






'''simple docstring'''





import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


a_      = logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):
		_UpperCAmelCase         =42
		_UpperCAmelCase         =None





def 			__UpperCAmelCase				(lowercase__	,lowercase__=0.999	,lowercase__="cosine"	,)		->				int:

						'''simple docstring'''


						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowercase__				):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowercase__				):
																		return math.exp(t * -12.0				)

						else:
												raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""				)

						a_				     =   []
						for i in range(lowercase__				):
												a_				     =   i / num_diffusion_timesteps
												a_				     =   (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowercase__				) / alpha_bar_fn(lowercase__				)	,lowercase__				)				)
						return torch.tensor(lowercase__	,dtype=torch.floataa				)





class 		SCREAMING_SNAKE_CASE__   (     lowercase_		,		lowercase_    ):
		_UpperCAmelCase         =[e.name for e in KarrasDiffusionSchedulers]
		_UpperCAmelCase         =1



		@register_to_config
		def __init__(    self:    str				,   a:    int = 10_00				,   a:    float = 0.0001				,   a:    float = 0.02				,   a:    str = "linear"				,   a:    Optional[Union[np.ndarray, List[float]]] = None				,   a:    str = "epsilon"				,   a:    str = "linear"				,   a:    Optional[bool] = False				,   a:    str = "linspace"				,   a:    int = 0				,   )  ->int:






								'''simple docstring'''


								if trained_betas is not None:
														a_				     =   torch.tensor(a				,   dtype=torch.floataa)
								elif beta_schedule == "linear":
														a_				     =   torch.linspace(a				,   a				,   a				,   dtype=torch.floataa)
								elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														a_				     =   (
														    torch.linspace(beta_start**0.5				,   beta_end**0.5				,   a				,   dtype=torch.floataa) ** 2
														)
								elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														a_				     =   betas_for_alpha_bar(a)
								else:
														raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""")

								a_				     =   1.0 - self.betas
								a_				     =   torch.cumprod(self.alphas				,   dim=0)

								a_				     =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								a_				     =   np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
								a_				     =   torch.from_numpy(a)

								# setable values
								a_				     =   None
								a_				     =   np.linspace(0				,   num_train_timesteps - 1				,   a				,   dtype=a)[::-1].copy()
								a_				     =   torch.from_numpy(a)
								a_				     =   False
								a_				     =   use_karras_sigmas



		@property
		def _lowerCAmelCase     (    self:    Optional[Any])  ->Optional[Any]:






								'''simple docstring'''


								if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5



		def _lowerCAmelCase     (    self:    str				,   a:    torch.FloatTensor				,   a:    Union[float, torch.FloatTensor])  ->torch.FloatTensor:






								'''simple docstring'''


								if isinstance(a				,   torch.Tensor):
														a_				     =   timestep.to(self.timesteps.device)
								a_				     =   (self.timesteps == timestep).nonzero().item()
								a_				     =   self.sigmas[step_index]

								a_				     =   sample / ((sigma**2 + 1) ** 0.5)

								a_				     =   True
								return sample



		def _lowerCAmelCase     (    self:    str				,   a:    int				,   a:    Union[str, torch.device] = None)  ->Union[str, Any]:






								'''simple docstring'''


								a_				     =   num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
														a_				     =   np.linspace(0				,   self.config.num_train_timesteps - 1				,   a				,   dtype=a)[
														    ::-1
														].copy()
								elif self.config.timestep_spacing == "leading":
														a_				     =   self.config.num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														a_				     =   (np.arange(0				,   a) * step_ratio).round()[::-1].copy().astype(a)
														timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
														a_				     =   self.config.num_train_timesteps / self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														a_				     =   (np.arange(self.config.num_train_timesteps				,   0				,   -step_ratio)).round().copy().astype(a)
														timesteps -= 1
								else:
														raise ValueError(
														    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

								a_				     =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
								a_				     =   np.log(a)

								if self.config.interpolation_type == "linear":
														a_				     =   np.interp(a				,   np.arange(0				,   len(a))				,   a)
								elif self.config.interpolation_type == "log_linear":
														a_				     =   torch.linspace(np.log(sigmas[-1])				,   np.log(sigmas[0])				,   num_inference_steps + 1).exp()
								else:
														raise ValueError(
														    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
														    " 'linear' or 'log_linear'")

								if self.use_karras_sigmas:
														a_				     =   self._convert_to_karras(in_sigmas=a				,   num_inference_steps=self.num_inference_steps)
														a_				     =   np.array([self._sigma_to_t(a				,   a) for sigma in sigmas])

								a_				     =   np.concatenate([sigmas, [0.0]]).astype(np.floataa)
								a_				     =   torch.from_numpy(a).to(device=a)
								if str(a).startswith("mps"):
														# mps does not support float64
														a_				     =   torch.from_numpy(a).to(a				,   dtype=torch.floataa)
								else:
														a_				     =   torch.from_numpy(a).to(device=a)



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






								'''simple docstring'''


								a_				     =   np.log(a)

								# get distribution
								a_				     =   log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								a_				     =   np.cumsum((dists >= 0)				,   axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
								a_				     =   low_idx + 1

								a_				     =   log_sigmas[low_idx]
								a_				     =   log_sigmas[high_idx]

								# interpolate sigmas
								a_				     =   (low - log_sigma) / (low - high)
								a_				     =   np.clip(a				,   0				,   1)

								# transform interpolation to time range
								a_				     =   (1 - w) * low_idx + w * high_idx
								a_				     =   t.reshape(sigma.shape)
								return t



		def _lowerCAmelCase     (    self:    List[str]				,   a:    torch.FloatTensor				,   a:    Dict)  ->torch.FloatTensor:






								'''simple docstring'''


								a_				     =   in_sigmas[-1].item()
								a_				     =   in_sigmas[0].item()

								a_				     =   7.0  # 7.0 is the value used in the paper
								a_				     =   np.linspace(0				,   1				,   a)
								a_				     =   sigma_min ** (1 / rho)
								a_				     =   sigma_max ** (1 / rho)
								a_				     =   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas



		def _lowerCAmelCase     (    self:    Optional[int]				,   a:    torch.FloatTensor				,   a:    Union[float, torch.FloatTensor]				,   a:    torch.FloatTensor				,   a:    float = 0.0				,   a:    float = 0.0				,   a:    float = float("inf")				,   a:    float = 1.0				,   a:    Optional[torch.Generator] = None				,   a:    bool = True				,   )  ->Union[EulerDiscreteSchedulerOutput, Tuple]:






								'''simple docstring'''


								if (
								    isinstance(a				,   a)
								    or isinstance(a				,   torch.IntTensor)
								    or isinstance(a				,   torch.LongTensor)
								):
														raise ValueError(
														    (
														        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
														        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
														        " one of the `scheduler.timesteps` as a timestep."
														    )				,   )

								if not self.is_scale_input_called:
														logger.warning(
														    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
														    "See `StableDiffusionPipeline` for a usage example.")

								if isinstance(a				,   torch.Tensor):
														a_				     =   timestep.to(self.timesteps.device)

								a_				     =   (self.timesteps == timestep).nonzero().item()
								a_				     =   self.sigmas[step_index]

								a_				     =   min(s_churn / (len(self.sigmas) - 1)				,   2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0

								a_				     =   randn_tensor(
								    model_output.shape				,   dtype=model_output.dtype				,   device=model_output.device				,   generator=a)

								a_				     =   noise * s_noise
								a_				     =   sigma * (gamma + 1)

								if gamma > 0:
														a_				     =   sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								# NOTE: "original_sample" should not be an expected prediction_type but is left in for
								# backwards compatibility
								if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
														a_				     =   model_output
								elif self.config.prediction_type == "epsilon":
														a_				     =   sample - sigma_hat * model_output
								elif self.config.prediction_type == "v_prediction":
														# * c_out + input * c_skip
														a_				     =   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
								a_				     =   (sample - pred_original_sample) / sigma_hat

								a_				     =   self.sigmas[step_index + 1] - sigma_hat

								a_				     =   sample + derivative * dt

								if not return_dict:
														return (prev_sample,)

								return EulerDiscreteSchedulerOutput(prev_sample=a				,   pred_original_sample=a)



		def _lowerCAmelCase     (    self:    int				,   a:    torch.FloatTensor				,   a:    torch.FloatTensor				,   a:    torch.FloatTensor				,   )  ->torch.FloatTensor:






								'''simple docstring'''


								a_				     =   self.sigmas.to(device=original_samples.device				,   dtype=original_samples.dtype)
								if original_samples.device.type == "mps" and torch.is_floating_point(a):
														# mps does not support float64
														a_				     =   self.timesteps.to(original_samples.device				,   dtype=torch.floataa)
														a_				     =   timesteps.to(original_samples.device				,   dtype=torch.floataa)
								else:
														a_				     =   self.timesteps.to(original_samples.device)
														a_				     =   timesteps.to(original_samples.device)

								a_				     =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

								a_				     =   sigmas[step_indices].flatten()
								while len(sigma.shape) < len(original_samples.shape):
														a_				     =   sigma.unsqueeze(-1)

								a_				     =   original_samples + noise * sigma
								return noisy_samples




		def __len__(    self:    int)  ->int:






								'''simple docstring'''


								return self.config.num_train_timesteps











'''simple docstring'''





import requests
from bsa import BeautifulSoup



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				str:

						'''simple docstring'''


						a_				     =   (
						    "https://www.horoscope.com/us/horoscopes/general/"
						    F"""horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"""
						)
						a_				     =   BeautifulSoup(requests.get(lowercase__				).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)



