

"""simple docstring"""

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left


@flax.struct.dataclass
class 			_snake_case     :
						# setable values
						__snake_case 			=     None
						__snake_case 			=     None
						__snake_case 			=     None







						@classmethod
						def        _UpperCamelCase    (cls							)			->						Optional[Any]:
											return cls()



@dataclass
class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     42
						__snake_case 			=     42
						__snake_case 			=     None



class 			_snake_case     (						UpperCamelCase						,			UpperCamelCase       ):







						@property
						def        _UpperCamelCase    (self							)			->						Union[str, Any]:
											return True







						@register_to_config
						def __init__(self     ,							UpperCAmelCase_ = 2_0_0_0     ,							UpperCAmelCase_ = 0.1_5     ,							UpperCAmelCase_ = 0.0_1     ,							UpperCAmelCase_ = 1_3_4_8.0     ,							UpperCAmelCase_ = 1e-5     ,							UpperCAmelCase_ = 1     ,							)			->						Union[str, Any]:
											pass







						def        _UpperCamelCase    (self							)			->						List[Any]:
											__lowercase												=			ScoreSdeVeSchedulerState.create()
											return self.set_sigmas(
											    UpperCAmelCase_     ,							self.config.num_train_timesteps     ,							self.config.sigma_min     ,							self.config.sigma_max     ,							self.config.sampling_eps     ,							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = ()     ,							UpperCAmelCase_ = None							)			->						ScoreSdeVeSchedulerState:
											__lowercase												=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

											__lowercase												=			jnp.linspace(1     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)
											return state.replace(timesteps=UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = None     ,							)			->						ScoreSdeVeSchedulerState:
											__lowercase												=			sigma_min if sigma_min is not None else self.config.sigma_min
											__lowercase												=			sigma_max if sigma_max is not None else self.config.sigma_max
											__lowercase												=			sampling_eps if sampling_eps is not None else self.config.sampling_eps
											if state.timesteps is None:
																__lowercase												=			self.set_timesteps(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)

											__lowercase												=			jnp.exp(jnp.linspace(jnp.log(UpperCAmelCase_							)     ,							jnp.log(UpperCAmelCase_							)     ,							UpperCAmelCase_							)							)
											__lowercase												=			jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]							)

											return state.replace(discrete_sigmas=UpperCAmelCase_     ,							sigmas=UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)			->						List[Any]:
											return jnp.where(timesteps == 0     ,							jnp.zeros_like(UpperCAmelCase_							)     ,							state.discrete_sigmas[timesteps - 1]							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = True     ,							)			->						Union[FlaxSdeVeOutput, Tuple]:
											if state.timesteps is None:
																raise ValueError(
																    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

											__lowercase												=			timestep * jnp.ones(
											    sample.shape[0]     ,							)
											__lowercase												=			(timestep * (len(state.timesteps							) - 1)).long()

											__lowercase												=			state.discrete_sigmas[timesteps]
											__lowercase												=			self.get_adjacent_sigma(UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_							)
											__lowercase												=			jnp.zeros_like(UpperCAmelCase_							)
											__lowercase												=			(sigma**2 - adjacent_sigma**2) ** 0.5

											# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
											# also equation 47 shows the analog from SDE models to ancestral sampling methods
											__lowercase												=			diffusion.flatten()
											__lowercase												=			broadcast_to_shape_from_left(UpperCAmelCase_     ,							sample.shape							)
											__lowercase												=			drift - diffusion**2 * model_output

											#  equation 6: sample noise for the diffusion term of
											__lowercase												=			random.split(UpperCAmelCase_     ,							num=1							)
											__lowercase												=			random.normal(key=UpperCAmelCase_     ,							shape=sample.shape							)
											__lowercase												=			sample - drift  # subtract because `dt` is a small negative timestep
											# TODO is the variable diffusion the correct scaling term for the noise?
											__lowercase												=			prev_sample_mean + diffusion * noise  # add impact of diffusion field g

											if not return_dict:
																return (prev_sample, prev_sample_mean, state)

											return FlaxSdeVeOutput(prev_sample=UpperCAmelCase_     ,							prev_sample_mean=UpperCAmelCase_     ,							state=UpperCAmelCase_							)







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = True     ,							)			->						Union[FlaxSdeVeOutput, Tuple]:
											if state.timesteps is None:
																raise ValueError(
																    '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

											# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
											# sample noise for correction
											__lowercase												=			random.split(UpperCAmelCase_     ,							num=1							)
											__lowercase												=			random.normal(key=UpperCAmelCase_     ,							shape=sample.shape							)

											# compute step size from the model_output, the noise, and the snr
											__lowercase												=			jnp.linalg.norm(UpperCAmelCase_							)
											__lowercase												=			jnp.linalg.norm(UpperCAmelCase_							)
											__lowercase												=			(self.config.snr * noise_norm / grad_norm) ** 2 * 2
											__lowercase												=			step_size * jnp.ones(sample.shape[0]							)

											# compute corrected sample: model_output term and noise term
											__lowercase												=			step_size.flatten()
											__lowercase												=			broadcast_to_shape_from_left(UpperCAmelCase_     ,							sample.shape							)
											__lowercase												=			sample + step_size * model_output
											__lowercase												=			prev_sample_mean + ((step_size * 2) ** 0.5) * noise

											if not return_dict:
																return (prev_sample, state)

											return FlaxSdeVeOutput(prev_sample=UpperCAmelCase_     ,							state=UpperCAmelCase_							)







						def __len__(self							)			->						List[Any]:
											return self.config.num_train_timesteps





"""simple docstring"""

from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


a__ :	int						= logging.get_logger(__name__)  # pylint: disable=invalid-name


class 			_snake_case     (						UpperCamelCase       ):
						__snake_case 			=     ['safety_checker', 'feature_extractor']







						def __init__(self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = True     ,							)			->						Optional[int]:
											super().__init__()
											if hasattr(scheduler.config     ,							'''steps_offset'''							) and scheduler.config.steps_offset != 1:
																__lowercase												=			(
																    f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
																    f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
																    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
																    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
																    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
																    ''' file'''
																)
																deprecate('''steps_offset!=1'''     ,							'''1.0.0'''     ,							UpperCAmelCase_     ,							standard_warn=UpperCAmelCase_							)
																__lowercase												=			dict(scheduler.config							)
																__lowercase												=			1
																__lowercase												=			FrozenDict(UpperCAmelCase_							)

											self.register_modules(
											    vae=UpperCAmelCase_     ,							text_encoder=UpperCAmelCase_     ,							tokenizer=UpperCAmelCase_     ,							unet=UpperCAmelCase_     ,							scheduler=UpperCAmelCase_     ,							safety_checker=UpperCAmelCase_     ,							feature_extractor=UpperCAmelCase_     ,							)
											self.register_to_config(requires_safety_checker=UpperCAmelCase_							)







						@property
						def        _UpperCamelCase    (self							)			->						Dict[str, Any]:
											return {k: getattr(self     ,							UpperCAmelCase_							) for k in self.config.keys() if not k.startswith('''_'''							)}







						def        _UpperCamelCase    (self     ,							UpperCAmelCase_ = "auto"							)			->						str:
											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowercase												=			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(UpperCAmelCase_							)







						def        _UpperCamelCase    (self							)			->						int:
											self.enable_attention_slicing(UpperCAmelCase_							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						str:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionInpaintPipelineLegacy(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							mask_image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 0.8     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							**UpperCAmelCase_     ,							)			->						List[str]:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionImgaImgPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							image=UpperCAmelCase_     ,							strength=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)







						@torch.no_grad()
						def        _UpperCamelCase    (self     ,							UpperCAmelCase_     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_1_2     ,							UpperCAmelCase_ = 5_0     ,							UpperCAmelCase_ = 7.5     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							UpperCAmelCase_ = 0.0     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = "pil"     ,							UpperCAmelCase_ = True     ,							UpperCAmelCase_ = None     ,							UpperCAmelCase_ = 1     ,							)			->						Optional[int]:
											# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
											return StableDiffusionPipeline(**self.components							)(
											    prompt=UpperCAmelCase_     ,							height=UpperCAmelCase_     ,							width=UpperCAmelCase_     ,							num_inference_steps=UpperCAmelCase_     ,							guidance_scale=UpperCAmelCase_     ,							negative_prompt=UpperCAmelCase_     ,							num_images_per_prompt=UpperCAmelCase_     ,							eta=UpperCAmelCase_     ,							generator=UpperCAmelCase_     ,							latents=UpperCAmelCase_     ,							output_type=UpperCAmelCase_     ,							return_dict=UpperCAmelCase_     ,							callback=UpperCAmelCase_     ,							callback_steps=UpperCAmelCase_     ,							)


