






'''simple docstring'''


# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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    _SCREAMING_SNAKE_CASE							:


			'''simple docstring'''





			__a							:							Union[str, Any]					     =					42

			# setable values
			__a							:							int					     =					42
			__a							:							List[Any]					     =					42
			__a							:							List[Any]					     =					None






			@classmethod
			def 				A      (   cls							:						Union[str, Any] ,      lowercase							:						Optional[int] ,      lowercase							:						Dict ,      lowercase							:						Optional[Any]			)							->    Tuple:

									'''simple docstring'''







									return cls(common=lowercase ,      init_noise_sigma=lowercase ,      timesteps=lowercase			)
@dataclass
class    _SCREAMING_SNAKE_CASE							(		__UpperCAmelCase	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					42
class    _SCREAMING_SNAKE_CASE							(		__UpperCAmelCase       ,__UpperCAmelCase	):


			'''simple docstring'''





			__a							:							Dict					     =					[e.name for e in FlaxKarrasDiffusionSchedulers]

			__a							:							List[Any]					     =					42






			@property
			def 				A      (   self							:						Optional[Any]			)							->    Union[str, Any]:

									'''simple docstring'''







									return True






			@register_to_config
			def __init__(   self							:						List[str] ,      lowercase							:						Optional[int] = 1_0_0_0 ,      lowercase							:						str = 0.0_0_0_1 ,      lowercase							:						List[Any] = 0.0_2 ,      lowercase							:						str = "linear" ,      lowercase							:						Union[str, Any] = None ,      lowercase							:						Tuple = "fixed_small" ,      lowercase							:						Optional[Any] = True ,      lowercase							:						int = "epsilon" ,      lowercase							:						str = jnp.floataa ,      )							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = dtype






			def 				A      (   self							:						Optional[Any] ,      lowercase							:						Optional[Any] = None			)							->    Optional[Any]:

									'''simple docstring'''







									if common is None:
															UpperCamelCase__         = CommonSchedulerState.create(self			)

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

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

									return DDPMSchedulerState.create(
									    common=lowercase ,      init_noise_sigma=lowercase ,      timesteps=lowercase ,      )






			def 				A      (   self							:						List[Any] ,      lowercase							:						Dict ,      lowercase							:						int ,      lowercase							:						str = None			)							->    Tuple:

									'''simple docstring'''







									return sample






			def 				A      (   self							:						Optional[Any] ,      lowercase							:						List[str] ,      lowercase							:						List[str] ,      lowercase							:						Tuple = ()			)							->    Tuple:

									'''simple docstring'''







									UpperCamelCase__         = 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
									UpperCamelCase__         = (jnp.arange(0 ,      lowercase			) * step_ratio).round()[::-1]

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






			def 				A      (   self							:						str ,      lowercase							:						Dict ,      lowercase							:						int ,      lowercase							:						List[Any]=None ,      lowercase							:						List[Any]=None			)							->    List[Any]:

									'''simple docstring'''







									UpperCamelCase__         = state.common.alphas_cumprod[t]
									UpperCamelCase__         = jnp.where(t > 0 ,      state.common.alphas_cumprod[t - 1] ,      jnp.array(1.0 ,      dtype=self.dtype			)			)

									# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
									# and sample from it to get previous sample
									# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
									UpperCamelCase__         = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

									if variance_type is None:
															UpperCamelCase__         = self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
															UpperCamelCase__         = jnp.clip(lowercase ,      a_min=1e-20			)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
															UpperCamelCase__         = jnp.log(jnp.clip(lowercase ,      a_min=1e-20			)			)
									elif variance_type == "fixed_large":
															UpperCamelCase__         = state.common.betas[t]
									elif variance_type == "fixed_large_log":
															# Glide max_log
															UpperCamelCase__         = jnp.log(state.common.betas[t]			)
									elif variance_type == "learned":
															return predicted_variance
									elif variance_type == "learned_range":
															UpperCamelCase__         = variance
															UpperCamelCase__         = state.common.betas[t]
															UpperCamelCase__         = (predicted_variance + 1) / 2
															UpperCamelCase__         = frac * max_log + (1 - frac) * min_log

									return variance






			def 				A      (   self							:						str ,      lowercase							:						Union[str, Any] ,      lowercase							:						Optional[int] ,      lowercase							:						int ,      lowercase							:						Union[str, Any] ,      lowercase							:						Any = None ,      lowercase							:						List[Any] = True ,      )							->    Dict:

									'''simple docstring'''







									UpperCamelCase__         = timestep

									if key is None:
															UpperCamelCase__         = jax.random.PRNGKey(0			)

									if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
															UpperCamelCase__    ,   UpperCamelCase__         = jnp.split(lowercase ,      sample.shape[1] ,      axis=1			)
									else:
															UpperCamelCase__         = None

									# 1. compute alphas, betas
									UpperCamelCase__         = state.common.alphas_cumprod[t]
									UpperCamelCase__         = jnp.where(t > 0 ,      state.common.alphas_cumprod[t - 1] ,      jnp.array(1.0 ,      dtype=self.dtype			)			)
									UpperCamelCase__         = 1 - alpha_prod_t
									UpperCamelCase__         = 1 - alpha_prod_t_prev

									# 2. compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
									if self.config.prediction_type == "epsilon":
															UpperCamelCase__         = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
															UpperCamelCase__         = model_output
									elif self.config.prediction_type == "v_prediction":
															UpperCamelCase__         = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									else:
															raise ValueError(
															    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
															    """ for the FlaxDDPMScheduler."""			)

									# 3. Clip "predicted x_0"
									if self.config.clip_sample:
															UpperCamelCase__         = jnp.clip(lowercase ,      -1 ,      1			)

									# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
									# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
									UpperCamelCase__         = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									UpperCamelCase__         = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

									# 5. Compute predicted previous sample µ_t
									# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
									UpperCamelCase__         = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
															UpperCamelCase__         = jax.random.split(lowercase ,      num=1			)
															UpperCamelCase__         = jax.random.normal(lowercase ,      shape=model_output.shape ,      dtype=self.dtype			)
															return (self._get_variance(lowercase ,      lowercase ,      predicted_variance=lowercase			) ** 0.5) * noise

									UpperCamelCase__         = jnp.where(t > 0 ,      random_variance() ,      jnp.zeros(model_output.shape ,      dtype=self.dtype			)			)

									UpperCamelCase__         = pred_prev_sample + variance

									if not return_dict:
															return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=lowercase ,      state=lowercase			)






			def 				A      (   self							:						int ,      lowercase							:						List[str] ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[int] ,      lowercase							:						Optional[int] ,      )							->    Optional[int]:

									'''simple docstring'''







									return add_noise_common(state.common ,      lowercase ,      lowercase ,      lowercase			)






			def 				A      (   self							:						List[str] ,      lowercase							:						List[Any] ,      lowercase							:						int ,      lowercase							:						Optional[Any] ,      lowercase							:						Optional[int] ,      )							->    Any:

									'''simple docstring'''







									return get_velocity_common(state.common ,      lowercase ,      lowercase ,      lowercase			)







			def __len__(   self							:						Any			)							->    Tuple:

									'''simple docstring'''







									return self.config.num_train_timesteps












'''simple docstring'''


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCamelCase_				: str    	=							transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 	__magic_name__( _A					):
						'''simple docstring'''





						if isinstance(_A						,      torch.Tensor					):
												return image
						elif isinstance(_A						,      PIL.Image.Image					):
												UpperCamelCase__         = [image]

						UpperCamelCase__         = [trans(img.convert("""RGB"""					)					) for img in image]
						UpperCamelCase__         = torch.stack(_A					)
						return image







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						Dict ,      lowercase							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									super().__init__()

									# make sure scheduler can always be converted to DDIM
									UpperCamelCase__         = DDIMScheduler.from_config(scheduler.config			)

									self.register_modules(unet=lowercase ,      scheduler=lowercase			)






			def 				A      (   self							:						Tuple ,      lowercase							:						Tuple			)							->    Any:

									'''simple docstring'''







									if strength < 0 or strength > 1:
															raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}"			)






			def 				A      (   self							:						str ,      lowercase							:						List[Any] ,      lowercase							:						Dict ,      lowercase							:						List[str]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = min(int(num_inference_steps * strength			) ,      lowercase			)

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

									return timesteps, num_inference_steps - t_start






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any] ,      lowercase							:						Dict ,      lowercase							:						Tuple ,      lowercase							:						int=None			)							->    int:

									'''simple docstring'''







									if not isinstance(lowercase ,      (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(lowercase			)}"			)

									UpperCamelCase__         = image.to(device=lowercase ,      dtype=lowercase			)

									if isinstance(lowercase ,      lowercase			) and len(lowercase			) != batch_size:
															raise ValueError(
															    f"You have passed a list of generators of length {len(lowercase			)}, but requested an effective batch"
															    f" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

									UpperCamelCase__         = init_latents.shape
									UpperCamelCase__         = randn_tensor(lowercase ,      generator=lowercase ,      device=lowercase ,      dtype=lowercase			)

									# get latents
									print("""add noise to latents at timestep""" ,      lowercase			)
									UpperCamelCase__         = self.scheduler.add_noise(lowercase ,      lowercase ,      lowercase			)
									UpperCamelCase__         = init_latents

									return latents







			@torch.no_grad()
			def __call__(   self							:						Any ,      lowercase							:						Union[torch.FloatTensor, PIL.Image.Image] = None ,      lowercase							:						float = 0.8 ,      lowercase							:						int = 1 ,      lowercase							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowercase							:						float = 0.0 ,      lowercase							:						int = 5_0 ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[str] = "pil" ,      lowercase							:						bool = True ,      )							->    Union[ImagePipelineOutput, Tuple]:

									'''simple docstring'''







									self.check_inputs(lowercase			)

									# 2. Preprocess image
									UpperCamelCase__         = preprocess(lowercase			)

									# 3. set timesteps
									self.scheduler.set_timesteps(lowercase ,      device=self.device			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.get_timesteps(lowercase ,      lowercase ,      self.device			)
									UpperCamelCase__         = timesteps[:1].repeat(lowercase			)

									# 4. Prepare latent variables
									UpperCamelCase__         = self.prepare_latents(lowercase ,      lowercase ,      lowercase ,      self.unet.dtype ,      self.device ,      lowercase			)
									UpperCamelCase__         = latents

									# 5. Denoising loop
									for t in self.progress_bar(lowercase			):
															# 1. predict noise model_output
															UpperCamelCase__         = self.unet(lowercase ,      lowercase			).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															UpperCamelCase__         = self.scheduler.step(
															    lowercase ,      lowercase ,      lowercase ,      eta=lowercase ,      use_clipped_model_output=lowercase ,      generator=lowercase ,      ).prev_sample

									UpperCamelCase__         = (image / 2 + 0.5).clamp(0 ,      1			)
									UpperCamelCase__         = image.cpu().permute(0 ,      2 ,      3 ,      1			).numpy()
									if output_type == "pil":
															UpperCamelCase__         = self.numpy_to_pil(lowercase			)

									if not return_dict:
															return (image, latent_timestep.item())

									return ImagePipelineOutput(images=lowercase			)
