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       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if isinstance(_lowerCamelCase		,  torch.Tensor						):
												return image
						elif isinstance(_lowerCamelCase		,  PIL.Image.Image						):
												SCREAMING_SNAKE_CASE							:    List[str]							=							[image]

						if isinstance(image[0]		,  PIL.Image.Image						):
												SCREAMING_SNAKE_CASE							:    Any							=							[np.array(i.resize((w, h)		,  resample=PIL_INTERPOLATION['''lanczos''']						)						)[None, :] for i in image]
												SCREAMING_SNAKE_CASE							:    Tuple							=							np.concatenate(_lowerCamelCase		,  axis=0						)
												SCREAMING_SNAKE_CASE							:    Any							=							np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
												SCREAMING_SNAKE_CASE							:    List[Any]							=							image.transpose(0		,  3		,  1		,  2						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							2.0 * image - 1.0
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							torch.from_numpy(_lowerCamelCase						)
						elif isinstance(image[0]		,  torch.Tensor						):
												SCREAMING_SNAKE_CASE							:    Any							=							torch.cat(_lowerCamelCase		,  dim=0						)
						return image
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase=0.9995						):
						if not isinstance(_lowerCamelCase		,  np.ndarray						):
												SCREAMING_SNAKE_CASE							:    List[str]							=							True
												SCREAMING_SNAKE_CASE							:    Tuple							=							va.device
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							va.cpu().numpy()
												SCREAMING_SNAKE_CASE							:    str							=							va.cpu().numpy()

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

						if inputs_are_torch:
												SCREAMING_SNAKE_CASE							:    Tuple							=							torch.from_numpy(_lowerCamelCase						).to(_lowerCamelCase						)

						return va
def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							F.normalize(_lowerCamelCase		,  dim=-1						)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							F.normalize(_lowerCamelCase		,  dim=-1						)
						return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)
def       A	(    _lowercase		,  _lowercase						):
						for param in model.parameters():
												SCREAMING_SNAKE_CASE							:    str							=							value





class 					lowercase__						(   __A):


						def __init__(       self	:				str    ,		UpperCamelCase__	:				AutoencoderKL    ,		UpperCamelCase__	:				CLIPTextModel    ,		UpperCamelCase__	:				CLIPModel    ,		UpperCamelCase__	:				CLIPTokenizer    ,		UpperCamelCase__	:				UNetaDConditionModel    ,		UpperCamelCase__	:				Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]    ,		UpperCamelCase__	:				CLIPFeatureExtractor    ,		UpperCamelCase__	:				Dict=None    ,		UpperCamelCase__	:				Tuple=None    ,		UpperCamelCase__	:				Tuple=None    ,		):





												'''simple docstring'''





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


						def        __A       (       self	:				Any    ,		UpperCamelCase__	:				Optional[Union[str, int]] = "auto"						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												self.enable_attention_slicing(lowercase_						)


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												set_requires_grad(self.vae    ,		lowercase_						)


						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												set_requires_grad(self.vae    ,		lowercase_						)


						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												set_requires_grad(self.unet    ,		lowercase_						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												set_requires_grad(self.unet    ,		lowercase_						)


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							min(int(num_inference_steps * strength						)    ,		lowercase_						)

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

												return timesteps, num_inference_steps - t_start


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				Optional[int]    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Tuple=None						):





												'''simple docstring'''





												if not isinstance(lowercase_    ,		torch.Tensor						):
																		raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(lowercase_						)}"""						)

												SCREAMING_SNAKE_CASE							:    int							=							image.to(device=lowercase_    ,		dtype=lowercase_						)

												if isinstance(lowercase_    ,		lowercase_						):
																		SCREAMING_SNAKE_CASE							:    Tuple							=							[
																		    self.vae.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(lowercase_						)
																		]
																		SCREAMING_SNAKE_CASE							:    Any							=							torch.cat(lowercase_    ,		dim=0						)
												else:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							self.vae.encode(lowercase_						).latent_dist.sample(lowercase_						)

												# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							0.1_8215 * init_latents
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							init_latents.repeat_interleave(lowercase_    ,		dim=0						)

												SCREAMING_SNAKE_CASE							:    int							=							randn_tensor(init_latents.shape    ,		generator=lowercase_    ,		device=lowercase_    ,		dtype=lowercase_						)

												# get latents
												SCREAMING_SNAKE_CASE							:    str							=							self.scheduler.add_noise(lowercase_    ,		lowercase_    ,		lowercase_						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							init_latents

												return latents


						def        __A       (       self	:				Dict    ,		UpperCamelCase__	:				Tuple						):





												'''simple docstring'''





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


						def        __A       (       self	:				Optional[int]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Any						):





												'''simple docstring'''





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


						@torch.enable_grad()
						def        __A       (       self	:				List[Any]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				Union[str, Any]    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				int    ,		UpperCamelCase__	:				int    ,		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							latents.detach().requires_grad_()

												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.scheduler.scale_model_input(lowercase_    ,		lowercase_						)

												# predict the noise residual
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.unet(lowercase_    ,		lowercase_    ,		encoder_hidden_states=lowercase_						).sample

												if isinstance(self.scheduler    ,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.scheduler.alphas_cumprod[timestep]
																		SCREAMING_SNAKE_CASE							:    Tuple							=							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
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.sqrt(lowercase_						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							pred_original_sample * (fac) + latents * (1 - fac)
												elif isinstance(self.scheduler    ,		lowercase_						):
																		SCREAMING_SNAKE_CASE							:    Tuple							=							self.scheduler.sigmas[index]
																		SCREAMING_SNAKE_CASE							:    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
												SCREAMING_SNAKE_CASE							:    Dict							=							1 / 0.1_8215 * sample
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.vae.decode(lowercase_						).sample
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(image / 2 + 0.5).clamp(0    ,		1						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							transforms.Resize(self.feature_extractor_size						)(lowercase_						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.normalize(lowercase_						).to(latents.dtype						)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							self.clip_model.get_image_features(lowercase_						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							image_embeddings_clip / image_embeddings_clip.norm(p=2    ,		dim=-1    ,		keepdim=lowercase_						)

												SCREAMING_SNAKE_CASE							:    int							=							spherical_dist_loss(lowercase_    ,		lowercase_						).mean() * clip_guidance_scale

												SCREAMING_SNAKE_CASE							:    int							=							-torch.autograd.grad(lowercase_    ,		lowercase_						)[0]

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



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





												'''simple docstring'''





												if isinstance(lowercase_    ,		lowercase_						) and len(lowercase_						) != batch_size:
																		raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(lowercase_						)} 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(lowercase_    ,		torch.Generator						) and batch_size > 1:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							[generator] + [None] * (batch_size - 1)

												SCREAMING_SNAKE_CASE							:    str							=							[
												    ("model", self.coca_model is None),
												    ("tokenizer", self.coca_tokenizer is None),
												    ("transform", self.coca_transform is None),
												]
												SCREAMING_SNAKE_CASE							:    Dict							=							[x[0] for x in coca_is_none if x[1]]
												SCREAMING_SNAKE_CASE							:    int							=							", ".join(lowercase_						)
												# generate prompts with coca model if prompt is None
												if content_prompt is None:
																		if len(lowercase_						):
																								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."""						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							self.get_image_description(lowercase_						)
												if style_prompt is None:
																		if len(lowercase_						):
																								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."""						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.get_image_description(lowercase_						)

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

												SCREAMING_SNAKE_CASE							:    List[str]							=							self.tokenizer(
												    lowercase_    ,		padding='''max_length'''    ,		max_length=self.tokenizer.model_max_length    ,		truncation=lowercase_    ,		return_tensors='''pt'''    ,		)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.text_encoder(style_text_input.input_ids.to(self.device						)						)[0]

												SCREAMING_SNAKE_CASE							:    int							=							slerp(lowercase_    ,		lowercase_    ,		lowercase_						)

												# duplicate text embeddings for each generation per prompt
												SCREAMING_SNAKE_CASE							:    Any							=							text_embeddings.repeat_interleave(lowercase_    ,		dim=0						)

												# set timesteps
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							"offset" in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							{}
												if accepts_offset:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							1

												self.scheduler.set_timesteps(lowercase_    ,		**lowercase_						)
												# 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						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.get_timesteps(lowercase_    ,		lowercase_    ,		self.device						)
												SCREAMING_SNAKE_CASE							:    str							=							timesteps[:1].repeat(lowercase_						)

												# Preprocess image
												SCREAMING_SNAKE_CASE							:    int							=							preprocess(lowercase_    ,		lowercase_    ,		lowercase_						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							self.prepare_latents(
												    lowercase_    ,		lowercase_    ,		lowercase_    ,		text_embeddings.dtype    ,		self.device    ,		lowercase_						)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							preprocess(lowercase_    ,		lowercase_    ,		lowercase_						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.prepare_latents(
												    lowercase_    ,		lowercase_    ,		lowercase_    ,		text_embeddings.dtype    ,		self.device    ,		lowercase_						)

												SCREAMING_SNAKE_CASE							:    Any							=							slerp(lowercase_    ,		lowercase_    ,		lowercase_						)

												if clip_guidance_scale > 0:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							self.get_clip_image_embeddings(lowercase_    ,		lowercase_						)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.get_clip_image_embeddings(lowercase_    ,		lowercase_						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							slerp(
																		    lowercase_    ,		lowercase_    ,		lowercase_						)

												# 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.
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																		SCREAMING_SNAKE_CASE							:    int							=							content_text_input.input_ids.shape[-1]
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.tokenizer(['''''']    ,		padding='''max_length'''    ,		max_length=lowercase_    ,		return_tensors='''pt'''						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
																		# duplicate unconditional embeddings for each generation per prompt
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							uncond_embeddings.repeat_interleave(lowercase_    ,		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
																		SCREAMING_SNAKE_CASE							:    Tuple							=							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`.
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							(batch_size, self.unet.config.in_channels, height // 8, width // 8)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							text_embeddings.dtype
												if latents is None:
																		if self.device.type == "mps":
																								# randn does not work reproducibly on mps
																								SCREAMING_SNAKE_CASE							:    str							=							torch.randn(lowercase_    ,		generator=lowercase_    ,		device='''cpu'''    ,		dtype=lowercase_						).to(
																								    self.device						)
																		else:
																								SCREAMING_SNAKE_CASE							:    Tuple							=							torch.randn(lowercase_    ,		generator=lowercase_    ,		device=self.device    ,		dtype=lowercase_						)
												else:
																		if latents.shape != latents_shape:
																								raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							latents.to(self.device						)

												# scale the initial noise by the standard deviation required by the scheduler
												SCREAMING_SNAKE_CASE							:    Any							=							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]
												SCREAMING_SNAKE_CASE							:    List[Any]							=							"eta" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							{}
												if accepts_eta:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							eta

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

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

																								# predict the noise residual
																								SCREAMING_SNAKE_CASE							:    Tuple							=							self.unet(lowercase_    ,		lowercase_    ,		encoder_hidden_states=lowercase_						).sample

																								# perform classifier free guidance
																								if do_classifier_free_guidance:
																														SCREAMING_SNAKE_CASE							:    int							=							noise_pred.chunk(2						)
																														SCREAMING_SNAKE_CASE							:    Dict							=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																								# perform clip guidance
																								if clip_guidance_scale > 0:
																														SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(
																														    text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
																														)
																														SCREAMING_SNAKE_CASE							:    Dict							=							self.cond_fn(
																														    lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		)

																								# compute the previous noisy sample x_t -> x_t-1
																								SCREAMING_SNAKE_CASE							:    Tuple							=							self.scheduler.step(lowercase_    ,		lowercase_    ,		lowercase_    ,		**lowercase_						).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							1 / 0.1_8215 * latents
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.vae.decode(lowercase_						).sample

												SCREAMING_SNAKE_CASE							:    List[Any]							=							(image / 2 + 0.5).clamp(0    ,		1						)
												SCREAMING_SNAKE_CASE							:    Dict							=							image.cpu().permute(0    ,		2    ,		3    ,		1						).numpy()

												if output_type == "pil":
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							self.numpy_to_pil(lowercase_						)

												if not return_dict:
																		return (image, None)

												return StableDiffusionPipelineOutput(images=lowercase_    ,		nsfw_content_detected=lowercase_						)






import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


UpperCamelCase  							=						logging.get_logger(__name__)


UpperCamelCase  							=						R'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''



class   snake_case_       (		__A       ):






   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     Optional[Any]	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     List[str]  )					->						bool:
       raise NotImplementedError("StoppingCriteria needs to be subclassed"  )



class   snake_case_       (		__A       ):






   def __init__(      self				:     Dict	,						lowercase_				:     int	,						lowercase_				:     Optional[int] = None  )					->						List[str]:
       lowercase__ :   str    					=				max_length
       lowercase__ :   Optional[int]    					=				max_position_embeddings







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     Tuple	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Union[str, Any]  )					->						bool:
       lowercase__ :   str    					=				input_ids.shape[-1]
       lowercase__ :   Any    					=				cur_len >= self.max_length
       if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
           logger.warning_once(
               "This is a friendly reminder - the current text generation call will exceed the model's predefined "
               F'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
               "exceptions, performance degradation, or nothing at all."  )
       return is_done



class   snake_case_       (		__A       ):






   def __init__(      self				:     Tuple	,						lowercase_				:     int	,						lowercase_				:     int  )					->						List[str]:
       warnings.warn(
           "The class `MaxNewTokensCriteria` is deprecated. "
           F'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
           "with `max_length = start_length + max_new_tokens` instead."	,						lowercase_	,						)
       lowercase__ :   Optional[int]    					=				start_length
       lowercase__ :   str    					=				max_new_tokens
       lowercase__ :   Tuple    					=				start_length + max_new_tokens







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     List[Any]	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Dict  )					->						bool:
       return input_ids.shape[-1] >= self.max_length



class   snake_case_       (		__A       ):






   def __init__(      self				:     Tuple	,						lowercase_				:     float	,						lowercase_				:     Optional[float] = None  )					->						Dict:
       lowercase__ :   List[str]    					=				max_time
       lowercase__ :   Tuple    					=				time.time() if initial_timestamp is None else initial_timestamp







   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     int	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     Union[str, Any]  )					->						bool:
       return time.time() - self.initial_timestamp > self.max_time



class   snake_case_       (		__A       ):






   @add_start_docstrings(lowercase_  )
   def __call__(      self				:     str	,						lowercase_				:     torch.LongTensor	,						lowercase_				:     torch.FloatTensor	,						**lowercase_				:     List[str]  )					->						bool:
       return any(criteria(lowercase_	,						lowercase_  ) for criteria in self  )







   @property
   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Optional[int]:
       for stopping_criterium in self:
           if isinstance(lowercase_	,						lowercase_  ):
               return stopping_criterium.max_length
           elif isinstance(lowercase_	,						lowercase_  ):
               return stopping_criterium.max_length
       return None



def 			lowercase_				(			_lowerCamelCase							:       StoppingCriteriaList		,	_lowerCamelCase							:       int):
    lowercase__ :   Optional[int]    					=				stopping_criteria.max_length
    lowercase__ :   str    					=				deepcopy(_lowerCamelCase)
    if stopping_max_length is not None and stopping_max_length != max_length:
        warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"		,	_lowerCamelCase)
    elif stopping_max_length is None:
        new_stopping_criteria.append(MaxLengthCriteria(max_length=_lowerCamelCase))
    return new_stopping_criteria



