





'''simple docstring'''







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 							__SCREAMING_SNAKE_CASE			:
      '''simple docstring'''



      lowerCAmelCase__       :      CommonSchedulerState

      # setable values
      lowerCAmelCase__       :      jnp.ndarray
      lowerCAmelCase__       :      jnp.ndarray
      lowerCAmelCase__       :      jnp.ndarray
      lowerCAmelCase__       :      Optional[int] 		=		None

      # running values
      lowerCAmelCase__       :      Optional[jnp.ndarray] 		=		None

      @classmethod
      def   _lowerCamelCase		(							cls    : Optional[int]   ,UpperCamelCase    : CommonSchedulerState   ,UpperCamelCase    : jnp.ndarray   ,UpperCamelCase    : jnp.ndarray   ,UpperCamelCase    : jnp.ndarray  )		->  Dict:
           return cls(common=UpperCamelCase   ,init_noise_sigma=UpperCamelCase   ,timesteps=UpperCamelCase   ,sigmas=UpperCamelCase  )



@dataclass
class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      LMSDiscreteSchedulerState



class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__							,   lowerCAmelCase__      ):
      '''simple docstring'''



      lowerCAmelCase__       :      List[Any] 		=		[e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCAmelCase__       :      jnp.dtype

      @property
      def   _lowerCamelCase		(							self    : Optional[Any]  )		->  Union[str, Any]:
           return True

      @register_to_config
      def __init__(							self    : Union[str, Any]   ,UpperCamelCase    : int = 1000   ,UpperCamelCase    : float = 0.0_0_0_1   ,UpperCamelCase    : float = 0.0_2   ,UpperCamelCase    : str = "linear"   ,UpperCamelCase    : Optional[jnp.ndarray] = None   ,UpperCamelCase    : str = "epsilon"   ,UpperCamelCase    : jnp.dtype = jnp.floataa   ,)		->  List[str]:
           _lowercase							:      Optional[int]  						=							dtype

      def   _lowerCamelCase		(							self    : Optional[Any]   ,UpperCamelCase    : Optional[CommonSchedulerState] = None  )		->  LMSDiscreteSchedulerState:
           if common is None:
                _lowercase							:      int  						=							CommonSchedulerState.create(self  )

           _lowercase							:      List[str]  						=							jnp.arange(0   ,self.config.num_train_timesteps  ).round()[::-1]
           _lowercase							:      str  						=							((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

           # standard deviation of the initial noise distribution
           _lowercase							:      int  						=							sigmas.max()

           return LMSDiscreteSchedulerState.create(
               common=UpperCamelCase   ,init_noise_sigma=UpperCamelCase   ,timesteps=UpperCamelCase   ,sigmas=UpperCamelCase   ,)

      def   _lowerCamelCase		(							self    : int   ,UpperCamelCase    : LMSDiscreteSchedulerState   ,UpperCamelCase    : jnp.ndarray   ,UpperCamelCase    : int  )		->  jnp.ndarray:
           ((_lowercase)						, )							:      str  						=							jnp.where(state.timesteps == timestep   ,size=1  )
           _lowercase							:      str  						=							step_index[0]

           _lowercase							:      List[str]  						=							state.sigmas[step_index]
           _lowercase							:      Optional[int]  						=							sample / ((sigma**2 + 1) ** 0.5)
           return sample

      def   _lowerCamelCase		(							self    : Optional[Any]   ,UpperCamelCase    : LMSDiscreteSchedulerState   ,UpperCamelCase    : Tuple   ,UpperCamelCase    : List[Any]   ,UpperCamelCase    : str  )		->  str:

           def lms_derivative(UpperCamelCase    : Tuple  ):
                _lowercase							:      Any  						=							1.0
                for k in range(UpperCamelCase  ):
                     if current_order == k:
                          continue
                     prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
                return prod

           _lowercase							:      int  						=							integrate.quad(UpperCamelCase   ,state.sigmas[t]   ,state.sigmas[t + 1]   ,epsrel=1e-4  )[0]

           return integrated_coeff

      def   _lowerCamelCase		(							self    : Any   ,UpperCamelCase    : LMSDiscreteSchedulerState   ,UpperCamelCase    : int   ,UpperCamelCase    : Tuple = ()  )		->  LMSDiscreteSchedulerState:
           _lowercase							:      Union[str, Any]  						=							jnp.linspace(self.config.num_train_timesteps - 1   ,0   ,UpperCamelCase   ,dtype=self.dtype  )

           _lowercase							:      List[Any]  						=							jnp.floor(UpperCamelCase  ).astype(jnp.intaa  )
           _lowercase							:      Tuple  						=							jnp.ceil(UpperCamelCase  ).astype(jnp.intaa  )

           _lowercase							:      Tuple  						=							jnp.mod(UpperCamelCase   ,1.0  )

           _lowercase							:      List[Any]  						=							((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
           _lowercase							:      int  						=							(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
           _lowercase							:      Optional[int]  						=							jnp.concatenate([sigmas, jnp.array([0.0]   ,dtype=self.dtype  )]  )

           _lowercase							:      List[Any]  						=							timesteps.astype(jnp.intaa  )

           # initial running values
           _lowercase							:      Tuple  						=							jnp.zeros((0,) + shape   ,dtype=self.dtype  )

           return state.replace(
               timesteps=UpperCamelCase   ,sigmas=UpperCamelCase   ,num_inference_steps=UpperCamelCase   ,derivatives=UpperCamelCase   ,)

      def   _lowerCamelCase		(							self    : int   ,UpperCamelCase    : LMSDiscreteSchedulerState   ,UpperCamelCase    : jnp.ndarray   ,UpperCamelCase    : int   ,UpperCamelCase    : jnp.ndarray   ,UpperCamelCase    : int = 4   ,UpperCamelCase    : bool = True   ,)		->  Union[FlaxLMSSchedulerOutput, Tuple]:
           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'  )

           _lowercase							:      List[Any]  						=							state.sigmas[timestep]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                _lowercase							:      Optional[Any]  						=							sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                _lowercase							:      Any  						=							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
           _lowercase							:      List[str]  						=							(sample - pred_original_sample) / sigma
           _lowercase							:      Tuple  						=							state.replace(derivatives=jnp.append(state.derivatives   ,UpperCamelCase  )  )
           if len(state.derivatives  ) > order:
                _lowercase							:      Union[str, Any]  						=							state.replace(derivatives=jnp.delete(state.derivatives   ,0  )  )

           # 3. Compute linear multistep coefficients
           _lowercase							:      List[Any]  						=							min(timestep + 1   ,UpperCamelCase  )
           _lowercase							:      Tuple  						=							[self.get_lms_coefficient(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  ) for curr_order in range(UpperCamelCase  )]

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

           if not return_dict:
                return (prev_sample, state)

           return FlaxLMSSchedulerOutput(prev_sample=UpperCamelCase   ,state=UpperCamelCase  )

      def   _lowerCamelCase		(							self    : List[str]   ,UpperCamelCase    : LMSDiscreteSchedulerState   ,UpperCamelCase    : jnp.ndarray   ,UpperCamelCase    : jnp.ndarray   ,UpperCamelCase    : jnp.ndarray   ,)		->  jnp.ndarray:
           _lowercase							:      int  						=							state.sigmas[timesteps].flatten()
           _lowercase							:      List[Any]  						=							broadcast_to_shape_from_left(UpperCamelCase   ,noise.shape  )

           _lowercase							:      List[Any]  						=							original_samples + noise * sigma

           return noisy_samples



      def __len__(							self    : Optional[Any]  )		->  Optional[Any]:
           return self.config.num_train_timesteps






'''simple docstring'''







import requests
from bsa import BeautifulSoup

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	int       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     _lowercase							:      int  						=							(
         'https://www.horoscope.com/us/horoscopes/general/'
         F'''horoscope-general-daily-{day}.aspx?sign={zodiac_sign}'''
     )
     _lowercase							:      Any  						=							BeautifulSoup(requests.get(lowerCAmelCase__).content       ,  'html.parser')
     return soup.find('div'       ,  class_='main-horoscope').p.text


if __name__ == "__main__":
    print('''Daily Horoscope. \n''')
    print(
        '''enter your Zodiac sign number:\n''',
        '''1. Aries\n''',
        '''2. Taurus\n''',
        '''3. Gemini\n''',
        '''4. Cancer\n''',
        '''5. Leo\n''',
        '''6. Virgo\n''',
        '''7. Libra\n''',
        '''8. Scorpio\n''',
        '''9. Sagittarius\n''',
        '''10. Capricorn\n''',
        '''11. Aquarius\n''',
        '''12. Pisces\n''',
    )
    A						    =							int(input('''number> ''').strip())
    print('''choose some day:\n''', '''yesterday\n''', '''today\n''', '''tomorrow\n''')
    A						    =							input('''enter the day> ''')
    A						    =							horoscope(zodiac_sign, day)
    print(horoscope_text)