
"""simple docstring"""






import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 							snake_case       (     __UpperCAmelCase		):
   a_					:		str     					=     (KDPMaAncestralDiscreteScheduler,)
   a_					:		List[Any]     					=     10







   def    UpperCAmelCase__	( self       ,							**__UpperCAmelCase)   ->Union[str, Any]:
      a_						   =     {
          "num_train_timesteps": 11_00,
          "beta_start": 0.0_001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**__UpperCAmelCase)
      return config







   def    UpperCAmelCase__	( self)   ->int:

      for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->Optional[int]:

      for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]       ,							[0.0_002, 0.002, 0.02]):
         self.check_over_configs(beta_start=__UpperCAmelCase       ,							beta_end=__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->Optional[Any]:

      for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->Optional[Any]:

      if torch_device == "mps":
         return
      a_						   =     self.scheduler_classes[0]
      a_						   =     self.get_scheduler_config()
      a_						   =     scheduler_class(**__UpperCAmelCase)

      scheduler.set_timesteps(self.num_inference_steps)

      a_						   =     torch.manual_seed(0)

      a_						   =     self.dummy_model()
      a_						   =     self.dummy_sample_deter * scheduler.init_noise_sigma
      a_						   =     sample.to(__UpperCAmelCase)

      for i, t in enumerate(scheduler.timesteps):
         a_						   =     scheduler.scale_model_input(__UpperCAmelCase       ,							__UpperCAmelCase)

         a_						   =     model(__UpperCAmelCase       ,							__UpperCAmelCase)

         a_						   =     scheduler.step(__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							generator=__UpperCAmelCase)
         a_						   =     output.prev_sample

      a_						   =     torch.sum(torch.abs(__UpperCAmelCase))
      a_						   =     torch.mean(torch.abs(__UpperCAmelCase))

      assert abs(result_sum.item() - 1_38_49.38_77) < 1E-2
      assert abs(result_mean.item() - 18.0_331) < 5E-3







   def    UpperCAmelCase__	( self)   ->int:

      for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->str:

      if torch_device == "mps":
         return
      a_						   =     self.scheduler_classes[0]
      a_						   =     self.get_scheduler_config(prediction_type="v_prediction")
      a_						   =     scheduler_class(**__UpperCAmelCase)

      scheduler.set_timesteps(self.num_inference_steps)

      a_						   =     self.dummy_model()
      a_						   =     self.dummy_sample_deter * scheduler.init_noise_sigma
      a_						   =     sample.to(__UpperCAmelCase)

      a_						   =     torch.manual_seed(0)

      for i, t in enumerate(scheduler.timesteps):
         a_						   =     scheduler.scale_model_input(__UpperCAmelCase       ,							__UpperCAmelCase)

         a_						   =     model(__UpperCAmelCase       ,							__UpperCAmelCase)

         a_						   =     scheduler.step(__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							generator=__UpperCAmelCase)
         a_						   =     output.prev_sample

      a_						   =     torch.sum(torch.abs(__UpperCAmelCase))
      a_						   =     torch.mean(torch.abs(__UpperCAmelCase))

      assert abs(result_sum.item() - 328.9_970) < 1E-2
      assert abs(result_mean.item() - 0.4_284) < 1E-3







   def    UpperCAmelCase__	( self)   ->str:

      if torch_device == "mps":
         return
      a_						   =     self.scheduler_classes[0]
      a_						   =     self.get_scheduler_config()
      a_						   =     scheduler_class(**__UpperCAmelCase)

      scheduler.set_timesteps(self.num_inference_steps       ,							device=__UpperCAmelCase)
      a_						   =     torch.manual_seed(0)

      a_						   =     self.dummy_model()
      a_						   =     self.dummy_sample_deter.to(__UpperCAmelCase) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
         a_						   =     scheduler.scale_model_input(__UpperCAmelCase       ,							__UpperCAmelCase)

         a_						   =     model(__UpperCAmelCase       ,							__UpperCAmelCase)

         a_						   =     scheduler.step(__UpperCAmelCase       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							generator=__UpperCAmelCase)
         a_						   =     output.prev_sample

      a_						   =     torch.sum(torch.abs(__UpperCAmelCase))
      a_						   =     torch.mean(torch.abs(__UpperCAmelCase))

      assert abs(result_sum.item() - 1_38_49.38_18) < 1E-1
      assert abs(result_mean.item() - 18.0_331) < 1E-3






import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowercase     :			Optional[int]     						=	{"""UserAgent""": UserAgent().random}
lowercase     :			Union[str, Any]     						=	"""https://www.mywaifulist.moe/random"""


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    requests.get(lowerCAmelCase     ,							headers=lowerCAmelCase       )
       with open(lowerCAmelCase     ,							'''wb'''       ) as file:
              file.write(image.content       )


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


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






