
"""simple docstring"""

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
  __SCREAMING_SNAKE_CASE   :							Optional[Any]							      =	(UniPCMultistepScheduler,)
  __SCREAMING_SNAKE_CASE   :							Optional[Any]							      =	(("num_inference_steps", 2_5),)




  def     a_      (							self   ,       **lowercase_							)						->					Dict:
      UpperCAmelCase									=					{
          '''num_train_timesteps''': 1_0_0_0,
          '''beta_start''': 0.0_0_0_1,
          '''beta_end''': 0.0_2,
          '''beta_schedule''': '''linear''',
          '''solver_order''': 2,
          '''solver_type''': '''bh2''',
      }

      config.update(**UpperCamelCase__							)
      return config




  def     a_      (							self   ,       lowercase_=0   ,       **lowercase_							)						->					Tuple:
      UpperCAmelCase									=					dict(self.forward_default_kwargs							)
      UpperCAmelCase									=					kwargs.pop('num_inference_steps'   ,       UpperCamelCase__							)
      UpperCAmelCase									=					self.dummy_sample
      UpperCAmelCase									=					0.1 * sample
      UpperCAmelCase									=					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          UpperCAmelCase									=					self.get_scheduler_config(**UpperCamelCase__							)
          UpperCAmelCase									=					scheduler_class(**UpperCamelCase__							)
          scheduler.set_timesteps(UpperCamelCase__							)
          # copy over dummy past residuals
          UpperCAmelCase									=					dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(UpperCamelCase__							)
              UpperCAmelCase									=					scheduler_class.from_pretrained(UpperCamelCase__							)
              new_scheduler.set_timesteps(UpperCamelCase__							)
              # copy over dummy past residuals
              UpperCAmelCase									=					dummy_past_residuals[: new_scheduler.config.solver_order]

          UpperCAmelCase									=					sample, sample
          for t in range(UpperCamelCase__   ,       time_step + scheduler.config.solver_order + 1							):
              UpperCAmelCase									=					scheduler.step(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__   ,       **UpperCamelCase__							).prev_sample
              UpperCAmelCase									=					new_scheduler.step(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__   ,       **UpperCamelCase__							).prev_sample

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




  def     a_      (							self   ,       lowercase_=0   ,       **lowercase_							)						->					Optional[Any]:
      UpperCAmelCase									=					dict(self.forward_default_kwargs							)
      UpperCAmelCase									=					kwargs.pop('num_inference_steps'   ,       UpperCamelCase__							)
      UpperCAmelCase									=					self.dummy_sample
      UpperCAmelCase									=					0.1 * sample
      UpperCAmelCase									=					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**UpperCamelCase__							)
          scheduler.set_timesteps(UpperCamelCase__							)

          # copy over dummy past residuals (must be after setting timesteps)
          UpperCAmelCase									=					dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(UpperCamelCase__							)
              UpperCAmelCase									=					scheduler_class.from_pretrained(UpperCamelCase__							)
              # copy over dummy past residuals
              new_scheduler.set_timesteps(UpperCamelCase__							)

              # copy over dummy past residual (must be after setting timesteps)
              UpperCAmelCase									=					dummy_past_residuals[: new_scheduler.config.solver_order]

          UpperCAmelCase									=					scheduler.step(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__   ,       **UpperCamelCase__							).prev_sample
          UpperCAmelCase									=					new_scheduler.step(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__   ,       **UpperCamelCase__							).prev_sample

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




  def     a_      (							self   ,       lowercase_=None   ,       **lowercase_							)						->					List[Any]:
      if scheduler is None:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(**UpperCamelCase__							)
          UpperCAmelCase									=					scheduler_class(**UpperCamelCase__							)

      UpperCAmelCase									=					self.scheduler_classes[0]
      UpperCAmelCase									=					self.get_scheduler_config(**UpperCamelCase__							)
      UpperCAmelCase									=					scheduler_class(**UpperCamelCase__							)

      UpperCAmelCase									=					1_0
      UpperCAmelCase									=					self.dummy_model()
      UpperCAmelCase									=					self.dummy_sample_deter
      scheduler.set_timesteps(UpperCamelCase__							)

      for i, t in enumerate(scheduler.timesteps							):
          UpperCAmelCase									=					model(UpperCamelCase__   ,       UpperCamelCase__							)
          UpperCAmelCase									=					scheduler.step(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__							).prev_sample

      return sample




  def     a_      (							self							)						->					List[str]:
      UpperCAmelCase									=					dict(self.forward_default_kwargs							)

      UpperCAmelCase									=					kwargs.pop('num_inference_steps'   ,       UpperCamelCase__							)

      for scheduler_class in self.scheduler_classes:
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**UpperCamelCase__							)

          UpperCAmelCase									=					self.dummy_sample
          UpperCAmelCase									=					0.1 * sample

          if num_inference_steps is not None and hasattr(UpperCamelCase__   ,       'set_timesteps'							):
              scheduler.set_timesteps(UpperCamelCase__							)
          elif num_inference_steps is not None and not hasattr(UpperCamelCase__   ,       'set_timesteps'							):
              UpperCAmelCase									=					num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          UpperCAmelCase									=					[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
          UpperCAmelCase									=					dummy_past_residuals[: scheduler.config.solver_order]

          UpperCAmelCase									=					scheduler.timesteps[5]
          UpperCAmelCase									=					scheduler.timesteps[6]

          UpperCAmelCase									=					scheduler.step(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__   ,       **UpperCamelCase__							).prev_sample
          UpperCAmelCase									=					scheduler.step(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__   ,       **UpperCamelCase__							).prev_sample

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




  def     a_      (							self							)						->					Any:
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      UpperCAmelCase									=					UniPCMultistepScheduler(**self.get_scheduler_config()							)
      UpperCAmelCase									=					self.full_loop(scheduler=UpperCamelCase__							)
      UpperCAmelCase									=					torch.mean(torch.abs(UpperCamelCase__							)							)

      assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3

      UpperCAmelCase									=					DPMSolverSinglestepScheduler.from_config(scheduler.config							)
      UpperCAmelCase									=					DEISMultistepScheduler.from_config(scheduler.config							)
      UpperCAmelCase									=					DPMSolverMultistepScheduler.from_config(scheduler.config							)
      UpperCAmelCase									=					UniPCMultistepScheduler.from_config(scheduler.config							)

      UpperCAmelCase									=					self.full_loop(scheduler=UpperCamelCase__							)
      UpperCAmelCase									=					torch.mean(torch.abs(UpperCamelCase__							)							)

      assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3




  def     a_      (							self							)						->					List[str]:
      for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=UpperCamelCase__							)




  def     a_      (							self							)						->					Dict:
      self.check_over_configs(thresholding=UpperCamelCase__							)
      for order in [1, 2, 3]:
          for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          thresholding=UpperCamelCase__   ,       prediction_type=UpperCamelCase__   ,       sample_max_value=UpperCamelCase__   ,       solver_order=UpperCamelCase__   ,       solver_type=UpperCamelCase__   ,       )




  def     a_      (							self							)						->					List[Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=UpperCamelCase__							)




  def     a_      (							self							)						->					Dict:
      for solver_type in ["bh1", "bh2"]:
          for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      solver_order=UpperCamelCase__   ,       solver_type=UpperCamelCase__   ,       prediction_type=UpperCamelCase__   ,       )
                  UpperCAmelCase									=					self.full_loop(
                      solver_order=UpperCamelCase__   ,       solver_type=UpperCamelCase__   ,       prediction_type=UpperCamelCase__   ,       )
                  assert not torch.isnan(UpperCamelCase__							).any(), "Samples have nan numbers"




  def     a_      (							self							)						->					List[Any]:
      self.check_over_configs(lower_order_final=UpperCamelCase__							)
      self.check_over_configs(lower_order_final=UpperCamelCase__							)




  def     a_      (							self							)						->					Dict:
      for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
          self.check_over_forward(num_inference_steps=UpperCamelCase__   ,       time_step=0							)




  def     a_      (							self							)						->					List[Any]:
      UpperCAmelCase									=					self.full_loop()
      UpperCAmelCase									=					torch.mean(torch.abs(UpperCamelCase__							)							)

      assert abs(result_mean.item() - 0.2_4_6_4							) < 1E-3




  def     a_      (							self							)						->					Optional[int]:
      UpperCAmelCase									=					self.full_loop(prediction_type='v_prediction'							)
      UpperCAmelCase									=					torch.mean(torch.abs(UpperCamelCase__							)							)

      assert abs(result_mean.item() - 0.1_0_1_4							) < 1E-3




  def     a_      (							self							)						->					Any:
      UpperCAmelCase									=					self.scheduler_classes[0]
      UpperCAmelCase									=					self.get_scheduler_config(thresholding=UpperCamelCase__   ,       dynamic_thresholding_ratio=0							)
      UpperCAmelCase									=					scheduler_class(**UpperCamelCase__							)

      UpperCAmelCase									=					1_0
      UpperCAmelCase									=					self.dummy_model()
      UpperCAmelCase									=					self.dummy_sample_deter.half()
      scheduler.set_timesteps(UpperCamelCase__							)

      for i, t in enumerate(scheduler.timesteps							):
          UpperCAmelCase									=					model(UpperCamelCase__   ,       UpperCamelCase__							)
          UpperCAmelCase									=					scheduler.step(UpperCamelCase__   ,       UpperCamelCase__   ,       UpperCamelCase__							).prev_sample

      assert sample.dtype == torch.floataa




  def     a_      (							self   ,       **lowercase_							)						->					Optional[Any]:
      for scheduler_class in self.scheduler_classes:
          UpperCAmelCase									=					self.get_scheduler_config(**UpperCamelCase__							)
          UpperCAmelCase									=					scheduler_class(**UpperCamelCase__							)

          scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
          assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps





"""simple docstring"""

import os
from collections.abc import Iterator




def     lowercase__						(					lowerCAmelCase				:					str = "." ) ->      Iterator[str]:






    """simple docstring"""




    for dir_path, dir_names, filenames in os.walk(lowerCAmelCase ):
        UpperCAmelCase									=					[d for d in dir_names if d != 'scripts' and d[0] not in '._']
        for filename in filenames:
            if filename == "__init__.py":
                continue
            if os.path.splitext(lowerCAmelCase )[1] in (".py", ".ipynb"):
                yield os.path.join(lowerCAmelCase ,				lowerCAmelCase ).lstrip('./' )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      Any:






    """simple docstring"""




    return F"{i * '  '}*" if i else "\n##"




def     lowercase__						(					lowerCAmelCase				:					str ,				lowerCAmelCase				:					str ) ->      str:






    """simple docstring"""




    UpperCAmelCase									=					old_path.split(os.sep )
    for i, new_part in enumerate(new_path.split(os.sep ) ):
        if (i + 1 > len(lowerCAmelCase ) or old_parts[i] != new_part) and new_part:
            print(F"{md_prefix(lowerCAmelCase )} {new_part.replace('_' ,				' ' ).title()}" )
    return new_path




def     lowercase__						(					lowerCAmelCase				:					str = "." ) ->      None:






    """simple docstring"""




    UpperCAmelCase									=					''
    for filepath in sorted(good_file_paths(lowerCAmelCase ) ):
        UpperCAmelCase			,							UpperCAmelCase									=					os.path.split(lowerCAmelCase )
        if filepath != old_path:
            UpperCAmelCase									=					print_path(lowerCAmelCase ,				lowerCAmelCase )
        UpperCAmelCase									=					(filepath.count(os.sep ) + 1) if filepath else 0
        UpperCAmelCase									=					F"{filepath}/{filename}".replace(' ' ,				'%20' )
        UpperCAmelCase									=					os.path.splitext(filename.replace('_' ,				' ' ).title() )[0]
        print(F"{md_prefix(lowerCAmelCase )} [{filename}]({url})" )


if __name__ == "__main__":
   print_directory_md('''.''')



