





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Tuple			   =   (UniPCMultistepScheduler,)
       _UpperCAmelCase				:  Dict			   =   (("num_inference_steps", 2_5),)







       def 						__lowerCamelCase   (				self					:     List[Any]		, **A					:     Union[str, Any]      )       ->Union[str, Any]:
          lowerCamelCase__       :						int        =							{
              '''num_train_timesteps''': 1_0_0_0,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''solver_order''': 2,
              '''solver_type''': '''bh2''',
          }

          config.update(**A      )
          return config







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Any=0		, **A					:     Union[str, Any]      )       ->Any:
          lowerCamelCase__       :						List[str]        =							dict(self.forward_default_kwargs      )
          lowerCamelCase__       :						Optional[Any]        =							kwargs.pop('''num_inference_steps'''		, A      )
          lowerCamelCase__       :						Any        =							self.dummy_sample
          lowerCamelCase__       :						Any        =							0.1 * sample
          lowerCamelCase__       :						int        =							[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

             lowerCamelCase__       ,							lowerCamelCase__       :						Union[str, Any]        =							sample, sample
             for t in range(A		, time_step + scheduler.config.solver_order + 1      ):
                lowerCamelCase__       :						Optional[Any]        =							scheduler.step(A		, A		, A		, **A      ).prev_sample
                lowerCamelCase__       :						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 						__lowerCamelCase   (				self					:     List[str]		, A					:     Union[str, Any]=0		, **A					:     Dict      )       ->List[str]:
          lowerCamelCase__       :						Dict        =							dict(self.forward_default_kwargs      )
          lowerCamelCase__       :						Tuple        =							kwargs.pop('''num_inference_steps'''		, A      )
          lowerCamelCase__       :						List[Any]        =							self.dummy_sample
          lowerCamelCase__       :						Tuple        =							0.1 * sample
          lowerCamelCase__       :						Tuple        =							[residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
             lowerCamelCase__       :						Tuple        =							self.get_scheduler_config()
             lowerCamelCase__       :						Optional[Any]        =							scheduler_class(**A      )
             scheduler.set_timesteps(A      )

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(A      )
                lowerCamelCase__       :						str        =							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)
                lowerCamelCase__       :						Tuple        =							dummy_past_residuals[: new_scheduler.config.solver_order]

             lowerCamelCase__       :						Any        =							scheduler.step(A		, A		, A		, **A      ).prev_sample
             lowerCamelCase__       :						Optional[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 						__lowerCamelCase   (				self					:     str		, A					:     List[str]=None		, **A					:     List[str]      )       ->Optional[int]:
          if scheduler is None:
             lowerCamelCase__       :						str        =							self.scheduler_classes[0]
             lowerCamelCase__       :						Any        =							self.get_scheduler_config(**A      )
             lowerCamelCase__       :						str        =							scheduler_class(**A      )

          lowerCamelCase__       :						List[str]        =							self.scheduler_classes[0]
          lowerCamelCase__       :						Optional[Any]        =							self.get_scheduler_config(**A      )
          lowerCamelCase__       :						Dict        =							scheduler_class(**A      )

          lowerCamelCase__       :						List[str]        =							1_0
          lowerCamelCase__       :						Dict        =							self.dummy_model()
          lowerCamelCase__       :						List[str]        =							self.dummy_sample_deter
          scheduler.set_timesteps(A      )

          for i, t in enumerate(scheduler.timesteps      ):
             lowerCamelCase__       :						Any        =							model(A		, A      )
             lowerCamelCase__       :						Optional[int]        =							scheduler.step(A		, A		, A      ).prev_sample

          return sample







       def 						__lowerCamelCase   (				self					:     Any      )       ->List[Any]:
          lowerCamelCase__       :						List[str]        =							dict(self.forward_default_kwargs      )

          lowerCamelCase__       :						List[str]        =							kwargs.pop('''num_inference_steps'''		, A      )

          for scheduler_class in self.scheduler_classes:
             lowerCamelCase__       :						Dict        =							self.get_scheduler_config()
             lowerCamelCase__       :						Any        =							scheduler_class(**A      )

             lowerCamelCase__       :						Optional[Any]        =							self.dummy_sample
             lowerCamelCase__       :						Union[str, Any]        =							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'''      ):
                lowerCamelCase__       :						Tuple        =							num_inference_steps

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

             lowerCamelCase__       :						Optional[Any]        =							scheduler.timesteps[5]
             lowerCamelCase__       :						Dict        =							scheduler.timesteps[6]

             lowerCamelCase__       :						Any        =							scheduler.step(A		, A		, A		, **A      ).prev_sample
             lowerCamelCase__       :						str        =							scheduler.step(A		, A		, A		, **A      ).prev_sample

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







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->str:
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          lowerCamelCase__       :						Dict        =							UniPCMultistepScheduler(**self.get_scheduler_config()      )
          lowerCamelCase__       :						Dict        =							self.full_loop(scheduler=A      )
          lowerCamelCase__       :						List[str]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_mean.item() - 0.24_64      ) < 1e-3

          lowerCamelCase__       :						Tuple        =							DPMSolverSinglestepScheduler.from_config(scheduler.config      )
          lowerCamelCase__       :						Optional[int]        =							DEISMultistepScheduler.from_config(scheduler.config      )
          lowerCamelCase__       :						Optional[Any]        =							DPMSolverMultistepScheduler.from_config(scheduler.config      )
          lowerCamelCase__       :						Dict        =							UniPCMultistepScheduler.from_config(scheduler.config      )

          lowerCamelCase__       :						List[str]        =							self.full_loop(scheduler=A      )
          lowerCamelCase__       :						Optional[int]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_mean.item() - 0.24_64      ) < 1e-3







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Dict:
          for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=A      )







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[int]:
          self.check_over_configs(thresholding=A      )
          for order in [1, 2, 3]:
             for solver_type in ["bh1", "bh2"]:
                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		, solver_order=A		, solver_type=A		, )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A      )







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->int:
          for solver_type in ["bh1", "bh2"]:
             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		, )
                   lowerCamelCase__       :						Union[str, Any]        =							self.full_loop(
                       solver_order=A		, solver_type=A		, prediction_type=A		, )
                   assert not torch.isnan(A      ).any(), "Samples have nan numbers"







       def 						__lowerCamelCase   (				self					:     str      )       ->Any:
          self.check_over_configs(lower_order_final=A      )
          self.check_over_configs(lower_order_final=A      )







       def 						__lowerCamelCase   (				self					:     str      )       ->List[Any]:
          for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
             self.check_over_forward(num_inference_steps=A		, time_step=0      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Any:
          lowerCamelCase__       :						int        =							self.full_loop()
          lowerCamelCase__       :						Optional[int]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_mean.item() - 0.24_64      ) < 1e-3







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Optional[Any]:
          lowerCamelCase__       :						Optional[int]        =							self.full_loop(prediction_type='''v_prediction'''      )
          lowerCamelCase__       :						Optional[int]        =							torch.mean(torch.abs(A      )      )

          assert abs(result_mean.item() - 0.10_14      ) < 1e-3







       def 						__lowerCamelCase   (				self					:     Any      )       ->Optional[int]:
          lowerCamelCase__       :						List[Any]        =							self.scheduler_classes[0]
          lowerCamelCase__       :						str        =							self.get_scheduler_config(thresholding=A		, dynamic_thresholding_ratio=0      )
          lowerCamelCase__       :						int        =							scheduler_class(**A      )

          lowerCamelCase__       :						str        =							1_0
          lowerCamelCase__       :						Union[str, Any]        =							self.dummy_model()
          lowerCamelCase__       :						Dict        =							self.dummy_sample_deter.half()
          scheduler.set_timesteps(A      )

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

          assert sample.dtype == torch.floataa







       def 						__lowerCamelCase   (				self					:     List[str]		, **A					:     int      )       ->Any:
          for scheduler_class in self.scheduler_classes:
             lowerCamelCase__       :						Union[str, Any]        =							self.get_scheduler_config(**A      )
             lowerCamelCase__       :						Union[str, Any]        =							scheduler_class(**A      )

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










import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
       # Create universe of discourse in Python using linspace ()
       _A :   Optional[int]  			=  np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

       # Create two fuzzy sets by defining any membership function
       # (trapmf(), gbellmf(), gaussmf(), etc).
       _A :   Any  			=  [0, 25, 50]
       _A :   Dict  			=  [25, 50, 75]
       _A :   Any  			=  fuzz.membership.trimf(X, abca)
       _A :   List[Any]  			=  fuzz.membership.trimf(X, abca)

       # Compute the different operations using inbuilt functions.
       _A :   List[str]  			=  np.ones(75)
       _A :   Optional[int]  			=  np.zeros((75,))
       # 1. Union = max(µA(x), µB(x))
       _A :   Optional[Any]  			=  fuzz.fuzzy_or(X, young, X, middle_aged)[1]
       # 2. Intersection = min(µA(x), µB(x))
       _A :   Union[str, Any]  			=  fuzz.fuzzy_and(X, young, X, middle_aged)[1]
       # 3. Complement (A) = (1- min(µA(x))
       _A :   List[Any]  			=  fuzz.fuzzy_not(young)
       # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
       _A :   Tuple  			=  fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
       # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
       _A :   List[Any]  			=  young + middle_aged - (young * middle_aged)
       # 6. Algebraic Product = (µA(x) * µB(x))
       _A :   Optional[Any]  			=  young * middle_aged
       # 7. Bounded Sum = min[1,(µA(x), µB(x))]
       _A :   Dict  			=  fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
       # 8. Bounded difference = min[0,(µA(x), µB(x))]
       _A :   List[str]  			=  fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

       # max-min composition
       # max-product composition

       # Plot each set A, set B and each operation result using plot() and subplot().
       from matplotlib import pyplot as plt

       plt.figure()

       plt.subplot(4, 3, 1)
       plt.plot(X, young)
       plt.title('Young')
       plt.grid(True)

       plt.subplot(4, 3, 2)
       plt.plot(X, middle_aged)
       plt.title('Middle aged')
       plt.grid(True)

       plt.subplot(4, 3, 3)
       plt.plot(X, union)
       plt.title('union')
       plt.grid(True)

       plt.subplot(4, 3, 4)
       plt.plot(X, intersection)
       plt.title('intersection')
       plt.grid(True)

       plt.subplot(4, 3, 5)
       plt.plot(X, complement_a)
       plt.title('complement_a')
       plt.grid(True)

       plt.subplot(4, 3, 6)
       plt.plot(X, difference)
       plt.title('difference a/b')
       plt.grid(True)

       plt.subplot(4, 3, 7)
       plt.plot(X, alg_sum)
       plt.title('alg_sum')
       plt.grid(True)

       plt.subplot(4, 3, 8)
       plt.plot(X, alg_product)
       plt.title('alg_product')
       plt.grid(True)

       plt.subplot(4, 3, 9)
       plt.plot(X, bdd_sum)
       plt.title('bdd_sum')
       plt.grid(True)

       plt.subplot(4, 3, 10)
       plt.plot(X, bdd_difference)
       plt.title('bdd_difference')
       plt.grid(True)

       plt.subplots_adjust(hspace=0.5)
       plt.show()



