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

UpperCamelCase_  =	"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"





def 							_lowerCamelCase			(					lowerCamelCase_:  Any				,	lowerCamelCase_:  Union[str, Any]				,	lowerCamelCase_:  Any=8		):



				'''simple docstring'''







				A     :  int   =				height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				A     :  Tuple   =				width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





def 							_lowerCamelCase			(					lowerCamelCase_:  List[str]				,	lowerCamelCase_:  Optional[int]=512				,	lowerCamelCase_:  Tuple=512		):



				'''simple docstring'''







				A     :  Tuple   =				pil_image.resize((w, h)				,	resample=Image.BICUBIC				,	reducing_gap=1		)
				A     :  Tuple   =				np.array(pil_image.convert('''RGB'''		)		)
				A     :  Optional[Any]   =				arr.astype(np.floataa		) / 127.5 - 1
				A     :  Union[str, Any]   =				np.transpose(_UpperCamelCase				,	[2, 0, 1]		)
				A     :  Optional[Any]   =				torch.from_numpy(_UpperCamelCase		).unsqueeze(0		)
				return image






class     _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE__							):





				def __init__(		self :		Optional[int]					,      snake_case_ :		UNetaDConditionModel					,      snake_case_ :		DDPMScheduler					,      snake_case_ :		VQModel					,      ):


								"""simple docstring"""

								super().__init__()

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





				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		Tuple					,      snake_case_ :		Dict					,      snake_case_ :		List[Any]							):


								"""simple docstring"""

								A     :  int   =				min(int(num_inference_steps * strength							)					,      a_							)

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

								return timesteps, num_inference_steps - t_start





				def        _UpperCAmelCase						(		self :		Tuple					,      snake_case_ :		Any					,      snake_case_ :		str					,      snake_case_ :		Any					,      snake_case_ :		str					,      snake_case_ :		Optional[int]					,      snake_case_ :		Tuple					,      snake_case_ :		int=None							):


								"""simple docstring"""

								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     :  Union[str, Any]   =				image.to(device=a_					,      dtype=a_							)

								A     :  List[str]   =				batch_size * num_images_per_prompt

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

												A     :  Tuple   =				self.movq.config.scaling_factor * init_latents

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

								A     :  Tuple   =				init_latents.shape
								A     :  Any   =				randn_tensor(a_					,      generator=a_					,      device=a_					,      dtype=a_							)

								# get latents
								A     :  Dict   =				self.scheduler.add_noise(a_					,      a_					,      a_							)

								A     :  List[str]   =				init_latents

								return latents





				def        _UpperCAmelCase						(		self :		Union[str, Any]					,      snake_case_ :		List[str]=0							):


								"""simple docstring"""

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

								A     :  Dict   =				torch.device(f"""cuda:{gpu_id}"""							)

								A     :  Optional[int]   =				[
								    self.unet,
								    self.movq,
								]
								for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																cpu_offload(a_					,      a_							)





				def        _UpperCAmelCase						(		self :		Dict					,      snake_case_ :		int=0							):


								"""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.'''							)

								A     :  List[Any]   =				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     :  Dict   =				None
								for cpu_offloaded_model in [self.unet, self.movq]:
												A ,       A     :  List[Any]   =				cpu_offload_with_hook(a_					,      a_					,      prev_module_hook=a_							)

								# We'll offload the last model manually.
								A     :  int   =				hook





				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def        _UpperCAmelCase						(		self :		Tuple							):


								"""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 :		Dict					,      snake_case_ :		Union[torch.FloatTensor, List[torch.FloatTensor]]					,      snake_case_ :		Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]					,      snake_case_ :		Union[torch.FloatTensor, List[torch.FloatTensor]]					,      snake_case_ :		torch.FloatTensor					,      snake_case_ :		int = 512					,      snake_case_ :		int = 512					,      snake_case_ :		int = 100					,      snake_case_ :		float = 4.0					,      snake_case_ :		float = 0.3					,      snake_case_ :		int = 1					,      snake_case_ :		Optional[Union[torch.Generator, List[torch.Generator]]] = None					,      snake_case_ :		Optional[str] = "pil"					,      snake_case_ :		bool = True					,      ):


								"""simple docstring"""

								A     :  Union[str, Any]   =				self._execution_device

								A     :  List[Any]   =				guidance_scale > 1.0

								if isinstance(a_					,      a_							):
												A     :  Any   =				torch.cat(a_					,      dim=0							)
								if isinstance(a_					,      a_							):
												A     :  List[str]   =				torch.cat(a_					,      dim=0							)
								if isinstance(a_					,      a_							):
												A     :  Any   =				torch.cat(a_					,      dim=0							)

								A     :  Optional[int]   =				image_embeds.shape[0]

								if do_classifier_free_guidance:
												A     :  str   =				image_embeds.repeat_interleave(a_					,      dim=0							)
												A     :  Tuple   =				negative_image_embeds.repeat_interleave(a_					,      dim=0							)
												A     :  Any   =				hint.repeat_interleave(a_					,      dim=0							)

								A     :  List[Any]   =				torch.cat([negative_image_embeds, image_embeds]					,      dim=0							).to(dtype=self.unet.dtype					,      device=a_							)
								A     :  str   =				torch.cat([hint, hint]					,      dim=0							).to(dtype=self.unet.dtype					,      device=a_							)

								if not isinstance(a_					,      a_							):
												A     :  str   =				[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     :  List[Any]   =				torch.cat([prepare_image(a_					,      a_					,      a_							) for i in image]					,      dim=0							)
								A     :  Optional[Any]   =				image.to(dtype=image_embeds.dtype					,      device=a_							)

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

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

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

												A     :  Union[str, Any]   =				self.scheduler.step(
												    a_					,      a_					,      a_					,      generator=a_					,      )[0]

								# post-processing
								A     :  Any   =				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     :  Any   =				image * 0.5 + 0.5
												A     :  Union[str, Any]   =				image.clamp(0					,      1							)
												A     :  List[str]   =				image.cpu().permute(0					,      2					,      3					,      1							).float().numpy()

								if output_type == "pil":
												A     :  List[str]   =				self.numpy_to_pil(a_							)

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=a_							)

UpperCamelCase_  =	10_00





def 							_lowerCamelCase			(					lowerCamelCase_:  float		):



				'''simple docstring'''







				return x * x





def 							_lowerCamelCase			(					lowerCamelCase_:  Tuple				,	lowerCamelCase_:  float				,	lowerCamelCase_:  float				,	lowerCamelCase_:  int = 4		):



				'''simple docstring'''







				assert callable(
				    lowerCamelCase_		), f"""the function(object) passed should be callable your input : {function}"""
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""a should be float or integer your input : {a}"""
				assert isinstance(function(lowerCamelCase_		)				,	(float, int)		), (
				    "the function should return integer or float return type of your function, "
				    f"""{type(lowerCamelCase_		)}"""
				)
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""b should be float or integer your input : {b}"""
				assert (
				    isinstance(lowerCamelCase_				,	lowerCamelCase_		) and precision > 0
				), f"""precision should be positive integer your input : {precision}"""

				# just applying the formula of simpson for approximate integration written in
				# mentioned article in first comment of this file and above this function

				A     :  Tuple   =				(b - a) / N_STEPS
				A     :  List[str]   =				function(lowerCamelCase_		) + function(lowerCamelCase_		)

				for i in range(1				,	lowerCamelCase_		):
								A     :  int   =				a + h * i
								result += function(lowerCamelCase_		) * (4 if i % 2 else 2)

				result *= h / 3
				return round(lowerCamelCase_				,	lowerCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()