


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

import flax
import jax.numpy as jnp
from scipy import integrate

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





@flax.struct.dataclass
class    A					:
							UpperCamelCase_     :							CommonSchedulerState

							# setable values
							UpperCamelCase_     :							jnp.ndarray
							UpperCamelCase_     :							jnp.ndarray
							UpperCamelCase_     :							jnp.ndarray
							UpperCamelCase_     :							Optional[int] =None

							# running values
							UpperCamelCase_     :							Optional[jnp.ndarray] =None


							@classmethod
							def     _A (cls   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
									return cls(common=lowerCAmelCase   ,       init_noise_sigma=lowerCAmelCase   ,       timesteps=lowerCAmelCase   ,       sigmas=lowerCAmelCase      )





@dataclass
class    A					(     A_				):
							UpperCamelCase_     :							LMSDiscreteSchedulerState





class    A					(     A_      ,       A_				):
							UpperCamelCase_     :							List[str] =[e.name for e in FlaxKarrasDiffusionSchedulers]

							UpperCamelCase_     :							jnp.dtype


							@property
							def     _A (self      ):
									return True


							@register_to_config
							def __init__(self   ,       lowerCAmelCase = 1_0_0_0   ,       lowerCAmelCase = 0.00_01   ,       lowerCAmelCase = 0.02   ,       lowerCAmelCase = "linear"   ,       lowerCAmelCase = None   ,       lowerCAmelCase = "epsilon"   ,       lowerCAmelCase = jnp.floataa   ,       ):
									__lowercase=    dtype


							def     _A (self   ,       lowerCAmelCase = None      ):
									if common is None:
											__lowercase=    CommonSchedulerState.create(self      )

									__lowercase=    jnp.arange(0   ,       self.config.num_train_timesteps      ).round()[::-1]
									__lowercase=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

									# standard deviation of the initial noise distribution
									__lowercase=    sigmas.max()

									return LMSDiscreteSchedulerState.create(
									    common=lowerCAmelCase   ,       init_noise_sigma=lowerCAmelCase   ,       timesteps=lowerCAmelCase   ,       sigmas=lowerCAmelCase   ,       )


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
									((__lowercase),							)=    jnp.where(state.timesteps == timestep   ,       size=1      )
									__lowercase=    step_index[0]

									__lowercase=    state.sigmas[step_index]
									__lowercase=    sample / ((sigma**2 + 1) ** 0.5)
									return sample


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):

									def lms_derivative(lowerCAmelCase      ):
											__lowercase=    1.0
											for k in range(lowerCAmelCase      ):
													if current_order == k:
															continue
													prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
											return prod

									__lowercase=    integrate.quad(lowerCAmelCase   ,       state.sigmas[t]   ,       state.sigmas[t + 1]   ,       epsrel=1E-4      )[0]

									return integrated_coeff


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase = ()      ):
									__lowercase=    jnp.linspace(self.config.num_train_timesteps - 1   ,       0   ,       lowerCAmelCase   ,       dtype=self.dtype      )

									__lowercase=    jnp.floor(lowerCAmelCase      ).astype(jnp.intaa      )
									__lowercase=    jnp.ceil(lowerCAmelCase      ).astype(jnp.intaa      )

									__lowercase=    jnp.mod(lowerCAmelCase   ,       1.0      )

									__lowercase=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
									__lowercase=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
									__lowercase=    jnp.concatenate([sigmas, jnp.array([0.0]   ,       dtype=self.dtype      )]      )

									__lowercase=    timesteps.astype(jnp.intaa      )

									# initial running values
									__lowercase=    jnp.zeros((0,) + shape   ,       dtype=self.dtype      )

									return state.replace(
									    timesteps=lowerCAmelCase   ,       sigmas=lowerCAmelCase   ,       num_inference_steps=lowerCAmelCase   ,       derivatives=lowerCAmelCase   ,       )


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase = 4   ,       lowerCAmelCase = True   ,       ):
									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'      )

									__lowercase=    state.sigmas[timestep]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
											__lowercase=    sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
											# * c_out + input * c_skip
											__lowercase=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
											raise ValueError(
											    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

									# 2. Convert to an ODE derivative
									__lowercase=    (sample - pred_original_sample) / sigma
									__lowercase=    state.replace(derivatives=jnp.append(state.derivatives   ,       lowerCAmelCase      )      )
									if len(state.derivatives      ) > order:
											__lowercase=    state.replace(derivatives=jnp.delete(state.derivatives   ,       0      )      )

									# 3. Compute linear multistep coefficients
									__lowercase=    min(timestep + 1   ,       lowerCAmelCase      )
									__lowercase=    [self.get_lms_coefficient(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ) for curr_order in range(lowerCAmelCase      )]

									# 4. Compute previous sample based on the derivatives path
									__lowercase=    sample + sum(
									    coeff * derivative for coeff, derivative in zip(lowerCAmelCase   ,       reversed(state.derivatives      )      )      )

									if not return_dict:
											return (prev_sample, state)

									return FlaxLMSSchedulerOutput(prev_sample=lowerCAmelCase   ,       state=lowerCAmelCase      )


							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       ):
									__lowercase=    state.sigmas[timesteps].flatten()
									__lowercase=    broadcast_to_shape_from_left(lowerCAmelCase   ,       noise.shape      )

									__lowercase=    original_samples + noise * sigma

									return noisy_samples







							def __len__(self      ):
									return self.config.num_train_timesteps










from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class    A					(     A_				):
							UpperCamelCase_     :							Optional[int] ={
							    '''sld_warmup_steps''': 15,
							    '''sld_guidance_scale''': 20,
							    '''sld_threshold''': 0.0,
							    '''sld_momentum_scale''': 0.0,
							    '''sld_mom_beta''': 0.0,
							}
							UpperCamelCase_     :							Tuple ={
							    '''sld_warmup_steps''': 10,
							    '''sld_guidance_scale''': 1_000,
							    '''sld_threshold''': 0.01,
							    '''sld_momentum_scale''': 0.3,
							    '''sld_mom_beta''': 0.4,
							}
							UpperCamelCase_     :							Union[str, Any] ={
							    '''sld_warmup_steps''': 7,
							    '''sld_guidance_scale''': 2_000,
							    '''sld_threshold''': 0.025,
							    '''sld_momentum_scale''': 0.5,
							    '''sld_mom_beta''': 0.7,
							}
							UpperCamelCase_     :							List[Any] ={
							    '''sld_warmup_steps''': 0,
							    '''sld_guidance_scale''': 5_000,
							    '''sld_threshold''': 1.0,
							    '''sld_momentum_scale''': 0.5,
							    '''sld_mom_beta''': 0.7,
							}





@dataclass
class    A					(     A_				):
							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
							UpperCamelCase_     :							Optional[List[bool]]
							UpperCamelCase_     :							Optional[Union[List[PIL.Image.Image], np.ndarray]]
							UpperCamelCase_     :							Optional[str]


if is_transformers_available() and is_torch_available():
	from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
	from .safety_checker import SafeStableDiffusionSafetyChecker






