






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    (PNDMScheduler,)
      A          =    (('''num_inference_steps''', 50),)
      def 			snake_case_      (   self    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**_lowerCAmelCase		)
          return config
      def 			snake_case_      (   self    ,	_lowerCAmelCase=0    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				dict(self.forward_default_kwargs		)
          lowerCAmelCase__   :int      							=				kwargs.pop("num_inference_steps"    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :List[Any]      							=				self.dummy_sample
          lowerCAmelCase__   :str      							=				0.1 * sample
          lowerCAmelCase__   :str      							=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__   :int      							=				self.get_scheduler_config(**_lowerCAmelCase		)
              lowerCAmelCase__   :str      							=				scheduler_class(**_lowerCAmelCase		)
              scheduler.set_timesteps(_lowerCAmelCase		)
              # copy over dummy past residuals
              lowerCAmelCase__   :List[str]      							=				dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_lowerCAmelCase		)
                  lowerCAmelCase__   :int      							=				scheduler_class.from_pretrained(_lowerCAmelCase		)
                  new_scheduler.set_timesteps(_lowerCAmelCase		)
                  # copy over dummy past residuals
                  lowerCAmelCase__   :Union[str, Any]      							=				dummy_past_residuals[:]

              lowerCAmelCase__   :int      							=				scheduler.step_prk(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample
              lowerCAmelCase__   :int      							=				new_scheduler.step_prk(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

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

              lowerCAmelCase__   :List[str]      							=				scheduler.step_plms(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample
              lowerCAmelCase__   :Any      							=				new_scheduler.step_plms(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

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



          '''simple docstring'''
          pass
      def 			snake_case_      (   self    ,	_lowerCAmelCase=0    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Union[str, Any]      							=				dict(self.forward_default_kwargs		)
          lowerCAmelCase__   :Optional[int]      							=				kwargs.pop("num_inference_steps"    ,	_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				self.dummy_sample
          lowerCAmelCase__   :Tuple      							=				0.1 * sample
          lowerCAmelCase__   :Dict      							=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__   :Tuple      							=				self.get_scheduler_config()
              lowerCAmelCase__   :List[Any]      							=				scheduler_class(**_lowerCAmelCase		)
              scheduler.set_timesteps(_lowerCAmelCase		)

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

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(_lowerCAmelCase		)
                  lowerCAmelCase__   :List[Any]      							=				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)
                  lowerCAmelCase__   :Union[str, Any]      							=				dummy_past_residuals[:]

              lowerCAmelCase__   :List[str]      							=				scheduler.step_prk(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample
              lowerCAmelCase__   :Tuple      							=				new_scheduler.step_prk(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

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

              lowerCAmelCase__   :List[Any]      							=				scheduler.step_plms(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample
              lowerCAmelCase__   :List[str]      							=				new_scheduler.step_plms(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

              assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"
      def 			snake_case_      (   self    ,	**_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.scheduler_classes[0]
          lowerCAmelCase__   :List[str]      							=				self.get_scheduler_config(**_lowerCAmelCase		)
          lowerCAmelCase__   :str      							=				scheduler_class(**_lowerCAmelCase		)

          lowerCAmelCase__   :Optional[Any]      							=				10
          lowerCAmelCase__   :int      							=				self.dummy_model()
          lowerCAmelCase__   :Union[str, Any]      							=				self.dummy_sample_deter
          scheduler.set_timesteps(_lowerCAmelCase		)

          for i, t in enumerate(scheduler.prk_timesteps		):
              lowerCAmelCase__   :Dict      							=				model(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Tuple      							=				scheduler.step_prk(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample

          for i, t in enumerate(scheduler.plms_timesteps		):
              lowerCAmelCase__   :Optional[Any]      							=				model(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :List[Any]      							=				scheduler.step_plms(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample

          return sample
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				dict(self.forward_default_kwargs		)

          lowerCAmelCase__   :List[Any]      							=				kwargs.pop("num_inference_steps"    ,	_lowerCAmelCase		)

          for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__   :Optional[int]      							=				self.get_scheduler_config()
              lowerCAmelCase__   :Union[str, Any]      							=				scheduler_class(**_lowerCAmelCase		)

              lowerCAmelCase__   :Optional[Any]      							=				self.dummy_sample
              lowerCAmelCase__   :List[str]      							=				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"		):
                  lowerCAmelCase__   :Optional[int]      							=				num_inference_steps

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

              lowerCAmelCase__   :List[str]      							=				scheduler.step_prk(_lowerCAmelCase    ,	0    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample
              lowerCAmelCase__   :List[Any]      							=				scheduler.step_prk(_lowerCAmelCase    ,	1    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

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

              lowerCAmelCase__   :Any      							=				scheduler.step_plms(_lowerCAmelCase    ,	0    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample
              lowerCAmelCase__   :Any      							=				scheduler.step_plms(_lowerCAmelCase    ,	1    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

              self.assertEqual(output_a.shape    ,	sample.shape		)
              self.assertEqual(output_a.shape    ,	output_a.shape		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_lowerCAmelCase		)

          lowerCAmelCase__   :int      							=				self.scheduler_classes[0]
          lowerCAmelCase__   :Any      							=				self.get_scheduler_config(steps_offset=1		)
          lowerCAmelCase__   :Any      							=				scheduler_class(**_lowerCAmelCase		)
          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 			snake_case_      (   self		):



          '''simple docstring'''
          for beta_start, beta_end in zip([0.0001, 0.001]    ,	[0.002, 0.02]		):
              self.check_over_configs(beta_start=_lowerCAmelCase    ,	beta_end=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for t in [1, 5, 10]:
              self.check_over_forward(time_step=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for t, num_inference_steps in zip([1, 5, 10]    ,	[10, 50, 100]		):
              self.check_over_forward(num_inference_steps=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
          lowerCAmelCase__   :Tuple      							=				27

          for scheduler_class in self.scheduler_classes:
              lowerCAmelCase__   :Optional[int]      							=				self.dummy_sample
              lowerCAmelCase__   :Optional[Any]      							=				0.1 * sample

              lowerCAmelCase__   :str      							=				self.get_scheduler_config()
              lowerCAmelCase__   :int      							=				scheduler_class(**_lowerCAmelCase		)

              scheduler.set_timesteps(_lowerCAmelCase		)

              # 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]		):
                  lowerCAmelCase__   :Union[str, Any]      							=				scheduler.step_prk(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		).prev_sample
      def 			snake_case_      (   self		):



          '''simple docstring'''
          with self.assertRaises(_lowerCAmelCase		):
              lowerCAmelCase__   :Any      							=				self.scheduler_classes[0]
              lowerCAmelCase__   :Dict      							=				self.get_scheduler_config()
              lowerCAmelCase__   :Dict      							=				scheduler_class(**_lowerCAmelCase		)

              scheduler.step_plms(self.dummy_sample    ,	1    ,	self.dummy_sample		).prev_sample
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.full_loop()
          lowerCAmelCase__   :Union[str, Any]      							=				torch.sum(torch.abs(_lowerCAmelCase		)		)
          lowerCAmelCase__   :Optional[int]      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_sum.item() - 198.1318		) < 1e-2
          assert abs(result_mean.item() - 0.2580		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Any      							=				self.full_loop(prediction_type="v_prediction"		)
          lowerCAmelCase__   :str      							=				torch.sum(torch.abs(_lowerCAmelCase		)		)
          lowerCAmelCase__   :Dict      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

          assert abs(result_sum.item() - 67.3986		) < 1e-2
          assert abs(result_mean.item() - 0.0878		) < 1e-3
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # We specify different beta, so that the first alpha is 0.99
          lowerCAmelCase__   :List[str]      							=				self.full_loop(set_alpha_to_one=_lowerCAmelCase    ,	beta_start=0.01		)
          lowerCAmelCase__   :List[Any]      							=				torch.sum(torch.abs(_lowerCAmelCase		)		)
          lowerCAmelCase__   :Any      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

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



      def 			snake_case_      (   self		):



          '''simple docstring'''
          # We specify different beta, so that the first alpha is 0.99
          lowerCAmelCase__   :Union[str, Any]      							=				self.full_loop(set_alpha_to_one=_lowerCAmelCase    ,	beta_start=0.01		)
          lowerCAmelCase__   :Dict      							=				torch.sum(torch.abs(_lowerCAmelCase		)		)
          lowerCAmelCase__   :List[Any]      							=				torch.mean(torch.abs(_lowerCAmelCase		)		)

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











from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean


def 				snake_case__							(							UpperCAmelCase							:  int       ):

    # A local function to see if a dot lands in the circle.
    def is_in_circle(UpperCAmelCase							:  float    ,			UpperCAmelCase							:  float       ) -> bool:
        lowerCAmelCase__   :Optional[int]      							=				sqrt((x**2) + (y**2)       )
        # Our circle has a radius of 1, so a distance
        # greater than 1 would land outside the circle.
        return distance_from_centre <= 1

    # The proportion of guesses that landed in the circle
    lowerCAmelCase__   :Union[str, Any]      							=				mean(
        int(is_in_circle(uniform(-1.0    ,			1.0       )    ,			uniform(-1.0    ,			1.0       )       )       )
        for _ in range(UpperCAmelCase       )       )
    # The ratio of the area for circle to square is pi/4.
    lowerCAmelCase__   :List[Any]      							=				proportion * 4
    print(F'''The estimated value of pi is {pi_estimate}'''       )
    print(F'''The numpy value of pi is {pi}'''       )
    print(F'''The total error is {abs(pi - pi_estimate       )}'''       )


def 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  Callable[[float], float]    ,			UpperCAmelCase							:  float = 0.0    ,			UpperCAmelCase							:  float = 1.0    ,			):
    return mean(
        function_to_integrate(uniform(UpperCAmelCase    ,			UpperCAmelCase       )       ) for _ in range(UpperCAmelCase       )       ) * (max_value - min_value)


def 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  float = 0.0    ,			UpperCAmelCase							:  float = 1.0       ):

    def identity_function(UpperCAmelCase							:  float       ) -> float:
        return x

    lowerCAmelCase__   :List[str]      							=				area_under_curve_estimator(
        UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )
    lowerCAmelCase__   :Tuple      							=				(max_value * max_value - min_value * min_value) / 2

    print("******************"       )
    print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}'''       )
    print(F'''Estimated value is {estimated_value}'''       )
    print(F'''Expected value is {expected_value}'''       )
    print(F'''Total error is {abs(estimated_value - expected_value       )}'''       )
    print("******************"       )


def 				snake_case__							(							UpperCAmelCase							:  int       ):

    def function_to_integrate(UpperCAmelCase							:  float       ) -> float:
        return sqrt(4.0 - x * x       )

    lowerCAmelCase__   :int      							=				area_under_curve_estimator(
        UpperCAmelCase    ,			UpperCAmelCase    ,			0.0    ,			2.0       )

    print("******************"       )
    print("Estimating pi using area_under_curve_estimator"       )
    print(F'''Estimated value is {estimated_value}'''       )
    print(F'''Expected value is {pi}'''       )
    print(F'''Total error is {abs(estimated_value - pi       )}'''       )
    print("******************"       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



