


"""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,
)


__magic_name__						: List[Any]    =				logging.get_logger(__name__)  # pylint: disable=invalid-name

__magic_name__						: int    =				'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> 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"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'





def       a_ (	lowercase__ :Dict,			lowercase__ :Tuple,			lowercase__ :Any=8     ):
			__lowerCamelCase          =      height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			__lowerCamelCase          =      width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor




class 			__snake_case   (lowerCamelCase  ):




							def __init__(     self:    str     ,							A_:    UNetaDConditionModel     ,							A_:    DDPMScheduler     ,							A_:    VQModel     ,							):
										super().__init__()

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




							def   __a   (     self:    Union[str, Any]     ,							A_:    str     ,							A_:    Any     ,							A_:    int     ,							A_:    List[str]     ,							A_:    Optional[Any]     ,							A_:    int   ):
										if latents is None:
													__lowerCamelCase          =      randn_tensor(A_     ,							generator=A_     ,							device=A_     ,							dtype=A_   )
										else:
													if latents.shape != shape:
																raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'   )
													__lowerCamelCase          =      latents.to(A_   )

										__lowerCamelCase          =      latents * scheduler.init_noise_sigma
										return latents




							def   __a   (     self:    Optional[Any]     ,							A_:    Optional[int]=0   ):
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError("""Please install accelerate via `pip install accelerate`"""   )

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

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




							def   __a   (     self:    Dict     ,							A_:    List[Any]=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."""   )

										__lowerCamelCase          =      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)

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

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




							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   __a   (     self:    List[Any]   ):
										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:    Dict     ,							A_:    Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							A_:    Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							A_:    torch.FloatTensor     ,							A_:    int = 5_12     ,							A_:    int = 5_12     ,							A_:    int = 1_00     ,							A_:    float = 4.0     ,							A_:    int = 1     ,							A_:    Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							A_:    Optional[torch.FloatTensor] = None     ,							A_:    Optional[str] = "pil"     ,							A_:    bool = True     ,							):
										__lowerCamelCase          =      self._execution_device

										__lowerCamelCase          =      guidance_scale > 1.0

										if isinstance(A_     ,							A_   ):
													__lowerCamelCase          =      torch.cat(A_     ,							dim=0   )
										if isinstance(A_     ,							A_   ):
													__lowerCamelCase          =      torch.cat(A_     ,							dim=0   )
										if isinstance(A_     ,							A_   ):
													__lowerCamelCase          =      torch.cat(A_     ,							dim=0   )

										__lowerCamelCase          =      image_embeds.shape[0] * num_images_per_prompt

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

										__lowerCamelCase          =      torch.cat([negative_image_embeds, image_embeds]     ,							dim=0   ).to(dtype=self.unet.dtype     ,							device=A_   )
										__lowerCamelCase          =      torch.cat([hint, hint]     ,							dim=0   ).to(dtype=self.unet.dtype     ,							device=A_   )
										self.scheduler.set_timesteps(A_     ,							device=A_   )
										__lowerCamelCase          =      self.scheduler.timesteps

										__lowerCamelCase          =      self.movq.config.latent_channels

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

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

										for i, t in enumerate(self.progress_bar(A_   )   ):
													# expand the latents if we are doing classifier free guidance
													__lowerCamelCase          =      torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

													__lowerCamelCase          =      {"""image_embeds""": image_embeds, """hint""": hint}
													__lowerCamelCase          =      self.unet(
													    sample=A_     ,							timestep=A_     ,							encoder_hidden_states=A_     ,							added_cond_kwargs=A_     ,							return_dict=A_     ,							)[0]

													if do_classifier_free_guidance:
																__lowerCamelCase     ,__lowerCamelCase          =      noise_pred.split(latents.shape[1]     ,							dim=1   )
																__lowerCamelCase     ,__lowerCamelCase          =      noise_pred.chunk(2   )
																__lowerCamelCase     ,__lowerCamelCase          =      variance_pred.chunk(2   )
																__lowerCamelCase          =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																__lowerCamelCase          =      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"]
													):
																__lowerCamelCase     ,__lowerCamelCase          =      noise_pred.split(latents.shape[1]     ,							dim=1   )

													# compute the previous noisy sample x_t -> x_t-1
													__lowerCamelCase          =      self.scheduler.step(
													    A_     ,							A_     ,							A_     ,							generator=A_     ,							)[0]
										# post-processing
										__lowerCamelCase          =      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"]:
													__lowerCamelCase          =      image * 0.5 + 0.5
													__lowerCamelCase          =      image.clamp(0     ,							1   )
													__lowerCamelCase          =      image.cpu().permute(0     ,							2     ,							3     ,							1   ).float().numpy()

										if output_type == "pil":
													__lowerCamelCase          =      self.numpy_to_pil(A_   )

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=A_   )








"""simple docstring"""




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

import numpy as np
import torch

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




@dataclass
class 			__snake_case   (lowerCamelCase  ):
							__a					 =       42


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_text_to_video_synth import TextToVideoSDPipeline
					from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
					from .pipeline_text_to_video_zero import TextToVideoZeroPipeline




