelCase							:      int = 100 ,				__lowerCamelCase							:      float = 4.0 ,				__lowerCamelCase							:      float = 0.3 ,				__lowerCamelCase							:      int = 1 ,				__lowerCamelCase							:      Optional[Union[torch.Generator, List[torch.Generator]]] = None ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				):


             '''simple docstring'''



             lowerCamelCase__		:    List[str]   =  self._execution_device

             lowerCamelCase__		:    Dict   =  guidance_scale > 1.0

             if isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    Any   =  torch.cat(__lowerCamelCase ,				dim=0	)
             if isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    str   =  torch.cat(__lowerCamelCase ,				dim=0	)
             if isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    int   =  torch.cat(__lowerCamelCase ,				dim=0	)

             lowerCamelCase__		:    Any   =  image_embeds.shape[0]

             if do_classifier_free_guidance:
                   lowerCamelCase__		:    Dict   =  image_embeds.repeat_interleave(__lowerCamelCase ,				dim=0	)
                   lowerCamelCase__		:    Union[str, Any]   =  negative_image_embeds.repeat_interleave(__lowerCamelCase ,				dim=0	)
                   lowerCamelCase__		:    Union[str, Any]   =  hint.repeat_interleave(__lowerCamelCase ,				dim=0	)

             lowerCamelCase__		:    Optional[int]   =  torch.cat([negative_image_embeds, image_embeds] ,				dim=0	).to(dtype=self.unet.dtype ,				device=__lowerCamelCase	)
             lowerCamelCase__		:    Dict   =  torch.cat([hint, hint] ,				dim=0	).to(dtype=self.unet.dtype ,				device=__lowerCamelCase	)

             if not isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    Union[str, Any]   =  [image]
             if not all(isinstance(__lowerCamelCase ,				(PIL.Image.Image, torch.Tensor)	) for i in image	):
                   raise ValueError(
                       f"Input is in incorrect format: {[type(__lowerCamelCase	) for i in image]}. Currently, we only support  PIL image and pytorch tensor"	)

             lowerCamelCase__		:    List[Any]   =  torch.cat([prepare_image(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	) for i in image] ,				dim=0	)
             lowerCamelCase__		:    List[Any]   =  image.to(dtype=image_embeds.dtype ,				device=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  self.movq.encode(__lowerCamelCase	)["latents"]
             lowerCamelCase__		:    str   =  latents.repeat_interleave(__lowerCamelCase ,				dim=0	)
             self.scheduler.set_timesteps(__lowerCamelCase ,				device=__lowerCamelCase	)
             lowerCamelCase__   ,       lowerCamelCase__		:    Dict   =  self.get_timesteps(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  timesteps[:1].repeat(batch_size * num_images_per_prompt	)
             lowerCamelCase__   ,       lowerCamelCase__		:    List[str]   =  downscale_height_and_width(__lowerCamelCase ,				__lowerCamelCase ,				self.movq_scale_factor	)
             lowerCamelCase__		:    Union[str, Any]   =  self.prepare_latents(
                 __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				image_embeds.dtype ,				__lowerCamelCase ,				__lowerCamelCase	)
             for i, t in enumerate(self.progress_bar(__lowerCamelCase	)	):
                   # expand the latents if we are doing classifier free guidance
                   lowerCamelCase__		:    Dict   =  torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

                   lowerCamelCase__		:    List[Any]   =  {"image_embeds": image_embeds, "hint": hint}
                   lowerCamelCase__		:    Union[str, Any]   =  self.unet(
                       sample=__lowerCamelCase ,				timestep=__lowerCamelCase ,				encoder_hidden_states=__lowerCamelCase ,				added_cond_kwargs=__lowerCamelCase ,				return_dict=__lowerCamelCase ,				)[0]

                   if do_classifier_free_guidance:
                         lowerCamelCase__   ,       lowerCamelCase__		:    Optional[int]   =  noise_pred.split(latents.shape[1] ,				dim=1	)
                         lowerCamelCase__   ,       lowerCamelCase__		:    Union[str, Any]   =  noise_pred.chunk(2	)
                         lowerCamelCase__   ,       lowerCamelCase__		:    Union[str, Any]   =  variance_pred.chunk(2	)
                         lowerCamelCase__		:    Optional[Any]   =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                         lowerCamelCase__		:    List[Any]   =  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"]
                   ):
                         lowerCamelCase__   ,       lowerCamelCase__		:    Tuple   =  noise_pred.split(latents.shape[1] ,				dim=1	)

                   # compute the previous noisy sample x_t -> x_t-1

                   lowerCamelCase__		:    Optional[int]   =  self.scheduler.step(
                       __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				generator=__lowerCamelCase ,				)[0]

             # post-processing
             lowerCamelCase__		:    Tuple   =  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"]:
                   lowerCamelCase__		:    str   =  image * 0.5 + 0.5
                   lowerCamelCase__		:    str   =  image.clamp(0 ,				1	)
                   lowerCamelCase__		:    List[str]   =  image.cpu().permute(0 ,				2 ,				3 ,				1	).float().numpy()

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

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=__lowerCamelCase	)






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():
    from PIL import Image

    from transformers import EfficientNetImageProcessor







class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def __init__(       self							:      Optional[Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      int=13 ,				__lowerCamelCase							:      Tuple=3 ,				__lowerCamelCase							:      Optional[Any]=18 ,				__lowerCamelCase							:      List[Any]=30 ,				__lowerCamelCase							:      Union[str, Any]=400 ,				__lowerCamelCase							:      Union[str, Any]=True ,				__lowerCamelCase							:      Union[str, Any]=None ,				__lowerCamelCase							:      Optional[Any]=True ,				__lowerCamelCase							:      str=[0.5, 0.5, 0.5] ,				__lowerCamelCase							:      Dict=[0.5, 0.5, 0.5] ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  size if size is not None else {"height": 18, "width": 18}
             lowerCamelCase__		:    Dict   =  parent
             lowerCamelCase__		:    str   =  batch_size
             lowerCamelCase__		:    Tuple   =  num_channels
             lowerCamelCase__		:    Tuple   =  image_size
             lowerCamelCase__		:    Any   =  min_resolution
             lowerCamelCase__		:    Dict   =  max_resolution
             lowerCamelCase__		:    Tuple   =  do_resize
             lowerCamelCase__		:    List[str]   =  size
             lowerCamelCase__		:    Optional[Any]   =  do_normalize
             lowerCamelCase__		:    Union[str, Any]   =  image_mean
             lowerCamelCase__		:    Optional[Any]   =  image_std





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''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,
             }







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


       """simple docstring"""




       A__ 						=      EfficientNetImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  EfficientNetImageProcessorTester(self	)





       @property
       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Union[str, Any]   =  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 ,				"size"	)	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  self.image_processing_class.from_dict(self.image_processor_dict	)
             self.assertEqual(image_processor.size ,				{"height": 18, "width": 18}	)

             lowerCamelCase__		:    Optional[int]   =  self.image_processing_class.from_dict(self.image_processor_dict ,				size=42	)
             self.assertEqual(image_processor.size ,				{"height": 42, "width": 42}	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



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

             # Test not batched input
             lowerCamelCase__		:    Union[str, Any]   =  image_processing(image_inputs[0] ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    Dict   =  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,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  self.image_processing_class(**self.image_processor_dict	)
             # create random numpy tensors
             lowerCamelCase__		:    List[Any]   =  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
             lowerCamelCase__		:    List[str]   =  image_processing(image_inputs[0] ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    Union[str, Any]   =  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,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  self.image_processing_class(**self.image_processor_dict	)
             # create random PyTorch tensors
             lowerCamelCase__		:    int   =  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
             lowerCamelCase__		:    Dict   =  image_processing(image_inputs[0] ,				return_tensors="pt"	).pixel_values
             self.assertEqual(
                 encoded_images.shape ,				(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)

             # Test batched
             lowerCamelCase__		:    List[Any]   =  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,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 ) ,				)


