




"""simple docstring"""






import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					List[str]      ,       snake_case			:					Union[str, Any]    ):
					if isinstance(snake_case      ,       torch.Tensor    ):
										return image
					elif isinstance(snake_case      ,       PIL.Image.Image    ):
										_lowerCAmelCase:List[str]									=  [image]

					if isinstance(image[0]      ,       PIL.Image.Image    ):
										_lowerCAmelCase:List[Any]									=  [np.array(i.resize((w, h)      ,       resample=PIL_INTERPOLATION['''lanczos''']    )    )[None, :] for i in image]
										_lowerCAmelCase:Optional[int]									=  np.concatenate(snake_case      ,       axis=0    )
										_lowerCAmelCase:Union[str, Any]									=  np.array(snake_case    ).astype(np.floataa    ) / 2_55.0
										_lowerCAmelCase:Tuple									=  image.transpose(0      ,       3      ,       1      ,       2    )
										_lowerCAmelCase:Any									=  2.0 * image - 1.0
										_lowerCAmelCase:Optional[Any]									=  torch.from_numpy(snake_case    )
					elif isinstance(image[0]      ,       torch.Tensor    ):
										_lowerCAmelCase:int									=  torch.cat(snake_case      ,       dim=0    )
					return image





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					List[str]      ,       snake_case			:					int      ,       snake_case			:					Optional[Any]=0.99_95    ):
					if not isinstance(snake_case      ,       np.ndarray    ):
										_lowerCAmelCase:List[Any]									=  True
										_lowerCAmelCase:List[Any]									=  va.device
										_lowerCAmelCase:Optional[int]									=  va.cpu().numpy()
										_lowerCAmelCase:Optional[int]									=  va.cpu().numpy()

					_lowerCAmelCase:Optional[Any]									=  np.sum(va * va / (np.linalg.norm(snake_case    ) * np.linalg.norm(snake_case    ))    )
					if np.abs(snake_case    ) > DOT_THRESHOLD:
										_lowerCAmelCase:Any									=  (1 - t) * va + t * va
					else:
										_lowerCAmelCase:str									=  np.arccos(snake_case    )
										_lowerCAmelCase:List[Any]									=  np.sin(snake_case    )
										_lowerCAmelCase:int									=  theta_a * t
										_lowerCAmelCase:List[Any]									=  np.sin(snake_case    )
										_lowerCAmelCase:Dict									=  np.sin(theta_a - theta_t    ) / sin_theta_a
										_lowerCAmelCase:Optional[Any]									=  sin_theta_t / sin_theta_a
										_lowerCAmelCase:List[Any]									=  sa * va + sa * va

					if inputs_are_torch:
										_lowerCAmelCase:str									=  torch.from_numpy(snake_case    ).to(snake_case    )

					return va





def 							UpperCAmelCase		(		snake_case			:					List[str]      ,       snake_case			:					str    ):
					_lowerCAmelCase:List[Any]									=  F.normalize(snake_case      ,       dim=-1    )
					_lowerCAmelCase:str									=  F.normalize(snake_case      ,       dim=-1    )
					return (x - y).norm(dim=-1    ).div(2    ).arcsin().pow(2    ).mul(2    )





def 							UpperCAmelCase		(		snake_case			:					Union[str, Any]      ,       snake_case			:					Union[str, Any]    ):
					for param in model.parameters():
										_lowerCAmelCase:Optional[Any]									=  value





class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	Dict     ,a__      :	AutoencoderKL     ,a__      :	CLIPTextModel     ,a__      :	CLIPModel     ,a__      :	CLIPTokenizer     ,a__      :	UNetaDConditionModel     ,a__      :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]     ,a__      :	CLIPFeatureExtractor     ,a__      :	str=None     ,a__      :	Optional[int]=None     ,a__      :	Any=None     ,)						->       List[str]:




											"""simple docstring"""







											super().__init__()
											self.register_modules(
											    vae=a__     ,text_encoder=a__     ,clip_model=a__     ,tokenizer=a__     ,unet=a__     ,scheduler=a__     ,feature_extractor=a__     ,coca_model=a__     ,coca_tokenizer=a__     ,coca_transform=a__     ,)
											_lowerCAmelCase:Optional[int]									=  (
											    feature_extractor.size
											    if isinstance(feature_extractor.size     ,a__)
											    else feature_extractor.size['''shortest_edge''']
											)
											_lowerCAmelCase:List[Any]									=  transforms.Normalize(mean=feature_extractor.image_mean     ,std=feature_extractor.image_std)
											set_requires_grad(self.text_encoder     ,a__)
											set_requires_grad(self.clip_model     ,a__)





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Optional[Union[str, int]] = "auto")						->       List[str]:




											"""simple docstring"""







											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																_lowerCAmelCase:Optional[int]									=  self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(a__)





						def       __UpperCamelCase     (							self      :	Optional[Any])						->       Optional[Any]:




											"""simple docstring"""







											self.enable_attention_slicing(a__)





						def       __UpperCamelCase     (							self      :	Optional[int])						->       int:




											"""simple docstring"""







											set_requires_grad(self.vae     ,a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       int:




											"""simple docstring"""







											set_requires_grad(self.vae     ,a__)





						def       __UpperCamelCase     (							self      :	Dict)						->       str:




											"""simple docstring"""







											set_requires_grad(self.unet     ,a__)





						def       __UpperCamelCase     (							self      :	Any)						->       Union[str, Any]:




											"""simple docstring"""







											set_requires_grad(self.unet     ,a__)





						def       __UpperCamelCase     (							self      :	Any     ,a__      :	Optional[Any]     ,a__      :	Optional[int]     ,a__      :	Optional[Any])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  min(int(num_inference_steps * strength)     ,a__)

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

											return timesteps, num_inference_steps - t_start





						def       __UpperCamelCase     (							self      :	Any     ,a__      :	List[str]     ,a__      :	Union[str, Any]     ,a__      :	Optional[int]     ,a__      :	Any     ,a__      :	Any     ,a__      :	int=None)						->       List[str]:




											"""simple docstring"""







											if not isinstance(a__     ,torch.Tensor):
																raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(a__)}')

											_lowerCAmelCase:Optional[Any]									=  image.to(device=a__     ,dtype=a__)

											if isinstance(a__     ,a__):
																_lowerCAmelCase:Dict									=  [
																    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(a__)
																]
																_lowerCAmelCase:Any									=  torch.cat(a__     ,dim=0)
											else:
																_lowerCAmelCase:Optional[Any]									=  self.vae.encode(a__).latent_dist.sample(a__)

											# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
											_lowerCAmelCase:Optional[Any]									=  0.18215 * init_latents
											_lowerCAmelCase:List[Any]									=  init_latents.repeat_interleave(a__     ,dim=0)

											_lowerCAmelCase:str									=  randn_tensor(init_latents.shape     ,generator=a__     ,device=a__     ,dtype=a__)

											# get latents
											_lowerCAmelCase:Any									=  self.scheduler.add_noise(a__     ,a__     ,a__)
											_lowerCAmelCase:Tuple									=  init_latents

											return latents





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	Any)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:Dict									=  self.coca_transform(a__).unsqueeze(0)
											with torch.no_grad(), torch.cuda.amp.autocast():
																_lowerCAmelCase:Dict									=  self.coca_model.generate(transformed_image.to(device=self.device     ,dtype=self.coca_model.dtype))
											_lowerCAmelCase:Any									=  self.coca_tokenizer.decode(generated[0].cpu().numpy())
											return generated.split('''<end_of_text>''')[0].replace('''<start_of_text>'''     ,'''''').rstrip(''' .,''')





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	Any     ,a__      :	Union[str, Any])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  self.feature_extractor.preprocess(a__)
											_lowerCAmelCase:Optional[Any]									=  torch.from_numpy(clip_image_input['''pixel_values'''][0]).unsqueeze(0).to(self.device).half()
											_lowerCAmelCase:Dict									=  self.clip_model.get_image_features(a__)
											_lowerCAmelCase:Dict									=  image_embeddings_clip / image_embeddings_clip.norm(p=2     ,dim=-1     ,keepdim=a__)
											_lowerCAmelCase:Dict									=  image_embeddings_clip.repeat_interleave(a__     ,dim=0)
											return image_embeddings_clip





						@torch.enable_grad()
						def       __UpperCamelCase     (							self      :	Optional[Any]     ,a__      :	Union[str, Any]     ,a__      :	Optional[int]     ,a__      :	int     ,a__      :	List[Any]     ,a__      :	int     ,a__      :	Optional[int]     ,a__      :	Union[str, Any]     ,)						->       Optional[int]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  latents.detach().requires_grad_()

											_lowerCAmelCase:Optional[int]									=  self.scheduler.scale_model_input(a__     ,a__)

											# predict the noise residual
											_lowerCAmelCase:Any									=  self.unet(a__     ,a__     ,encoder_hidden_states=a__).sample

											if isinstance(self.scheduler     ,(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
																_lowerCAmelCase:int									=  self.scheduler.alphas_cumprod[timestep]
																_lowerCAmelCase:List[str]									=  1 - alpha_prod_t
																# compute predicted original sample from predicted noise also called
																# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
																_lowerCAmelCase:Optional[int]									=  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																_lowerCAmelCase:str									=  torch.sqrt(a__)
																_lowerCAmelCase:Union[str, Any]									=  pred_original_sample * (fac) + latents * (1 - fac)
											elif isinstance(self.scheduler     ,a__):
																_lowerCAmelCase:Tuple									=  self.scheduler.sigmas[index]
																_lowerCAmelCase:Union[str, Any]									=  latents - sigma * noise_pred
											else:
																raise ValueError(F'scheduler type {type(self.scheduler)} not supported')

											# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
											_lowerCAmelCase:str									=  1 / 0.18215 * sample
											_lowerCAmelCase:int									=  self.vae.decode(a__).sample
											_lowerCAmelCase:int									=  (image / 2 + 0.5).clamp(0     ,1)

											_lowerCAmelCase:List[str]									=  transforms.Resize(self.feature_extractor_size)(a__)
											_lowerCAmelCase:Any									=  self.normalize(a__).to(latents.dtype)

											_lowerCAmelCase:Tuple									=  self.clip_model.get_image_features(a__)
											_lowerCAmelCase:Dict									=  image_embeddings_clip / image_embeddings_clip.norm(p=2     ,dim=-1     ,keepdim=a__)

											_lowerCAmelCase:List[Any]									=  spherical_dist_loss(a__     ,a__).mean() * clip_guidance_scale

											_lowerCAmelCase:int									=  -torch.autograd.grad(a__     ,a__)[0]

											if isinstance(self.scheduler     ,a__):
																_lowerCAmelCase:List[str]									=  latents.detach() + grads * (sigma**2)
																_lowerCAmelCase:str									=  noise_pred_original
											else:
																_lowerCAmelCase:Optional[int]									=  noise_pred_original - torch.sqrt(a__) * grads
											return noise_pred, latents







						@torch.no_grad()
						def __call__(							self      :	List[Any]     ,a__      :	Union[torch.FloatTensor, PIL.Image.Image]     ,a__      :	Union[torch.FloatTensor, PIL.Image.Image]     ,a__      :	Optional[str] = None     ,a__      :	Optional[str] = None     ,a__      :	Optional[int] = 512     ,a__      :	Optional[int] = 512     ,a__      :	float = 0.6     ,a__      :	Optional[int] = 50     ,a__      :	Optional[float] = 7.5     ,a__      :	Optional[int] = 1     ,a__      :	float = 0.0     ,a__      :	Optional[float] = 100     ,a__      :	Optional[torch.Generator] = None     ,a__      :	Optional[str] = "pil"     ,a__      :	bool = True     ,a__      :	float = 0.8     ,a__      :	float = 0.1     ,a__      :	float = 0.1     ,)						->       int:




											"""simple docstring"""







											if isinstance(a__     ,a__) and len(a__) != batch_size:
																raise ValueError(F'You have passed {batch_size} batch_size, but only {len(a__)} generators.')

											if height % 8 != 0 or width % 8 != 0:
																raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.')

											if isinstance(a__     ,torch.Generator) and batch_size > 1:
																_lowerCAmelCase:Optional[Any]									=  [generator] + [None] * (batch_size - 1)

											_lowerCAmelCase:Any									=  [
											    ('''model''', self.coca_model is None),
											    ('''tokenizer''', self.coca_tokenizer is None),
											    ('''transform''', self.coca_transform is None),
											]
											_lowerCAmelCase:List[Any]									=  [x[0] for x in coca_is_none if x[1]]
											_lowerCAmelCase:List[str]									=  ''', '''.join(a__)
											# generate prompts with coca model if prompt is None
											if content_prompt is None:
																if len(a__):
																					raise ValueError(
																					    F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
																					    F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.')
																_lowerCAmelCase:Optional[int]									=  self.get_image_description(a__)
											if style_prompt is None:
																if len(a__):
																					raise ValueError(
																					    F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
																					    F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.')
																_lowerCAmelCase:str									=  self.get_image_description(a__)

											# get prompt text embeddings for content and style
											_lowerCAmelCase:Tuple									=  self.tokenizer(
											    a__     ,padding='''max_length'''     ,max_length=self.tokenizer.model_max_length     ,truncation=a__     ,return_tensors='''pt'''     ,)
											_lowerCAmelCase:List[str]									=  self.text_encoder(content_text_input.input_ids.to(self.device))[0]

											_lowerCAmelCase:int									=  self.tokenizer(
											    a__     ,padding='''max_length'''     ,max_length=self.tokenizer.model_max_length     ,truncation=a__     ,return_tensors='''pt'''     ,)
											_lowerCAmelCase:Union[str, Any]									=  self.text_encoder(style_text_input.input_ids.to(self.device))[0]

											_lowerCAmelCase:Dict									=  slerp(a__     ,a__     ,a__)

											# duplicate text embeddings for each generation per prompt
											_lowerCAmelCase:str									=  text_embeddings.repeat_interleave(a__     ,dim=0)

											# set timesteps
											_lowerCAmelCase:Tuple									=  '''offset''' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
											_lowerCAmelCase:Dict									=  {}
											if accepts_offset:
																_lowerCAmelCase:Optional[Any]									=  1

											self.scheduler.set_timesteps(a__     ,**a__)
											# Some schedulers like PNDM have timesteps as arrays
											# It's more optimized to move all timesteps to correct device beforehand
											self.scheduler.timesteps.to(self.device)

											_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  self.get_timesteps(a__     ,a__     ,self.device)
											_lowerCAmelCase:str									=  timesteps[:1].repeat(a__)

											# Preprocess image
											_lowerCAmelCase:List[str]									=  preprocess(a__     ,a__     ,a__)
											_lowerCAmelCase:List[Any]									=  self.prepare_latents(
											    a__     ,a__     ,a__     ,text_embeddings.dtype     ,self.device     ,a__)

											_lowerCAmelCase:Union[str, Any]									=  preprocess(a__     ,a__     ,a__)
											_lowerCAmelCase:Dict									=  self.prepare_latents(
											    a__     ,a__     ,a__     ,text_embeddings.dtype     ,self.device     ,a__)

											_lowerCAmelCase:Optional[Any]									=  slerp(a__     ,a__     ,a__)

											if clip_guidance_scale > 0:
																_lowerCAmelCase:str									=  self.get_clip_image_embeddings(a__     ,a__)
																_lowerCAmelCase:Tuple									=  self.get_clip_image_embeddings(a__     ,a__)
																_lowerCAmelCase:int									=  slerp(
																    a__     ,a__     ,a__)

											# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
											# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
											# corresponds to doing no classifier free guidance.
											_lowerCAmelCase:Any									=  guidance_scale > 1.0
											# get unconditional embeddings for classifier free guidance
											if do_classifier_free_guidance:
																_lowerCAmelCase:Union[str, Any]									=  content_text_input.input_ids.shape[-1]
																_lowerCAmelCase:Dict									=  self.tokenizer(['''''']     ,padding='''max_length'''     ,max_length=a__     ,return_tensors='''pt''')
																_lowerCAmelCase:List[Any]									=  self.text_encoder(uncond_input.input_ids.to(self.device))[0]
																# duplicate unconditional embeddings for each generation per prompt
																_lowerCAmelCase:Dict									=  uncond_embeddings.repeat_interleave(a__     ,dim=0)

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																_lowerCAmelCase:Union[str, Any]									=  torch.cat([uncond_embeddings, text_embeddings])

											# get the initial random noise unless the user supplied it

											# Unlike in other pipelines, latents need to be generated in the target device
											# for 1-to-1 results reproducibility with the CompVis implementation.
											# However this currently doesn't work in `mps`.
											_lowerCAmelCase:str									=  (batch_size, self.unet.config.in_channels, height // 8, width // 8)
											_lowerCAmelCase:Tuple									=  text_embeddings.dtype
											if latents is None:
																if self.device.type == "mps":
																					# randn does not work reproducibly on mps
																					_lowerCAmelCase:str									=  torch.randn(a__     ,generator=a__     ,device='''cpu'''     ,dtype=a__).to(
																					    self.device)
																else:
																					_lowerCAmelCase:str									=  torch.randn(a__     ,generator=a__     ,device=self.device     ,dtype=a__)
											else:
																if latents.shape != latents_shape:
																					raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
																_lowerCAmelCase:Any									=  latents.to(self.device)

											# scale the initial noise by the standard deviation required by the scheduler
											_lowerCAmelCase:str									=  latents * self.scheduler.init_noise_sigma

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
											# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
											# and should be between [0, 1]
											_lowerCAmelCase:List[str]									=  '''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
											_lowerCAmelCase:str									=  {}
											if accepts_eta:
																_lowerCAmelCase:str									=  eta

											# check if the scheduler accepts generator
											_lowerCAmelCase:str									=  '''generator''' in set(inspect.signature(self.scheduler.step).parameters.keys())
											if accepts_generator:
																_lowerCAmelCase:Optional[Any]									=  generator

											with self.progress_bar(total=a__):
																for i, t in enumerate(a__):
																					# expand the latents if we are doing classifier free guidance
																					_lowerCAmelCase:str									=  torch.cat([latents] * 2) if do_classifier_free_guidance else latents
																					_lowerCAmelCase:Dict									=  self.scheduler.scale_model_input(a__     ,a__)

																					# predict the noise residual
																					_lowerCAmelCase:List[Any]									=  self.unet(a__     ,a__     ,encoder_hidden_states=a__).sample

																					# perform classifier free guidance
																					if do_classifier_free_guidance:
																										_lowerCAmelCase							,   _lowerCAmelCase:str									=  noise_pred.chunk(2)
																										_lowerCAmelCase:List[str]									=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					# perform clip guidance
																					if clip_guidance_scale > 0:
																										_lowerCAmelCase:int									=  (
																										    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
																										)
																										_lowerCAmelCase							,   _lowerCAmelCase:Dict									=  self.cond_fn(
																										    a__     ,a__     ,a__     ,a__     ,a__     ,a__     ,a__     ,)

																					# compute the previous noisy sample x_t -> x_t-1
																					_lowerCAmelCase:Optional[Any]									=  self.scheduler.step(a__     ,a__     ,a__     ,**a__).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
											_lowerCAmelCase:str									=  1 / 0.18215 * latents
											_lowerCAmelCase:Optional[int]									=  self.vae.decode(a__).sample

											_lowerCAmelCase:Tuple									=  (image / 2 + 0.5).clamp(0     ,1)
											_lowerCAmelCase:Any									=  image.cpu().permute(0     ,2     ,3     ,1).numpy()

											if output_type == "pil":
																_lowerCAmelCase:List[str]									=  self.numpy_to_pil(a__)

											if not return_dict:
																return (image, None)

											return StableDiffusionPipelineOutput(images=a__     ,nsfw_content_detected=a__)












"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class   a__			(       UpperCamelCase_  ):
						snake_case__             = 42
						snake_case__             = None





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int=0.9_99      ,       snake_case			:					Union[str, Any]="cosine"      ,       ):
					if alpha_transform_type == "cosine":

										def alpha_bar_fn(snake_case			:					Dict    ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2    ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(snake_case			:					Optional[Any]    ):
															return math.exp(t * -12.0    )

					else:
										raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'    )

					_lowerCAmelCase:List[str]									=  []
					for i in range(snake_case    ):
										_lowerCAmelCase:Union[str, Any]									=  i / num_diffusion_timesteps
										_lowerCAmelCase:Dict									=  (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(snake_case    ) / alpha_bar_fn(snake_case    )      ,       snake_case    )    )
					return torch.tensor(snake_case      ,       dtype=torch.floataa    )





class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Optional[Any]     ,a__      :	int = 1000     ,a__      :	float = 0.0001     ,a__      :	float = 0.02     ,a__      :	str = "linear"     ,a__      :	Optional[Union[np.ndarray, List[float]]] = None     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	int = 0     ,a__      :	str = "epsilon"     ,a__      :	float = 1.0     ,**a__      :	Any     ,)						->       str:




											"""simple docstring"""







											if kwargs.get('''set_alpha_to_one'''     ,a__) is not None:
																_lowerCAmelCase:Tuple									=  (
																    '''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
																)
																deprecate('''set_alpha_to_one'''     ,'''1.0.0'''     ,a__     ,standard_warn=a__)
																_lowerCAmelCase:Optional[Any]									=  kwargs['''set_alpha_to_one''']
											if trained_betas is not None:
																_lowerCAmelCase:Any									=  torch.tensor(a__     ,dtype=torch.floataa)
											elif beta_schedule == "linear":
																_lowerCAmelCase:str									=  torch.linspace(a__     ,a__     ,a__     ,dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																_lowerCAmelCase:int									=  (
																    torch.linspace(beta_start**0.5     ,beta_end**0.5     ,a__     ,dtype=torch.floataa) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																_lowerCAmelCase:Optional[int]									=  betas_for_alpha_bar(a__)
											else:
																raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

											_lowerCAmelCase:Any									=  1.0 - self.betas
											_lowerCAmelCase:Optional[Any]									=  torch.cumprod(self.alphas     ,dim=0)

											# At every step in inverted ddim, we are looking into the next alphas_cumprod
											# For the final step, there is no next alphas_cumprod, and the index is out of bounds
											# `set_alpha_to_zero` decides whether we set this parameter simply to zero
											# in this case, self.step() just output the predicted noise
											# or whether we use the final alpha of the "non-previous" one.
											_lowerCAmelCase:Optional[Any]									=  torch.tensor(0.0) if set_alpha_to_zero else self.alphas_cumprod[-1]

											# standard deviation of the initial noise distribution
											_lowerCAmelCase:Any									=  1.0

											# setable values
											_lowerCAmelCase:Dict									=  None
											_lowerCAmelCase:List[Any]									=  torch.from_numpy(np.arange(0     ,a__).copy().astype(np.intaa))





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	torch.FloatTensor     ,a__      :	Optional[int] = None)						->       torch.FloatTensor:




											"""simple docstring"""







											return sample





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	int     ,a__      :	Union[str, torch.device] = None)						->       List[Any]:




											"""simple docstring"""







											if num_inference_steps > self.config.num_train_timesteps:
																raise ValueError(
																    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
																    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
																    F' maximal {self.config.num_train_timesteps} timesteps.')

											_lowerCAmelCase:Union[str, Any]									=  num_inference_steps
											_lowerCAmelCase:Union[str, Any]									=  self.config.num_train_timesteps // self.num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# casting to int to avoid issues when num_inference_step is power of 3
											_lowerCAmelCase:str									=  (np.arange(0     ,a__) * step_ratio).round().copy().astype(np.intaa)
											_lowerCAmelCase:str									=  torch.from_numpy(a__).to(a__)
											self.timesteps += self.config.steps_offset





						def       __UpperCamelCase     (							self      :	int     ,a__      :	torch.FloatTensor     ,a__      :	int     ,a__      :	torch.FloatTensor     ,a__      :	float = 0.0     ,a__      :	bool = False     ,a__      :	Optional[torch.FloatTensor] = None     ,a__      :	bool = True     ,)						->       Union[DDIMSchedulerOutput, Tuple]:




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=  timestep + self.config.num_train_timesteps // self.num_inference_steps

											# 2. compute alphas, betas
											# change original implementation to exactly match noise levels for analogous forward process
											_lowerCAmelCase:Optional[Any]									=  self.alphas_cumprod[timestep]
											_lowerCAmelCase:List[Any]									=  (
											    self.alphas_cumprod[prev_timestep]
											    if prev_timestep < self.config.num_train_timesteps
											    else self.final_alpha_cumprod
											)

											_lowerCAmelCase:str									=  1 - alpha_prod_t

											# 3. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											if self.config.prediction_type == "epsilon":
																_lowerCAmelCase:int									=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																_lowerCAmelCase:str									=  model_output
											elif self.config.prediction_type == "sample":
																_lowerCAmelCase:List[str]									=  model_output
																_lowerCAmelCase:Optional[int]									=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
																_lowerCAmelCase:str									=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																_lowerCAmelCase:Dict									=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
											else:
																raise ValueError(
																    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
																    ''' `v_prediction`''')

											# 4. Clip or threshold "predicted x_0"
											if self.config.clip_sample:
																_lowerCAmelCase:List[Any]									=  pred_original_sample.clamp(
																    -self.config.clip_sample_range     ,self.config.clip_sample_range)

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:str									=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

											# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											_lowerCAmelCase:Optional[Any]									=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

											if not return_dict:
																return (prev_sample, pred_original_sample)
											return DDIMSchedulerOutput(prev_sample=a__     ,pred_original_sample=a__)







						def __len__(							self      :	Dict)						->       Union[str, Any]:




											"""simple docstring"""







											return self.config.num_train_timesteps






