



"""simple docstring"""







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (DDPMScheduler,)



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      Dict       ):
        lowerCAmelCase_   :				List[str]								= {
            'num_train_timesteps': 1_0_0_0,
            'beta_start': 0.00_01,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'variance_type': 'fixed_small',
            'clip_sample': True,
        }

        config.update(**__UpperCamelCase       )
        return config



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):


        for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):


        for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]    ,			[0.0_02, 0.02, 0.2, 2]       ):
          self.check_over_configs(beta_start=__UpperCamelCase    ,			beta_end=__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):


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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):


        for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):


        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        self.check_over_configs(thresholding=__UpperCamelCase       )
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
            self.check_over_configs(
                thresholding=__UpperCamelCase    ,			prediction_type=__UpperCamelCase    ,			sample_max_value=__UpperCamelCase    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):


        for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(prediction_type=__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):


        for t in [0, 5_0_0, 9_9_9]:
          self.check_over_forward(time_step=__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Union[str, Any]								= self.scheduler_classes[0]
        lowerCAmelCase_   :				str								= self.get_scheduler_config()
        lowerCAmelCase_   :				List[Any]								= scheduler_class(**__UpperCamelCase       )

        assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_09_79       )       ) < 1E-5
        assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1E-5



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Dict								= self.scheduler_classes[0]
        lowerCAmelCase_   :				Tuple								= self.get_scheduler_config()
        lowerCAmelCase_   :				Optional[int]								= scheduler_class(**__UpperCamelCase       )

        lowerCAmelCase_   :				int								= len(__UpperCamelCase       )

        lowerCAmelCase_   :				Optional[int]								= self.dummy_model()
        lowerCAmelCase_   :				Union[str, Any]								= self.dummy_sample_deter
        lowerCAmelCase_   :				str								= torch.manual_seed(0       )

        for t in reversed(range(__UpperCamelCase       )       ):
          # 1. predict noise residual
          lowerCAmelCase_   :				Tuple								= model(__UpperCamelCase    ,			__UpperCamelCase       )

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase_   :				Dict								= scheduler.step(__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			generator=__UpperCamelCase       ).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase_   :				str								= pred_prev_sample

        lowerCAmelCase_   :				Union[str, Any]								= torch.sum(torch.abs(__UpperCamelCase       )       )
        lowerCAmelCase_   :				Dict								= torch.mean(torch.abs(__UpperCamelCase       )       )

        assert abs(result_sum.item() - 2_58.96_06       ) < 1E-2
        assert abs(result_mean.item() - 0.33_72       ) < 1E-3



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				Union[str, Any]								= self.scheduler_classes[0]
        lowerCAmelCase_   :				Tuple								= self.get_scheduler_config(prediction_type='v_prediction'       )
        lowerCAmelCase_   :				Tuple								= scheduler_class(**__UpperCamelCase       )

        lowerCAmelCase_   :				Optional[Any]								= len(__UpperCamelCase       )

        lowerCAmelCase_   :				Optional[int]								= self.dummy_model()
        lowerCAmelCase_   :				str								= self.dummy_sample_deter
        lowerCAmelCase_   :				Optional[Any]								= torch.manual_seed(0       )

        for t in reversed(range(__UpperCamelCase       )       ):
          # 1. predict noise residual
          lowerCAmelCase_   :				List[str]								= model(__UpperCamelCase    ,			__UpperCamelCase       )

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase_   :				Union[str, Any]								= scheduler.step(__UpperCamelCase    ,			__UpperCamelCase    ,			__UpperCamelCase    ,			generator=__UpperCamelCase       ).prev_sample

          # if t > 0:
          #     noise = self.dummy_sample_deter
          #     variance = scheduler.get_variance(t) ** (0.5) * noise
          #
          # sample = pred_prev_sample + variance
          lowerCAmelCase_   :				Dict								= pred_prev_sample

        lowerCAmelCase_   :				List[Any]								= torch.sum(torch.abs(__UpperCamelCase       )       )
        lowerCAmelCase_   :				Optional[int]								= torch.mean(torch.abs(__UpperCamelCase       )       )

        assert abs(result_sum.item() - 2_02.02_96       ) < 1E-2
        assert abs(result_mean.item() - 0.26_31       ) < 1E-3



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				int								= self.scheduler_classes[0]
        lowerCAmelCase_   :				Optional[Any]								= self.get_scheduler_config()
        lowerCAmelCase_   :				Any								= scheduler_class(**__UpperCamelCase       )

        lowerCAmelCase_   :				Dict								= [1_0_0, 8_7, 5_0, 1, 0]

        scheduler.set_timesteps(timesteps=__UpperCamelCase       )

        lowerCAmelCase_   :				Tuple								= scheduler.timesteps

        for i, timestep in enumerate(__UpperCamelCase       ):
          if i == len(__UpperCamelCase       ) - 1:
            lowerCAmelCase_   :				Tuple								= -1
          else:
            lowerCAmelCase_   :				Dict								= timesteps[i + 1]

          lowerCAmelCase_   :				int								= scheduler.previous_timestep(__UpperCamelCase       )
          lowerCAmelCase_   :				List[Any]								= prev_t.item()

          self.assertEqual(__UpperCamelCase    ,			__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				int								= self.scheduler_classes[0]
        lowerCAmelCase_   :				int								= self.get_scheduler_config()
        lowerCAmelCase_   :				Union[str, Any]								= scheduler_class(**__UpperCamelCase       )

        lowerCAmelCase_   :				Any								= [1_0_0, 8_7, 5_0, 5_1, 0]

        with self.assertRaises(__UpperCamelCase    ,			msg='`custom_timesteps` must be in descending order.'       ):
          scheduler.set_timesteps(timesteps=__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				Tuple								= self.scheduler_classes[0]
        lowerCAmelCase_   :				Any								= self.get_scheduler_config()
        lowerCAmelCase_   :				Optional[Any]								= scheduler_class(**__UpperCamelCase       )

        lowerCAmelCase_   :				List[str]								= [1_0_0, 8_7, 5_0, 1, 0]
        lowerCAmelCase_   :				Tuple								= len(__UpperCamelCase       )

        with self.assertRaises(__UpperCamelCase    ,			msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'       ):
          scheduler.set_timesteps(num_inference_steps=__UpperCamelCase    ,			timesteps=__UpperCamelCase       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				int								= self.scheduler_classes[0]
        lowerCAmelCase_   :				str								= self.get_scheduler_config()
        lowerCAmelCase_   :				str								= scheduler_class(**__UpperCamelCase       )

        lowerCAmelCase_   :				Any								= [scheduler.config.num_train_timesteps]

        with self.assertRaises(
            __UpperCamelCase    ,			msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'    ,			):
          scheduler.set_timesteps(timesteps=__UpperCamelCase       )






"""simple docstring"""







from __future__ import annotations

import math
from collections.abc import Callable



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Callable[[int | float], int | float]     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int = 1_00     ,			):
     lowercase_	:				Dict													=	x_start
     lowercase_	:				Optional[Any]													=	fnc(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	0.0

     for _ in range(__SCREAMING_SNAKE_CASE      ):
          # Approximates curve as a sequence of linear lines and sums their length
          lowercase_	:				Union[str, Any]													=	(x_end - x_start) / steps + xa
          lowercase_	:				str													=	fnc(__SCREAMING_SNAKE_CASE      )
          length += math.hypot(xa - xa     ,			fxa - fxa      )

          # Increment step
          lowercase_	:				List[Any]													=	xa
          lowercase_	:				Optional[Any]													=	fxa

     return length


if __name__ == "__main__":



       def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
            return math.sin(10 * x      )

       print("f(x) = sin(10 * x)")
       print("The length of the curve from x = -10 to x = 10 is:")
       __SCREAMING_SNAKE_CASE    	=10
       while i <= 10_0000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10

