

# 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		:







    """simple docstring"""





    lowerCamelCase						      =				None
    lowerCamelCase						      =				None
    lowerCamelCase						      =				None


    @classmethod
    def 		snake_case__	( cls					:			List[Any]						)->       Optional[Any]:





        '''simple docstring'''







        return cls()




@dataclass
class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				42
    lowerCamelCase						      =				42
    lowerCamelCase						      =				None




class 					A		(				_UpperCAmelCase       ,				_UpperCAmelCase					):







    """simple docstring"""





    @property
    def 		snake_case__	( self					:			Any						)->       Dict:





        '''simple docstring'''







        return True


    @register_to_config
    def __init__( self					:			Tuple,lowercase_					:			int = 2_0_0_0,lowercase_					:			float = 0.15,lowercase_					:			float = 0.01,lowercase_					:			float = 1_348.0,lowercase_					:			float = 1E-5,lowercase_					:			int = 1,)->       Any:





        '''simple docstring'''







        pass


    def 		snake_case__	( self					:			List[Any]						)->       Dict:





        '''simple docstring'''







        A__        =  ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            lowercase_,self.config.num_train_timesteps,self.config.sigma_min,self.config.sigma_max,self.config.sampling_eps,)


    def 		snake_case__	( self					:			List[Any],lowercase_					:			ScoreSdeVeSchedulerState,lowercase_					:			int,lowercase_					:			Tuple = (),lowercase_					:			float = None						)->       ScoreSdeVeSchedulerState:





        '''simple docstring'''







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

        A__        =  jnp.linspace(1,lowercase_,lowercase_						)
        return state.replace(timesteps=lowercase_						)


    def 		snake_case__	( self					:			Any,lowercase_					:			ScoreSdeVeSchedulerState,lowercase_					:			int,lowercase_					:			float = None,lowercase_					:			float = None,lowercase_					:			float = None,)->       ScoreSdeVeSchedulerState:





        '''simple docstring'''







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

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

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


    def 		snake_case__	( self					:			Dict,lowercase_					:			Optional[int],lowercase_					:			Any,lowercase_					:			Optional[Any]						)->       Optional[Any]:





        '''simple docstring'''







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


    def 		snake_case__	( self					:			Tuple,lowercase_					:			ScoreSdeVeSchedulerState,lowercase_					:			jnp.ndarray,lowercase_					:			int,lowercase_					:			jnp.ndarray,lowercase_					:			random.KeyArray,lowercase_					:			bool = True,)->       Union[FlaxSdeVeOutput, Tuple]:





        '''simple docstring'''







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

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

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

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


    def 		snake_case__	( self					:			int,lowercase_					:			ScoreSdeVeSchedulerState,lowercase_					:			jnp.ndarray,lowercase_					:			jnp.ndarray,lowercase_					:			random.KeyArray,lowercase_					:			bool = True,)->       Union[FlaxSdeVeOutput, Tuple]:





        '''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
        A__        =  random.split(lowercase_,num=1						)
        A__        =  random.normal(key=lowercase_,shape=sample.shape						)

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

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

        if not return_dict:
            return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=lowercase_,state=lowercase_						)


    def __len__( self					:			int						)->       Optional[int]:





        '''simple docstring'''







        return self.config.num_train_timesteps




import timeit


class 					A		:







    """simple docstring"""





    def __init__( self					:			Optional[int],lowercase_					:			Union[str, Any]						)->       List[str]:





        '''simple docstring'''







        if isinstance(lowercase_,lowercase_						):  # Initializes a matrix identical to the one provided.
            A__        =  arg
            A__        =  len(lowercase_						)
        else:  # Initializes a square matrix of the given size and set values to zero.
            A__        =  arg
            A__        =  [[0 for _ in range(self.n						)] for _ in range(self.n						)]


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





        '''simple docstring'''







        A__        =  Matrix(self.n						)
        for i in range(self.n						):
            for j in range(self.n						):
                for k in range(self.n						):
                    matrix.t[i][j] += self.t[i][k] * b.t[k][j]
        return matrix




def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      List[Any]       ,     SCREAMING_SNAKE_CASE__     :      Optional[Any] )				-> Union[str, Any]:





    '''simple docstring'''






    A__        =  Matrix([[1, 0], [0, 1]] )
    while b > 0:
        if b & 1:
            matrix *= a
        a *= a
        b >>= 1
    return matrix
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Optional[int]       ,     SCREAMING_SNAKE_CASE__     :      str       ,     SCREAMING_SNAKE_CASE__     :      Any )				-> List[str]:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa
    A__        =  Matrix([[1, 1], [1, 0]] )
    A__        =  modular_exponentiation(SCREAMING_SNAKE_CASE__       ,     n - 2 )
    return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      Dict       ,     SCREAMING_SNAKE_CASE__     :      Tuple       ,     SCREAMING_SNAKE_CASE__     :      Tuple )				-> Dict:





    '''simple docstring'''






    if n == 1:
        return fa
    elif n == 2:
        return fa

    A__        =  fa
    A__        =  fa
    n -= 2

    while n > 0:
        A__ ,		A__        =  fn_a + fn_a, fn_a
        n -= 1

    return fn_a
def 		_snake_case(       )				-> Optional[Any]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
    A__        =  'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print('With matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Optional[int]:





    '''simple docstring'''






    A__        =  '\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
    A__        =  'simple_fibonacci(randint(1,70000), 1, 1)'
    A__        =  timeit.timeit(setup=SCREAMING_SNAKE_CASE__       ,     stmt=SCREAMING_SNAKE_CASE__       ,     number=100 )
    print(
        'Without matrix exponentiation the average execution time is '       ,     exec_time / 100 )
    return exec_time
def 		_snake_case(       )				-> Union[str, Any]:





    '''simple docstring'''






    matrix_exponentiation_time()
    simple_fibonacci_time()


if __name__ == "__main__":
     main()

