





from typing import Any

import numpy as np





class 	_lowercase		:

 def __init__(				self			:							Dict     ,   snake_case			:							np.ndarray     ,   snake_case			:							int    )     ->		None:

  """simple docstring"""





  if np.any(tableau[:, -1]     ,   where=tableau[:, -1] < 0    ):
   raise ValueError('RHS must be > 0'    )

  UpperCamelCase_      :		List[str]				=      tableau
  UpperCamelCase_      :		Tuple				=      tableau.shape

  # Number of decision variables x1, x2, x3...
  UpperCamelCase_      :		Dict				=      n_vars

  # Number of artificial variables to be minimised
  UpperCamelCase_      :		str				=      len(np.where(tableau[self.n_vars : -1] == -1    )[0]    )

  # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  UpperCamelCase_      :		int				=      (self.n_art_vars > 0) + 1

  # Number of slack variables added to make inequalities into equalities
  UpperCamelCase_      :		Union[str, Any]				=      self.n_rows - self.n_stages

  # Objectives for each stage
  UpperCamelCase_      :		Dict				=      ['max']

  # In two stage simplex, first minimise then maximise
  if self.n_art_vars:
   self.objectives.append('min'    )

  UpperCamelCase_      :		str				=      ['']

  # Index of current pivot row and column
  UpperCamelCase_      :		str				=      None
  UpperCamelCase_      :		Optional[int]				=      None

  # Does objective row only contain (non)-negative values?
  UpperCamelCase_      :		Any				=      False

 @staticmethod
 def 		SCREAMING_SNAKE_CASE__     (				*snake_case			:							int    )     ->		list[str]:

  """simple docstring"""





  if len(snake_case    ) != 3:
   raise ValueError('Must provide n_vars, n_slack, and n_art_vars'    )

  if not all(x >= 0 and isinstance(snake_case     ,   snake_case    ) for x in args    ):
   raise ValueError('All arguments must be non-negative integers'    )

  # decision | slack | artificial
  UpperCamelCase_      :		Tuple				=      ['x', 's', 'a']
  UpperCamelCase_      :		Optional[int]				=      []
  for i in range(3    ):
   for j in range(args[i]    ):
    titles.append(string_starts[i] + str(j + 1    )    )
  titles.append('RHS'    )
  return titles

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray    )     ->		tuple[Any, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      self.objectives[-1]

  # Find entries of highest magnitude in objective rows
  UpperCamelCase_      :		str				=      (objective == 'min') - (objective == 'max')
  UpperCamelCase_      :		str				=      np.argmax(sign * tableau[0, : self.n_vars]    )

  # Choice is only valid if below 0 for maximise, and above for minimise
  if sign * self.tableau[0, col_idx] <= 0:
   UpperCamelCase_      :		Tuple				=      True
   return 0, 0

  # Pivot row is chosen as having the lowest quotient when elements of
  # the pivot column divide the right-hand side

  # Slice excluding the objective rows
  UpperCamelCase_      :		Dict				=      slice(self.n_stages     ,   self.n_rows    )

  # RHS
  UpperCamelCase_      :		str				=      tableau[s, -1]

  # Elements of pivot column within slice
  UpperCamelCase_      :		List[Any]				=      tableau[s, col_idx]

  # Array filled with nans
  UpperCamelCase_      :		Optional[int]				=      np.full(self.n_rows - self.n_stages     ,   np.nan    )

  # If element in pivot column is greater than zeron_stages, return
  # quotient or nan otherwise
  UpperCamelCase_      :		int				=      np.divide(snake_case     ,   snake_case     ,   out=snake_case     ,   where=divisor > 0    )

  # Arg of minimum quotient excluding the nan values. n_stages is added
  # to compensate for earlier exclusion of objective columns
  UpperCamelCase_      :		int				=      np.nanargmin(snake_case    ) + self.n_stages
  return row_idx, col_idx

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							np.ndarray     ,   snake_case			:							int     ,   snake_case			:							int    )     ->		np.ndarray:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      tableau[row_idx].copy()

  UpperCamelCase_      :		List[Any]				=      piv_row[col_idx]

  # Entry becomes 1
  piv_row *= 1 / piv_val

  # Variable in pivot column becomes basic, ie the only non-zero entry
  for idx, coeff in enumerate(tableau[:, col_idx]    ):
   tableau[idx] += -coeff * piv_row
  UpperCamelCase_      :		Any				=      piv_row
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							np.ndarray    )     ->		np.ndarray:

  """simple docstring"""





  self.objectives.pop()

  if not self.objectives:
   return tableau

  # Slice containing ids for artificial columns
  UpperCamelCase_      :		int				=      slice(-self.n_art_vars - 1     ,   -1    )

  # Delete the artificial variable columns
  UpperCamelCase_      :		Any				=      np.delete(snake_case     ,   snake_case     ,   axis=1    )

  # Delete the objective row of the first stage
  UpperCamelCase_      :		Dict				=      np.delete(snake_case     ,   0     ,   axis=0    )

  UpperCamelCase_      :		List[Any]				=      1
  self.n_rows -= 1
  UpperCamelCase_      :		int				=      0
  UpperCamelCase_      :		Optional[int]				=      False
  return tableau

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		dict[Any, Any]:

  """simple docstring"""





  for _ in range(1_0_0    ):
   # Completion of each stage removes an objective. If both stages
   # are complete, then no objectives are left
   if not self.objectives:
    UpperCamelCase_      :		Any				=      self.generate_col_titles(
        self.n_vars     ,   self.n_slack     ,   self.n_art_vars    )

    # Find the values of each variable at optimal solution
    return self.interpret_tableau(self.tableau     ,   self.col_titles    )

   UpperCamelCase_      :		Tuple				=      self.find_pivot(self.tableau    )

   # If there are no more negative values in objective row
   if self.stop_iter:
    # Delete artificial variable columns and rows. Update attributes
    UpperCamelCase_      :		Union[str, Any]				=      self.change_stage(self.tableau    )
   else:
    UpperCamelCase_      :		Union[str, Any]				=      self.pivot(self.tableau     ,   snake_case     ,   snake_case    )
  return {}


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple     ,   snake_case			:							np.ndarray     ,   snake_case			:							list[str]    )     ->		dict[str, float]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      {'P': abs(tableau[0, -1]    )}

  for i in range(self.n_vars    ):
   # Gives ids of nonzero entries in the ith column
   UpperCamelCase_      :		Dict				=      np.nonzero(tableau[:, i]    )
   UpperCamelCase_      :		Union[str, Any]				=      len(nonzero[0]    )

   # First entry in the nonzero ids
   UpperCamelCase_      :		Optional[int]				=      nonzero[0][0]
   UpperCamelCase_      :		Union[str, Any]				=      tableau[nonzero_rowidx, i]

   # If there is only one nonzero value in column, which is one
   if n_nonzero == nonzero_val == 1:
    UpperCamelCase_      :		Optional[int]				=      tableau[nonzero_rowidx, -1]
    UpperCamelCase_      :		Union[str, Any]				=      rhs_val

        # Check for basic variables
  for title in col_titles:
   # Don't add RHS or slack variables to output dict
   if title[0] not in "R-s-a":
    output_dict.setdefault(snake_case     ,   0    )
  return output_dict


if __name__ == "__main__":
   import doctest

   doctest.testmod()




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

a_					      = '\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  (		lowerCamelCase      :  str						,						lowerCamelCase      :  List[str]						,						lowerCamelCase      :  str=8			):
 UpperCamelCase_      :		Union[str, Any]				=      height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 UpperCamelCase_      :		List[str]				=      width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor





def      __lowercase  (		lowerCamelCase      :  Optional[Any]						,						lowerCamelCase      :  Optional[int]=512						,						lowerCamelCase      :  str=512			):
 UpperCamelCase_      :		Any				=      pil_image.resize((w, h)						,						resample=Image.BICUBIC						,						reducing_gap=1			)
 UpperCamelCase_      :		Tuple				=      np.array(pil_image.convert('RGB'			)			)
 UpperCamelCase_      :		Optional[int]				=      arr.astype(np.floataa			) / 1_2_7.5 - 1
 UpperCamelCase_      :		int				=      np.transpose(lowerCamelCase						,						[2, 0, 1]			)
 UpperCamelCase_      :		List[Any]				=      torch.from_numpy(lowerCamelCase			).unsqueeze(0			)
 return image






class 	_lowercase		(      snake_case_ ):

 def __init__(				self			:							Union[str, Any]     ,   snake_case			:							UNetaDConditionModel     ,   snake_case			:							DDPMScheduler     ,   snake_case			:							VQModel     ,   )     ->		str:

  """simple docstring"""





  super().__init__()

  self.register_modules(
      unet=snake_case     ,   scheduler=snake_case     ,   movq=snake_case     ,   )
  UpperCamelCase_      :		Optional[Any]				=      2 ** (len(self.movq.config.block_out_channels    ) - 1)

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							str     ,   snake_case			:							List[Any]     ,   snake_case			:							int    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      min(int(num_inference_steps * strength    )     ,   snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      max(num_inference_steps - init_timestep     ,   0    )
  UpperCamelCase_      :		Union[str, Any]				=      self.scheduler.timesteps[t_start:]

  return timesteps, num_inference_steps - t_start

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							int     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							List[str]     ,   snake_case			:							str     ,   snake_case			:							Optional[int]     ,   snake_case			:							Dict     ,   snake_case			:							List[Any]=None    )     ->		Union[str, Any]:

  """simple docstring"""





  if not isinstance(snake_case     ,   (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(snake_case    )}"    )

  UpperCamelCase_      :		Union[str, Any]				=      image.to(device=snake_case     ,   dtype=snake_case    )

  UpperCamelCase_      :		Any				=      batch_size * num_images_per_prompt

  if image.shape[1] == 4:
   UpperCamelCase_      :		List[Any]				=      image

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

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

   UpperCamelCase_      :		List[Any]				=      self.movq.config.scaling_factor * init_latents

  UpperCamelCase_      :		List[str]				=      torch.cat([init_latents]     ,   dim=0    )

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

  # get latents
  UpperCamelCase_      :		Union[str, Any]				=      self.scheduler.add_noise(snake_case     ,   snake_case     ,   snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      init_latents

  return latents

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							Dict=0    )     ->		Optional[Any]:

  """simple docstring"""





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

  UpperCamelCase_      :		str				=      torch.device(f"cuda:{gpu_id}"    )

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

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Tuple=0    )     ->		Optional[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_      :		Any				=      torch.device(f"cuda:{gpu_id}"    )

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

  UpperCamelCase_      :		Any				=      None
  for cpu_offloaded_model in [self.unet, self.movq]:
   UpperCamelCase_,				UpperCamelCase_      :		Any				=      cpu_offload_with_hook(snake_case     ,   snake_case     ,   prev_module_hook=snake_case    )

  # We'll offload the last model manually.
  UpperCamelCase_      :		Optional[int]				=      hook

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Optional[Any]:

  """simple docstring"""





  if not hasattr(self.unet     ,   '_hf_hook'    ):
   return self.device
  for module in self.unet.modules():
   if (
       hasattr(snake_case     ,   '_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(snake_case    )
 def __call__(				self			:							Optional[int]     ,   snake_case			:							Union[torch.FloatTensor, List[torch.FloatTensor]]     ,   snake_case			:							Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     ,   snake_case			:							Union[torch.FloatTensor, List[torch.FloatTensor]]     ,   snake_case			:							torch.FloatTensor     ,   snake_case			:							int = 5_1_2     ,   snake_case			:							int = 5_1_2     ,   snake_case			:							int = 1_0_0     ,   snake_case			:							float = 4.0     ,   snake_case			:							float = 0.3     ,   snake_case			:							int = 1     ,   snake_case			:							Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,   snake_case			:							Optional[str] = "pil"     ,   snake_case			:							bool = True     ,   )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      self._execution_device

  UpperCamelCase_      :		str				=      guidance_scale > 1.0

  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Dict				=      torch.cat(snake_case     ,   dim=0    )
  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Optional[int]				=      torch.cat(snake_case     ,   dim=0    )
  if isinstance(snake_case     ,   snake_case    ):
   UpperCamelCase_      :		Dict				=      torch.cat(snake_case     ,   dim=0    )

  UpperCamelCase_      :		int				=      image_embeds.shape[0]

  if do_classifier_free_guidance:
   UpperCamelCase_      :		Optional[Any]				=      image_embeds.repeat_interleave(snake_case     ,   dim=0    )
   UpperCamelCase_      :		Any				=      negative_image_embeds.repeat_interleave(snake_case     ,   dim=0    )
   UpperCamelCase_      :		Tuple				=      hint.repeat_interleave(snake_case     ,   dim=0    )

  UpperCamelCase_      :		int				=      torch.cat([negative_image_embeds, image_embeds]     ,   dim=0    ).to(dtype=self.unet.dtype     ,   device=snake_case    )
  UpperCamelCase_      :		Union[str, Any]				=      torch.cat([hint, hint]     ,   dim=0    ).to(dtype=self.unet.dtype     ,   device=snake_case    )

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

  UpperCamelCase_      :		Union[str, Any]				=      torch.cat([prepare_image(snake_case     ,   snake_case     ,   snake_case    ) for i in image]     ,   dim=0    )
  UpperCamelCase_      :		Dict				=      image.to(dtype=image_embeds.dtype     ,   device=snake_case    )

  UpperCamelCase_      :		List[Any]				=      self.movq.encode(snake_case    )['latents']
  UpperCamelCase_      :		str				=      latents.repeat_interleave(snake_case     ,   dim=0    )
  self.scheduler.set_timesteps(snake_case     ,   device=snake_case    )
  UpperCamelCase_,				UpperCamelCase_      :		int				=      self.get_timesteps(snake_case     ,   snake_case     ,   snake_case    )
  UpperCamelCase_      :		Any				=      timesteps[:1].repeat(batch_size * num_images_per_prompt    )
  UpperCamelCase_,				UpperCamelCase_      :		Optional[Any]				=      downscale_height_and_width(snake_case     ,   snake_case     ,   self.movq_scale_factor    )
  UpperCamelCase_      :		List[Any]				=      self.prepare_latents(
      snake_case     ,   snake_case     ,   snake_case     ,   snake_case     ,   image_embeds.dtype     ,   snake_case     ,   snake_case    )
  for i, t in enumerate(self.progress_bar(snake_case    )    ):
   # expand the latents if we are doing classifier free guidance
   UpperCamelCase_      :		int				=      torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

   UpperCamelCase_      :		Optional[Any]				=      {'image_embeds': image_embeds, 'hint': hint}
   UpperCamelCase_      :		Optional[Any]				=      self.unet(
       sample=snake_case     ,   timestep=snake_case     ,   encoder_hidden_states=snake_case     ,   added_cond_kwargs=snake_case     ,   return_dict=snake_case     ,   )[0]

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

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

   UpperCamelCase_      :		List[Any]				=      self.scheduler.step(
       snake_case     ,   snake_case     ,   snake_case     ,   generator=snake_case     ,   )[0]

  # post-processing
  UpperCamelCase_      :		Any				=      self.movq.decode(snake_case     ,   force_not_quantize=snake_case    )['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_      :		List[Any]				=      image * 0.5 + 0.5
   UpperCamelCase_      :		Optional[int]				=      image.clamp(0     ,   1    )
   UpperCamelCase_      :		Dict				=      image.cpu().permute(0     ,   2     ,   3     ,   1    ).float().numpy()

  if output_type == "pil":
   UpperCamelCase_      :		Optional[Any]				=      self.numpy_to_pil(snake_case    )

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=snake_case    )



