




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,
)


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

_lowerCamelCase     :		List[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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_=8						)       ->	Dict:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_=512			,    lowercase_=512						)       ->	Dict:



       """simple docstring"""

       A__							=   pil_image.resize((w, h)			,    resample=Image.BICUBIC			,    reducing_gap=1						)
       A__							=   np.array(pil_image.convert('''RGB'''						)						)
       A__							=   arr.astype(np.floataa						) / 1_27.5 - 1
       A__							=   np.transpose(__lowerCAmelCase			,    [2, 0, 1]						)
       A__							=   torch.from_numpy(__lowerCAmelCase						).unsqueeze(0						)
       return image


class     UpperCamelCase_   (			lowerCamelCase_					):

    '''simple docstring'''






    def __init__(      self				:     List[Any] ,			UpperCAmelCase__				:     UNetaDConditionModel ,			UpperCAmelCase__				:     DDPMScheduler ,			UpperCAmelCase__				:     VQModel ,			)						->Tuple:





           '''simple docstring'''

           super().__init__()

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   min(int(num_inference_steps * strength) ,			__snake_case)

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

           return timesteps, num_inference_steps - t_start


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple=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)}""")

           A__							=   image.to(device=__snake_case ,			dtype=__snake_case)

           A__							=   batch_size * num_images_per_prompt

           if image.shape[1] == 4:
                  A__							=   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):
                         A__							=   [
                             self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(__snake_case)
                         ]
                         A__							=   torch.cat(__snake_case ,			dim=0)
                  else:
                         A__							=   self.movq.encode(__snake_case).latent_dist.sample(__snake_case)

                  A__							=   self.movq.config.scaling_factor * init_latents

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

           A__							=   init_latents.shape
           A__							=   randn_tensor(__snake_case ,			generator=__snake_case ,			device=__snake_case ,			dtype=__snake_case)

           # get latents
           A__							=   self.scheduler.add_noise(__snake_case ,			__snake_case ,			__snake_case)

           A__							=   init_latents

           return latents


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     List[str]=0)						->str:





           '''simple docstring'''

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

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

           A__							=   [
               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				:     List[str] ,			UpperCAmelCase__				:     Optional[int]=0)						->int:





           '''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.''')

           A__							=   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)

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

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


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





           '''simple docstring'''

           A__							=   self._execution_device

           A__							=   guidance_scale > 1.0

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

           if do_classifier_free_guidance:
                  A__							=   image_embeds.repeat_interleave(__snake_case ,			dim=0)
                  A__							=   negative_image_embeds.repeat_interleave(__snake_case ,			dim=0)

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

           if not isinstance(__snake_case ,			__snake_case):
                  A__							=   [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""")

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

           A__							=   self.movq.encode(__snake_case)['''latents''']
           A__							=   latents.repeat_interleave(__snake_case ,			dim=0)
           self.scheduler.set_timesteps(__snake_case ,			device=__snake_case)
           A__			,							A__							=   self.get_timesteps(__snake_case ,			__snake_case ,			__snake_case)
           A__							=   timesteps[:1].repeat(batch_size * num_images_per_prompt)
           A__			,							A__							=   downscale_height_and_width(__snake_case ,			__snake_case ,			self.movq_scale_factor)
           A__							=   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
                  A__							=   torch.cat([latents] * 2) if do_classifier_free_guidance else latents

                  A__							=   {'''image_embeds''': image_embeds}
                  A__							=   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:
                         A__			,							A__							=   noise_pred.split(latents.shape[1] ,			dim=1)
                         A__			,							A__							=   noise_pred.chunk(2)
                         A__			,							A__							=   variance_pred.chunk(2)
                         A__							=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                         A__							=   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"]
                  ):
                         A__			,							A__							=   noise_pred.split(latents.shape[1] ,			dim=1)

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

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

           if output_type == "pil":
                  A__							=   self.numpy_to_pil(__snake_case)

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=__snake_case)








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       return " ".join(
           ''''''.join(word[::-1]						) if len(lowercase_						) > 4 else word for word in sentence.split()						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(reverse_long_words("""Hey wollef sroirraw"""))


