






"""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        lowerCAmelCase__       :
   # setable values
   SCREAMING_SNAKE_CASE_					      =None
   SCREAMING_SNAKE_CASE_					      =None
   SCREAMING_SNAKE_CASE_					      =None
   @classmethod
   def 	__a				(						cls	:     Union[str, Any]				):


        '''simple docstring'''
        return cls()




@dataclass
class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      =42
   SCREAMING_SNAKE_CASE_					      =42
   SCREAMING_SNAKE_CASE_					      =None




class        lowerCAmelCase__       (  __magic_name__				,		__magic_name__					):
   @property
   def 	__a				(						self	:     Tuple				):


        '''simple docstring'''
        return True
   @register_to_config
   def __init__(						self	:     int				,       snake_case__	:     int = 2_0_0_0				,       snake_case__	:     float = 0.15				,       snake_case__	:     float = 0.01				,       snake_case__	:     float = 1348.0				,       snake_case__	:     float = 1e-5				,       snake_case__	:     int = 1				,       ):


        '''simple docstring'''
        pass
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            snake_case__				,       self.config.num_train_timesteps				,       self.config.sigma_min				,       self.config.sigma_max				,       self.config.sampling_eps				,       )
   def 	__a				(						self	:     Dict				,       snake_case__	:     ScoreSdeVeSchedulerState				,       snake_case__	:     int				,       snake_case__	:     Tuple = ()				,       snake_case__	:     float = None				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

        UpperCAmelCase__		:  Optional[Any]				=				jnp.linspace(1				,       snake_case__				,       snake_case__				)
        return state.replace(timesteps=snake_case__				)
   def 	__a				(						self	:     List[Any]				,       snake_case__	:     ScoreSdeVeSchedulerState				,       snake_case__	:     int				,       snake_case__	:     float = None				,       snake_case__	:     float = None				,       snake_case__	:     float = None				,       ):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				sigma_min if sigma_min is not None else self.config.sigma_min
        UpperCAmelCase__		:  List[Any]				=				sigma_max if sigma_max is not None else self.config.sigma_max
        UpperCAmelCase__		:  Union[str, Any]				=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
             UpperCAmelCase__		:  List[str]				=				self.set_timesteps(snake_case__				,       snake_case__				,       snake_case__				)

        UpperCAmelCase__		:  Any				=				jnp.exp(jnp.linspace(jnp.log(snake_case__				)				,       jnp.log(snake_case__				)				,       snake_case__				)				)
        UpperCAmelCase__		:  Any				=				jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

        return state.replace(discrete_sigmas=snake_case__				,       sigmas=snake_case__				)
   def 	__a				(						self	:     Tuple				,       snake_case__	:     Any				,       snake_case__	:     List[str]				,       snake_case__	:     str				):


        '''simple docstring'''
        return jnp.where(timesteps == 0				,       jnp.zeros_like(snake_case__				)				,       state.discrete_sigmas[timesteps - 1]				)
   def 	__a				(						self	:     Any				,       snake_case__	:     ScoreSdeVeSchedulerState				,       snake_case__	:     jnp.ndarray				,       snake_case__	:     int				,       snake_case__	:     jnp.ndarray				,       snake_case__	:     random.KeyArray				,       snake_case__	:     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"				)

        UpperCAmelCase__		:  str				=				timestep * jnp.ones(
            sample.shape[0]				,       )
        UpperCAmelCase__		:  List[Any]				=				(timestep * (len(state.timesteps				) - 1)).long()

        UpperCAmelCase__		:  Tuple				=				state.discrete_sigmas[timesteps]
        UpperCAmelCase__		:  List[str]				=				self.get_adjacent_sigma(snake_case__				,       snake_case__				,       snake_case__				)
        UpperCAmelCase__		:  Union[str, Any]				=				jnp.zeros_like(snake_case__				)
        UpperCAmelCase__		:  str				=				(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
        UpperCAmelCase__		:  List[Any]				=				diffusion.flatten()
        UpperCAmelCase__		:  Optional[int]				=				broadcast_to_shape_from_left(snake_case__				,       sample.shape				)
        UpperCAmelCase__		:  Any				=				drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        UpperCAmelCase__		:  List[str]				=				random.split(snake_case__				,       num=1				)
        UpperCAmelCase__		:  Optional[int]				=				random.normal(key=snake_case__				,       shape=sample.shape				)
        UpperCAmelCase__		:  str				=				sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        UpperCAmelCase__		:  Optional[Any]				=				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=snake_case__				,       prev_sample_mean=snake_case__				,       state=snake_case__				)
   def 	__a				(						self	:     Optional[int]				,       snake_case__	:     ScoreSdeVeSchedulerState				,       snake_case__	:     jnp.ndarray				,       snake_case__	:     jnp.ndarray				,       snake_case__	:     random.KeyArray				,       snake_case__	:     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
        UpperCAmelCase__		:  Union[str, Any]				=				random.split(snake_case__				,       num=1				)
        UpperCAmelCase__		:  int				=				random.normal(key=snake_case__				,       shape=sample.shape				)

        # compute step size from the model_output, the noise, and the snr
        UpperCAmelCase__		:  Union[str, Any]				=				jnp.linalg.norm(snake_case__				)
        UpperCAmelCase__		:  Any				=				jnp.linalg.norm(snake_case__				)
        UpperCAmelCase__		:  List[str]				=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        UpperCAmelCase__		:  Tuple				=				step_size * jnp.ones(sample.shape[0]				)

        # compute corrected sample: model_output term and noise term
        UpperCAmelCase__		:  Dict				=				step_size.flatten()
        UpperCAmelCase__		:  Union[str, Any]				=				broadcast_to_shape_from_left(snake_case__				,       sample.shape				)
        UpperCAmelCase__		:  List[Any]				=				sample + step_size * model_output
        UpperCAmelCase__		:  int				=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
             return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=snake_case__				,       state=snake_case__				)




   def __len__(						self	:     int				):


        '''simple docstring'''
        return self.config.num_train_timesteps










"""simple docstring"""



import shutil

import requests


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      bool = False       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				"https://api.nasa.gov/planetary/apod"
     return requests.get(snake_case       , params={"api_key": api_key}							).json()


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				get_apod_data(snake_case							)
     UpperCAmelCase__		:  str				=				apod_data["url"]
     UpperCAmelCase__		:  Tuple				=				img_url.split("/"							)[-1]
     UpperCAmelCase__		:  Dict				=				requests.get(snake_case       , stream=snake_case							)

     with open(f'{path}/{img_name}'       , "wb+"							) as img_file:
          shutil.copyfileobj(response.raw       , snake_case							)
     del response
     return apod_data


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				"https://images-api.nasa.gov/search"
     return requests.get(snake_case       , params={"q": query}							).json()


if __name__ == "__main__":
 print(save_apod("""YOUR API KEY"""))
 _lowerCAmelCase     :     List[Any]            =	get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
 print(apollo_aaaa_items[0]["""data"""][0]["""description"""])


