

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 						__snake_case						:
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       42

       # setable values
       __SCREAMING_SNAKE_CASE													=       42
       __SCREAMING_SNAKE_CASE													=       42
       __SCREAMING_SNAKE_CASE													=       42
       __SCREAMING_SNAKE_CASE													=       None

       # running values
       __SCREAMING_SNAKE_CASE													=       None


       @classmethod
       def  a		(		cls			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )			->       Optional[Any]:






              """simple docstring"""


              return cls(common=__UpperCamelCase			,   init_noise_sigma=__UpperCamelCase			,   timesteps=__UpperCamelCase			,   sigmas=__UpperCamelCase      )






@dataclass
class 						__snake_case						(							UpperCAmelCase_     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       42






class 						__snake_case						(							UpperCAmelCase_ ,  UpperCAmelCase_     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       [e.name for e in FlaxKarrasDiffusionSchedulers]

       __SCREAMING_SNAKE_CASE													=       42


       @property
       def  a		(		self      )			->       Any:






              """simple docstring"""


              return True


       @register_to_config
       def __init__(		self			,   _UpperCamelCase = 10_00			,   _UpperCamelCase = 0.0001			,   _UpperCamelCase = 0.02			,   _UpperCamelCase = "linear"			,   _UpperCamelCase = None			,   _UpperCamelCase = "epsilon"			,   _UpperCamelCase = jnp.floataa			,   )			->       Optional[Any]:






              """simple docstring"""


              __snake_case					  =       dtype


       def  a		(		self			,   _UpperCamelCase = None      )			->       Optional[Any]:






              """simple docstring"""


              if common is None:
                     __snake_case					  =       CommonSchedulerState.create(self      )

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

              # standard deviation of the initial noise distribution
              __snake_case					  =       sigmas.max()

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


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )			->       int:






              """simple docstring"""


              ((__snake_case					)  ,											)					  =       jnp.where(state.timesteps == timestep			,   size=1      )
              __snake_case					  =       step_index[0]

              __snake_case					  =       state.sigmas[step_index]
              __snake_case					  =       sample / ((sigma**2 + 1) ** 0.5)
              return sample


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )			->       Any:






              """simple docstring"""


              def lms_derivative(_UpperCamelCase      ):
                     __snake_case					  =       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

              __snake_case					  =       integrate.quad(__UpperCamelCase			,   state.sigmas[t]			,   state.sigmas[t + 1]			,   epsrel=1E-4      )[0]

              return integrated_coeff


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = ()      )			->       int:






              """simple docstring"""


              __snake_case					  =       jnp.linspace(self.config.num_train_timesteps - 1			,   0			,   __UpperCamelCase			,   dtype=self.dtype      )

              __snake_case					  =       jnp.floor(__UpperCamelCase      ).astype(jnp.intaa      )
              __snake_case					  =       jnp.ceil(__UpperCamelCase      ).astype(jnp.intaa      )

              __snake_case					  =       jnp.mod(__UpperCamelCase			,   1.0      )

              __snake_case					  =       ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
              __snake_case					  =       (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
              __snake_case					  =       jnp.concatenate([sigmas, jnp.array([0.0]			,   dtype=self.dtype      )]      )

              __snake_case					  =       timesteps.astype(jnp.intaa      )

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

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


       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = 4			,   _UpperCamelCase = True			,   )			->       Tuple:






              """simple docstring"""


              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"""      )

              __snake_case					  =       state.sigmas[timestep]

              # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
              if self.config.prediction_type == "epsilon":
                     __snake_case					  =       sample - sigma * model_output
              elif self.config.prediction_type == "v_prediction":
                     # * c_out + input * c_skip
                     __snake_case					  =       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
              __snake_case					  =       (sample - pred_original_sample) / sigma
              __snake_case					  =       state.replace(derivatives=jnp.append(state.derivatives			,   __UpperCamelCase      )      )
              if len(state.derivatives      ) > order:
                     __snake_case					  =       state.replace(derivatives=jnp.delete(state.derivatives			,   0      )      )

              # 3. Compute linear multistep coefficients
              __snake_case					  =       min(timestep + 1			,   __UpperCamelCase      )
              __snake_case					  =       [self.get_lms_coefficient(__UpperCamelCase			,   __UpperCamelCase			,   __UpperCamelCase			,   __UpperCamelCase      ) for curr_order in range(__UpperCamelCase      )]

              # 4. Compute previous sample based on the derivatives path
              __snake_case					  =       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  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   )			->       List[Any]:






              """simple docstring"""


              __snake_case					  =       state.sigmas[timesteps].flatten()
              __snake_case					  =       broadcast_to_shape_from_left(__UpperCamelCase			,   noise.shape      )

              __snake_case					  =       original_samples + noise * sigma

              return noisy_samples






       def __len__(		self      )			->       str:






              """simple docstring"""


              return self.config.num_train_timesteps




import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCamelCase__		   =    {'''UserAgent''': UserAgent().random}
UpperCamelCase__		   =    '''https://www.mywaifulist.moe/random'''







def 	lowerCamelCase__			(   __A						:str  ,__A						:str		):


       """simple docstring"""




       __snake_case					  =       requests.get(__A  ,headers=__A		)
       with open(__A  ,"""wb"""		) as file:
              file.write(image.content		)







def 	lowerCamelCase__			(   ):


       """simple docstring"""




       __snake_case					  =       BeautifulSoup(requests.get(__A  ,headers=__A		).text  ,"""html.parser"""		)
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:title"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""meta"""  ,attrs={"""property""": """og:image"""}		).attrs["""content"""]
       __snake_case					  =       soup.find("""p"""  ,id="""description"""		).get_text()
       __snake_case  ,						__snake_case					  =       os.path.splitext(os.path.basename(__A		)		)
       __snake_case					  =       title.strip().replace(""" """  ,"""_"""		)
       __snake_case					  =       F'{image_title}{image_extension}'
       save_image(__A  ,__A		)
       return (title, description, image_title)


if __name__ == "__main__":
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__		   =    random_anime_character()
    print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')

