'''simple docstring'''

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

_A			:      Any					      =				'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''







def      UpperCamelCase_						(					snake_case_       :       Tuple						,		snake_case_       :       Optional[Any]						,		snake_case_       :       Any=8      )					->     Union[str, Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 __lowerCAmelCase											=						width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor







def      UpperCamelCase_						(					snake_case_       :       Optional[int]						,		snake_case_       :       Dict=5_12						,		snake_case_       :       Optional[Any]=5_12      )					->     List[str]:






 '''simple docstring'''

 __lowerCAmelCase											=						pil_image.resize((w, h)						,		resample=Image.BICUBIC						,		reducing_gap=1      )
 __lowerCAmelCase											=						np.array(pil_image.convert("""RGB"""      )      )
 __lowerCAmelCase											=						arr.astype(np.floataa      ) / 1_2_7.5 - 1
 __lowerCAmelCase											=						np.transpose(snake_case_						,		[2, 0, 1]      )
 __lowerCAmelCase											=						torch.from_numpy(snake_case_      ).unsqueeze(0      )
 return image







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	UNetaDConditionModel      ,       SCREAMING_SNAKE_CASE__   :	DDPMScheduler      ,       SCREAMING_SNAKE_CASE__   :	VQModel      ,       )  ->    List[str]:
  super().__init__()

  self.register_modules(
      unet=SCREAMING_SNAKE_CASE__      ,       scheduler=SCREAMING_SNAKE_CASE__      ,       movq=SCREAMING_SNAKE_CASE__      ,       )
  __lowerCAmelCase											=						2 ** (len(self.movq.config.block_out_channels							) - 1)
 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]							)  ->    Dict:
  # get the original timestep using init_timestep
  __lowerCAmelCase											=						min(int(num_inference_steps * strength							)      ,       SCREAMING_SNAKE_CASE__							)

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

  return timesteps, num_inference_steps - t_start
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Any=None							)  ->    Dict:
  if not isinstance(SCREAMING_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(SCREAMING_SNAKE_CASE__							)}"""							)

  __lowerCAmelCase											=						image.to(device=SCREAMING_SNAKE_CASE__      ,       dtype=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						batch_size * num_images_per_prompt

  if image.shape[1] == 4:
   __lowerCAmelCase											=						image

  else:
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) and len(SCREAMING_SNAKE_CASE__							) != batch_size:
    raise ValueError(
        f"""You have passed a list of generators of length {len(SCREAMING_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(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[
        self.movq.encode(image[i : i + 1]							).latent_dist.sample(generator[i]							) for i in range(SCREAMING_SNAKE_CASE__							)
    ]
    __lowerCAmelCase											=						torch.cat(SCREAMING_SNAKE_CASE__      ,       dim=0							)
   else:
    __lowerCAmelCase											=						self.movq.encode(SCREAMING_SNAKE_CASE__							).latent_dist.sample(SCREAMING_SNAKE_CASE__							)

   __lowerCAmelCase											=						self.movq.config.scaling_factor * init_latents

  __lowerCAmelCase											=						torch.cat([init_latents]      ,       dim=0							)

  __lowerCAmelCase											=						init_latents.shape
  __lowerCAmelCase											=						randn_tensor(SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__      ,       device=SCREAMING_SNAKE_CASE__      ,       dtype=SCREAMING_SNAKE_CASE__							)

  # get latents
  __lowerCAmelCase											=						self.scheduler.add_noise(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						init_latents

  return latents
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0							)  ->    int:
  if is_accelerate_available():
   from accelerate import cpu_offload
  else:
   raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

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

  __lowerCAmelCase											=						[
      self.unet,
      self.movq,
  ]
  for cpu_offloaded_model in models:
   if cpu_offloaded_model is not None:
    cpu_offload(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	List[str]=0							)  ->    Any:
  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											=						torch.device(f"""cuda:{gpu_id}"""							)

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

  __lowerCAmelCase											=						None
  for cpu_offloaded_model in [self.unet, self.movq]:
   __lowerCAmelCase							,       __lowerCAmelCase											=						cpu_offload_with_hook(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       prev_module_hook=SCREAMING_SNAKE_CASE__							)

  # We'll offload the last model manually.
  __lowerCAmelCase											=						hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def a					(      self   :	int							)  ->    Optional[int]:
  if not hasattr(self.unet      ,       """_hf_hook"""							):
   return self.device
  for module in self.unet.modules():
   if (
       hasattr(SCREAMING_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(SCREAMING_SNAKE_CASE__							)
 def __call__(      self   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Union[torch.FloatTensor, List[torch.FloatTensor]]      ,       SCREAMING_SNAKE_CASE__   :	Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]      ,       SCREAMING_SNAKE_CASE__   :	Union[torch.FloatTensor, List[torch.FloatTensor]]      ,       SCREAMING_SNAKE_CASE__   :	int = 5_12      ,       SCREAMING_SNAKE_CASE__   :	int = 5_12      ,       SCREAMING_SNAKE_CASE__   :	int = 1_00      ,       SCREAMING_SNAKE_CASE__   :	float = 4.0      ,       SCREAMING_SNAKE_CASE__   :	float = 0.3      ,       SCREAMING_SNAKE_CASE__   :	int = 1      ,       SCREAMING_SNAKE_CASE__   :	Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,       SCREAMING_SNAKE_CASE__   :	Optional[str] = "pil"      ,       SCREAMING_SNAKE_CASE__   :	bool = True      ,       )  ->    List[str]:
  __lowerCAmelCase											=						self._execution_device

  __lowerCAmelCase											=						guidance_scale > 1.0

  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						torch.cat(SCREAMING_SNAKE_CASE__      ,       dim=0							)
  __lowerCAmelCase											=						image_embeds.shape[0]
  if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						torch.cat(SCREAMING_SNAKE_CASE__      ,       dim=0							)

  if do_classifier_free_guidance:
   __lowerCAmelCase											=						image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__      ,       dim=0							)
   __lowerCAmelCase											=						negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__      ,       dim=0							)

  __lowerCAmelCase											=						torch.cat([negative_image_embeds, image_embeds]      ,       dim=0							).to(dtype=self.unet.dtype      ,       device=SCREAMING_SNAKE_CASE__							)

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

  __lowerCAmelCase											=						torch.cat([prepare_image(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							) for i in image]      ,       dim=0							)
  __lowerCAmelCase											=						image.to(dtype=image_embeds.dtype      ,       device=SCREAMING_SNAKE_CASE__							)

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

   __lowerCAmelCase											=						{"""image_embeds""": image_embeds}
   __lowerCAmelCase											=						self.unet(
       sample=SCREAMING_SNAKE_CASE__      ,       timestep=SCREAMING_SNAKE_CASE__      ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__      ,       added_cond_kwargs=SCREAMING_SNAKE_CASE__      ,       return_dict=SCREAMING_SNAKE_CASE__      ,       )[0]

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

   # compute the previous noisy sample x_t -> x_t-1
   __lowerCAmelCase											=						self.scheduler.step(
       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       generator=SCREAMING_SNAKE_CASE__      ,       )[0]

  # post-processing
  __lowerCAmelCase											=						self.movq.decode(SCREAMING_SNAKE_CASE__      ,       force_not_quantize=SCREAMING_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"]:
   __lowerCAmelCase											=						image * 0.5 + 0.5
   __lowerCAmelCase											=						image.clamp(0      ,       1							)
   __lowerCAmelCase											=						image.cpu().permute(0      ,       2      ,       3      ,       1							).float().numpy()

  if output_type == "pil":
   __lowerCAmelCase											=						self.numpy_to_pil(SCREAMING_SNAKE_CASE__							)

  if not return_dict:
   return (image,)

  return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_A			:      Optional[int]					      =				logging.get_logger(__name__)







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def __init__(      self   :	Dict      ,       *SCREAMING_SNAKE_CASE__   :	Optional[int]      ,       **SCREAMING_SNAKE_CASE__   :	List[str]							)  ->    None:
  warnings.warn(
      """The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
      """ Please use SegformerImageProcessor instead."""      ,       SCREAMING_SNAKE_CASE__      ,       )
  super().__init__(*SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)






