






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

lowerCamelCase : List[str]						   =       '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> 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 = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.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)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''
def       snake_case_		(     lowerCAmelCase_       :						List[str]       ,       lowerCAmelCase_       :						int       ,       lowerCAmelCase_       :						Dict=8      ):
							__lowercase      :							Any				=     height // scale_factor**2
							if height % scale_factor**2 != 0:
														new_height += 1
							__lowercase      :							Optional[int]				=     width // scale_factor**2
							if width % scale_factor**2 != 0:
														new_width += 1
							return new_height * scale_factor, new_width * scale_factor



class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Optional[int]		,     __a				:	UNetaDConditionModel		,     __a				:	DDPMScheduler		,     __a				:	VQModel		,     )			->  Optional[int]:


												"""simple docstring"""

												super().__init__()

												self.register_modules(
												    unet=__a		,     scheduler=__a		,     movq=__a		,     )
												__lowercase      :							str				=     2 ** (len(self.movq.config.block_out_channels							) - 1)



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	List[Any]		,     __a				:	List[Any]		,     __a				:	Any		,     __a				:	List[Any]		,     __a				:	str		,     __a				:	List[Any]							)			->  Optional[int]:


												"""simple docstring"""

												if latents is None:
																			__lowercase      :							List[str]				=     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}"							)
																			__lowercase      :							str				=     latents.to(__a							)

												__lowercase      :							Optional[Any]				=     latents * scheduler.init_noise_sigma
												return latents



					def       lowerCAmelCase				(  self				:	Dict		,     __a				:	Optional[Any]=0							)			->  int:


												"""simple docstring"""

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

												__lowercase      :							int				=     torch.device(F"cuda:{gpu_id}"							)

												__lowercase      :							List[str]				=     [
												    self.unet,
												    self.movq,
												]
												for cpu_offloaded_model in models:
																			if cpu_offloaded_model is not None:
																										cpu_offload(__a		,     __a							)



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	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."""							)

												__lowercase      :							str				=     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[Any]				=     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       lowerCAmelCase				(  self				:	List[str]							)			->  List[str]:


												"""simple docstring"""

												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				:	List[Any]		,     __a				:	Union[torch.FloatTensor, List[torch.FloatTensor]]		,     __a				:	Union[torch.FloatTensor, List[torch.FloatTensor]]		,     __a				:	torch.FloatTensor		,     __a				:	int = 512		,     __a				:	int = 512		,     __a				:	int = 100		,     __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		,     )			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self._execution_device

												__lowercase      :							Optional[Any]				=     guidance_scale > 1.0

												if isinstance(__a		,     __a							):
																			__lowercase      :							Any				=     torch.cat(__a		,     dim=0							)
												if isinstance(__a		,     __a							):
																			__lowercase      :							Any				=     torch.cat(__a		,     dim=0							)
												if isinstance(__a		,     __a							):
																			__lowercase      :							Optional[int]				=     torch.cat(__a		,     dim=0							)

												__lowercase      :							Any				=     image_embeds.shape[0] * num_images_per_prompt

												if do_classifier_free_guidance:
																			__lowercase      :							List[str]				=     image_embeds.repeat_interleave(__a		,     dim=0							)
																			__lowercase      :							Tuple				=     negative_image_embeds.repeat_interleave(__a		,     dim=0							)
																			__lowercase      :							Dict				=     hint.repeat_interleave(__a		,     dim=0							)

												__lowercase      :							Dict				=     torch.cat([negative_image_embeds, image_embeds]		,     dim=0							).to(dtype=self.unet.dtype		,     device=__a							)
												__lowercase      :							List[Any]				=     torch.cat([hint, hint]		,     dim=0							).to(dtype=self.unet.dtype		,     device=__a							)
												self.scheduler.set_timesteps(__a		,     device=__a							)
												__lowercase      :							Union[str, Any]				=     self.scheduler.timesteps

												__lowercase      :							List[str]				=     self.movq.config.latent_channels

												__lowercase			,			__lowercase      :							Optional[int]				=     downscale_height_and_width(__a		,     __a		,     self.movq_scale_factor							)

												# create initial latent
												__lowercase      :							Optional[Any]				=     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
																			__lowercase      :							int				=     torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

																			__lowercase      :							str				=     {"""image_embeds""": image_embeds, """hint""": hint}
																			__lowercase      :							List[str]				=     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      :							List[Any]				=     noise_pred.split(latents.shape[1]		,     dim=1							)
																										__lowercase			,			__lowercase      :							List[str]				=     noise_pred.chunk(2							)
																										__lowercase			,			__lowercase      :							Any				=     variance_pred.chunk(2							)
																										__lowercase      :							Optional[int]				=     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																										__lowercase      :							Union[str, 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      :							Union[str, Any]				=     noise_pred.split(latents.shape[1]		,     dim=1							)

																			# compute the previous noisy sample x_t -> x_t-1
																			__lowercase      :							int				=     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      :							Union[str, Any]				=     image * 0.5 + 0.5
																			__lowercase      :							Optional[int]				=     image.clamp(0		,     1							)
																			__lowercase      :							Union[str, Any]				=     image.cpu().permute(0		,     2		,     3		,     1							).float().numpy()

												if output_type == "pil":
																			__lowercase      :							Tuple				=     self.numpy_to_pil(__a							)

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__a							)







from .imports import is_tqdm_available


if is_tqdm_available():
	from tqdm.auto import tqdm as _tqdm

from ..state import PartialState
def       snake_case_		(     lowerCAmelCase_       :						bool = True       ,       *lowerCAmelCase_       :						int       ,       **lowerCAmelCase_       :						List[str]      ):
							if not is_tqdm_available():
														raise ImportError("""Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."""      )
							__lowercase      :							List[str]				=     False
							if main_process_only:
														__lowercase      :							Optional[int]				=     PartialState().local_process_index == 0
							return _tqdm(*lowerCAmelCase_       ,       **lowerCAmelCase_       ,       disable=lowerCAmelCase_      )