
'''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 				A       :
	# setable values
	snake_case__ :Optional[jnp.ndarray]				       =       None
	snake_case__ :Optional[jnp.ndarray]				       =       None
	snake_case__ :Optional[jnp.ndarray]				       =       None





	@classmethod
	def 							__SCREAMING_SNAKE_CASE					(   cls     :    Union[str, Any]							):


				"""simple docstring"""

				return cls()






@dataclass
class 				A       (		SCREAMING_SNAKE_CASE__      ):
	snake_case__ :ScoreSdeVeSchedulerState
	snake_case__ :jnp.ndarray
	snake_case__ :Optional[jnp.ndarray]				       =       None






class 				A       (		SCREAMING_SNAKE_CASE__					, SCREAMING_SNAKE_CASE__      ):





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				return True





	@register_to_config
	def __init__(   self     :    str ,							__magic_name__     :    int = 2000 ,							__magic_name__     :    float = 0.15 ,							__magic_name__     :    float = 0.01 ,							__magic_name__     :    float = 1348.0 ,							__magic_name__     :    float = 1E-5 ,							__magic_name__     :    int = 1 ,							):


				"""simple docstring"""

				pass





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


				"""simple docstring"""

				lowerCAmelCase__									=							ScoreSdeVeSchedulerState.create()
				return self.set_sigmas(
				    __magic_name__ ,							self.config.num_train_timesteps ,							self.config.sigma_min ,							self.config.sigma_max ,							self.config.sampling_eps ,							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    ScoreSdeVeSchedulerState ,							__magic_name__     :    int ,							__magic_name__     :    Tuple = () ,							__magic_name__     :    float = None							):


				"""simple docstring"""

				lowerCAmelCase__									=							sampling_eps if sampling_eps is not None else self.config.sampling_eps

				lowerCAmelCase__									=							jnp.linspace(1 ,							__magic_name__ ,							__magic_name__							)
				return state.replace(timesteps=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Union[str, Any] ,							__magic_name__     :    ScoreSdeVeSchedulerState ,							__magic_name__     :    int ,							__magic_name__     :    float = None ,							__magic_name__     :    float = None ,							__magic_name__     :    float = None ,							):


				"""simple docstring"""

				lowerCAmelCase__									=							sigma_min if sigma_min is not None else self.config.sigma_min
				lowerCAmelCase__									=							sigma_max if sigma_max is not None else self.config.sigma_max
				lowerCAmelCase__									=							sampling_eps if sampling_eps is not None else self.config.sampling_eps
				if state.timesteps is None:
							lowerCAmelCase__									=							self.set_timesteps(__magic_name__ ,							__magic_name__ ,							__magic_name__							)

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

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





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    Optional[int] ,							__magic_name__     :    Any ,							__magic_name__     :    str							):


				"""simple docstring"""

				return jnp.where(timesteps == 0 ,							jnp.zeros_like(__magic_name__							) ,							state.discrete_sigmas[timesteps - 1]							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str] ,							__magic_name__     :    ScoreSdeVeSchedulerState ,							__magic_name__     :    jnp.ndarray ,							__magic_name__     :    int ,							__magic_name__     :    jnp.ndarray ,							__magic_name__     :    random.KeyArray ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				if state.timesteps is None:
							raise ValueError(
							    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"							)

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

				lowerCAmelCase__									=							state.discrete_sigmas[timesteps]
				lowerCAmelCase__									=							self.get_adjacent_sigma(__magic_name__ ,							__magic_name__ ,							__magic_name__							)
				lowerCAmelCase__									=							jnp.zeros_like(__magic_name__							)
				lowerCAmelCase__									=							(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
				lowerCAmelCase__									=							diffusion.flatten()
				lowerCAmelCase__									=							broadcast_to_shape_from_left(__magic_name__ ,							sample.shape							)
				lowerCAmelCase__									=							drift - diffusion**2 * model_output

				#  equation 6: sample noise for the diffusion term of
				lowerCAmelCase__									=							random.split(__magic_name__ ,							num=1							)
				lowerCAmelCase__									=							random.normal(key=__magic_name__ ,							shape=sample.shape							)
				lowerCAmelCase__									=							sample - drift  # subtract because `dt` is a small negative timestep
				# TODO is the variable diffusion the correct scaling term for the noise?
				lowerCAmelCase__									=							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=__magic_name__ ,							prev_sample_mean=__magic_name__ ,							state=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Any ,							__magic_name__     :    ScoreSdeVeSchedulerState ,							__magic_name__     :    jnp.ndarray ,							__magic_name__     :    jnp.ndarray ,							__magic_name__     :    random.KeyArray ,							__magic_name__     :    bool = True ,							):


				"""simple docstring"""

				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
				lowerCAmelCase__									=							random.split(__magic_name__ ,							num=1							)
				lowerCAmelCase__									=							random.normal(key=__magic_name__ ,							shape=sample.shape							)

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

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

				if not return_dict:
							return (prev_sample, state)

				return FlaxSdeVeOutput(prev_sample=__magic_name__ ,							state=__magic_name__							)







	def __len__(   self     :    str							):


				"""simple docstring"""

				return self.config.num_train_timesteps







'''simple docstring'''

UpperCAmelCase__				:	List[str]					=						"\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

UpperCAmelCase__				:	Any					=						[{"type": "code", "content": INSTALL_CONTENT}]
UpperCAmelCase__				:	Union[str, Any]					=						{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





