
"""simple docstring"""





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		(PNDMScheduler,)
  SCREAMING_SNAKE_CASE__			:						Tuple       =		(("""num_inference_steps""", 50),)







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    {
         "num_train_timesteps": 1000,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_  )
     return config







  def        UpperCamelCase__					(		self				,   lowercase_=0				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    dict(self.forward_default_kwargs  )
     UpperCAmelCase_					:					str			     =    kwargs.pop("num_inference_steps"				,   lowercase_  )
     UpperCAmelCase_					:					Optional[Any]			     =    self.dummy_sample
     UpperCAmelCase_					:					Dict			     =    0.1 * sample
     UpperCAmelCase_					:					Optional[Any]			     =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
        UpperCAmelCase_					:					List[Any]			     =    self.get_scheduler_config(**lowercase_  )
        UpperCAmelCase_					:					Union[str, Any]			     =    scheduler_class(**lowercase_  )
        scheduler.set_timesteps(lowercase_  )
        # copy over dummy past residuals
        UpperCAmelCase_					:					List[Any]			     =    dummy_past_residuals[:]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowercase_  )
           UpperCAmelCase_					:					List[str]			     =    scheduler_class.from_pretrained(lowercase_  )
           new_scheduler.set_timesteps(lowercase_  )
           # copy over dummy past residuals
           UpperCAmelCase_					:					Union[str, Any]			     =    dummy_past_residuals[:]

        UpperCAmelCase_					:					Optional[Any]			     =    scheduler.step_prk(lowercase_				,   lowercase_				,   lowercase_				,   **lowercase_  ).prev_sample
        UpperCAmelCase_					:					Dict			     =    new_scheduler.step_prk(lowercase_				,   lowercase_				,   lowercase_				,   **lowercase_  ).prev_sample

        assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

        UpperCAmelCase_					:					List[str]			     =    scheduler.step_plms(lowercase_				,   lowercase_				,   lowercase_				,   **lowercase_  ).prev_sample
        UpperCAmelCase_					:					Dict			     =    new_scheduler.step_plms(lowercase_				,   lowercase_				,   lowercase_				,   **lowercase_  ).prev_sample

        assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  def        UpperCamelCase__					(		self				,   lowercase_=0				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    dict(self.forward_default_kwargs  )
     UpperCAmelCase_					:					Optional[Any]			     =    kwargs.pop("num_inference_steps"				,   lowercase_  )
     UpperCAmelCase_					:					Any			     =    self.dummy_sample
     UpperCAmelCase_					:					Union[str, Any]			     =    0.1 * sample
     UpperCAmelCase_					:					Any			     =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
        UpperCAmelCase_					:					Union[str, Any]			     =    self.get_scheduler_config()
        UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )
        scheduler.set_timesteps(lowercase_  )

        # copy over dummy past residuals (must be after setting timesteps)
        UpperCAmelCase_					:					Optional[Any]			     =    dummy_past_residuals[:]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(lowercase_  )
           UpperCAmelCase_					:					List[Any]			     =    scheduler_class.from_pretrained(lowercase_  )
           # copy over dummy past residuals
           new_scheduler.set_timesteps(lowercase_  )

           # copy over dummy past residual (must be after setting timesteps)
           UpperCAmelCase_					:					List[str]			     =    dummy_past_residuals[:]

        UpperCAmelCase_					:					Union[str, Any]			     =    scheduler.step_prk(lowercase_				,   lowercase_				,   lowercase_				,   **lowercase_  ).prev_sample
        UpperCAmelCase_					:					List[str]			     =    new_scheduler.step_prk(lowercase_				,   lowercase_				,   lowercase_				,   **lowercase_  ).prev_sample

        assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"

        UpperCAmelCase_					:					List[Any]			     =    scheduler.step_plms(lowercase_				,   lowercase_				,   lowercase_				,   **lowercase_  ).prev_sample
        UpperCAmelCase_					:					str			     =    new_scheduler.step_plms(lowercase_				,   lowercase_				,   lowercase_				,   **lowercase_  ).prev_sample

        assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config(**lowercase_  )
     UpperCAmelCase_					:					int			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_					:					Any			     =    10
     UpperCAmelCase_					:					Tuple			     =    self.dummy_model()
     UpperCAmelCase_					:					str			     =    self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_  )

     for i, t in enumerate(scheduler.prk_timesteps  ):
        UpperCAmelCase_					:					List[str]			     =    model(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Optional[int]			     =    scheduler.step_prk(lowercase_				,   lowercase_				,   lowercase_  ).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps  ):
        UpperCAmelCase_					:					str			     =    model(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Dict			     =    scheduler.step_plms(lowercase_				,   lowercase_				,   lowercase_  ).prev_sample

     return sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    dict(self.forward_default_kwargs  )

     UpperCAmelCase_					:					Union[str, Any]			     =    kwargs.pop("num_inference_steps"				,   lowercase_  )

     for scheduler_class in self.scheduler_classes:
        UpperCAmelCase_					:					int			     =    self.get_scheduler_config()
        UpperCAmelCase_					:					List[Any]			     =    scheduler_class(**lowercase_  )

        UpperCAmelCase_					:					List[str]			     =    self.dummy_sample
        UpperCAmelCase_					:					List[Any]			     =    0.1 * sample

        if num_inference_steps is not None and hasattr(lowercase_				,   "set_timesteps"  ):
           scheduler.set_timesteps(lowercase_  )
        elif num_inference_steps is not None and not hasattr(lowercase_				,   "set_timesteps"  ):
           UpperCAmelCase_					:					Tuple			     =    num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        UpperCAmelCase_					:					Any			     =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        UpperCAmelCase_					:					List[Any]			     =    dummy_past_residuals[:]

        UpperCAmelCase_					:					Dict			     =    scheduler.step_prk(lowercase_				,   0				,   lowercase_				,   **lowercase_  ).prev_sample
        UpperCAmelCase_					:					Dict			     =    scheduler.step_prk(lowercase_				,   1				,   lowercase_				,   **lowercase_  ).prev_sample

        self.assertEqual(output_a.shape				,   sample.shape  )
        self.assertEqual(output_a.shape				,   output_a.shape  )

        UpperCAmelCase_					:					Optional[Any]			     =    scheduler.step_plms(lowercase_				,   0				,   lowercase_				,   **lowercase_  ).prev_sample
        UpperCAmelCase_					:					Union[str, Any]			     =    scheduler.step_plms(lowercase_				,   1				,   lowercase_				,   **lowercase_  ).prev_sample

        self.assertEqual(output_a.shape				,   sample.shape  )
        self.assertEqual(output_a.shape				,   output_a.shape  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Optional[int]			     =    self.get_scheduler_config(steps_offset=1  )
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )
     scheduler.set_timesteps(10  )
     assert torch.equal(
         scheduler.timesteps				,   torch.LongTensor(
             [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]  )				,   )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for beta_start, beta_end in zip([0.00_01, 0.0_01]				,   [0.0_02, 0.02]  ):
        self.check_over_configs(beta_start=lowercase_				,   beta_end=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t in [1, 5, 10]:
        self.check_over_forward(time_step=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t, num_inference_steps in zip([1, 5, 10]				,   [10, 50, 100]  ):
        self.check_over_forward(num_inference_steps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     UpperCAmelCase_					:					Tuple			     =    27

     for scheduler_class in self.scheduler_classes:
        UpperCAmelCase_					:					Optional[Any]			     =    self.dummy_sample
        UpperCAmelCase_					:					Optional[int]			     =    0.1 * sample

        UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config()
        UpperCAmelCase_					:					Union[str, Any]			     =    scheduler_class(**lowercase_  )

        scheduler.set_timesteps(lowercase_  )

        # before power of 3 fix, would error on first step, so we only need to do two
        for i, t in enumerate(scheduler.prk_timesteps[:2]  ):
           UpperCAmelCase_					:					List[str]			     =    scheduler.step_prk(lowercase_				,   lowercase_				,   lowercase_  ).prev_sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     with self.assertRaises(lowercase_  ):
        UpperCAmelCase_					:					Any			     =    self.scheduler_classes[0]
        UpperCAmelCase_					:					Dict			     =    self.get_scheduler_config()
        UpperCAmelCase_					:					List[Any]			     =    scheduler_class(**lowercase_  )

        scheduler.step_plms(self.dummy_sample				,   1				,   self.dummy_sample  ).prev_sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.full_loop()
     UpperCAmelCase_					:					List[str]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					str			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_98.13_18  ) < 1E-2
     assert abs(result_mean.item() - 0.25_80  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    self.full_loop(prediction_type="v_prediction"  )
     UpperCAmelCase_					:					Any			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Union[str, Any]			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 67.39_86  ) < 1E-2
     assert abs(result_mean.item() - 0.08_78  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					Dict			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					Union[str, Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					int			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 2_30.03_99  ) < 1E-2
     assert abs(result_mean.item() - 0.29_95  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					Optional[int]			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					Dict			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					str			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_86.94_82  ) < 1E-2
     assert abs(result_mean.item() - 0.24_34  ) < 1E-3






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if divisor % 5 == 0 or divisor % 2 == 0:
    return 0
  lowerCamelCase :					List[Any]    =				1
  lowerCamelCase :					Union[str, Any]    =				1
  while repunit:
    lowerCamelCase :					Union[str, Any]    =				(10 * repunit + 1) % divisor
    repunit_index += 1
  return repunit_index






def 		lowercase_(  SCREAMING_SNAKE_CASE_ = 1000000	):


  '''simple docstring'''

  lowerCamelCase :					List[str]    =				limit - 1
  if divisor % 2 == 0:
    divisor += 1
  while least_divisible_repunit(SCREAMING_SNAKE_CASE_	) <= limit:
    divisor += 2
  return divisor


if __name__ == "__main__":
   print(f'''{solution() = }''')



