
'''simple docstring'''





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			(DPMSolverMultistepScheduler,)
  _UpperCamelCase							=			(('num_inference_steps', 25),)

  def 		_lowerCamelCase						(self	:			Any						,			**lowerCAmelCase_	:			Union[str, Any]     ) ->							str:
        snake_case_    							=				{
            "num_train_timesteps": 10_00,
            "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",
            "lower_order_final": False,
            "lambda_min_clipped": -float("inf"     ),
            "variance_type": None,
        }

        config.update(**lowerCAmelCase_     )
        return config

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

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

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

              snake_case_							,						snake_case_    							=				sample, sample
              for t in range(lowerCAmelCase_						,			time_step + scheduler.config.solver_order + 1     ):
                    snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample
                    snake_case_    							=				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	:			List[Any]     ) ->							List[str]:
        pass

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			Union[str, Any]=0						,			**lowerCAmelCase_	:			Dict     ) ->							Optional[Any]:
        snake_case_    							=				dict(self.forward_default_kwargs     )
        snake_case_    							=				kwargs.pop("num_inference_steps"						,			lowerCAmelCase_     )
        snake_case_    							=				self.dummy_sample
        snake_case_    							=				0.1 * sample
        snake_case_    							=				[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
              snake_case_    							=				self.get_scheduler_config()
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )
              scheduler.set_timesteps(lowerCAmelCase_     )

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(lowerCAmelCase_     )
                    snake_case_    							=				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)
                    snake_case_    							=				dummy_past_residuals[: new_scheduler.config.solver_order]

              snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample
              snake_case_    							=				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	:			str						,			lowerCAmelCase_	:			int=None						,			**lowerCAmelCase_	:			Optional[Any]     ) ->							Dict:
        if scheduler is None:
              snake_case_    							=				self.scheduler_classes[0]
              snake_case_    							=				self.get_scheduler_config(**lowerCAmelCase_     )
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )

        snake_case_    							=				10
        snake_case_    							=				self.dummy_model()
        snake_case_    							=				self.dummy_sample_deter
        scheduler.set_timesteps(lowerCAmelCase_     )

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

        return sample

  def 		_lowerCamelCase						(self	:			Dict     ) ->							List[str]:
        snake_case_    							=				dict(self.forward_default_kwargs     )

        snake_case_    							=				kwargs.pop("num_inference_steps"						,			lowerCAmelCase_     )

        for scheduler_class in self.scheduler_classes:
              snake_case_    							=				self.get_scheduler_config()
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )

              snake_case_    							=				self.dummy_sample
              snake_case_    							=				0.1 * sample

              if num_inference_steps is not None and hasattr(lowerCAmelCase_						,			"set_timesteps"     ):
                    scheduler.set_timesteps(lowerCAmelCase_     )
              elif num_inference_steps is not None and not hasattr(lowerCAmelCase_						,			"set_timesteps"     ):
                    snake_case_    							=				num_inference_steps

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

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

              snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample
              snake_case_    							=				scheduler.step(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			**lowerCAmelCase_     ).prev_sample

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

  def 		_lowerCamelCase						(self	:			Dict     ) ->							List[Any]:
        for timesteps in [25, 50, 1_00, 9_99, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Union[str, Any]:
        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	:			List[Any]     ) ->							Tuple:
        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Dict:
        for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
              for solver_type in ["midpoint", "heun"]:
                    for order in [1, 2, 3]:
                          for prediction_type in ["epsilon", "sample"]:
                                if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                      if order == 3:
                                            continue
                                else:
                                      self.check_over_configs(
                                          solver_order=lowerCAmelCase_						,			solver_type=lowerCAmelCase_						,			prediction_type=lowerCAmelCase_						,			algorithm_type=lowerCAmelCase_						,			)
                                snake_case_    							=				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	:			Optional[int]     ) ->							Optional[Any]:
        self.check_over_configs(lower_order_final=lowerCAmelCase_     )
        self.check_over_configs(lower_order_final=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        self.check_over_configs(lambda_min_clipped=-float("inf"     )     )
        self.check_over_configs(lambda_min_clipped=-5.1     )

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Union[str, Any]:
        self.check_over_configs(variance_type=lowerCAmelCase_     )
        self.check_over_configs(variance_type="learned_range"     )

  def 		_lowerCamelCase						(self	:			str     ) ->							List[Any]:
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
              self.check_over_forward(num_inference_steps=lowerCAmelCase_						,			time_step=0     )

  def 		_lowerCamelCase						(self	:			Dict     ) ->							Union[str, Any]:
        snake_case_    							=				self.full_loop()
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.3_301     ) < 1E-3

  def 		_lowerCamelCase						(self	:			List[Any]     ) ->							List[str]:
        snake_case_    							=				self.full_loop(thresholding=lowerCAmelCase_						,			dynamic_thresholding_ratio=0.87						,			sample_max_value=0.5     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 1.1_364     ) < 1E-3

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Union[str, Any]:
        snake_case_    							=				self.full_loop(prediction_type="v_prediction"     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.2_251     ) < 1E-3

  def 		_lowerCamelCase						(self	:			str     ) ->							List[Any]:
        snake_case_    							=				self.full_loop(prediction_type="v_prediction"						,			use_karras_sigmas=lowerCAmelCase_     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.2_096     ) < 1E-3

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Tuple:
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        snake_case_    							=				DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
        snake_case_    							=				self.full_loop(scheduler=lowerCAmelCase_     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.3_301     ) < 1E-3

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

        snake_case_    							=				self.full_loop(scheduler=lowerCAmelCase_     )
        snake_case_    							=				torch.mean(torch.abs(lowerCAmelCase_     )     )

        assert abs(result_mean.item() - 0.3_301     ) < 1E-3

  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Tuple:
        snake_case_    							=				self.scheduler_classes[0]
        snake_case_    							=				self.get_scheduler_config(thresholding=lowerCAmelCase_						,			dynamic_thresholding_ratio=0     )
        snake_case_    							=				scheduler_class(**lowerCAmelCase_     )

        snake_case_    							=				10
        snake_case_    							=				self.dummy_model()
        snake_case_    							=				self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowerCAmelCase_     )

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

        assert sample.dtype == torch.floataa


  def 		_lowerCamelCase						(self	:			Any						,			**lowerCAmelCase_	:			Union[str, Any]     ) ->							Dict:
        for scheduler_class in self.scheduler_classes:
              snake_case_    							=				self.get_scheduler_config(**lowerCAmelCase_     )
              snake_case_    							=				scheduler_class(**lowerCAmelCase_     )

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



'''simple docstring'''





from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap



@total_ordering
class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			int     ) ->							None:
        snake_case_    							=				word
        snake_case_    							=				count

  def __eq__(self	:			List[Any]						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count == other.count


  def __lt__(self	:			Tuple						,			lowerCAmelCase_	:			object     ) ->							bool:
        if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return NotImplemented
        return self.count < other.count

def 	snake_case_							(				_a  :				list[str]			,       _a  :				int      ):
      snake_case_    							=				Heap()
      snake_case_    							=				Counter(_a      )
      heap.build_max_heap(
          [WordCount(_a			,       _a      ) for word, count in count_by_word.items()]      )
      return [heap.extract_max().word for _ in range(min(_a			,       len(_a      )      )      )]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

