

'''simple docstring'''

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class   lowerCAmelCase     :
       lowerCAmelCase_		= 4_2
       lowerCAmelCase_		= 4_2

       # setable values
       lowerCAmelCase_		= 4_2
       lowerCAmelCase_		= 4_2
       lowerCAmelCase_		= None







       @classmethod
       def 			snake_case   (							cls			: Any						,      __lowercase			: str						,      __lowercase			: List[Any]						,      __lowercase			: str						,      __lowercase			: Union[str, Any]						,      ):







            """simple docstring"""







            return cls(
                common=__lowercase						,      final_alpha_cumprod=__lowercase						,      init_noise_sigma=__lowercase						,      timesteps=__lowercase						,      )






@dataclass
class   lowerCAmelCase     (      snake_case_   ):
       lowerCAmelCase_		= 4_2






class   lowerCAmelCase     (      snake_case_   ,			snake_case_   ):
       lowerCAmelCase_		= [e.name for e in FlaxKarrasDiffusionSchedulers]

       lowerCAmelCase_		= 4_2







       @property
       def 			snake_case   (							self			: int      ):







            """simple docstring"""







            return True







       @register_to_config
       def __init__(							self			: int						,      __lowercase			: List[str] = 1000						,      __lowercase			: Dict = 0.0_0_0_1						,      __lowercase			: Tuple = 0.0_2						,      __lowercase			: str = "linear"						,      __lowercase			: List[Any] = None						,      __lowercase			: Optional[Any] = True						,      __lowercase			: Dict = 0						,      __lowercase			: List[str] = "epsilon"						,      __lowercase			: Optional[int] = jnp.floataa						,      ):







            """simple docstring"""







            __lowercase					=dtype







       def 			snake_case   (							self			: Any						,      __lowercase			: Tuple = None      ):







            """simple docstring"""







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

            # At every step in ddim, we are looking into the previous alphas_cumprod
            # For the final step, there is no previous alphas_cumprod because we are already at 0
            # `set_alpha_to_one` decides whether we set this parameter simply to one or
            # whether we use the final alpha of the "non-previous" one.
            __lowercase					=(
                jnp.array(1.0						,      dtype=self.dtype      ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
            )

            # standard deviation of the initial noise distribution
            __lowercase					=jnp.array(1.0						,      dtype=self.dtype      )

            __lowercase					=jnp.arange(0						,      self.config.num_train_timesteps      ).round()[::-1]

            return DDIMSchedulerState.create(
                common=__lowercase						,      final_alpha_cumprod=__lowercase						,      init_noise_sigma=__lowercase						,      timesteps=__lowercase						,      )







       def 			snake_case   (							self			: Dict						,      __lowercase			: Dict						,      __lowercase			: List[str]						,      __lowercase			: List[Any] = None      ):







            """simple docstring"""







            return sample







       def 			snake_case   (							self			: str						,      __lowercase			: int						,      __lowercase			: Tuple						,      __lowercase			: Dict = ()      ):







            """simple docstring"""







            __lowercase					=self.config.num_train_timesteps // num_inference_steps
            # creates integer timesteps by multiplying by ratio
            # rounding to avoid issues when num_inference_step is power of 3
            __lowercase					=(jnp.arange(0						,      __lowercase      ) * step_ratio).round()[::-1] + self.config.steps_offset

            return state.replace(
                num_inference_steps=__lowercase						,      timesteps=__lowercase						,      )







       def 			snake_case   (							self			: Optional[int]						,      __lowercase			: Optional[int]						,      __lowercase			: List[str]						,      __lowercase			: Any      ):







            """simple docstring"""







            __lowercase					=state.common.alphas_cumprod[timestep]
            __lowercase					=jnp.where(
                prev_timestep >= 0						,      state.common.alphas_cumprod[prev_timestep]						,      state.final_alpha_cumprod      )
            __lowercase					=1 - alpha_prod_t
            __lowercase					=1 - alpha_prod_t_prev

            __lowercase					=(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

            return variance







       def 			snake_case   (							self			: List[Any]						,      __lowercase			: Optional[int]						,      __lowercase			: Optional[Any]						,      __lowercase			: List[str]						,      __lowercase			: Optional[int]						,      __lowercase			: str = 0.0						,      __lowercase			: str = True						,      ):







            """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'      )

            # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
            # Ideally, read DDIM paper in-detail understanding

            # Notation (<variable name> -> <name in paper>
            # - pred_noise_t -> e_theta(x_t, t)
            # - pred_original_sample -> f_theta(x_t, t) or x_0
            # - std_dev_t -> sigma_t
            # - eta -> η
            # - pred_sample_direction -> "direction pointing to x_t"
            # - pred_prev_sample -> "x_t-1"

            # 1. get previous step value (=t-1)
            __lowercase					=timestep - self.config.num_train_timesteps // state.num_inference_steps

            __lowercase					=state.common.alphas_cumprod
            __lowercase					=state.final_alpha_cumprod

            # 2. compute alphas, betas
            __lowercase					=alphas_cumprod[timestep]
            __lowercase					=jnp.where(prev_timestep >= 0						,      alphas_cumprod[prev_timestep]						,      __lowercase      )

            __lowercase					=1 - alpha_prod_t

            # 3. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            if self.config.prediction_type == "epsilon":
                 __lowercase					=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                 __lowercase					=model_output
            elif self.config.prediction_type == "sample":
                 __lowercase					=model_output
                 __lowercase					=(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
            elif self.config.prediction_type == "v_prediction":
                 __lowercase					=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                 __lowercase					=(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
            else:
                 raise ValueError(
                     f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
                     ' `v_prediction`'      )

            # 4. compute variance: "sigma_t(η)" -> see formula (16)
            # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
            __lowercase					=self._get_variance(__lowercase						,      __lowercase						,      __lowercase      )
            __lowercase					=eta * variance ** 0.5

            # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            __lowercase					=(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

            # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            __lowercase					=alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

            if not return_dict:
                 return (prev_sample, state)

            return FlaxDDIMSchedulerOutput(prev_sample=__lowercase						,      state=__lowercase      )







       def 			snake_case   (							self			: List[Any]						,      __lowercase			: Dict						,      __lowercase			: Union[str, Any]						,      __lowercase			: Any						,      __lowercase			: Dict						,      ):







            """simple docstring"""







            return add_noise_common(state.common						,      __lowercase						,      __lowercase						,      __lowercase      )







       def 			snake_case   (							self			: Any						,      __lowercase			: List[str]						,      __lowercase			: Union[str, Any]						,      __lowercase			: int						,      __lowercase			: int						,      ):







            """simple docstring"""







            return get_velocity_common(state.common						,      __lowercase						,      __lowercase						,      __lowercase      )







       def __len__(							self			: Tuple      ):







            """simple docstring"""







            return self.config.num_train_timesteps





'''simple docstring'''

import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

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

def 				__UpperCamelCase							(	lowercase__						:  str,					lowercase__						:  str ):






     '''simple docstring'''





     __lowercase					=requests.get(lowercase__,					headers=lowercase__ )
     with open(lowercase__,					'wb' ) as file:
          file.write(image.content )

def 				__UpperCamelCase							(	):






     '''simple docstring'''





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


