



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,
)


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

UpperCAmelCase							 =					"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

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

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""

def 			A_      (	__a     :				Tuple ,		__a     :				Optional[Any] ,		__a     :				int=8		):

				"""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

def 			A_      (	__a     :				List[str] ,		__a     :				Any=512 ,		__a     :				List[Any]=512		):

				"""simple docstring"""
				a__				      =		pil_image.resize((w, h) ,		resample=Image.BICUBIC ,		reducing_gap=1		)
				a__				      =		np.array(pil_image.convert("""RGB"""		)		)
				a__				      =		arr.astype(np.floataa		) / 1_2_7.5 - 1
				a__				      =		np.transpose(__a ,		[2, 0, 1]		)
				a__				      =		torch.from_numpy(__a		).unsqueeze(0		)
				return image




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






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

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


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											# get the original timestep using init_timestep
											a__				      =		min(int(num_inference_steps * strength		)  ,  a_		)

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

											return timesteps, num_inference_steps - t_start


							def _a      (							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_		)}'''		)

											a__				      =		image.to(device=a_  ,  dtype=a_		)

											a__				      =		batch_size * num_images_per_prompt

											if image.shape[1] == 4:
															a__				      =		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_		):
																			a__				      =		[
																			    self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(a_		)
																			]
																			a__				      =		torch.cat(a_  ,  dim=0		)
															else:
																			a__				      =		self.movq.encode(a_		).latent_dist.sample(a_		)

															a__				      =		self.movq.config.scaling_factor * init_latents

											a__				      =		torch.cat([init_latents]  ,  dim=0		)

											a__				      =		init_latents.shape
											a__				      =		randn_tensor(a_  ,  generator=a_  ,  device=a_  ,  dtype=a_		)

											# get latents
											a__				      =		self.scheduler.add_noise(a_  ,  a_  ,  a_		)

											a__				      =		init_latents

											return latents


							def _a      (							self  ,  a_=0		):
											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(a_  ,  a_		)


							def _a      (							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."""		)

											a__				      =		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)

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

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


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def _a      (							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_ = 512  ,  a_ = 512  ,  a_ = 100  ,  a_ = 4.0  ,  a_ = 0.3  ,  a_ = 1  ,  a_ = None  ,  a_ = "pil"  ,  a_ = True  ,  ):
											a__				      =		self._execution_device

											a__				      =		guidance_scale > 1.0

											if isinstance(a_  ,  a_		):
															a__				      =		torch.cat(a_  ,  dim=0		)
											a__				      =		image_embeds.shape[0]
											if isinstance(a_  ,  a_		):
															a__				      =		torch.cat(a_  ,  dim=0		)

											if do_classifier_free_guidance:
															a__				      =		image_embeds.repeat_interleave(a_  ,  dim=0		)
															a__				      =		negative_image_embeds.repeat_interleave(a_  ,  dim=0		)

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

											if not isinstance(a_  ,  a_		):
															a__				      =		[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'''		)

											a__				      =		torch.cat([prepare_image(a_  ,  a_  ,  a_		) for i in image]  ,  dim=0		)
											a__				      =		image.to(dtype=image_embeds.dtype  ,  device=a_		)

											a__				      =		self.movq.encode(a_		)["""latents"""]
											a__				      =		latents.repeat_interleave(a_  ,  dim=0		)
											self.scheduler.set_timesteps(a_  ,  device=a_		)
											a__      ,			a__				      =		self.get_timesteps(a_  ,  a_  ,  a_		)
											a__				      =		timesteps[:1].repeat(batch_size * num_images_per_prompt		)
											a__      ,			a__				      =		downscale_height_and_width(a_  ,  a_  ,  self.movq_scale_factor		)
											a__				      =		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
															a__				      =		torch.cat([latents] * 2		) if do_classifier_free_guidance else latents

															a__				      =		{"""image_embeds""": image_embeds}
															a__				      =		self.unet(
															    sample=a_  ,  timestep=a_  ,  encoder_hidden_states=a_  ,  added_cond_kwargs=a_  ,  return_dict=a_  ,  )[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(
															    a_  ,  a_  ,  a_  ,  generator=a_  ,  )[0]

											# post-processing
											a__				      =		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"]:
															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(a_		)

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=a_		)






import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase							 =					[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]

def 			A_      (	):

				"""simple docstring"""
				a__				      =		Github(os.environ["""GITHUB_TOKEN"""]		)
				a__				      =		g.get_repo("""huggingface/diffusers"""		)
				a__				      =		repo.get_issues(state="""open"""		)

				for issue in open_issues:
								a__				      =		sorted(issue.get_comments() ,		key=lambda __a		: i.created_at ,		reverse=__a		)
								a__				      =		comments[0] if len(__a		) > 0 else None
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and (dt.utcnow() - issue.updated_at).days > 7
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()		)
								):
												# Closes the issue after 7 days of inactivity since the Stalebot notification.
												issue.edit(state="""closed"""		)
								elif (
								    "stale" in issue.get_labels()
								    and last_comment is not None
								    and last_comment.user.login != "github-actions[bot]"
								):
												# Opens the issue if someone other than Stalebot commented.
												issue.edit(state="""open"""		)
												issue.remove_from_labels("""stale"""		)
								elif (
								    (dt.utcnow() - issue.updated_at).days > 23
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()		)
								):
												# Post a Stalebot notification after 23 days of inactivity.
												issue.create_comment(
												    """This issue has been automatically marked as stale because it has not had """
												    """recent activity. If you think this still needs to be addressed """
												    """please comment on this thread.\n\nPlease note that issues that do not follow the """
												    """[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
												    """are likely to be ignored."""		)
												issue.add_to_labels("""stale"""		)


if __name__ == "__main__":
	main()

