'''simple docstring'''


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Dict						   =		(DEISMultistepScheduler,)
  lowerCamelCase_		:List[Any]						   =		(('''num_inference_steps''', 25),)


  def _UpperCamelCase					(			self				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				{
           'num_train_timesteps': 1_0_0_0,
           'beta_start': 0.00_01,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
           'solver_order': 2,
       }

       config.update(**snake_case_						)
       return config


  def _UpperCamelCase					(			self				,		snake_case_=0				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				dict(self.forward_default_kwargs						)
       UpperCAmelCase_							:       Union[str, Any]       =				kwargs.pop('num_inference_steps'				,		snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				self.dummy_sample
       UpperCAmelCase_							:       List[Any]       =				0.1 * sample
       UpperCAmelCase_							:       Optional[int]       =				[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            UpperCAmelCase_							:       str       =				self.get_scheduler_config(**snake_case_						)
            UpperCAmelCase_							:       Optional[Any]       =				scheduler_class(**snake_case_						)
            scheduler.set_timesteps(snake_case_						)
            # copy over dummy past residuals
            UpperCAmelCase_							:       Optional[Any]       =				dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(snake_case_						)
                 UpperCAmelCase_							:       Dict       =				scheduler_class.from_pretrained(snake_case_						)
                 new_scheduler.set_timesteps(snake_case_						)
                 # copy over dummy past residuals
                 UpperCAmelCase_							:       Dict       =				dummy_past_residuals[: new_scheduler.config.solver_order]

            UpperCAmelCase_							:       List[Any]       =				sample, sample
            for t in range(snake_case_				,		time_step + scheduler.config.solver_order + 1						):
                 UpperCAmelCase_							:       Union[str, Any]       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						).prev_sample
                 UpperCAmelCase_							:       Union[str, Any]       =				new_scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						).prev_sample

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  def _UpperCamelCase					(			self				,		snake_case_=0				,		**snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				dict(self.forward_default_kwargs						)
       UpperCAmelCase_							:       Dict       =				kwargs.pop('num_inference_steps'				,		snake_case_						)
       UpperCAmelCase_							:       List[str]       =				self.dummy_sample
       UpperCAmelCase_							:       str       =				0.1 * sample
       UpperCAmelCase_							:       Any       =				[residual + 0.2, residual + 0.15, residual + 0.10]

       for scheduler_class in self.scheduler_classes:
            UpperCAmelCase_							:       str       =				self.get_scheduler_config()
            UpperCAmelCase_							:       Optional[Any]       =				scheduler_class(**snake_case_						)
            scheduler.set_timesteps(snake_case_						)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(snake_case_						)
                 UpperCAmelCase_							:       Dict       =				scheduler_class.from_pretrained(snake_case_						)
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(snake_case_						)

                 # copy over dummy past residual (must be after setting timesteps)
                 UpperCAmelCase_							:       Any       =				dummy_past_residuals[: new_scheduler.config.solver_order]

            UpperCAmelCase_							:       List[str]       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						).prev_sample
            UpperCAmelCase_							:       Any       =				new_scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						).prev_sample

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


  def _UpperCamelCase					(			self				,		snake_case_=None				,		**snake_case_						):




       '''simple docstring'''

       if scheduler is None:
            UpperCAmelCase_							:       Union[str, Any]       =				self.scheduler_classes[0]
            UpperCAmelCase_							:       Any       =				self.get_scheduler_config(**snake_case_						)
            UpperCAmelCase_							:       str       =				scheduler_class(**snake_case_						)

       UpperCAmelCase_							:       List[Any]       =				self.scheduler_classes[0]
       UpperCAmelCase_							:       Union[str, Any]       =				self.get_scheduler_config(**snake_case_						)
       UpperCAmelCase_							:       List[str]       =				scheduler_class(**snake_case_						)

       UpperCAmelCase_							:       Any       =				1_0
       UpperCAmelCase_							:       int       =				self.dummy_model()
       UpperCAmelCase_							:       int       =				self.dummy_sample_deter
       scheduler.set_timesteps(snake_case_						)

       for i, t in enumerate(scheduler.timesteps						):
            UpperCAmelCase_							:       Union[str, Any]       =				model(snake_case_				,		snake_case_						)
            UpperCAmelCase_							:       Dict       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_						).prev_sample

       return sample


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				dict(self.forward_default_kwargs						)

       UpperCAmelCase_							:       Tuple       =				kwargs.pop('num_inference_steps'				,		snake_case_						)

       for scheduler_class in self.scheduler_classes:
            UpperCAmelCase_							:       List[str]       =				self.get_scheduler_config()
            UpperCAmelCase_							:       List[Any]       =				scheduler_class(**snake_case_						)

            UpperCAmelCase_							:       int       =				self.dummy_sample
            UpperCAmelCase_							:       Any       =				0.1 * sample

            if num_inference_steps is not None and hasattr(snake_case_				,		'set_timesteps'						):
                 scheduler.set_timesteps(snake_case_						)
            elif num_inference_steps is not None and not hasattr(snake_case_				,		'set_timesteps'						):
                 UpperCAmelCase_							:       str       =				num_inference_steps

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

            UpperCAmelCase_							:       Optional[Any]       =				scheduler.timesteps[5]
            UpperCAmelCase_							:       List[str]       =				scheduler.timesteps[6]

            UpperCAmelCase_							:       Tuple       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						).prev_sample
            UpperCAmelCase_							:       List[str]       =				scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						).prev_sample

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


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				DEISMultistepScheduler(**self.get_scheduler_config()						)
       UpperCAmelCase_							:       Dict       =				self.full_loop(scheduler=snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_mean.item() - 0.2_39_16						) < 1E-3

       UpperCAmelCase_							:       Dict       =				DPMSolverSinglestepScheduler.from_config(scheduler.config						)
       UpperCAmelCase_							:       List[Any]       =				DPMSolverMultistepScheduler.from_config(scheduler.config						)
       UpperCAmelCase_							:       Any       =				UniPCMultistepScheduler.from_config(scheduler.config						)
       UpperCAmelCase_							:       str       =				DEISMultistepScheduler.from_config(scheduler.config						)

       UpperCAmelCase_							:       Optional[int]       =				self.full_loop(scheduler=snake_case_						)
       UpperCAmelCase_							:       Optional[Any]       =				torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_mean.item() - 0.2_39_16						) < 1E-3


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.check_over_configs(thresholding=snake_case_						)
       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=snake_case_				,		prediction_type=snake_case_				,		sample_max_value=snake_case_				,		algorithm_type='deis'				,		solver_order=snake_case_				,		solver_type=snake_case_				,		)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self						):




       '''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=snake_case_				,		solver_type=snake_case_				,		prediction_type=snake_case_				,		algorithm_type=snake_case_				,		)
                           UpperCAmelCase_							:       Optional[Any]       =				self.full_loop(
                               solver_order=snake_case_				,		solver_type=snake_case_				,		prediction_type=snake_case_				,		algorithm_type=snake_case_				,		)
                           assert not torch.isnan(snake_case_						).any(), "Samples have nan numbers"


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.check_over_configs(lower_order_final=snake_case_						)
       self.check_over_configs(lower_order_final=snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       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=snake_case_				,		time_step=0						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.full_loop()
       UpperCAmelCase_							:       Any       =				torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_mean.item() - 0.2_39_16						) < 1E-3


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.full_loop(prediction_type='v_prediction'						)
       UpperCAmelCase_							:       int       =				torch.mean(torch.abs(snake_case_						)						)

       assert abs(result_mean.item() - 0.0_91						) < 1E-3






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.scheduler_classes[0]
       UpperCAmelCase_							:       Tuple       =				self.get_scheduler_config(thresholding=snake_case_				,		dynamic_thresholding_ratio=0						)
       UpperCAmelCase_							:       int       =				scheduler_class(**snake_case_						)

       UpperCAmelCase_							:       Union[str, Any]       =				1_0
       UpperCAmelCase_							:       List[Any]       =				self.dummy_model()
       UpperCAmelCase_							:       str       =				self.dummy_sample_deter.half()
       scheduler.set_timesteps(snake_case_						)

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

       assert sample.dtype == torch.floataa







'''simple docstring'''


from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_	):







  '''simple docstring'''






  @register_to_config
  def __init__(			self				,		snake_case_ = 7_6_8				,		):




       '''simple docstring'''

       super().__init__()

       UpperCAmelCase_							:       int       =				nn.Parameter(torch.zeros(1				,		snake_case_						)						)
       UpperCAmelCase_							:       str       =				nn.Parameter(torch.ones(1				,		snake_case_						)						)


  def _UpperCamelCase					(			self				,		snake_case_ = None				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				nn.Parameter(self.mean.to(snake_case_						).to(snake_case_						)						)
       UpperCAmelCase_							:       Tuple       =				nn.Parameter(self.std.to(snake_case_						).to(snake_case_						)						)
       return self


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				(embeds - self.mean) * 1.0 / self.std
       return embeds






  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				(embeds * self.std) + self.mean
       return embeds






