





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					(UnCLIPScheduler,)






  def 							lowercase						(							self					:						str   ,    **lowerCAmelCase_					:						str	)				->		str:
     __lowerCAmelCase				      =      {
         'num_train_timesteps': 1_0_0_0,
         'variance_type': 'fixed_small_log',
         'clip_sample': True,
         'clip_sample_range': 1.0,
         'prediction_type': 'epsilon',
     }

     config.update(**lowerCAmelCase_	)
     return config






  def 							lowercase						(							self					:						Optional[Any]	)				->		Optional[int]:
     for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]	)				->		List[Any]:
     for variance in ["fixed_small_log", "learned_range"]:
        self.check_over_configs(variance_type=lowerCAmelCase_	)






  def 							lowercase						(							self					:						int	)				->		Union[str, Any]:
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Tuple	)				->		List[Any]:
     for clip_sample_range in [1, 5, 1_0, 2_0]:
        self.check_over_configs(clip_sample_range=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Dict	)				->		Any:
     for prediction_type in ["epsilon", "sample"]:
        self.check_over_configs(prediction_type=lowerCAmelCase_	)






  def 							lowercase						(							self					:						int	)				->		Tuple:
     for time_step in [0, 5_0_0, 9_9_9]:
        for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
           if prev_timestep is not None and prev_timestep >= time_step:
              continue

           self.check_over_forward(time_step=lowerCAmelCase_   ,    prev_timestep=lowerCAmelCase_	)






  def 							lowercase						(							self					:						Any	)				->		Dict:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config(variance_type='fixed_small_log'	)
     __lowerCAmelCase				      =      scheduler_class(**lowerCAmelCase_	)

     assert torch.sum(torch.abs(scheduler._get_variance(0	) - 1.00_00e-10	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(4_8_7	) - 0.0_54_96_25	)	) < 1e-5
     assert torch.sum(torch.abs(scheduler._get_variance(9_9_9	) - 0.9_99_49_87	)	) < 1e-5






  def 							lowercase						(							self					:						List[Any]	)				->		int:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config(variance_type='learned_range'	)
     __lowerCAmelCase				      =      scheduler_class(**lowerCAmelCase_	)

     __lowerCAmelCase				      =      0.5

     assert scheduler._get_variance(1   ,    predicted_variance=lowerCAmelCase_	) - -10.1_71_27_90 < 1e-5
     assert scheduler._get_variance(4_8_7   ,    predicted_variance=lowerCAmelCase_	) - -5.7_99_80_52 < 1e-5
     assert scheduler._get_variance(9_9_9   ,    predicted_variance=lowerCAmelCase_	) - -0.0_01_00_11 < 1e-5






  def 							lowercase						(							self					:						Dict	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config()
     __lowerCAmelCase				      =      scheduler_class(**lowerCAmelCase_	)

     __lowerCAmelCase				      =      scheduler.timesteps

     __lowerCAmelCase				      =      self.dummy_model()
     __lowerCAmelCase				      =      self.dummy_sample_deter
     __lowerCAmelCase				      =      torch.manual_seed(0	)

     for i, t in enumerate(lowerCAmelCase_	):
        # 1. predict noise residual
        __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    lowerCAmelCase_	)

        # 2. predict previous mean of sample x_t-1
        __lowerCAmelCase				      =      scheduler.step(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_   ,    generator=lowerCAmelCase_	).prev_sample

        __lowerCAmelCase				      =      pred_prev_sample

     __lowerCAmelCase				      =      torch.sum(torch.abs(lowerCAmelCase_	)	)
     __lowerCAmelCase				      =      torch.mean(torch.abs(lowerCAmelCase_	)	)

     assert abs(result_sum.item() - 2_52.2_68_24_95	) < 1e-2
     assert abs(result_mean.item() - 0.3_28_47_43	) < 1e-3






  def 							lowercase						(							self					:						List[str]	)				->		Optional[int]:
     __lowerCAmelCase				      =      self.scheduler_classes[0]
     __lowerCAmelCase				      =      self.get_scheduler_config()
     __lowerCAmelCase				      =      scheduler_class(**lowerCAmelCase_	)

     scheduler.set_timesteps(2_5	)

     __lowerCAmelCase				      =      scheduler.timesteps

     __lowerCAmelCase				      =      self.dummy_model()
     __lowerCAmelCase				      =      self.dummy_sample_deter
     __lowerCAmelCase				      =      torch.manual_seed(0	)

     for i, t in enumerate(lowerCAmelCase_	):
        # 1. predict noise residual
        __lowerCAmelCase				      =      model(lowerCAmelCase_   ,    lowerCAmelCase_	)

        if i + 1 == timesteps.shape[0]:
           __lowerCAmelCase				      =      None
        else:
           __lowerCAmelCase				      =      timesteps[i + 1]

        # 2. predict previous mean of sample x_t-1
        __lowerCAmelCase				      =      scheduler.step(
            lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_   ,    prev_timestep=lowerCAmelCase_   ,    generator=lowerCAmelCase_	).prev_sample

        __lowerCAmelCase				      =      pred_prev_sample

     __lowerCAmelCase				      =      torch.sum(torch.abs(lowerCAmelCase_	)	)
     __lowerCAmelCase				      =      torch.mean(torch.abs(lowerCAmelCase_	)	)

     assert abs(result_sum.item() - 2_58.2_04_49_83	) < 1e-2
     assert abs(result_mean.item() - 0.3_36_20_38	) < 1e-3






  def 							lowercase						(							self					:						Union[str, Any]	)				->		Optional[Any]:
     pass






  def 							lowercase						(							self					:						Tuple	)				->		Tuple:
     pass





import requests
from bsa import BeautifulSoup





def 						_lowerCAmelCase							(     _lowerCAmelCase = "AAPL"			)    ->						str:


						'''simple docstring'''






						__snake_case				=		F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
						__snake_case				=		BeautifulSoup(requests.get(_lowerCAmelCase			).text   ,						"html.parser"			)
						__snake_case				=		"My(6px) Pos(r) smartphone_Mt(6px)"
						return soup.find("div"   ,						class_=class_			).find("span"			).text


if __name__ == "__main__":
	for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
		print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')





