






'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class    _snake_case   :

					"""simple docstring"""





					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42

					# setable values
					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      42
					_UpperCamelCase		 =      None




					@classmethod
					def  __SCREAMING_SNAKE_CASE (     cls   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	Optional[Any]:
										return cls(
										    common=UpperCAmelCase__   , final_alpha_cumprod=UpperCAmelCase__   , init_noise_sigma=UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )



@dataclass
class    _snake_case   (				snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      42



class    _snake_case   (				snake_case     ,					snake_case					):

					"""simple docstring"""





					_UpperCamelCase		 =      [e.name for e in FlaxKarrasDiffusionSchedulers]

					_UpperCamelCase		 =      42




					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	str:
										return True




					@register_to_config
					def __init__(     self   , UpperCAmelCase__ = 1000   , UpperCAmelCase__ = 0.0_0_0_1   , UpperCAmelCase__ = 0.0_2   , UpperCAmelCase__ = "linear"   , UpperCAmelCase__ = None   , UpperCAmelCase__ = True   , UpperCAmelCase__ = 0   , UpperCAmelCase__ = "epsilon"   , UpperCAmelCase__ = jnp.floataa   , )      ->	str:
										a_									=   dtype




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__ = None     )      ->	DDIMSchedulerState:
										if common is None:
															a_									=   CommonSchedulerState.create(self     )

										# At every step in ddim, we are looking into the previous alphas_cumprod
										# For the final step, there is no previous alphas_cumprod because we are already at 0
										# `set_alpha_to_one` decides whether we set this parameter simply to one or
										# whether we use the final alpha of the "non-previous" one.
										a_									=   (
										    jnp.array(1.0   , dtype=self.dtype     ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
										)

										# standard deviation of the initial noise distribution
										a_									=   jnp.array(1.0   , dtype=self.dtype     )

										a_									=   jnp.arange(0   , self.config.num_train_timesteps     ).round()[::-1]

										return DDIMSchedulerState.create(
										    common=UpperCAmelCase__   , final_alpha_cumprod=UpperCAmelCase__   , init_noise_sigma=UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = None     )      ->	jnp.ndarray:
										return sample




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = ()     )      ->	DDIMSchedulerState:
										a_									=   self.config.num_train_timesteps // num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# rounding to avoid issues when num_inference_step is power of 3
										a_									=   (jnp.arange(0   , UpperCAmelCase__     ) * step_ratio).round()[::-1] + self.config.steps_offset

										return state.replace(
										    num_inference_steps=UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										a_									=   state.common.alphas_cumprod[timestep]
										a_									=   jnp.where(
										    prev_timestep >= 0   , state.common.alphas_cumprod[prev_timestep]   , state.final_alpha_cumprod     )
										a_									=   1 - alpha_prod_t
										a_									=   1 - alpha_prod_t_prev

										a_									=   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

										return variance




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.0   , UpperCAmelCase__ = True   , )      ->	Union[FlaxDDIMSchedulerOutput, Tuple]:
										if state.num_inference_steps is None:
															raise ValueError(
															    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'     )

										# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
										# Ideally, read DDIM paper in-detail understanding

										# Notation (<variable name> -> <name in paper>
										# - pred_noise_t -> e_theta(x_t, t)
										# - pred_original_sample -> f_theta(x_t, t) or x_0
										# - std_dev_t -> sigma_t
										# - eta -> η
										# - pred_sample_direction -> "direction pointing to x_t"
										# - pred_prev_sample -> "x_t-1"

										# 1. get previous step value (=t-1)
										a_									=   timestep - self.config.num_train_timesteps // state.num_inference_steps

										a_									=   state.common.alphas_cumprod
										a_									=   state.final_alpha_cumprod

										# 2. compute alphas, betas
										a_									=   alphas_cumprod[timestep]
										a_									=   jnp.where(prev_timestep >= 0   , alphas_cumprod[prev_timestep]   , UpperCAmelCase__     )

										a_									=   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":
															a_									=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
															a_									=   model_output
										elif self.config.prediction_type == "sample":
															a_									=   model_output
															a_									=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
										elif self.config.prediction_type == "v_prediction":
															a_									=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
															a_									=   (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. compute variance: "sigma_t(η)" -> see formula (16)
										# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
										a_									=   self._get_variance(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )
										a_									=   eta * variance ** 0.5

										# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										a_									=   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

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

										if not return_dict:
															return (prev_sample, state)

										return FlaxDDIMSchedulerOutput(prev_sample=UpperCAmelCase__   , state=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	jnp.ndarray:
										return add_noise_common(state.common   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	jnp.ndarray:
										return get_velocity_common(state.common   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )




					def __len__(     self     )      ->	Tuple:
										return self.config.num_train_timesteps









'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

										self.register_modules(vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

										torch.manual_seed(UpperCAmelCase__     )
										a_									=   torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)   , ).to(self.device     )

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

										a_									=   torch.cat([latents] * 2     )

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

										with torch.no_grad():
															a_									=   self.unet(
															    UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , encoder_hidden_states=UpperCAmelCase__   , ).sample

										a_							,					a_									=   pred.chunk(2     )
										a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps     )     ):
															if i > tmax:
																				if i < tmin:  # layout generation phase
																									a_									=   self.scheduler.add_noise(
																									    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )

																									a_									=   (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									a_									=   torch.cat([input] * 2     )

																				else:  # content generation phase
																									a_									=   torch.cat([latents] * 2     )

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

																				a_							,					a_									=   pred.chunk(2     )
																				a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

