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   lowercase			:
							lowercase__  :				List[Any]									=							42

							# setable values
							lowercase__  :				int									=							42
							lowercase__  :				Tuple									=							42
							lowercase__  :				List[str]									=							42
							lowercase__  :				Union[str, Any]									=							None

							# running values
							lowercase__  :				Tuple									=							None




							@classmethod
							def 		__snake_case(  cls      :    List[str]		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Optional[int]		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    str					)    -> List[str]:



												'''simple docstring'''

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



@dataclass
class   lowercase			(   SCREAMING_SNAKE_CASE__   ):
							lowercase__  :				Dict									=							42



class   lowercase			(   SCREAMING_SNAKE_CASE__ ,				SCREAMING_SNAKE_CASE__   ):
							lowercase__  :				int									=							[e.name for e in FlaxKarrasDiffusionSchedulers]

							lowercase__  :				Dict									=							42




							@property
							def 		__snake_case(  self      :    List[str]					)    -> Tuple:



												'''simple docstring'''

												return True




							@register_to_config
							def __init__(  self      :    Tuple		,							_UpperCamelCase      :    Optional[Any] = 1_000		,							_UpperCamelCase      :    Dict = 0.0_0_0_1		,							_UpperCamelCase      :    Optional[Any] = 0.0_2		,							_UpperCamelCase      :    List[Any] = "linear"		,							_UpperCamelCase      :    int = None		,							_UpperCamelCase      :    Optional[Any] = "epsilon"		,							_UpperCamelCase      :    List[str] = jnp.floataa		,							)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				dtype




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    str = None					)    -> List[Any]:



												'''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=snake_case__		,							init_noise_sigma=snake_case__		,							timesteps=snake_case__		,							sigmas=snake_case__		,							)




							def 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    Union[str, Any]					)    -> Any:



												'''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 		__snake_case(  self      :    int		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    str					)    -> Dict:



												'''simple docstring'''

												def lms_derivative(_UpperCamelCase      :    Optional[Any]					):
																	SCREAMING_SNAKE_CASE				=				1.0
																	for k in range(snake_case__					):
																						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(snake_case__		,							state.sigmas[t]		,							state.sigmas[t + 1]		,							epsrel=1e-4					)[0]

												return integrated_coeff




							def 		__snake_case(  self      :    Optional[int]		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Optional[int] = ()					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				jnp.linspace(self.config.num_train_timesteps - 1		,							0		,							snake_case__		,							dtype=self.dtype					)

												SCREAMING_SNAKE_CASE				=				jnp.floor(snake_case__					).astype(jnp.intaa					)
												SCREAMING_SNAKE_CASE				=				jnp.ceil(snake_case__					).astype(jnp.intaa					)

												SCREAMING_SNAKE_CASE				=				jnp.mod(snake_case__		,							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=snake_case__		,							sigmas=snake_case__		,							num_inference_steps=snake_case__		,							derivatives=snake_case__		,							)




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    Union[str, Any]		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    List[str] = 4		,							_UpperCamelCase      :    List[Any] = True		,							)    -> Optional[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		,							snake_case__					)					)
												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		,							snake_case__					)
												SCREAMING_SNAKE_CASE				=				[self.get_lms_coefficient(snake_case__		,							snake_case__		,							snake_case__		,							snake_case__					) for curr_order in range(snake_case__					)]

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

												if not return_dict:
																	return (prev_sample, state)

												return FlaxLMSSchedulerOutput(prev_sample=snake_case__		,							state=snake_case__					)




							def 		__snake_case(  self      :    Dict		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    str		,							)    -> Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				state.sigmas[timesteps].flatten()
												SCREAMING_SNAKE_CASE				=				broadcast_to_shape_from_left(snake_case__		,							noise.shape					)

												SCREAMING_SNAKE_CASE				=				original_samples + noise * sigma

												return noisy_samples





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



												'''simple docstring'''

												return self.config.num_train_timesteps












"""simple docstring"""







def 						lowercase__(A						)				->int:




						"""simple docstring"""



						lowercase__				:      str=       0
						for plain_chr in plain_text:
												lowercase__				:      List[str]=       (
												    ord(A						) + (hash_value << 6) + (hash_value << 16) - hash_value
												)
						return hash_value






