"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 				a (						lowerCAmelCase_      ):
    _snake_case   :     Optional[int]   							=							(DPMSolverSinglestepScheduler,)
    _snake_case   :     Optional[Any]   							=							(('num_inference_steps', 25),)


    def    lowerCAmelCase_     (    self		:     Any      , **__lowerCAmelCase		:     Any    ):
      _UpperCAmelCase         =					{
          """num_train_timesteps""": 1000,
          """beta_start""": 0.0_001,
          """beta_end""": 0.02,
          """beta_schedule""": """linear""",
          """solver_order""": 2,
          """prediction_type""": """epsilon""",
          """thresholding""": False,
          """sample_max_value""": 1.0,
          """algorithm_type""": """dpmsolver++""",
          """solver_type""": """midpoint""",
          """lambda_min_clipped""": -float("""inf"""    ),
          """variance_type""": None,
      }

      config.update(**__lowerCAmelCase    )
      return config


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     Tuple=0      , **__lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					dict(self.forward_default_kwargs    )
      _UpperCAmelCase         =					kwargs.pop("""num_inference_steps"""      , __lowerCAmelCase    )
      _UpperCAmelCase         =					self.dummy_sample
      _UpperCAmelCase         =					0.1 * sample
      _UpperCAmelCase         =					[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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


    def    lowerCAmelCase_     (    self		:     Any    ):
      pass


    def    lowerCAmelCase_     (    self		:     List[str]      , __lowerCAmelCase		:     Tuple=0      , **__lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					dict(self.forward_default_kwargs    )
      _UpperCAmelCase         =					kwargs.pop("""num_inference_steps"""      , __lowerCAmelCase    )
      _UpperCAmelCase         =					self.dummy_sample
      _UpperCAmelCase         =					0.1 * sample
      _UpperCAmelCase         =					[residual + 0.2, residual + 0.15, residual + 0.10]

      for scheduler_class in self.scheduler_classes:
        _UpperCAmelCase         =					self.get_scheduler_config()
        _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )
        scheduler.set_timesteps(__lowerCAmelCase    )

        # 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(__lowerCAmelCase    )
          _UpperCAmelCase         =					scheduler_class.from_pretrained(__lowerCAmelCase    )
          # copy over dummy past residuals
          new_scheduler.set_timesteps(__lowerCAmelCase    )

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

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample
        _UpperCAmelCase         =					new_scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample

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


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     List[str]=None      , **__lowerCAmelCase		:     Optional[Any]    ):
      if scheduler is None:
        _UpperCAmelCase         =					self.scheduler_classes[0]
        _UpperCAmelCase         =					self.get_scheduler_config(**__lowerCAmelCase    )
        _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )

      _UpperCAmelCase         =					self.scheduler_classes[0]
      _UpperCAmelCase         =					self.get_scheduler_config(**__lowerCAmelCase    )
      _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )

      _UpperCAmelCase         =					10
      _UpperCAmelCase         =					self.dummy_model()
      _UpperCAmelCase         =					self.dummy_sample_deter
      scheduler.set_timesteps(__lowerCAmelCase    )

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

      return sample


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
      _UpperCAmelCase         =					50
      _UpperCAmelCase         =					self.dummy_model()
      _UpperCAmelCase         =					self.dummy_sample_deter
      scheduler.set_timesteps(__lowerCAmelCase    )

      # make sure that the first t is uneven
      for i, t in enumerate(scheduler.timesteps[3:]    ):
        _UpperCAmelCase         =					model(__lowerCAmelCase      , __lowerCAmelCase    )
        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    ).prev_sample

      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      assert abs(result_mean.item() - 0.2_574    ) < 1e-3


    def    lowerCAmelCase_     (    self		:     Dict    ):
      for timesteps in [25, 50, 100, 999, 1000]:
        self.check_over_configs(num_train_timesteps=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      # make sure that iterating over schedulers with same config names gives same results
      # for defaults
      _UpperCAmelCase         =					DPMSolverSinglestepScheduler(**self.get_scheduler_config()    )
      _UpperCAmelCase         =					self.full_loop(scheduler=__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      assert abs(result_mean.item() - 0.2_791    ) < 1e-3

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

      _UpperCAmelCase         =					self.full_loop(scheduler=__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      assert abs(result_mean.item() - 0.2_791    ) < 1e-3


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      self.check_over_configs(thresholding=__lowerCAmelCase    )
      for order in [1, 2, 3]:
        for solver_type in ["midpoint", "heun"]:
          for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  thresholding=__lowerCAmelCase      , prediction_type=__lowerCAmelCase      , sample_max_value=__lowerCAmelCase      , algorithm_type="""dpmsolver++"""      , solver_order=__lowerCAmelCase      , solver_type=__lowerCAmelCase      , )


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Any    ):
      for algorithm_type in ["dpmsolver", "dpmsolver++"]:
        for solver_type in ["midpoint", "heun"]:
          for order in [1, 2, 3]:
            for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(
                  solver_order=__lowerCAmelCase      , solver_type=__lowerCAmelCase      , prediction_type=__lowerCAmelCase      , algorithm_type=__lowerCAmelCase      , )
              _UpperCAmelCase         =					self.full_loop(
                  solver_order=__lowerCAmelCase      , solver_type=__lowerCAmelCase      , prediction_type=__lowerCAmelCase      , algorithm_type=__lowerCAmelCase      , )
              assert not torch.isnan(__lowerCAmelCase    ).any(), "Samples have nan numbers"


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      self.check_over_configs(lower_order_final=__lowerCAmelCase    )
      self.check_over_configs(lower_order_final=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     str    ):
      self.check_over_configs(lambda_min_clipped=-float("""inf"""    )    )
      self.check_over_configs(lambda_min_clipped=-5.1    )


    def    lowerCAmelCase_     (    self		:     int    ):
      self.check_over_configs(variance_type=__lowerCAmelCase    )
      self.check_over_configs(variance_type="""learned_range"""    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
        self.check_over_forward(num_inference_steps=__lowerCAmelCase      , time_step=0    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					self.full_loop()
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      assert abs(result_mean.item() - 0.2_791    ) < 1e-3


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					self.full_loop(use_karras_sigmas=__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      assert abs(result_mean.item() - 0.2_248    ) < 1e-3


    def    lowerCAmelCase_     (    self		:     int    ):
      _UpperCAmelCase         =					self.full_loop(prediction_type="""v_prediction"""    )
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      assert abs(result_mean.item() - 0.1_453    ) < 1e-3


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.full_loop(prediction_type="""v_prediction"""      , use_karras_sigmas=__lowerCAmelCase    )
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      assert abs(result_mean.item() - 0.0_649    ) < 1e-3


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.scheduler_classes[0]
      _UpperCAmelCase         =					self.get_scheduler_config(thresholding=__lowerCAmelCase      , dynamic_thresholding_ratio=0    )
      _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )

      _UpperCAmelCase         =					10
      _UpperCAmelCase         =					self.dummy_model()
      _UpperCAmelCase         =					self.dummy_sample_deter.half()
      scheduler.set_timesteps(__lowerCAmelCase    )

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

      assert sample.dtype == torch.floataa

"""simple docstring"""


import math


class 				a :


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     list[list[float]]      , __lowerCAmelCase		:     list[int]    ):
      _UpperCAmelCase         =					0.0
      _UpperCAmelCase         =					0.0
      for i in range(len(__lowerCAmelCase    )    ):
        da += math.pow((sample[i] - weights[0][i])      , 2    )
        da += math.pow((sample[i] - weights[1][i])      , 2    )
        return 0 if da > da else 1
      return 0


    def    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     list[list[int | float]]      , __lowerCAmelCase		:     list[int]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     float    ):
      for i in range(len(__lowerCAmelCase    )    ):
        weights[j][i] += alpha * (sample[i] - weights[j][i])
      return weights






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  # Training Examples ( m, n )
  _UpperCAmelCase         =					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _UpperCAmelCase         =					[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _UpperCAmelCase         =					SelfOrganizingMap()
  _UpperCAmelCase         =					3
  _UpperCAmelCase         =					0.5

  for _ in range(lowercase						):
    for j in range(len(lowercase						)						):
      # training sample
      _UpperCAmelCase         =					training_samples[j]

      # Compute the winning vector
      _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

      # Update the winning vector
      _UpperCAmelCase         =					self_organizing_map.update(lowercase      ,lowercase      ,lowercase      ,lowercase						)

    # classify test sample
  _UpperCAmelCase         =					[0, 0, 0, 1]
  _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''						)
  print(f'''Weights that have been trained : {weights}'''						)


# running the main() function
if __name__ == "__main__":
    main()
