




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_					:
	A					       =   42

	# setable values
	A					       =   42
	A					       =   42
	A					       =   42
	A					       =   None

	# running values
	A					       =   None





	@classmethod
	def 	A_(       cls			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Any:




				"""simple docstring"""



				return cls(common=_lowerCAmelCase			,    init_noise_sigma=_lowerCAmelCase			,    timesteps=_lowerCAmelCase			,    sigmas=_lowerCAmelCase			)




@dataclass
class 			a_					(    __lowerCamelCase    ):
	A					       =   42




class 			a_					(    __lowerCamelCase     ,						__lowerCamelCase    ):
	A					       =   [e.name for e in FlaxKarrasDiffusionSchedulers]

	A					       =   42





	@property
	def 	A_(       self			)     ->				Any:




				"""simple docstring"""



				return True





	@register_to_config
	def __init__(       self			,    SCREAMING_SNAKE_CASE = 1000			,    SCREAMING_SNAKE_CASE = 0.0_0_0_1			,    SCREAMING_SNAKE_CASE = 0.0_2			,    SCREAMING_SNAKE_CASE = "linear"			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = "epsilon"			,    SCREAMING_SNAKE_CASE = jnp.floataa			,    )     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	dtype





	def 	A_(       self			,    SCREAMING_SNAKE_CASE = None			)     ->				Tuple:




				"""simple docstring"""



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

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

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

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





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				List[str]:




				"""simple docstring"""



				(SCREAMING_SNAKE_CASE_		)			=	jnp.where(state.timesteps == timestep			,    size=1			)
				SCREAMING_SNAKE_CASE_			=	step_index[0]

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





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:




				"""simple docstring"""



				def lms_derivative(SCREAMING_SNAKE_CASE			):
							SCREAMING_SNAKE_CASE_			=	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

				SCREAMING_SNAKE_CASE_			=	integrate.quad(_lowerCAmelCase			,    state.sigmas[t]			,    state.sigmas[t + 1]			,    epsrel=1e-4			)[0]

				return integrated_coeff





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = ()			)     ->				str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	jnp.linspace(self.config.num_train_timesteps - 1			,    0			,    _lowerCAmelCase			,    dtype=self.dtype			)

				SCREAMING_SNAKE_CASE_			=	jnp.floor(_lowerCAmelCase			).astype(jnp.intaa			)
				SCREAMING_SNAKE_CASE_			=	jnp.ceil(_lowerCAmelCase			).astype(jnp.intaa			)

				SCREAMING_SNAKE_CASE_			=	jnp.mod(_lowerCAmelCase			,    1.0			)

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

				SCREAMING_SNAKE_CASE_			=	timesteps.astype(jnp.intaa			)

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

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





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = 4			,    SCREAMING_SNAKE_CASE = True			,    )     ->				Any:




				"""simple docstring"""



				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'			)

				SCREAMING_SNAKE_CASE_			=	state.sigmas[timestep]

				# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
				if self.config.prediction_type == "epsilon":
							SCREAMING_SNAKE_CASE_			=	sample - sigma * model_output
				elif self.config.prediction_type == "v_prediction":
							# * c_out + input * c_skip
							SCREAMING_SNAKE_CASE_			=	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
				SCREAMING_SNAKE_CASE_			=	(sample - pred_original_sample) / sigma
				SCREAMING_SNAKE_CASE_			=	state.replace(derivatives=jnp.append(state.derivatives			,    _lowerCAmelCase			)			)
				if len(state.derivatives			) > order:
							SCREAMING_SNAKE_CASE_			=	state.replace(derivatives=jnp.delete(state.derivatives			,    0			)			)

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

				# 4. Compute previous sample based on the derivatives path
				SCREAMING_SNAKE_CASE_			=	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			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    )     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	state.sigmas[timesteps].flatten()
				SCREAMING_SNAKE_CASE_			=	broadcast_to_shape_from_left(_lowerCAmelCase			,    noise.shape			)

				SCREAMING_SNAKE_CASE_			=	original_samples + noise * sigma

				return noisy_samples





	def __len__(       self			)     ->				List[str]:




				"""simple docstring"""



				return self.config.num_train_timesteps






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

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available



@dataclass
class 			a_					(    SCREAMING_SNAKE_CASE__    ):
	A						       =   42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
				from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
				from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline
