

'''simple docstring'''







from typing import List, Optional, Union

import torch

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

__lowerCamelCase						    =      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__=8			)     ->     Optional[int]:
       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







class    A__ ( _snake_case     ):



    def __init__(   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			)  -> List[str]:





           '''simple docstring'''
           super().__init__()

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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Union[str, Any]:





           '''simple docstring'''
           if latents is None:
                  A_		   = randn_tensor(UpperCamelCase__						,			generator=UpperCamelCase__						,			device=UpperCamelCase__						,			dtype=UpperCamelCase__    )
           else:
                  if latents.shape != shape:
                         raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''    )
                  A_		   = latents.to(UpperCamelCase__    )

           A_		   = latents * scheduler.init_noise_sigma
           return latents



    def    snake_case_     (   self						,			UpperCamelCase__=0    )  -> Tuple:





           '''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(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__=0    )  -> List[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."""    )

           A_		   = 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)

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

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



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





           '''simple docstring'''
           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						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = 512						,			UpperCamelCase__ = 512						,			UpperCamelCase__ = 100						,			UpperCamelCase__ = 4.0						,			UpperCamelCase__ = 1						,			UpperCamelCase__ = None						,			UpperCamelCase__ = None						,			UpperCamelCase__ = "pil"						,			UpperCamelCase__ = True						,			)  -> Any:





           '''simple docstring'''
           A_		   = self._execution_device

           A_		   = guidance_scale > 1.0

           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  A_		   = torch.cat(UpperCamelCase__						,			dim=0    )
           A_		   = image_embeds.shape[0] * num_images_per_prompt
           if isinstance(UpperCamelCase__						,			UpperCamelCase__    ):
                  A_		   = torch.cat(UpperCamelCase__						,			dim=0    )

           if do_classifier_free_guidance:
                  A_		   = image_embeds.repeat_interleave(UpperCamelCase__						,			dim=0    )
                  A_		   = negative_image_embeds.repeat_interleave(UpperCamelCase__						,			dim=0    )

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

           self.scheduler.set_timesteps(UpperCamelCase__						,			device=UpperCamelCase__    )
           A_		   = self.scheduler.timesteps

           A_		   = self.unet.config.in_channels

           A_   ,     A_		   = downscale_height_and_width(UpperCamelCase__						,			UpperCamelCase__						,			self.movq_scale_factor    )

           # create initial latent
           A_		   = self.prepare_latents(
               (batch_size, num_channels_latents, height, width)						,			image_embeds.dtype						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			self.scheduler						,			)

           for i, t in enumerate(self.progress_bar(UpperCamelCase__    )    ):
                  # 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=UpperCamelCase__						,			timestep=UpperCamelCase__						,			encoder_hidden_states=UpperCamelCase__						,			added_cond_kwargs=UpperCamelCase__						,			return_dict=UpperCamelCase__						,			)[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(
                      UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			generator=UpperCamelCase__						,			)[0]
           # post-processing
           A_		   = 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"]:
                  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(UpperCamelCase__    )

           if not return_dict:
                  return (image,)

           return ImagePipelineOutput(images=UpperCamelCase__    )



'''simple docstring'''







from __future__ import annotations

from typing import Any
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if not postfix_notation:
              return 0

       A_		   = {"""+""", """-""", """*""", """/"""}
       A_		   = []

       for token in postfix_notation:
              if token in operations:
                     A_   ,     A_		   = stack.pop(), stack.pop()
                     if token == "+":
                            stack.append(a + b			)
                     elif token == "-":
                            stack.append(a - b			)
                     elif token == "*":
                            stack.append(a * b			)
                     else:
                            if a * b < 0 and a % b != 0:
                                   stack.append(a // b + 1			)
                            else:
                                   stack.append(a // b			)
              else:
                     stack.append(int(UpperCAmelCase__			)			)

       return stack.pop()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
