

"""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,
)


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

lowercase_			=  '''
    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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]=8				)							->				Any:





    '''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


class 			A						(							lowerCamelCase_			):







    """simple docstring"""





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





        '''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 						snake_case__					(				self			:	Tuple,lowercase_			:	Any,lowercase_			:	Optional[Any],lowercase_			:	Any,lowercase_			:	str,lowercase_			:	int,lowercase_			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        if latents is None:
            A__   				=       randn_tensor(__snake_case,generator=__snake_case,device=__snake_case,dtype=__snake_case		)
        else:
            if latents.shape != shape:
                raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
            A__   				=       latents.to(__snake_case		)

        A__   				=       latents * scheduler.init_noise_sigma
        return latents


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	int=0		)->   int:





        '''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 						snake_case__					(				self			:	Any,lowercase_			:	Optional[Any]=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 						snake_case__					(				self			:	Union[str, Any]		)->   int:





        '''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			:	Union[str, Any],lowercase_			:	Union[torch.FloatTensor, List[torch.FloatTensor]],lowercase_			:	Union[torch.FloatTensor, List[torch.FloatTensor]],lowercase_			:	int = 5_1_2,lowercase_			:	int = 5_1_2,lowercase_			:	int = 1_0_0,lowercase_			:	float = 4.0,lowercase_			:	int = 1,lowercase_			:	Optional[Union[torch.Generator, List[torch.Generator]]] = None,lowercase_			:	Optional[torch.FloatTensor] = None,lowercase_			:	Optional[str] = "pil",lowercase_			:	bool = True,)->   Optional[int]:





        '''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] * num_images_per_prompt
        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		)

        self.scheduler.set_timesteps(__snake_case,device=__snake_case		)
        A__   				=       self.scheduler.timesteps

        A__   				=       self.unet.config.in_channels

        A__				,						A__   				=       downscale_height_and_width(__snake_case,__snake_case,self.movq_scale_factor		)

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

        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		)




from __future__ import annotations

from typing import Any
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list				)							->				int:





    '''simple docstring'''






    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(SCREAMING_SNAKE_CASE__				)				)

    return stack.pop()


if __name__ == "__main__":
     import doctest

     doctest.testmod()

