





"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class      __SCREAMING_SNAKE_CASE    (						UpperCamelCase_   ):
  SCREAMING_SNAKE_CASE__							:Any	     =					(DEISMultistepScheduler,)
  SCREAMING_SNAKE_CASE__							:int	     =					(("num_inference_steps", 25),)

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   **__a   :       List[Any]	)		->				List[str]:
        _UpperCamelCase      :	Dict						=       {
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.00_01,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
        }

        config.update(**__a	)
        return config

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       Dict=0					,   **__a   :       Any	)		->				Optional[Any]:
        _UpperCamelCase      :	str						=       dict(self.forward_default_kwargs	)
        _UpperCamelCase      :	Optional[int]						=       kwargs.pop("num_inference_steps"					,   __a	)
        _UpperCamelCase      :	str						=       self.dummy_sample
        _UpperCamelCase      :	str						=       0.1 * sample
        _UpperCamelCase      :	List[str]						=       [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
              _UpperCamelCase      :	str						=       self.get_scheduler_config(**__a	)
              _UpperCamelCase      :	Union[str, Any]						=       scheduler_class(**__a	)
              scheduler.set_timesteps(__a	)
              # copy over dummy past residuals
              _UpperCamelCase      :	Optional[Any]						=       dummy_past_residuals[: scheduler.config.solver_order]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(__a	)
                    _UpperCamelCase      :	Optional[int]						=       scheduler_class.from_pretrained(__a	)
                    new_scheduler.set_timesteps(__a	)
                    # copy over dummy past residuals
                    _UpperCamelCase      :	Tuple						=       dummy_past_residuals[: new_scheduler.config.solver_order]

              _UpperCamelCase      :	List[Any]						=       sample, sample
              for t in range(__a					,   time_step + scheduler.config.solver_order + 1	):
                    _UpperCamelCase      :	Optional[int]						=       scheduler.step(__a					,   __a					,   __a					,   **__a	).prev_sample
                    _UpperCamelCase      :	str						=       new_scheduler.step(__a					,   __a					,   __a					,   **__a	).prev_sample

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				int:
        pass

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       Dict=0					,   **__a   :       Dict	)		->				List[str]:
        _UpperCamelCase      :	str						=       dict(self.forward_default_kwargs	)
        _UpperCamelCase      :	List[str]						=       kwargs.pop("num_inference_steps"					,   __a	)
        _UpperCamelCase      :	Union[str, Any]						=       self.dummy_sample
        _UpperCamelCase      :	int						=       0.1 * sample
        _UpperCamelCase      :	int						=       [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
              _UpperCamelCase      :	Tuple						=       self.get_scheduler_config()
              _UpperCamelCase      :	Union[str, Any]						=       scheduler_class(**__a	)
              scheduler.set_timesteps(__a	)

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

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

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

              _UpperCamelCase      :	Tuple						=       scheduler.step(__a					,   __a					,   __a					,   **__a	).prev_sample
              _UpperCamelCase      :	Union[str, Any]						=       new_scheduler.step(__a					,   __a					,   __a					,   **__a	).prev_sample

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       List[str]=None					,   **__a   :       List[Any]	)		->				Any:
        if scheduler is None:
              _UpperCamelCase      :	Dict						=       self.scheduler_classes[0]
              _UpperCamelCase      :	Optional[Any]						=       self.get_scheduler_config(**__a	)
              _UpperCamelCase      :	int						=       scheduler_class(**__a	)

        _UpperCamelCase      :	Optional[int]						=       self.scheduler_classes[0]
        _UpperCamelCase      :	str						=       self.get_scheduler_config(**__a	)
        _UpperCamelCase      :	Any						=       scheduler_class(**__a	)

        _UpperCamelCase      :	Dict						=       10
        _UpperCamelCase      :	Optional[int]						=       self.dummy_model()
        _UpperCamelCase      :	Any						=       self.dummy_sample_deter
        scheduler.set_timesteps(__a	)

        for i, t in enumerate(scheduler.timesteps	):
              _UpperCamelCase      :	List[str]						=       model(__a					,   __a	)
              _UpperCamelCase      :	Any						=       scheduler.step(__a					,   __a					,   __a	).prev_sample

        return sample

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				List[str]:
        _UpperCamelCase      :	List[Any]						=       dict(self.forward_default_kwargs	)

        _UpperCamelCase      :	Dict						=       kwargs.pop("num_inference_steps"					,   __a	)

        for scheduler_class in self.scheduler_classes:
              _UpperCamelCase      :	Optional[Any]						=       self.get_scheduler_config()
              _UpperCamelCase      :	List[Any]						=       scheduler_class(**__a	)

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

              if num_inference_steps is not None and hasattr(__a					,   "set_timesteps"	):
                    scheduler.set_timesteps(__a	)
              elif num_inference_steps is not None and not hasattr(__a					,   "set_timesteps"	):
                    _UpperCamelCase      :	List[str]						=       num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              _UpperCamelCase      :	Tuple						=       [residual + 0.2, residual + 0.15, residual + 0.10]
              _UpperCamelCase      :	List[str]						=       dummy_past_residuals[: scheduler.config.solver_order]

              _UpperCamelCase      :	Dict						=       scheduler.timesteps[5]
              _UpperCamelCase      :	List[str]						=       scheduler.timesteps[6]

              _UpperCamelCase      :	List[str]						=       scheduler.step(__a					,   __a					,   __a					,   **__a	).prev_sample
              _UpperCamelCase      :	Optional[int]						=       scheduler.step(__a					,   __a					,   __a					,   **__a	).prev_sample

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				List[Any]:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        _UpperCamelCase      :	str						=       DEISMultistepScheduler(**self.get_scheduler_config()	)
        _UpperCamelCase      :	List[str]						=       self.full_loop(scheduler=__a	)
        _UpperCamelCase      :	Tuple						=       torch.mean(torch.abs(__a	)	)

        assert abs(result_mean.item() - 0.2_39_16	) < 1e-3

        _UpperCamelCase      :	int						=       DPMSolverSinglestepScheduler.from_config(scheduler.config	)
        _UpperCamelCase      :	int						=       DPMSolverMultistepScheduler.from_config(scheduler.config	)
        _UpperCamelCase      :	str						=       UniPCMultistepScheduler.from_config(scheduler.config	)
        _UpperCamelCase      :	str						=       DEISMultistepScheduler.from_config(scheduler.config	)

        _UpperCamelCase      :	Optional[int]						=       self.full_loop(scheduler=__a	)
        _UpperCamelCase      :	Tuple						=       torch.mean(torch.abs(__a	)	)

        assert abs(result_mean.item() - 0.2_39_16	) < 1e-3

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Union[str, Any]:
        for timesteps in [25, 50, 100, 999, 1000]:
              self.check_over_configs(num_train_timesteps=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				List[str]:
        self.check_over_configs(thresholding=__a	)
        for order in [1, 2, 3]:
              for solver_type in ["logrho"]:
                    for threshold in [0.5, 1.0, 2.0]:
                          for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    thresholding=__a					,   prediction_type=__a					,   sample_max_value=__a					,   algorithm_type="deis"					,   solver_order=__a					,   solver_type=__a					,   )

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Optional[int]:
        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Optional[Any]:
        for algorithm_type in ["deis"]:
              for solver_type in ["logrho"]:
                    for order in [1, 2, 3]:
                          for prediction_type in ["epsilon", "sample"]:
                                self.check_over_configs(
                                    solver_order=__a					,   solver_type=__a					,   prediction_type=__a					,   algorithm_type=__a					,   )
                                _UpperCamelCase      :	List[Any]						=       self.full_loop(
                                    solver_order=__a					,   solver_type=__a					,   prediction_type=__a					,   algorithm_type=__a					,   )
                                assert not torch.isnan(__a	).any(), "Samples have nan numbers"

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict:
        self.check_over_configs(lower_order_final=__a	)
        self.check_over_configs(lower_order_final=__a	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				int:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
              self.check_over_forward(num_inference_steps=__a					,   time_step=0	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				Tuple:
        _UpperCamelCase      :	List[Any]						=       self.full_loop()
        _UpperCamelCase      :	Optional[Any]						=       torch.mean(torch.abs(__a	)	)

        assert abs(result_mean.item() - 0.2_39_16	) < 1e-3

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				List[Any]:
        _UpperCamelCase      :	List[Any]						=       self.full_loop(prediction_type="v_prediction"	)
        _UpperCamelCase      :	str						=       torch.mean(torch.abs(__a	)	)

        assert abs(result_mean.item() - 0.0_91	) < 1e-3


  def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				List[str]:
        _UpperCamelCase      :	Union[str, Any]						=       self.scheduler_classes[0]
        _UpperCamelCase      :	Tuple						=       self.get_scheduler_config(thresholding=__a					,   dynamic_thresholding_ratio=0	)
        _UpperCamelCase      :	str						=       scheduler_class(**__a	)

        _UpperCamelCase      :	List[Any]						=       10
        _UpperCamelCase      :	str						=       self.dummy_model()
        _UpperCamelCase      :	List[str]						=       self.dummy_sample_deter.half()
        scheduler.set_timesteps(__a	)

        for i, t in enumerate(scheduler.timesteps	):
              _UpperCamelCase      :	int						=       model(__a					,   __a	)
              _UpperCamelCase      :	Dict						=       scheduler.step(__a					,   __a					,   __a	).prev_sample

        assert sample.dtype == torch.floataa







"""simple docstring"""


import math



class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       list[list[float]]					,   __a   :       list[int]	)		->				int:
        _UpperCamelCase      :	List[Any]						=       0.0
        _UpperCamelCase      :	Union[str, Any]						=       0.0
        for i in range(len(__a	)	):
              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    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       list[list[int | float]]					,   __a   :       list[int]					,   __a   :       int					,   __a   :       float	)		->				list[list[int | float]]:
        for i in range(len(__a	)	):
              weights[j][i] += alpha * (sample[i] - weights[j][i])
        return weights





def 					lowercase__    (					)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	Optional[int]						=       [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

      # weight initialization ( n, C )
      _UpperCamelCase      :	List[str]						=       [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

      # training
      _UpperCamelCase      :	List[Any]						=       SelfOrganizingMap()
      _UpperCamelCase      :	int						=       3
      _UpperCamelCase      :	List[Any]						=       0.5

      for _ in range(lowercase_			):
            for j in range(len(lowercase_			)			):
                  # training sample
                  _UpperCamelCase      :	int						=       training_samples[j]

                  # Compute the winning vector
                  _UpperCamelCase      :	Tuple						=       self_organizing_map.get_winner(lowercase_		,lowercase_			)

                  # Update the winning vector
                  _UpperCamelCase      :	int						=       self_organizing_map.update(lowercase_		,lowercase_		,lowercase_		,lowercase_			)

    # classify test sample
      _UpperCamelCase      :	Optional[int]						=       [0, 0, 0, 1]
      _UpperCamelCase      :	Dict						=       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()
