

"""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,
)


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

lowerCamelCase__				:  List[Any]													=  '''
    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       UpperCamelCase					(			_lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						Any,  _lowerCAmelCase     :						Dict=8 )				->			int:
	_UpperCAmelCase    :							List[Any]									=					height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	_UpperCAmelCase    :							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					(			_lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						Any=512,  _lowerCAmelCase     :						Optional[Any]=512 )				->			Any:
	_UpperCAmelCase    :							int									=					pil_image.resize((w, h),  resample=Image.BICUBIC,  reducing_gap=1 )
	_UpperCAmelCase    :							Tuple									=					np.array(pil_image.convert("""RGB""" ) )
	_UpperCAmelCase    :							Tuple									=					arr.astype(np.floataa ) / 127.5 - 1
	_UpperCAmelCase    :							Dict									=					np.transpose(_lowerCAmelCase,  [2, 0, 1] )
	_UpperCAmelCase    :							Optional[Any]									=					torch.from_numpy(_lowerCAmelCase ).unsqueeze(0 )
	return image






class     _UpperCAmelCase			(		__a):
			def __init__(		self      ,		_A      ,		_A      ,		_A      ,		)    ->    Any:






				'''simple docstring'''







				super().__init__()

				self.register_modules(
				    unet=_A      ,		scheduler=_A      ,		movq=_A      ,		)
				_UpperCAmelCase    :							List[str]									=					2 ** (len(self.movq.config.block_out_channels     ) - 1)
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A     )    ->    Any:






				'''simple docstring'''







				_UpperCAmelCase    :							Tuple									=					min(int(num_inference_steps * strength     )      ,		_A     )

				_UpperCAmelCase    :							List[str]									=					max(num_inference_steps - init_timestep      ,		0     )
				_UpperCAmelCase    :							Optional[Any]									=					self.scheduler.timesteps[t_start:]

				return timesteps, num_inference_steps - t_start
			def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A=None     )    ->    Optional[int]:






				'''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     )}'''     )

				_UpperCAmelCase    :							Tuple									=					image.to(device=_A      ,		dtype=_A     )

				_UpperCAmelCase    :							str									=					batch_size * num_images_per_prompt

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

					_UpperCAmelCase    :							Dict									=					self.movq.config.scaling_factor * init_latents

				_UpperCAmelCase    :							Dict									=					torch.cat([init_latents]      ,		dim=0     )

				_UpperCAmelCase    :							List[Any]									=					init_latents.shape
				_UpperCAmelCase    :							str									=					randn_tensor(_A      ,		generator=_A      ,		device=_A      ,		dtype=_A     )

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

				_UpperCAmelCase    :							Any									=					init_latents

				return latents
			def 							__snake_case (		self      ,		_A=0     )    ->    List[str]:






				'''simple docstring'''







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

				_UpperCAmelCase    :							str									=					torch.device(f'''cuda:{gpu_id}'''     )

				_UpperCAmelCase    :							Dict									=					[
				    self.unet,
				    self.movq,
				]
				for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
						cpu_offload(_A      ,		_A     )
			def 							__snake_case (		self      ,		_A=0     )    ->    Tuple:






				'''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."""     )

				_UpperCAmelCase    :							Tuple									=					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)

				_UpperCAmelCase    :							Any									=					None
				for cpu_offloaded_model in [self.unet, self.movq]:
					_UpperCAmelCase						,   _UpperCAmelCase    :							Tuple									=					cpu_offload_with_hook(_A      ,		_A      ,		prev_module_hook=_A     )

				# We'll offload the last model manually.
				_UpperCAmelCase    :							Any									=					hook
			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							__snake_case (		self     )    ->    List[str]:






				'''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      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = 5_12      ,		_A = 5_12      ,		_A = 1_00      ,		_A = 4.0      ,		_A = 0.3      ,		_A = 1      ,		_A = None      ,		_A = "pil"      ,		_A = True      ,		)    ->    Dict:






				'''simple docstring'''







				_UpperCAmelCase    :							List[Any]									=					self._execution_device

				_UpperCAmelCase    :							List[Any]									=					guidance_scale > 1.0

				if isinstance(_A      ,		_A     ):
					_UpperCAmelCase    :							str									=					torch.cat(_A      ,		dim=0     )
				if isinstance(_A      ,		_A     ):
					_UpperCAmelCase    :							List[str]									=					torch.cat(_A      ,		dim=0     )
				if isinstance(_A      ,		_A     ):
					_UpperCAmelCase    :							int									=					torch.cat(_A      ,		dim=0     )

				_UpperCAmelCase    :							Tuple									=					image_embeds.shape[0]

				if do_classifier_free_guidance:
					_UpperCAmelCase    :							List[Any]									=					image_embeds.repeat_interleave(_A      ,		dim=0     )
					_UpperCAmelCase    :							List[str]									=					negative_image_embeds.repeat_interleave(_A      ,		dim=0     )
					_UpperCAmelCase    :							Tuple									=					hint.repeat_interleave(_A      ,		dim=0     )

				_UpperCAmelCase    :							Optional[int]									=					torch.cat([negative_image_embeds, image_embeds]      ,		dim=0     ).to(dtype=self.unet.dtype      ,		device=_A     )
				_UpperCAmelCase    :							Tuple									=					torch.cat([hint, hint]      ,		dim=0     ).to(dtype=self.unet.dtype      ,		device=_A     )

				if not isinstance(_A      ,		_A     ):
					_UpperCAmelCase    :							Any									=					[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'''     )

				_UpperCAmelCase    :							Tuple									=					torch.cat([prepare_image(_A      ,		_A      ,		_A     ) for i in image]      ,		dim=0     )
				_UpperCAmelCase    :							List[Any]									=					image.to(dtype=image_embeds.dtype      ,		device=_A     )

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

					_UpperCAmelCase    :							Union[str, Any]									=					{"""image_embeds""": image_embeds, """hint""": hint}
					_UpperCAmelCase    :							Union[str, Any]									=					self.unet(
					    sample=_A      ,		timestep=_A      ,		encoder_hidden_states=_A      ,		added_cond_kwargs=_A      ,		return_dict=_A      ,		)[0]

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

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

					_UpperCAmelCase    :							Dict									=					self.scheduler.step(
					    _A      ,		_A      ,		_A      ,		generator=_A      ,		)[0]

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

				if output_type == "pil":
					_UpperCAmelCase    :							int									=					self.numpy_to_pil(_A     )

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=_A     )








"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int = 1000 )				->			str:
	if (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (digit_position <= 0):
		raise ValueError("""Digit position must be a positive integer""" )
	elif (not isinstance(_lowerCAmelCase,  _lowerCAmelCase )) or (precision < 0):
		raise ValueError("""Precision must be a nonnegative integer""" )

	# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
	# accurate
	_UpperCAmelCase    :							List[Any]									=					(
	    4 * _subsum(_lowerCAmelCase,  1,  _lowerCAmelCase )
	    - 2 * _subsum(_lowerCAmelCase,  4,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  5,  _lowerCAmelCase )
	    - _subsum(_lowerCAmelCase,  6,  _lowerCAmelCase )
	)

	# return the first hex digit of the fractional part of the result
	return hex(int((sum_result % 1) * 16 ) )[2:]
def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						int )				->			float:
	_UpperCAmelCase    :							Optional[int]									=					0.0
	for sum_index in range(digit_pos_to_extract + precision ):
		_UpperCAmelCase    :							Optional[Any]									=					8 * sum_index + denominator_addend
		if sum_index < digit_pos_to_extract:
			# if the exponential term is an integer and we mod it by the denominator
			# before dividing, only the integer part of the sum will change;
			# the fractional part will not
			_UpperCAmelCase    :							Optional[Any]									=					pow(
			    16,  digit_pos_to_extract - 1 - sum_index,  _lowerCAmelCase )
		else:
			_UpperCAmelCase    :							int									=					pow(16,  digit_pos_to_extract - 1 - sum_index )
		total += exponential_term / denominator
	return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()





