




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(DDIMParallelScheduler,)
    UpperCAmelCase__							  =				(('''eta''', 0.0), ('''num_inference_steps''', 50))


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			**UpperCAmelCase__				:     Any)						->List[str]:





           '''simple docstring'''

           A__							=   {
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''clip_sample''': True,
           }

           config.update(**UpperCAmelCase__)
           return config


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			**UpperCAmelCase__				:     Tuple)						->Optional[Any]:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config(**UpperCAmelCase__)
           A__							=   scheduler_class(**UpperCAmelCase__)

           A__			,							A__							=   10, 0.0

           A__							=   self.dummy_model()
           A__							=   self.dummy_sample_deter

           scheduler.set_timesteps(UpperCAmelCase__)

           for t in scheduler.timesteps:
                  A__							=   model(UpperCAmelCase__ ,			UpperCAmelCase__)
                  A__							=   scheduler.step(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__).prev_sample

           return sample


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->str:





           '''simple docstring'''

           for timesteps in [100, 500, 1_000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->str:





           '''simple docstring'''

           for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=UpperCAmelCase__)

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config(steps_offset=1)
           A__							=   scheduler_class(**UpperCAmelCase__)
           scheduler.set_timesteps(5)
           assert torch.equal(scheduler.timesteps ,			torch.LongTensor([801, 601, 401, 201, 1]))


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->Tuple:





           '''simple docstring'''

           for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,			[0.002, 0.02, 0.2, 2]):
                  self.check_over_configs(beta_start=UpperCAmelCase__ ,			beta_end=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Union[str, Any]:





           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->str:





           '''simple docstring'''

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


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





           '''simple docstring'''

           for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Union[str, Any]:





           '''simple docstring'''

           for timestep_spacing in ["trailing", "leading"]:
                  self.check_over_configs(timestep_spacing=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[str]:





           '''simple docstring'''

           for rescale_betas_zero_snr in [True, False]:
                  self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Any:





           '''simple docstring'''

           self.check_over_configs(thresholding=UpperCAmelCase__)
           for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "v_prediction"]:
                         self.check_over_configs(
                             thresholding=UpperCAmelCase__ ,			prediction_type=UpperCAmelCase__ ,			sample_max_value=UpperCAmelCase__ ,			)


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





           '''simple docstring'''

           for t in [1, 10, 49]:
                  self.check_over_forward(time_step=UpperCAmelCase__)


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





           '''simple docstring'''

           for t, num_inference_steps in zip([1, 10, 50] ,			[10, 50, 500]):
                  self.check_over_forward(time_step=UpperCAmelCase__ ,			num_inference_steps=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Tuple:





           '''simple docstring'''

           for t, eta in zip([1, 10, 49] ,			[0.0, 0.5, 1.0]):
                  self.check_over_forward(time_step=UpperCAmelCase__ ,			eta=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[Any]:





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config()
           A__							=   scheduler_class(**UpperCAmelCase__)

           assert torch.sum(torch.abs(scheduler._get_variance(0 ,			0) - 0.0)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(420 ,			400) - 0.14771)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(980 ,			960) - 0.32460)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(0 ,			0) - 0.0)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(487 ,			486) - 0.00979)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999 ,			998) - 0.02)) < 1e-5


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





           '''simple docstring'''

           A__							=   self.scheduler_classes[0]
           A__							=   self.get_scheduler_config()
           A__							=   scheduler_class(**UpperCAmelCase__)

           A__			,							A__							=   10, 0.0
           scheduler.set_timesteps(UpperCAmelCase__)

           A__							=   self.dummy_model()
           A__							=   self.dummy_sample_deter
           A__							=   self.dummy_sample_deter + 0.1
           A__							=   self.dummy_sample_deter - 0.1

           A__							=   samplea.shape[0]
           A__							=   torch.stack([samplea, samplea, samplea] ,			dim=0)
           A__							=   torch.arange(UpperCAmelCase__)[0:3, None].repeat(1 ,			UpperCAmelCase__)

           A__							=   model(samples.flatten(0 ,			1) ,			timesteps.flatten(0 ,			1))
           A__							=   scheduler.batch_step_no_noise(UpperCAmelCase__ ,			timesteps.flatten(0 ,			1) ,			samples.flatten(0 ,			1) ,			UpperCAmelCase__)

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 1147.7904) < 1e-2
           assert abs(result_mean.item() - 0.4982) < 1e-3


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





           '''simple docstring'''

           A__							=   self.full_loop()

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 172.0067) < 1e-2
           assert abs(result_mean.item() - 0.223967) < 1e-3


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str])						->Optional[Any]:





           '''simple docstring'''

           A__							=   self.full_loop(prediction_type='''v_prediction''')

           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 52.5302) < 1e-2
           assert abs(result_mean.item() - 0.0684) < 1e-3


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   self.full_loop(set_alpha_to_one=UpperCAmelCase__ ,			beta_start=0.01)
           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 149.8295) < 1e-2
           assert abs(result_mean.item() - 0.1951) < 1e-3





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





           '''simple docstring'''

           A__							=   self.full_loop(set_alpha_to_one=UpperCAmelCase__ ,			beta_start=0.01)
           A__							=   torch.sum(torch.abs(UpperCAmelCase__))
           A__							=   torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 149.0784) < 1e-2
           assert abs(result_mean.item() - 0.1941) < 1e-3








from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""						)       ->	dict[str, float]:



       """simple docstring"""

       A__							=   url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
       A__							=   BeautifulSoup(requests.get(lowercase_						).text			,    '''html.parser'''						)
       A__							=   soup.find_all('''td'''			,    attrs='''titleColumn'''						)
       A__							=   soup.find_all('''td'''			,    class_='''ratingColumn imdbRating'''						)
       return {
           title.a.text: float(rating.strong.text						)
           for title, rating in zip(lowercase_			,    lowercase_						)
       }







def SCREAMING_SNAKE_CASE    (	lowercase_ = "IMDb_Top_250_Movies.csv"						)       ->	None:



       """simple docstring"""

       A__							=   get_imdb_top_aaa_movies()
       with open(lowercase_			,    '''w'''			,    newline=''''''						) as out_file:
              A__							=   csv.writer(lowercase_						)
              writer.writerow(['''Movie title''', '''IMDb rating''']						)
              for title, rating in movies.items():
                     writer.writerow([title, rating]						)


if __name__ == "__main__":
  write_movies()


