


"""simple docstring"""


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

UpperCamelCase						:				Tuple             =      "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=8	)      ->		List[str]:







		"""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  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__=512 ,			UpperCamelCase__=512	)      ->		Optional[int]:







		"""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(UpperCamelCase__ ,			[2, 0, 1]	)
		A					       = torch.from_numpy(UpperCamelCase__	).unsqueeze(0	)
		return image



class 			lowerCamelCase__						(   UpperCAmelCase_			):







						def __init__(							self   :    Tuple			,			_lowercase   :    UNetaDConditionModel			,			_lowercase   :    DDPMScheduler			,			_lowercase   :    VQModel			,			):
								super().__init__()

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







						def __a						(							self   :    int			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Optional[Any]			,			_lowercase   :    List[Any]		):
								# get the original timestep using init_timestep
								A					       = min(int(num_inference_steps * strength		)			,			_lowercase		)

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

								return timesteps, num_inference_steps - t_start







						def __a						(							self   :    List[Any]			,			_lowercase   :    Tuple			,			_lowercase   :    Tuple			,			_lowercase   :    Union[str, Any]			,			_lowercase   :    List[str]			,			_lowercase   :    List[Any]			,			_lowercase   :    str			,			_lowercase   :    List[str]=None		):
								if not isinstance(_lowercase			,			(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(_lowercase		)}'		)

								A					       = image.to(device=_lowercase			,			dtype=_lowercase		)

								A					       = batch_size * num_images_per_prompt

								if image.shape[1] == 4:
										A					       = image

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

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

										A					       = self.movq.config.scaling_factor * init_latents

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

								A					       = init_latents.shape
								A					       = randn_tensor(_lowercase			,			generator=_lowercase			,			device=_lowercase			,			dtype=_lowercase		)

								# get latents
								A					       = self.scheduler.add_noise(_lowercase			,			_lowercase			,			_lowercase		)

								A					       = init_latents

								return latents







						def __a						(							self   :    Union[str, Any]			,			_lowercase   :    Any=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(_lowercase			,			_lowercase		)







						def __a						(							self   :    List[str]			,			_lowercase   :    Optional[int]=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=_lowercase		)
										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(_lowercase			,			_lowercase			,			prev_module_hook=_lowercase		)

								# 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   :    Dict		):
								if not hasattr(self.unet			,			'_hf_hook'		):
										return self.device
								for module in self.unet.modules():
										if (
										    hasattr(_lowercase			,			'_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(_lowercase		)
						def __call__(							self   :    Any			,			_lowercase   :    Union[torch.FloatTensor, List[torch.FloatTensor]]			,			_lowercase   :    Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]			,			_lowercase   :    Union[torch.FloatTensor, List[torch.FloatTensor]]			,			_lowercase   :    int = 512			,			_lowercase   :    int = 512			,			_lowercase   :    int = 100			,			_lowercase   :    float = 4.0			,			_lowercase   :    float = 0.3			,			_lowercase   :    int = 1			,			_lowercase   :    Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			_lowercase   :    Optional[str] = "pil"			,			_lowercase   :    bool = True			,			):
								A					       = self._execution_device

								A					       = guidance_scale > 1.0

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

								if do_classifier_free_guidance:
										A					       = image_embeds.repeat_interleave(_lowercase			,			dim=0		)
										A					       = negative_image_embeds.repeat_interleave(_lowercase			,			dim=0		)

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

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

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

								A					       = self.movq.encode(_lowercase		)['latents']
								A					       = latents.repeat_interleave(_lowercase			,			dim=0		)
								self.scheduler.set_timesteps(_lowercase			,			device=_lowercase		)
								A       , A					       = self.get_timesteps(_lowercase			,			_lowercase			,			_lowercase		)
								A					       = timesteps[:1].repeat(batch_size * num_images_per_prompt		)
								A       , A					       = downscale_height_and_width(_lowercase			,			_lowercase			,			self.movq_scale_factor		)
								A					       = self.prepare_latents(
								    _lowercase			,			_lowercase			,			_lowercase			,			_lowercase			,			image_embeds.dtype			,			_lowercase			,			_lowercase		)
								for i, t in enumerate(self.progress_bar(_lowercase		)		):
										# 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=_lowercase			,			timestep=_lowercase			,			encoder_hidden_states=_lowercase			,			added_cond_kwargs=_lowercase			,			return_dict=_lowercase			,			)[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(
										    _lowercase			,			_lowercase			,			_lowercase			,			generator=_lowercase			,			)[0]

								# post-processing
								A					       = self.movq.decode(_lowercase			,			force_not_quantize=_lowercase		)['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(_lowercase		)

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=_lowercase		)









"""simple docstring"""


from datetime import datetime as dt
import os

from github import Github


UpperCamelCase						:				Union[str, Any]             =      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def  __snake_case       (   )      ->		int:







		"""simple docstring"""





		A					       = Github(os.environ['GITHUB_TOKEN']	)
		A					       = g.get_repo('huggingface/transformers'	)
		A					       = repo.get_issues(state='open'	)

		for issue in open_issues:
				A					       = sorted([comment for comment in issue.get_comments()] ,			key=lambda UpperCamelCase__	: i.created_at ,			reverse=UpperCamelCase__	)
				A					       = comments[0] if len(UpperCamelCase__	) > 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()	)
				):
						# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
						issue.edit(state='closed'	)
				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()	)
				):
						# print(f"Would add stale comment to {issue.number}")
						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/transformers/blob/main/CONTRIBUTING.md) '
						    'are likely to be ignored.'	)


if __name__ == "__main__":
	main()





