




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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class     A:



 '''simple docstring'''


 UpperCamelCase		  =		None
 UpperCamelCase		  =		None
 UpperCamelCase		  =		None  # sigma(t_i)

 @classmethod
 def 							a__					(							cls   :    str    )  ->      List[Any]:

       """simple docstring"""






       return cls()







@dataclass
class     A(		UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		42
 UpperCamelCase		  =		42
 UpperCamelCase		  =		42







class     A(		UpperCamelCase				, UpperCamelCase  ):



 '''simple docstring'''


 @property
 def 							a__					(							self   :    Optional[int]    )  ->      int:

       """simple docstring"""






       return True

 @register_to_config
 def __init__(							self   :    Any		,				A_   :    float = 0.02		,				A_   :    float = 100		,				A_   :    float = 1.007		,				A_   :    float = 80		,				A_   :    float = 0.05		,				A_   :    float = 50		,				)  ->      Union[str, Any]:

       """simple docstring"""






       pass

 def 							a__					(							self   :    Union[str, Any]    )  ->      List[str]:

       """simple docstring"""






       return KarrasVeSchedulerState.create()

 def 							a__					(							self   :    List[Any]		,				A_   :    KarrasVeSchedulerState		,				A_   :    int		,				A_   :    Tuple = ()    )  ->      KarrasVeSchedulerState:

       """simple docstring"""






       lowerCamelCase_							=       jnp.arange(0		,				A_    )[::-1].copy()
       lowerCamelCase_							=       [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in timesteps
       ]

       return state.replace(
           num_inference_steps=A_		,				schedule=jnp.array(A_		,				dtype=jnp.floataa    )		,				timesteps=A_		,				)

 def 							a__					(							self   :    Optional[int]		,				A_   :    KarrasVeSchedulerState		,				A_   :    jnp.ndarray		,				A_   :    float		,				A_   :    random.KeyArray		,				)  ->      Tuple[jnp.ndarray, float]:

       """simple docstring"""






       if self.config.s_min <= sigma <= self.config.s_max:
             lowerCamelCase_							=       min(self.config.s_churn / state.num_inference_steps		,				2**0.5 - 1    )
       else:
             lowerCamelCase_							=       0

       # sample eps ~ N(0, S_noise^2 * I)
       lowerCamelCase_							=       random.split(A_		,				num=1    )
       lowerCamelCase_							=       self.config.s_noise * random.normal(key=A_		,				shape=sample.shape    )
       lowerCamelCase_							=       sigma + gamma * sigma
       lowerCamelCase_							=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat

 def 							a__					(							self   :    Optional[Any]		,				A_   :    KarrasVeSchedulerState		,				A_   :    jnp.ndarray		,				A_   :    float		,				A_   :    float		,				A_   :    jnp.ndarray		,				A_   :    bool = True		,				)  ->      Union[FlaxKarrasVeOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_							=       sample_hat + sigma_hat * model_output
       lowerCamelCase_							=       (sample_hat - pred_original_sample) / sigma_hat
       lowerCamelCase_							=       sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
             return (sample_prev, derivative, state)

       return FlaxKarrasVeOutput(prev_sample=A_		,				derivative=A_		,				state=A_    )

 def 							a__					(							self   :    Optional[Any]		,				A_   :    KarrasVeSchedulerState		,				A_   :    jnp.ndarray		,				A_   :    float		,				A_   :    float		,				A_   :    jnp.ndarray		,				A_   :    jnp.ndarray		,				A_   :    jnp.ndarray		,				A_   :    bool = True		,				)  ->      Union[FlaxKarrasVeOutput, Tuple]:

       """simple docstring"""






       lowerCamelCase_							=       sample_prev + sigma_prev * model_output
       lowerCamelCase_							=       (sample_prev - pred_original_sample) / sigma_prev
       lowerCamelCase_							=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
             return (sample_prev, derivative, state)

       return FlaxKarrasVeOutput(prev_sample=A_		,				derivative=A_		,				state=A_    )





 def 							a__					(							self   :    Union[str, Any]		,				A_   :    KarrasVeSchedulerState		,				A_   :    Dict		,				A_   :    List[str]		,				A_   :    List[Any]    )  ->      List[str]:

       """simple docstring"""






       raise NotImplementedError()







import requests
from bsa import BeautifulSoup





def 	_lowerCAmelCase					( UpperCamelCase__:  str = "https://www.worldometers.info/coronavirus"    )			-> dict:





	"""simple docstring"""







	A             =					BeautifulSoup(requests.get(UpperCamelCase__    ).text				,       """html.parser"""    )
	A             =					soup.findAll("""h1"""    )
	A             =					soup.findAll("""div"""				,       {"""class""": """maincounter-number"""}    )
	keys += soup.findAll("""span"""				,       {"""class""": """panel-title"""}    )
	values += soup.findAll("""div"""				,       {"""class""": """number-table-main"""}    )
	return {key.text.strip(): value.text.strip() for key, value in zip(UpperCamelCase__				,       UpperCamelCase__    )}


if __name__ == "__main__":
				print("\033[1m" + "COVID-19 Status of the World" + "\033[0m\n")
				for key, value in world_covidaa_stats().items():
								print(f'''{key}\n{value}\n''')



