"""simple docstring"""






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_A   					=    'scheduler_config.json'

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		1
					a   	=		2
					a   	=		3
					a   	=		4
					a   	=		5



@dataclass
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42



class lowerCamelCase		:






					'''simple docstring'''


					a   	=		SCHEDULER_CONFIG_NAME
					a   	=		["dtype"]
					a   	=		[]
					a   	=		True



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Dict   , _snake_case			:				Dict[str, Any] = None   , _snake_case			:				Optional[str] = None   , _snake_case			:				Union[str, Any]=False   , **_snake_case			:				Optional[int]   , )							->       int:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				cls.load_config(
									    pretrained_model_name_or_path=_snake_case   , subfolder=_snake_case   , return_unused_kwargs=_snake_case   , **_snake_case   , )
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				cls.from_config(_snake_case   , return_unused_kwargs=_snake_case   , **_snake_case		)

									if hasattr(_snake_case   , "create_state"		) and getattr(_snake_case   , "has_state"   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				scheduler.create_state()

									if return_unused_kwargs:
													return scheduler, state, unused_kwargs

									return scheduler, state



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				Union[str, os.PathLike]   , _snake_case			:				bool = False   , **_snake_case			:				Union[str, Any]		)							->       Dict:
									self.save_config(save_directory=_snake_case   , push_to_hub=_snake_case   , **_snake_case		)



					@property
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									return self._get_compatibles()



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				List[Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				list(set([cls.__name__] + cls._compatibles		)		)
									SCREAMING_SNAKE_CASE__		  =				importlib.import_module(__name__.split("."		)[0]		)
									SCREAMING_SNAKE_CASE__		  =				[
									    getattr(_snake_case   , _snake_case		) for c in compatible_classes_str if hasattr(_snake_case   , _snake_case		)
									]
									return compatible_classes




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   jnp.ndarray:
				assert len(__UpperCAmelCase       ) >= x.ndim
				return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__UpperCAmelCase       ) - x.ndim)       )  ,					__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=0.999  ,					__UpperCAmelCase=jnp.floataa       )							->   jnp.ndarray:

				def alpha_bar(__UpperCAmelCase       ):
								return math.cos((time_step + 0.008) / 1.008 * math.pi / 2       ) ** 2

				SCREAMING_SNAKE_CASE__		  =				[]
				for i in range(__UpperCAmelCase       ):
								SCREAMING_SNAKE_CASE__		  =				i / num_diffusion_timesteps
								SCREAMING_SNAKE_CASE__		  =				(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar(__UpperCAmelCase       ) / alpha_bar(__UpperCAmelCase       )  ,					__UpperCAmelCase       )       )
				return jnp.array(__UpperCAmelCase  ,					dtype=__UpperCAmelCase       )

@flax.struct.dataclass
class lowerCamelCase		:






					'''simple docstring'''


					a   	=		42
					a   	=		42
					a   	=		42



					@classmethod
					def 			lowerCAmelCase_					(						cls			:				Dict   , _snake_case			:				Any		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				scheduler.config

									if config.trained_betas is not None:
													SCREAMING_SNAKE_CASE__		  =				jnp.asarray(config.trained_betas   , dtype=scheduler.dtype		)
									elif config.beta_schedule == "linear":
													SCREAMING_SNAKE_CASE__		  =				jnp.linspace(config.beta_start   , config.beta_end   , config.num_train_timesteps   , dtype=scheduler.dtype		)
									elif config.beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													SCREAMING_SNAKE_CASE__		  =				(
													    jnp.linspace(
													        config.beta_start**0.5   , config.beta_end**0.5   , config.num_train_timesteps   , dtype=scheduler.dtype		)
													    ** 2
													)
									elif config.beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													SCREAMING_SNAKE_CASE__		  =				betas_for_alpha_bar(config.num_train_timesteps   , dtype=scheduler.dtype		)
									else:
													raise NotImplementedError(
													    F"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""		)

									SCREAMING_SNAKE_CASE__		  =				1.0 - betas

									SCREAMING_SNAKE_CASE__		  =				jnp.cumprod(_snake_case   , axis=0		)

									return cls(
									    alphas=_snake_case   , betas=_snake_case   , alphas_cumprod=_snake_case   , )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				state.alphas_cumprod

				SCREAMING_SNAKE_CASE__		  =				alphas_cumprod[timesteps] ** 0.5
				SCREAMING_SNAKE_CASE__		  =				sqrt_alpha_prod.flatten()
				SCREAMING_SNAKE_CASE__		  =				broadcast_to_shape_from_left(__UpperCAmelCase  ,					original_samples.shape       )

				SCREAMING_SNAKE_CASE__		  =				(1 - alphas_cumprod[timesteps]) ** 0.5
				SCREAMING_SNAKE_CASE__		  =				sqrt_one_minus_alpha_prod.flatten()
				SCREAMING_SNAKE_CASE__		  =				broadcast_to_shape_from_left(__UpperCAmelCase  ,					original_samples.shape       )

				return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				get_sqrt_alpha_prod(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				get_sqrt_alpha_prod(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
				return velocity





"""simple docstring"""






from __future__ import annotations

from collections.abc import Generator

import requests
from bsa import BeautifulSoup

_A   					=    'https://www.indeed.co.in/jobs?q=mobile+app+development&l='




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase = "mumbai"       )							->   Generator[tuple[str, str], None, None]:
				SCREAMING_SNAKE_CASE__		  =				BeautifulSoup(requests.get(url + location       ).content  ,					"html.parser"       )
				# This attribute finds out all the specifics listed in a job
				for job in soup.find_all("div"  ,					attrs={"data-tn-component": "organicJob"}       ):
								SCREAMING_SNAKE_CASE__		  =				job.find("a"  ,					attrs={"data-tn-element": "jobTitle"}       ).text.strip()
								SCREAMING_SNAKE_CASE__		  =				job.find("span"  ,					{"class": "company"}       ).text.strip()
								yield job_title, company_name


if __name__ == "__main__":
						for i, job in enumerate(fetch_jobs('Bangalore'), 1):
												print(F'Job {i:>2} is {job[0]} at {job[1]}')




