code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import hashlib
  import importlib.util
  import json
  import os
  import pathlib
  from types import ModuleType
  
  import pytest
  import requests
  
  _lowerCamelCase				:	Optional[Any]				    =       pathlib.Path.cwd().joinpath('project_euler')
  _lowerCamelCase				:	Optional[int]				    =       pathlib.Path.cwd().joinpath(
      'scripts', 'project_euler_answers.json'
  )
  
  with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
  				_lowerCamelCase				:	dict[str, str]				    =       json.load(file_handle)
  
  
  
  
  def    __a (     UpperCAmelCase    )      ->ModuleType:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      importlib.util.spec_from_file_location(file_path.name       ,			str(UpperCAmelCase    )    )
  	A							  =      importlib.util.module_from_spec(UpperCAmelCase    )  # type: ignore
  	spec.loader.exec_module(UpperCAmelCase    )  # type: ignore
  	return module
  
  
  
  
  def    __a (     )      ->list[pathlib.Path]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      []
  	for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
  		if problem_dir_path.is_file() or problem_dir_path.name.startswith("""_"""    ):
  			continue
  		for file_path in problem_dir_path.iterdir():
  			if file_path.suffix != ".py" or file_path.name.startswith(("""_""", """test""")    ):
  				continue
  			solution_file_paths.append(UpperCAmelCase    )
  	return solution_file_paths
  
  
  
  
  def    __a (     )      ->str:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	with open(os.environ["""GITHUB_EVENT_PATH"""]    ) as file:
  		A							  =      json.load(UpperCAmelCase    )
  	return event["pull_request"]["url"] + "/files"
  
  
  
  
  def    __a (     )      ->list[pathlib.Path]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      []
  	A							  =      {
  	    """Accept""": """application/vnd.github.v3+json""",
  	    """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
  	}
  	A							  =      requests.get(get_files_url()       ,			headers=UpperCAmelCase    ).json()
  	for file in files:
  		A							  =      pathlib.Path.cwd().joinpath(file["""filename"""]    )
  		if (
  		    filepath.suffix != ".py"
  		    or filepath.name.startswith(("""_""", """test""")    )
  		    or not filepath.name.startswith("""sol"""    )
  		):
  			continue
  		solution_file_paths.append(UpperCAmelCase    )
  	return solution_file_paths
  
  
  
  
  def    __a (     )      ->list[pathlib.Path]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if os.environ.get("""CI"""    ) and os.environ.get("""GITHUB_EVENT_NAME"""    ) == "pull_request":
  		# Return only if there are any, otherwise default to all solutions
  		if filepaths := added_solution_file_path():
  			return filepaths
  	return all_solution_file_paths()
  
  
  
  
  @pytest.mark.parametrize(
      """solution_path"""       ,			collect_solution_file_paths()       ,			ids=lambda UpperCAmelCase    : f"""{path.parent.name}/{path.name}"""       ,			)
  def    __a (     UpperCAmelCase    )      ->None:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      solution_path.parent.name[8:].zfill(3    )
  	A							  =      PROBLEM_ANSWERS[problem_number]
  	A							  =      convert_path_to_module(UpperCAmelCase    )
  	A							  =      str(solution_module.solution()    )  # type: ignore
  	A							  =      hashlib.shaaaa(answer.encode()    ).hexdigest()
  	assert (
  	    answer == expected
  	), f"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    (KDPMaAncestralDiscreteScheduler,)
  		__lowerCAmelCase 				=    10
  
  
  
  
  
  
  
  		def        A     (self						:      Any      ,      **_lowerCAmelCase						:      List[Any]      ):
  			A							  =      {
  			    """num_train_timesteps""": 1100,
  			    """beta_start""": 0.0_001,
  			    """beta_end""": 0.02,
  			    """beta_schedule""": """linear""",
  			}
  
  			config.update(**_lowerCAmelCase      )
  			return config
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			for timesteps in [10, 50, 100, 1000]:
  				self.check_over_configs(num_train_timesteps=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (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=_lowerCAmelCase      ,      beta_end=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			for schedule in ["linear", "scaled_linear"]:
  				self.check_over_configs(beta_schedule=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config()
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			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(_lowerCAmelCase      )
  
  			for i, t in enumerate(scheduler.timesteps      ):
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  			assert abs(result_sum.item() - 13_849.3_877      ) < 1e-2
  			assert abs(result_mean.item() - 18.0_331      ) < 5e-3
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ):
  			for prediction_type in ["epsilon", "v_prediction"]:
  				self.check_over_configs(prediction_type=_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config(prediction_type="""v_prediction"""      )
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			scheduler.set_timesteps(self.num_inference_steps      )
  
  			A							  =      self.dummy_model()
  			A							  =      self.dummy_sample_deter * scheduler.init_noise_sigma
  			A							  =      sample.to(_lowerCAmelCase      )
  
  			A							  =      torch.manual_seed(0      )
  
  			for i, t in enumerate(scheduler.timesteps      ):
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  			assert abs(result_sum.item() - 328.9_970      ) < 1e-2
  			assert abs(result_mean.item() - 0.4_284      ) < 1e-3
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ):
  			if torch_device == "mps":
  				return
  			A							  =      self.scheduler_classes[0]
  			A							  =      self.get_scheduler_config()
  			A							  =      scheduler_class(**_lowerCAmelCase      )
  
  			scheduler.set_timesteps(self.num_inference_steps      ,      device=_lowerCAmelCase      )
  			A							  =      torch.manual_seed(0      )
  
  			A							  =      self.dummy_model()
  			A							  =      self.dummy_sample_deter.to(_lowerCAmelCase      ) * scheduler.init_noise_sigma
  
  			for t in scheduler.timesteps:
  				A							  =      scheduler.scale_model_input(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      model(_lowerCAmelCase      ,      _lowerCAmelCase      )
  
  				A							  =      scheduler.step(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      generator=_lowerCAmelCase      )
  				A							  =      output.prev_sample
  
  			A							  =      torch.sum(torch.abs(_lowerCAmelCase      )      )
  			A							  =      torch.mean(torch.abs(_lowerCAmelCase      )      )
  
  			assert abs(result_sum.item() - 13_849.3_818      ) < 1e-1
  			assert abs(result_mean.item() - 18.0_331      ) < 1e-3
  
  