import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest

class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  (DEISMultistepScheduler,)
  lowercase_								=  (('num_inference_steps', 25),)




  def 						__lowercase(   self  :						List[str]  ,			**a_  :						int					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		{
           'num_train_timesteps': 1000,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'solver_order': 2,
       }

       config.update(**a_					)
       return config




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						List[str]=0  ,			**a_  :						str					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		dict(self.forward_default_kwargs					)
       SCREAMING_SNAKE_CASE__					:					str      =		kwargs.pop('num_inference_steps'  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.dummy_sample
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		0.1 * sample
       SCREAMING_SNAKE_CASE__					:					int      =		[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_scheduler_config(**a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scheduler_class(**a_					)
            scheduler.set_timesteps(a_					)
            # copy over dummy past residuals
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(a_					)
                 SCREAMING_SNAKE_CASE__					:					Dict      =		scheduler_class.from_pretrained(a_					)
                 new_scheduler.set_timesteps(a_					)
                 # copy over dummy past residuals
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		dummy_past_residuals[: new_scheduler.config.solver_order]

            SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		sample, sample
            for t in range(a_  ,			time_step + scheduler.config.solver_order + 1					):
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		scheduler.step(a_  ,			a_  ,			a_  ,			**a_					).prev_sample
                 SCREAMING_SNAKE_CASE__					:					List[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 						__lowercase(   self  :						int					)->							Dict:




       """simple docstring"""
       pass




  def 						__lowercase(   self  :						List[str]  ,			a_  :						int=0  ,			**a_  :						Optional[Any]					)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		dict(self.forward_default_kwargs					)
       SCREAMING_SNAKE_CASE__					:					str      =		kwargs.pop('num_inference_steps'  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		self.dummy_sample
       SCREAMING_SNAKE_CASE__					:					Dict      =		0.1 * sample
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__					:					int      =		self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		scheduler_class(**a_					)
            scheduler.set_timesteps(a_					)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(a_					)
                 SCREAMING_SNAKE_CASE__					:					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)
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		dummy_past_residuals[: new_scheduler.config.solver_order]

            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		scheduler.step(a_  ,			a_  ,			a_  ,			**a_					).prev_sample
            SCREAMING_SNAKE_CASE__					:					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 						__lowercase(   self  :						Dict  ,			a_  :						List[str]=None  ,			**a_  :						List[Any]					)->							Any:




       """simple docstring"""
       if scheduler is None:
            SCREAMING_SNAKE_CASE__					:					str      =		self.scheduler_classes[0]
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_scheduler_config(**a_					)
            SCREAMING_SNAKE_CASE__					:					int      =		scheduler_class(**a_					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					int      =		self.get_scheduler_config(**a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		scheduler_class(**a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		10
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.dummy_sample_deter
       scheduler.set_timesteps(a_					)

       for i, t in enumerate(scheduler.timesteps					):
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		model(a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					Any      =		scheduler.step(a_  ,			a_  ,			a_					).prev_sample

       return sample




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		dict(self.forward_default_kwargs					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		kwargs.pop('num_inference_steps'  ,			a_					)

       for scheduler_class in self.scheduler_classes:
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.get_scheduler_config()
            SCREAMING_SNAKE_CASE__					:					Tuple      =		scheduler_class(**a_					)

            SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.dummy_sample
            SCREAMING_SNAKE_CASE__					:					int      =		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'					):
                 SCREAMING_SNAKE_CASE__					:					int      =		num_inference_steps

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

            SCREAMING_SNAKE_CASE__					:					Tuple      =		scheduler.timesteps[5]
            SCREAMING_SNAKE_CASE__					:					List[str]      =		scheduler.timesteps[6]

            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		scheduler.step(a_  ,			a_  ,			a_  ,			**a_					).prev_sample
            SCREAMING_SNAKE_CASE__					:					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 						__lowercase(   self  :						Tuple					)->							Any:




       """simple docstring"""
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       SCREAMING_SNAKE_CASE__					:					List[str]      =		DEISMultistepScheduler(**self.get_scheduler_config()					)
       SCREAMING_SNAKE_CASE__					:					str      =		self.full_loop(scheduler=a_					)
       SCREAMING_SNAKE_CASE__					:					int      =		torch.mean(torch.abs(a_					)					)

       assert abs(result_mean.item() - 0.2_3916					) < 1e-3

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

       SCREAMING_SNAKE_CASE__					:					Any      =		self.full_loop(scheduler=a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.mean(torch.abs(a_					)					)

       assert abs(result_mean.item() - 0.2_3916					) < 1e-3




  def 						__lowercase(   self  :						Tuple					)->							Tuple:




       """simple docstring"""
       for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=a_					)




  def 						__lowercase(   self  :						str					)->							Tuple:




       """simple docstring"""
       self.check_over_configs(thresholding=a_					)
       for order in [1, 2, 3]:
            for solver_type in ["logrho"]:
                 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_  ,			algorithm_type='deis'  ,			solver_order=a_  ,			solver_type=a_  ,			)




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




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




  def 						__lowercase(   self  :						str					)->							Dict:




       """simple docstring"""
       for algorithm_type in ["deis"]:
            for solver_type in ["logrho"]:
                 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_  ,			algorithm_type=a_  ,			)
                           SCREAMING_SNAKE_CASE__					:					Tuple      =		self.full_loop(
                               solver_order=a_  ,			solver_type=a_  ,			prediction_type=a_  ,			algorithm_type=a_  ,			)
                           assert not torch.isnan(a_					).any(), "Samples have nan numbers"




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




       """simple docstring"""
       self.check_over_configs(lower_order_final=a_					)
       self.check_over_configs(lower_order_final=a_					)




  def 						__lowercase(   self  :						List[Any]					)->							str:




       """simple docstring"""
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=a_  ,			time_step=0					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.full_loop()
       SCREAMING_SNAKE_CASE__					:					Any      =		torch.mean(torch.abs(a_					)					)

       assert abs(result_mean.item() - 0.2_3916					) < 1e-3




  def 						__lowercase(   self  :						Any					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.full_loop(prediction_type='v_prediction'					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.mean(torch.abs(a_					)					)

       assert abs(result_mean.item() - 0.091					) < 1e-3




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__					:					str      =		self.get_scheduler_config(thresholding=a_  ,			dynamic_thresholding_ratio=0					)
       SCREAMING_SNAKE_CASE__					:					str      =		scheduler_class(**a_					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		10
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.dummy_model()
       SCREAMING_SNAKE_CASE__					:					str      =		self.dummy_sample_deter.half()
       scheduler.set_timesteps(a_					)

       for i, t in enumerate(scheduler.timesteps					):
            SCREAMING_SNAKE_CASE__					:					int      =		model(a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		scheduler.step(a_  ,			a_  ,			a_					).prev_sample

       assert sample.dtype == torch.floataa







"""simple docstring"""
import mpmath  # for roots of unity
import numpy as np




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None)  ->  Tuple:
      # Input as list
      _lowerCamelCase	:					Any   =		list(poly_a or [0])[:]
      _lowerCamelCase	:					Optional[Any]   =		list(poly_b or [0])[:]

      # Remove leading zero coefficients
      while self.polyA[-1] == 0:
         self.polyA.pop()
      _lowerCamelCase	:					int   =		len(self.polyA)

      while self.polyB[-1] == 0:
         self.polyB.pop()
      _lowerCamelCase	:					Union[str, Any]   =		len(self.polyB)

      # Add 0 to make lengths equal a power of 2
      _lowerCamelCase	:					List[Any]   =		int(
          2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

      while len(self.polyA) < self.c_max_length:
         self.polyA.append(0)
      while len(self.polyB) < self.c_max_length:
         self.polyB.append(0)
      # A complex root used for the fourier transform
      _lowerCamelCase	:					Optional[Any]   =		complex(mpmath.root(x=1      ,   n=self.c_max_length      ,   k=1))

      # The product
      _lowerCamelCase	:					int   =		self.__multiply()





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					Dict   =		[[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
      # Corner case
      if len(SCREAMING_SNAKE_CASE) <= 1:
         return dft[0]
      #
      _lowerCamelCase	:					str   =		self.c_max_length // 2
      while next_ncol > 0:
         _lowerCamelCase	:					Dict   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					Tuple   =		self.root**next_ncol

         # First half of next step
         _lowerCamelCase	:					int   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
            current_root *= root
         # Second half of next step
         _lowerCamelCase	:					Optional[int]   =		1
         for j in range(self.c_max_length // (next_ncol * 2)):
            for i in range(SCREAMING_SNAKE_CASE):
               new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
            current_root *= root
         # Update
         _lowerCamelCase	:					Union[str, Any]   =		new_dft
         _lowerCamelCase	:					List[str]   =		next_ncol // 2
      return dft[0]





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		self.__dft("""A""")
      _lowerCamelCase	:					List[str]   =		self.__dft("""B""")
      _lowerCamelCase	:					List[Any]   =		[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
      del dft_a
      del dft_b

      # Corner Case
      if len(inverce_c[0]) <= 1:
         return inverce_c[0]
      # Inverse DFT
      _lowerCamelCase	:					List[str]   =		2
      while next_ncol <= self.c_max_length:
         _lowerCamelCase	:					Any   =		[[] for i in range(SCREAMING_SNAKE_CASE)]
         _lowerCamelCase	:					List[Any]   =		self.root ** (next_ncol // 2)
         _lowerCamelCase	:					str   =		1
         # First half of next step
         for j in range(self.c_max_length // next_ncol):
            for i in range(next_ncol // 2):
               # Even positions
               new_inverse_c[i].append(
                   (
                       inverce_c[i][j]
                       + inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / 2)
               # Odd positions
               new_inverse_c[i + next_ncol // 2].append(
                   (
                       inverce_c[i][j]
                       - inverce_c[i][j + self.c_max_length // next_ncol]
                   )
                   / (2 * current_root))
            current_root *= root
         # Update
         _lowerCamelCase	:					Any   =		new_inverse_c
         next_ncol *= 2
      # Unpack
      _lowerCamelCase	:					Optional[Any]   =		[round(x[0].real      ,   8) + round(x[0].imag      ,   8) * 1j for x in inverce_c]

      # Remove leading 0's
      while inverce_c[-1] == 0:
         inverce_c.pop()
      return inverce_c





   def __str__(							self)  ->  Any:
      _lowerCamelCase	:					Dict   =		"""A = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyA[: self.len_A]))
      _lowerCamelCase	:					List[Any]   =		"""B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.polyB[: self.len_B]))
      _lowerCamelCase	:					int   =		"""A*B = """ + """ + """.join(
          F'{coef}*x^{i}' for coef, i in enumerate(self.product))

      return F'{a}\n{b}\n{c}'


# Unit tests
if __name__ == "__main__":
 import doctest

 doctest.testmod()

