





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	List[str]				      = (PNDMScheduler,)
   _UpperCamelCase					:	Optional[int]				      = (('num_inference_steps', 50),)


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  **a    :       Any )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						{
         'num_train_timesteps': 1_000,
         'beta_start': 0.0001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
     }

     config.update(**a )
     return config


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  a    :       List[Any]=0    ,  **a    :       int )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						dict(self.forward_default_kwargs )
     lowercase__			    =						kwargs.pop('num_inference_steps'    ,  a )
     lowercase__			    =						self.dummy_sample
     lowercase__			    =						0.1 * sample
     lowercase__			    =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
       lowercase__			    =						self.get_scheduler_config(**a )
       lowercase__			    =						scheduler_class(**a )
       scheduler.set_timesteps(a )
       # copy over dummy past residuals
       lowercase__			    =						dummy_past_residuals[:]

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

       lowercase__			    =						scheduler.step_prk(a    ,  a    ,  a    ,  **a ).prev_sample
       lowercase__			    =						new_scheduler.step_prk(a    ,  a    ,  a    ,  **a ).prev_sample

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

       lowercase__			    =						scheduler.step_plms(a    ,  a    ,  a    ,  **a ).prev_sample
       lowercase__			    =						new_scheduler.step_plms(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] )->     Optional[Any]:





     """simple docstring"""







     pass


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  a    :       List[Any]=0    ,  **a    :       List[Any] )->     str:





     """simple docstring"""







     lowercase__			    =						dict(self.forward_default_kwargs )
     lowercase__			    =						kwargs.pop('num_inference_steps'    ,  a )
     lowercase__			    =						self.dummy_sample
     lowercase__			    =						0.1 * sample
     lowercase__			    =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

     for scheduler_class in self.scheduler_classes:
       lowercase__			    =						self.get_scheduler_config()
       lowercase__			    =						scheduler_class(**a )
       scheduler.set_timesteps(a )

       # copy over dummy past residuals (must be after setting timesteps)
       lowercase__			    =						dummy_past_residuals[:]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(a )
         lowercase__			    =						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)
         lowercase__			    =						dummy_past_residuals[:]

       lowercase__			    =						scheduler.step_prk(a    ,  a    ,  a    ,  **a ).prev_sample
       lowercase__			    =						new_scheduler.step_prk(a    ,  a    ,  a    ,  **a ).prev_sample

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

       lowercase__			    =						scheduler.step_plms(a    ,  a    ,  a    ,  **a ).prev_sample
       lowercase__			    =						new_scheduler.step_plms(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[Any]    ,  **a    :       Dict )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.scheduler_classes[0]
     lowercase__			    =						self.get_scheduler_config(**a )
     lowercase__			    =						scheduler_class(**a )

     lowercase__			    =						10
     lowercase__			    =						self.dummy_model()
     lowercase__			    =						self.dummy_sample_deter
     scheduler.set_timesteps(a )

     for i, t in enumerate(scheduler.prk_timesteps ):
       lowercase__			    =						model(a    ,  a )
       lowercase__			    =						scheduler.step_prk(a    ,  a    ,  a ).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps ):
       lowercase__			    =						model(a    ,  a )
       lowercase__			    =						scheduler.step_plms(a    ,  a    ,  a ).prev_sample

     return sample


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						dict(self.forward_default_kwargs )

     lowercase__			    =						kwargs.pop('num_inference_steps'    ,  a )

     for scheduler_class in self.scheduler_classes:
       lowercase__			    =						self.get_scheduler_config()
       lowercase__			    =						scheduler_class(**a )

       lowercase__			    =						self.dummy_sample
       lowercase__			    =						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' ):
         lowercase__			    =						num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       lowercase__			    =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
       lowercase__			    =						dummy_past_residuals[:]

       lowercase__			    =						scheduler.step_prk(a    ,  0    ,  a    ,  **a ).prev_sample
       lowercase__			    =						scheduler.step_prk(a    ,  1    ,  a    ,  **a ).prev_sample

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

       lowercase__			    =						scheduler.step_plms(a    ,  0    ,  a    ,  **a ).prev_sample
       lowercase__			    =						scheduler.step_plms(a    ,  1    ,  a    ,  **a ).prev_sample

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


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[Any]:





     """simple docstring"""







     for timesteps in [100, 1_000]:
       self.check_over_configs(num_train_timesteps=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     int:





     """simple docstring"""







     for steps_offset in [0, 1]:
       self.check_over_configs(steps_offset=a )

     lowercase__			    =						self.scheduler_classes[0]
     lowercase__			    =						self.get_scheduler_config(steps_offset=1 )
     lowercase__			    =						scheduler_class(**a )
     scheduler.set_timesteps(10 )
     assert torch.equal(
         scheduler.timesteps    ,  torch.LongTensor(
             [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] )    ,  )


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[Any]:





     """simple docstring"""







     for beta_start, beta_end in zip([0.0001, 0.001]    ,  [0.002, 0.02] ):
       self.check_over_configs(beta_start=a    ,  beta_end=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Optional[Any]:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Tuple:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Any:





     """simple docstring"""







     for t in [1, 5, 10]:
       self.check_over_forward(time_step=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Any:





     """simple docstring"""







     for t, num_inference_steps in zip([1, 5, 10]    ,  [10, 50, 100] ):
       self.check_over_forward(num_inference_steps=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Tuple:





     """simple docstring"""







     lowercase__			    =						27

     for scheduler_class in self.scheduler_classes:
       lowercase__			    =						self.dummy_sample
       lowercase__			    =						0.1 * sample

       lowercase__			    =						self.get_scheduler_config()
       lowercase__			    =						scheduler_class(**a )

       scheduler.set_timesteps(a )

       # before power of 3 fix, would error on first step, so we only need to do two
       for i, t in enumerate(scheduler.prk_timesteps[:2] ):
         lowercase__			    =						scheduler.step_prk(a    ,  a    ,  a ).prev_sample


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Optional[int]:





     """simple docstring"""







     with self.assertRaises(a ):
       lowercase__			    =						self.scheduler_classes[0]
       lowercase__			    =						self.get_scheduler_config()
       lowercase__			    =						scheduler_class(**a )

       scheduler.step_plms(self.dummy_sample    ,  1    ,  self.dummy_sample ).prev_sample


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Any:





     """simple docstring"""







     lowercase__			    =						self.full_loop()
     lowercase__			    =						torch.sum(torch.abs(a ) )
     lowercase__			    =						torch.mean(torch.abs(a ) )

     assert abs(result_sum.item() - 198.1318 ) < 1E-2
     assert abs(result_mean.item() - 0.2580 ) < 1E-3


   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.full_loop(prediction_type='v_prediction' )
     lowercase__			    =						torch.sum(torch.abs(a ) )
     lowercase__			    =						torch.mean(torch.abs(a ) )

     assert abs(result_sum.item() - 67.3986 ) < 1E-2
     assert abs(result_mean.item() - 0.0878 ) < 1E-3


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						self.full_loop(set_alpha_to_one=a    ,  beta_start=0.01 )
     lowercase__			    =						torch.sum(torch.abs(a ) )
     lowercase__			    =						torch.mean(torch.abs(a ) )

     assert abs(result_sum.item() - 230.0399 ) < 1E-2
     assert abs(result_mean.item() - 0.2995 ) < 1E-3


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						self.full_loop(set_alpha_to_one=a    ,  beta_start=0.01 )
     lowercase__			    =						torch.sum(torch.abs(a ) )
     lowercase__			    =						torch.mean(torch.abs(a ) )

     assert abs(result_sum.item() - 186.9482 ) < 1E-2
     assert abs(result_mean.item() - 0.2434 ) < 1E-3










from __future__ import annotations

import math
from collections.abc import Callable




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 100	,		)       ->     float:
  lowercase__			    =						x_start
  lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						0.0

  for _ in range(_SCREAMING_SNAKE_CASE	):
    # Approximates curve as a sequence of linear lines and sums their length
    lowercase__			    =						(x_end - x_start) / steps + xa
    lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
    length += math.hypot(xa - xa	,		fxa - fxa	)

    # Increment step
    lowercase__			    =						xa
    lowercase__			    =						fxa

  return length


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return math.sin(10 * x	)

      print("""f(x) = sin(10 * x)""")
      print("""The length of the curve from x = -10 to x = 10 is:""")
      lowercase_					     =	10
      while i <= 100_000:
            print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
            i *= 10


