

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
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]=8    ):



     '''simple docstring'''





     lowercase__					:     str   = height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     lowercase__					:     List[str]   = width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Dict			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=512			,  SCREAMING_SNAKE_CASE_	:					List[str]=512    ):



     '''simple docstring'''





     lowercase__					:     Any   = pil_image.resize((w, h)			,  resample=Image.BICUBIC			,  reducing_gap=1    )
     lowercase__					:     Any   = np.array(pil_image.convert('RGB'    )    )
     lowercase__					:     Tuple   = arr.astype(np.floataa    ) / 127.5 - 1
     lowercase__					:     Optional[Any]   = np.transpose(SCREAMING_SNAKE_CASE_			,  [2, 0, 1]    )
     lowercase__					:     Any   = torch.from_numpy(SCREAMING_SNAKE_CASE_    ).unsqueeze(0    )
     return image


class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def __init__(   self		,     a		,     a		,     a		,     ):
         super().__init__()

         self.register_modules(
             unet=a		,     scheduler=a		,     movq=a		,     )
         lowercase__					:     Any   = 2 ** (len(self.movq.config.block_out_channels) - 1)





    def    snake_case_       (   self		,     a		,     a		,     a):
         # get the original timestep using init_timestep
         lowercase__					:     int   = min(int(num_inference_steps * strength)		,     a)

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

         return timesteps, num_inference_steps - t_start





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a		,     a		,     a=None):
         if not isinstance(a		,     (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(a)}""")

         lowercase__					:     Tuple   = image.to(device=a		,     dtype=a)

         lowercase__					:     Tuple   = batch_size * num_images_per_prompt

         if image.shape[1] == 4:
              lowercase__					:     List[str]   = image

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

              elif isinstance(a		,     a):
                   lowercase__					:     Any   = [
                       self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(a)
                   ]
                   lowercase__					:     int   = torch.cat(a		,     dim=0)
              else:
                   lowercase__					:     Dict   = self.movq.encode(a).latent_dist.sample(a)

              lowercase__					:     Dict   = self.movq.config.scaling_factor * init_latents

         lowercase__					:     Optional[int]   = torch.cat([init_latents]		,     dim=0)

         lowercase__					:     List[str]   = init_latents.shape
         lowercase__					:     Dict   = randn_tensor(a		,     generator=a		,     device=a		,     dtype=a)

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

         lowercase__					:     Optional[Any]   = init_latents

         return latents





    def    snake_case_       (   self		,     a=0):
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError('Please install accelerate via `pip install accelerate`')

         lowercase__					:     Optional[Any]   = torch.device(f"""cuda:{gpu_id}""")

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





    def    snake_case_       (   self		,     a=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.')

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

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

         lowercase__					:     List[str]   = None
         for cpu_offloaded_model in [self.unet, self.movq]:
              lowercase__   ,				lowercase__					:     List[str]   = cpu_offload_with_hook(a		,     a		,     prev_module_hook=a)

         # We'll offload the last model manually.
         lowercase__					:     List[Any]   = hook





    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def    snake_case_       (   self):
         if not hasattr(self.unet		,     '_hf_hook'):
              return self.device
         for module in self.unet.modules():
              if (
                  hasattr(a		,     '_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(a)
    def __call__(   self		,     a		,     a		,     a		,     a		,     a = 512		,     a = 512		,     a = 100		,     a = 4.0		,     a = 0.3		,     a = 1		,     a = None		,     a = "pil"		,     a = True		,     ):
         lowercase__					:     Union[str, Any]   = self._execution_device

         lowercase__					:     Optional[int]   = guidance_scale > 1.0

         if isinstance(a		,     a):
              lowercase__					:     str   = torch.cat(a		,     dim=0)
         if isinstance(a		,     a):
              lowercase__					:     Union[str, Any]   = torch.cat(a		,     dim=0)
         if isinstance(a		,     a):
              lowercase__					:     Dict   = torch.cat(a		,     dim=0)

         lowercase__					:     Optional[int]   = image_embeds.shape[0]

         if do_classifier_free_guidance:
              lowercase__					:     Tuple   = image_embeds.repeat_interleave(a		,     dim=0)
              lowercase__					:     Optional[int]   = negative_image_embeds.repeat_interleave(a		,     dim=0)
              lowercase__					:     Tuple   = hint.repeat_interleave(a		,     dim=0)

         lowercase__					:     List[Any]   = torch.cat([negative_image_embeds, image_embeds]		,     dim=0).to(dtype=self.unet.dtype		,     device=a)
         lowercase__					:     Dict   = torch.cat([hint, hint]		,     dim=0).to(dtype=self.unet.dtype		,     device=a)

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

         lowercase__					:     Union[str, Any]   = torch.cat([prepare_image(a		,     a		,     a) for i in image]		,     dim=0)
         lowercase__					:     Optional[Any]   = image.to(dtype=image_embeds.dtype		,     device=a)

         lowercase__					:     Any   = self.movq.encode(a)['latents']
         lowercase__					:     Tuple   = latents.repeat_interleave(a		,     dim=0)
         self.scheduler.set_timesteps(a		,     device=a)
         lowercase__   ,				lowercase__					:     Optional[Any]   = self.get_timesteps(a		,     a		,     a)
         lowercase__					:     Tuple   = timesteps[:1].repeat(batch_size * num_images_per_prompt)
         lowercase__   ,				lowercase__					:     str   = downscale_height_and_width(a		,     a		,     self.movq_scale_factor)
         lowercase__					:     Optional[Any]   = self.prepare_latents(
             a		,     a		,     a		,     a		,     image_embeds.dtype		,     a		,     a)
         for i, t in enumerate(self.progress_bar(a)):
              # expand the latents if we are doing classifier free guidance
              lowercase__					:     Tuple   = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

              lowercase__					:     Any   = {'image_embeds': image_embeds, 'hint': hint}
              lowercase__					:     Optional[int]   = self.unet(
                  sample=a		,     timestep=a		,     encoder_hidden_states=a		,     added_cond_kwargs=a		,     return_dict=a		,     )[0]

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

              # compute the previous noisy sample x_t -> x_t-1

              lowercase__					:     List[str]   = self.scheduler.step(
                  a		,     a		,     a		,     generator=a		,     )[0]

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

         if output_type == "pil":
              lowercase__					:     Dict   = self.numpy_to_pil(a)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=a)




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available


@dataclass
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
       from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
       from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

