






'''simple docstring'''

from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 	_UpperCAmelCase    (  a		:	Optional[int]							)			->			List[Any]:





						"""simple docstring"""
						for param in module.parameters():
												lowercase_      :   Union[str, Any]        =			False






def 	_UpperCAmelCase    (  )			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   Dict        =			'cuda' if torch.cuda.is_available() else 'cpu'
						if torch.backends.mps.is_available() and torch.backends.mps.is_built():
												lowercase_      :   Tuple        =			'mps'
						if device == "mps":
												print(
												    'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
												    ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
												    ' with generations.'							)
						return device






def 	_UpperCAmelCase    (  a		:	List[str]							)			->			List[str]:





						"""simple docstring"""
						lowercase_      :   str        =			plt.imshow(a							)
						fig.axes.get_xaxis().set_visible(a							)
						fig.axes.get_yaxis().set_visible(a							)
						plt.show()






def 	_UpperCAmelCase    (  )			->			Tuple:





						"""simple docstring"""
						lowercase_      :   Optional[int]        =			datetime.now()
						lowercase_      :   Tuple        =			current_time.strftime('%H:%M:%S'							)
						return timestamp








'''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,
)


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

A:      Union[str, Any]  				=				"\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 	_UpperCAmelCase    (  a		:	Tuple						,			a		:	Union[str, Any]						,			a		:	List[Any]=8							)			->			Dict:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			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 	_UpperCAmelCase    (  a		:	Any						,			a		:	Dict=5_1_2						,			a		:	Optional[Any]=5_1_2							)			->			Tuple:





						"""simple docstring"""
						lowercase_      :   int        =			pil_image.resize((w, h)						,			resample=Image.BICUBIC						,			reducing_gap=1							)
						lowercase_      :   int        =			np.array(pil_image.convert('RGB'							)							)
						lowercase_      :   Optional[int]        =			arr.astype(np.floataa							) / 1_27.5 - 1
						lowercase_      :   Any        =			np.transpose(a						,			[2, 0, 1]							)
						lowercase_      :   Any        =			torch.from_numpy(a							).unsqueeze(0							)
						return image






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )						->  List[Any]:
							super().__init__()

							self.register_modules(
							    unet=_lowercase  ,      scheduler=_lowercase  ,      movq=_lowercase  ,      )
							lowercase_      :   Dict        =			2 ** (len(self.movq.config.block_out_channels       ) - 1)
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  int:
							# get the original timestep using init_timestep
							lowercase_      :   List[Any]        =			min(int(num_inference_steps * strength       )  ,      _lowercase       )

							lowercase_      :   Tuple        =			max(num_inference_steps - init_timestep  ,      0       )
							lowercase_      :   Optional[Any]        =			self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase=None       )						->  Any:
							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       )}"       )

							lowercase_      :   Dict        =			image.to(device=_lowercase  ,      dtype=_lowercase       )

							lowercase_      :   Dict        =			batch_size * num_images_per_prompt

							if image.shape[1] == 4:
													lowercase_      :   str        =			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       ):
																			lowercase_      :   List[Any]        =			[
																			    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(_lowercase       )
																			]
																			lowercase_      :   Union[str, Any]        =			torch.cat(_lowercase  ,      dim=0       )
													else:
																			lowercase_      :   Union[str, Any]        =			self.movq.encode(_lowercase       ).latent_dist.sample(_lowercase       )

													lowercase_      :   str        =			self.movq.config.scaling_factor * init_latents

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

							lowercase_      :   Dict        =			init_latents.shape
							lowercase_      :   Dict        =			randn_tensor(_lowercase  ,      generator=_lowercase  ,      device=_lowercase  ,      dtype=_lowercase       )

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

							lowercase_      :   Optional[Any]        =			init_latents

							return latents
	def        lowerCamelCase__		(							self  ,      _lowercase=0       )						->  int:
							if is_accelerate_available():
													from accelerate import cpu_offload
							else:
													raise ImportError('Please install accelerate via `pip install accelerate`'       )

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

							lowercase_      :   Optional[Any]        =			[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																			cpu_offload(_lowercase  ,      _lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase=0       )						->  int:
							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_      :   List[Any]        =			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)

							lowercase_      :   Tuple        =			None
							for cpu_offloaded_model in [self.unet, self.movq]:
													lowercase_  ,      lowercase_      :   Dict        =			cpu_offload_with_hook(_lowercase  ,      _lowercase  ,      prev_module_hook=_lowercase       )

							# We'll offload the last model manually.
							lowercase_      :   List[str]        =			hook
	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def        lowerCamelCase__		(							self       )						->  List[str]:
							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  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = 512  ,      _lowercase = 512  ,      _lowercase = 100  ,      _lowercase = 4.0  ,      _lowercase = 0.3  ,      _lowercase = 1  ,      _lowercase = None  ,      _lowercase = "pil"  ,      _lowercase = True  ,      )						->  str:
							lowercase_      :   List[Any]        =			self._execution_device

							lowercase_      :   List[Any]        =			guidance_scale > 1.0

							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   Union[str, Any]        =			torch.cat(_lowercase  ,      dim=0       )
							lowercase_      :   Optional[Any]        =			image_embeds.shape[0]
							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   List[str]        =			torch.cat(_lowercase  ,      dim=0       )

							if do_classifier_free_guidance:
													lowercase_      :   List[str]        =			image_embeds.repeat_interleave(_lowercase  ,      dim=0       )
													lowercase_      :   Dict        =			negative_image_embeds.repeat_interleave(_lowercase  ,      dim=0       )

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

							if not isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   Union[str, Any]        =			[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"       )

							lowercase_      :   List[Any]        =			torch.cat([prepare_image(_lowercase  ,      _lowercase  ,      _lowercase       ) for i in image]  ,      dim=0       )
							lowercase_      :   Dict        =			image.to(dtype=image_embeds.dtype  ,      device=_lowercase       )

							lowercase_      :   Dict        =			self.movq.encode(_lowercase       )['latents']
							lowercase_      :   Optional[Any]        =			latents.repeat_interleave(_lowercase  ,      dim=0       )
							self.scheduler.set_timesteps(_lowercase  ,      device=_lowercase       )
							lowercase_  ,      lowercase_      :   str        =			self.get_timesteps(_lowercase  ,      _lowercase  ,      _lowercase       )
							lowercase_      :   int        =			timesteps[:1].repeat(batch_size * num_images_per_prompt       )
							lowercase_  ,      lowercase_      :   Union[str, Any]        =			downscale_height_and_width(_lowercase  ,      _lowercase  ,      self.movq_scale_factor       )
							lowercase_      :   List[str]        =			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
													lowercase_      :   int        =			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

													lowercase_      :   str        =			{'image_embeds': image_embeds}
													lowercase_      :   str        =			self.unet(
													    sample=_lowercase  ,      timestep=_lowercase  ,      encoder_hidden_states=_lowercase  ,      added_cond_kwargs=_lowercase  ,      return_dict=_lowercase  ,      )[0]

													if do_classifier_free_guidance:
																			lowercase_  ,      lowercase_      :   Dict        =			noise_pred.split(latents.shape[1]  ,      dim=1       )
																			lowercase_  ,      lowercase_      :   Optional[int]        =			noise_pred.chunk(2       )
																			lowercase_  ,      lowercase_      :   Tuple        =			variance_pred.chunk(2       )
																			lowercase_      :   List[str]        =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			lowercase_      :   Tuple        =			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_      :   List[str]        =			noise_pred.split(latents.shape[1]  ,      dim=1       )

													# compute the previous noisy sample x_t -> x_t-1
													lowercase_      :   Dict        =			self.scheduler.step(
													    _lowercase  ,      _lowercase  ,      _lowercase  ,      generator=_lowercase  ,      )[0]

							# post-processing
							lowercase_      :   Any        =			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"]:
													lowercase_      :   Dict        =			image * 0.5 + 0.5
													lowercase_      :   Dict        =			image.clamp(0  ,      1       )
													lowercase_      :   int        =			image.cpu().permute(0  ,      2  ,      3  ,      1       ).float().numpy()

							if output_type == "pil":
													lowercase_      :   int        =			self.numpy_to_pil(_lowercase       )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=_lowercase       )
