






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,
)


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

snake_case    				=		"""
    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   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=8				):

  """simple docstring"""






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


def   lowerCamelCase__			(     lowercase						,     lowercase=512						,     lowercase=512				):

  """simple docstring"""






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


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     def __init__(				self       :	List[str]					, UpperCAmelCase_       :	UNetaDConditionModel					, UpperCAmelCase_       :	DDPMScheduler					, UpperCAmelCase_       :	VQModel					, ):
       super().__init__()

       self.register_modules(
           unet=UpperCAmelCase_					, scheduler=UpperCAmelCase_					, movq=UpperCAmelCase_					, )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    2 ** (len(self.movq.config.block_out_channels      ) - 1)


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Dict      ):
       # get the original timestep using init_timestep
       SCREAMING_SNAKE_CASE       :      str							=    min(int(num_inference_steps * strength      )					, UpperCAmelCase_      )

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

       return timesteps, num_inference_steps - t_start


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[str]=None      ):
       if not isinstance(UpperCAmelCase_					, (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(UpperCAmelCase_      )}'''      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    image.to(device=UpperCAmelCase_					, dtype=UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    batch_size * num_images_per_prompt

       if image.shape[1] == 4:
         SCREAMING_SNAKE_CASE       :      str							=    image

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

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

         SCREAMING_SNAKE_CASE       :      str							=    self.movq.config.scaling_factor * init_latents

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

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    init_latents.shape
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    randn_tensor(UpperCAmelCase_					, generator=UpperCAmelCase_					, device=UpperCAmelCase_					, dtype=UpperCAmelCase_      )

       # get latents
       SCREAMING_SNAKE_CASE       :      List[str]							=    self.scheduler.add_noise(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    init_latents

       return latents


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	int=0      ):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("Please install accelerate via `pip install accelerate`"      )

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

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


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Optional[int]=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."      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    torch.device(f'''cuda:{gpu_id}'''      )

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

       SCREAMING_SNAKE_CASE       :      List[Any]							=    None
       for cpu_offloaded_model in [self.unet, self.movq]:
         SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[int]							=    cpu_offload_with_hook(UpperCAmelCase_					, UpperCAmelCase_					, prev_module_hook=UpperCAmelCase_      )

       # We'll offload the last model manually.
       SCREAMING_SNAKE_CASE       :      int							=    hook


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 				_A       (				self       :	Optional[int]      ):
       if not hasattr(self.unet					, "_hf_hook"      ):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(UpperCAmelCase_					, "_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(UpperCAmelCase_      )
     def __call__(				self       :	List[Any]					, UpperCAmelCase_       :	Union[torch.FloatTensor, List[torch.FloatTensor]]					, UpperCAmelCase_       :	Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]					, UpperCAmelCase_       :	Union[torch.FloatTensor, List[torch.FloatTensor]]					, UpperCAmelCase_       :	int = 512					, UpperCAmelCase_       :	int = 512					, UpperCAmelCase_       :	int = 100					, UpperCAmelCase_       :	float = 4.0					, UpperCAmelCase_       :	float = 0.3					, UpperCAmelCase_       :	int = 1					, UpperCAmelCase_       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None					, UpperCAmelCase_       :	Optional[str] = "pil"					, UpperCAmelCase_       :	bool = True					, ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self._execution_device

       SCREAMING_SNAKE_CASE       :      Tuple							=    guidance_scale > 1.0

       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      Any							=    torch.cat(UpperCAmelCase_					, dim=0      )
       SCREAMING_SNAKE_CASE       :      int							=    image_embeds.shape[0]
       if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         SCREAMING_SNAKE_CASE       :      str							=    torch.cat(UpperCAmelCase_					, dim=0      )

       if do_classifier_free_guidance:
         SCREAMING_SNAKE_CASE       :      Tuple							=    image_embeds.repeat_interleave(UpperCAmelCase_					, dim=0      )
         SCREAMING_SNAKE_CASE       :      int							=    negative_image_embeds.repeat_interleave(UpperCAmelCase_					, dim=0      )

       SCREAMING_SNAKE_CASE       :      str							=    torch.cat([negative_image_embeds, image_embeds]					, dim=0      ).to(dtype=self.unet.dtype					, device=UpperCAmelCase_      )

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

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    torch.cat([prepare_image(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_      ) for i in image]					, dim=0      )
       SCREAMING_SNAKE_CASE       :      str							=    image.to(dtype=image_embeds.dtype					, device=UpperCAmelCase_      )

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

         SCREAMING_SNAKE_CASE       :      List[Any]							=    {"image_embeds": image_embeds}
         SCREAMING_SNAKE_CASE       :      Any							=    self.unet(
             sample=UpperCAmelCase_					, timestep=UpperCAmelCase_					, encoder_hidden_states=UpperCAmelCase_					, added_cond_kwargs=UpperCAmelCase_					, return_dict=UpperCAmelCase_					, )[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.scheduler.step(
             UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, generator=UpperCAmelCase_					, )[0]

       # post-processing
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.movq.decode(UpperCAmelCase_					, force_not_quantize=UpperCAmelCase_      )["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"]:
         SCREAMING_SNAKE_CASE       :      int							=    image * 0.5 + 0.5
         SCREAMING_SNAKE_CASE       :      Any							=    image.clamp(0					, 1      )
         SCREAMING_SNAKE_CASE       :      Tuple							=    image.cpu().permute(0					, 2					, 3					, 1      ).float().numpy()

       if output_type == "pil":
         SCREAMING_SNAKE_CASE       :      Any							=    self.numpy_to_pil(UpperCAmelCase_      )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=UpperCAmelCase_      )














snake_case    				=		"""Tobias Carryer"""

from time import time


class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	List[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	int=int(time()      )      ):  # noqa: B008
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    multiplier
       SCREAMING_SNAKE_CASE       :      Tuple							=    increment
       SCREAMING_SNAKE_CASE       :      int							=    modulo
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    seed






     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (self.multiplier * self.seed + self.increment) % self.modulo
       return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   snake_case    				=		LinearCongruentialGenerator(1_664_525, 1_013_904_223, 2 << 31)
   while True:
      print(lcg.next_number())






