
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,
)


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

snake_case__  							=							"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				List[Any]							,			UpperCAmelCase_							:				int=8       ):
						lowercase       :    int											=  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

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[Any]							,			UpperCAmelCase_							:				List[Any]=5_12							,			UpperCAmelCase_							:				List[Any]=5_12       ):
						lowercase       :    str											=  pil_image.resize((w, h)							,			resample=Image.BICUBIC							,			reducing_gap=1       )
						lowercase       :    List[Any]											=  np.array(pil_image.convert('''RGB'''       )       )
						lowercase       :    int											=  arr.astype(np.floataa       ) / 1_27.5 - 1
						lowercase       :    Tuple											=  np.transpose(UpperCAmelCase_							,			[2, 0, 1]       )
						lowercase       :    Dict											=  torch.from_numpy(UpperCAmelCase_       ).unsqueeze(0       )
						return image







class 							UpperCAmelCase	(   __lowerCamelCase					):

					def __init__(			self      :					Dict ,      lowerCAmelCase      :					UNetaDConditionModel ,      lowerCAmelCase      :					DDPMScheduler ,      lowerCAmelCase      :					VQModel ,      ):
											super().__init__()

											self.register_modules(
											    unet=lowerCAmelCase ,      scheduler=lowerCAmelCase ,      movq=lowerCAmelCase ,      )
											lowercase       :    Any											=  2 ** (len(self.movq.config.block_out_channels					) - 1)

					def        _lowerCAmelCase	(			self      :					Any ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					Optional[int] ,      lowerCAmelCase      :					str					):
											# get the original timestep using init_timestep
											lowercase       :    int											=  min(int(num_inference_steps * strength					) ,      lowerCAmelCase					)

											lowercase       :    int											=  max(num_inference_steps - init_timestep ,      0					)
											lowercase       :    Union[str, Any]											=  self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start

					def        _lowerCAmelCase	(			self      :					Optional[Any] ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					int ,      lowerCAmelCase      :					str ,      lowerCAmelCase      :					Optional[Any] ,      lowerCAmelCase      :					Tuple=None					):
											if not isinstance(lowerCAmelCase ,      (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(lowerCAmelCase					)}'''					)

											lowercase       :    Any											=  image.to(device=lowerCAmelCase ,      dtype=lowerCAmelCase					)

											lowercase       :    int											=  batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																	lowercase       :    Tuple											=  image

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

																	elif isinstance(lowerCAmelCase ,      lowerCAmelCase					):
																							lowercase       :    List[str]											=  [
																							    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(lowerCAmelCase					)
																							]
																							lowercase       :    Optional[Any]											=  torch.cat(lowerCAmelCase ,      dim=0					)
																	else:
																							lowercase       :    int											=  self.movq.encode(lowerCAmelCase					).latent_dist.sample(lowerCAmelCase					)

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

											lowercase       :    List[str]											=  torch.cat([init_latents] ,      dim=0					)

											lowercase       :    Optional[int]											=  init_latents.shape
											lowercase       :    Any											=  randn_tensor(lowerCAmelCase ,      generator=lowerCAmelCase ,      device=lowerCAmelCase ,      dtype=lowerCAmelCase					)

											# get latents
											lowercase       :    List[Any]											=  self.scheduler.add_noise(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					)

											lowercase       :    int											=  init_latents

											return latents

					def        _lowerCAmelCase	(			self      :					Optional[int] ,      lowerCAmelCase      :					Union[str, Any]=0					):
											if is_accelerate_available():
																	from accelerate import cpu_offload
											else:
																	raise ImportError('''Please install accelerate via `pip install accelerate`'''					)

											lowercase       :    Union[str, Any]											=  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(lowerCAmelCase ,      lowerCAmelCase					)

					def        _lowerCAmelCase	(			self      :					List[str] ,      lowerCAmelCase      :					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.'''					)

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

											if self.device.type != "cpu":
																	self.to('''cpu''' ,      silence_dtype_warnings=lowerCAmelCase					)
																	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[Any]											=  cpu_offload_with_hook(lowerCAmelCase ,      lowerCAmelCase ,      prev_module_hook=lowerCAmelCase					)

											# We'll offload the last model manually.
											lowercase       :    Tuple											=  hook

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def        _lowerCAmelCase	(			self      :					Dict					):
											if not hasattr(self.unet ,      '''_hf_hook'''					):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(lowerCAmelCase ,      '''_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(lowerCAmelCase					)
					def __call__(			self      :					str ,      lowerCAmelCase      :					Union[torch.FloatTensor, List[torch.FloatTensor]] ,      lowerCAmelCase      :					Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] ,      lowerCAmelCase      :					Union[torch.FloatTensor, List[torch.FloatTensor]] ,      lowerCAmelCase      :					torch.FloatTensor ,      lowerCAmelCase      :					int = 512 ,      lowerCAmelCase      :					int = 512 ,      lowerCAmelCase      :					int = 100 ,      lowerCAmelCase      :					float = 4.0 ,      lowerCAmelCase      :					float = 0.3 ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      ):
											lowercase       :    Any											=  self._execution_device

											lowercase       :    Union[str, Any]											=  guidance_scale > 1.0

											if isinstance(lowerCAmelCase ,      lowerCAmelCase					):
																	lowercase       :    str											=  torch.cat(lowerCAmelCase ,      dim=0					)
											if isinstance(lowerCAmelCase ,      lowerCAmelCase					):
																	lowercase       :    Tuple											=  torch.cat(lowerCAmelCase ,      dim=0					)
											if isinstance(lowerCAmelCase ,      lowerCAmelCase					):
																	lowercase       :    List[Any]											=  torch.cat(lowerCAmelCase ,      dim=0					)

											lowercase       :    List[Any]											=  image_embeds.shape[0]

											if do_classifier_free_guidance:
																	lowercase       :    Tuple											=  image_embeds.repeat_interleave(lowerCAmelCase ,      dim=0					)
																	lowercase       :    Dict											=  negative_image_embeds.repeat_interleave(lowerCAmelCase ,      dim=0					)
																	lowercase       :    Optional[Any]											=  hint.repeat_interleave(lowerCAmelCase ,      dim=0					)

											lowercase       :    Dict											=  torch.cat([negative_image_embeds, image_embeds] ,      dim=0					).to(dtype=self.unet.dtype ,      device=lowerCAmelCase					)
											lowercase       :    Optional[Any]											=  torch.cat([hint, hint] ,      dim=0					).to(dtype=self.unet.dtype ,      device=lowerCAmelCase					)

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

											lowercase       :    int											=  torch.cat([prepare_image(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					) for i in image] ,      dim=0					)
											lowercase       :    Optional[int]											=  image.to(dtype=image_embeds.dtype ,      device=lowerCAmelCase					)

											lowercase       :    int											=  self.movq.encode(lowerCAmelCase					)['''latents''']
											lowercase       :    Dict											=  latents.repeat_interleave(lowerCAmelCase ,      dim=0					)
											self.scheduler.set_timesteps(lowerCAmelCase ,      device=lowerCAmelCase					)
											lowercase				,				lowercase       :    Tuple											=  self.get_timesteps(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					)
											lowercase       :    Tuple											=  timesteps[:1].repeat(batch_size * num_images_per_prompt					)
											lowercase				,				lowercase       :    Dict											=  downscale_height_and_width(lowerCAmelCase ,      lowerCAmelCase ,      self.movq_scale_factor					)
											lowercase       :    Any											=  self.prepare_latents(
											    lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase ,      image_embeds.dtype ,      lowerCAmelCase ,      lowerCAmelCase					)
											for i, t in enumerate(self.progress_bar(lowerCAmelCase					)					):
																	# expand the latents if we are doing classifier free guidance
																	lowercase       :    Tuple											=  torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

																	lowercase       :    Dict											=  {'''image_embeds''': image_embeds, '''hint''': hint}
																	lowercase       :    int											=  self.unet(
																	    sample=lowerCAmelCase ,      timestep=lowerCAmelCase ,      encoder_hidden_states=lowerCAmelCase ,      added_cond_kwargs=lowerCAmelCase ,      return_dict=lowerCAmelCase ,      )[0]

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

																	# compute the previous noisy sample x_t -> x_t-1

																	lowercase       :    Dict											=  self.scheduler.step(
																	    lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase ,      generator=lowerCAmelCase ,      )[0]

											# post-processing
											lowercase       :    Tuple											=  self.movq.decode(lowerCAmelCase ,      force_not_quantize=lowerCAmelCase					)['''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       :    int											=  image * 0.5 + 0.5
																	lowercase       :    int											=  image.clamp(0 ,      1					)
																	lowercase       :    Any											=  image.cpu().permute(0 ,      2 ,      3 ,      1					).float().numpy()

											if output_type == "pil":
																	lowercase       :    List[Any]											=  self.numpy_to_pil(lowerCAmelCase					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)




snake_case__  							=							"""
# Diffusers installation
! pip install diffusers transformers datasets accelerate
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/diffusers.git
"""

snake_case__  							=							[{"""type""": """code""", """content""": INSTALL_CONTENT}]


