


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'''


      __snake_case					       =   None
      __snake_case					       =   None
      __snake_case					       =   None




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



@dataclass
class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      __snake_case					       =   42
      __snake_case					       =   42
      __snake_case					       =   None



class        A_					(       __UpperCamelCase					,      __UpperCamelCase    ):




      '''simple docstring'''


      @property
      def      _snake_case	(				self:       Tuple				):
       return True




      @register_to_config
      def __init__(				self:       int						,      a:       int = 2000						,      a:       float = 0.1_5						,      a:       float = 0.0_1						,      a:       float = 1_3_4_8.0						,      a:       float = 1e-5						,      a:       int = 1						,      ):
       pass




      def      _snake_case	(				self:       Dict				):
       __lowerCamelCase		:							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      _snake_case	(				self:       Dict						,      a:       ScoreSdeVeSchedulerState						,      a:       int						,      a:       Tuple = ()						,      a:       float = None				):
       __lowerCamelCase		:							Dict							=						sampling_eps if sampling_eps is not None else self.config.sampling_eps

       __lowerCamelCase		:							Optional[Any]							=						jnp.linspace(1						,      snake_case__						,      snake_case__				)
       return state.replace(timesteps=snake_case__				)




      def      _snake_case	(				self:       List[Any]						,      a:       ScoreSdeVeSchedulerState						,      a:       int						,      a:       float = None						,      a:       float = None						,      a:       float = None						,      ):
       __lowerCamelCase		:							Dict							=						sigma_min if sigma_min is not None else self.config.sigma_min
       __lowerCamelCase		:							List[Any]							=						sigma_max if sigma_max is not None else self.config.sigma_max
       __lowerCamelCase		:							Union[str, Any]							=						sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if state.timesteps is None:
        __lowerCamelCase		:							List[str]							=						self.set_timesteps(snake_case__						,      snake_case__						,      snake_case__				)

       __lowerCamelCase		:							Any							=						jnp.exp(jnp.linspace(jnp.log(snake_case__				)						,      jnp.log(snake_case__				)						,      snake_case__				)				)
       __lowerCamelCase		:							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      _snake_case	(				self:       Tuple						,      a:       Any						,      a:       List[str]						,      a:       str				):
       return jnp.where(timesteps == 0						,      jnp.zeros_like(snake_case__				)						,      state.discrete_sigmas[timesteps - 1]				)




      def      _snake_case	(				self:       Any						,      a:       ScoreSdeVeSchedulerState						,      a:       jnp.ndarray						,      a:       int						,      a:       jnp.ndarray						,      a:       random.KeyArray						,      a:       bool = True						,      ):
       if state.timesteps is None:
        raise ValueError(
            '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'				)

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

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

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

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

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


