

from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available


def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						Any    ):





				"""simple docstring"""


				if is_torch_available():
								import torch

								if isinstance(SCREAMING_SNAKE_CASE_	,				torch.Tensor    ):
												return True

				return isinstance(SCREAMING_SNAKE_CASE_	,				np.ndarray    )





class  __snake_case					(      _UpperCAmelCase    ):

		def   lowerCAmelCase__       (   self):
						SCREAMING_SNAKE_CASE_													=  fields(self)

						# Safety and consistency checks
						if not len(lowercase__):
										raise ValueError(f"""{self.__class__.__name__} has no fields.""")

						SCREAMING_SNAKE_CASE_													=  getattr(self  ,						class_fields[0].name)
						SCREAMING_SNAKE_CASE_													=  all(getattr(self  ,						field.name) is None for field in class_fields[1:])

						if other_fields_are_none and isinstance(lowercase__  ,						lowercase__):
										for key, value in first_field.items():
														SCREAMING_SNAKE_CASE_													=  value
						else:
										for field in class_fields:
														SCREAMING_SNAKE_CASE_													=  getattr(self  ,						field.name)
														if v is not None:
																		SCREAMING_SNAKE_CASE_													=  v

		def __delitem__(   self  ,						*_A  ,						**_A):
						raise Exception(f"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.""")

		def   lowerCAmelCase__       (   self  ,						*_A  ,						**_A):
						raise Exception(f"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance.""")

		def   lowerCAmelCase__       (   self  ,						*_A  ,						**_A):
						raise Exception(f"""You cannot use ``pop`` on a {self.__class__.__name__} instance.""")

		def   lowerCAmelCase__       (   self  ,						*_A  ,						**_A):
						raise Exception(f"""You cannot use ``update`` on a {self.__class__.__name__} instance.""")

		def __getitem__(   self  ,						_A):






						if isinstance(lowercase__  ,						lowercase__):
										SCREAMING_SNAKE_CASE_													=  dict(self.items())
										return inner_dict[k]
						else:
										return self.to_tuple()[k]

		def __setattr__(   self  ,						_A  ,						_A):






						if name in self.keys() and value is not None:
										# Don't call self.__setitem__ to avoid recursion errors
										super().__setitem__(lowercase__  ,						lowercase__)
						super().__setattr__(lowercase__  ,						lowercase__)

		def __setitem__(   self  ,						_A  ,						_A):
						super().__setitem__(lowercase__  ,						lowercase__)
						# Don't call self.__setattr__ to avoid recursion errors
						super().__setattr__(lowercase__  ,						lowercase__)


		def   lowerCAmelCase__       (   self):
						return tuple(self[k] for k in self.keys())





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		Union[str, Any]  	=					(HeunDiscreteScheduler,)
  __lowerCAmelCase       :		Optional[int]  	=					10

  def   lowerCAmelCase__       (   self  ,						**_A):
      SCREAMING_SNAKE_CASE_													=  {
          'num_train_timesteps': 1100,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
      }

      config.update(**_A)
      return config

  def   lowerCAmelCase__       (   self):
      for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=_A)

  def   lowerCAmelCase__       (   self):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
          self.check_over_configs(beta_start=_A  ,						beta_end=_A)

  def   lowerCAmelCase__       (   self):
      for schedule in ["linear", "scaled_linear", "exp"]:
          self.check_over_configs(beta_schedule=_A)

  def   lowerCAmelCase__       (   self):
      for clip_sample_range in [1.0, 2.0, 3.0]:
          self.check_over_configs(clip_sample_range=_A  ,						clip_sample=_A)

  def   lowerCAmelCase__       (   self):
      for prediction_type in ["epsilon", "v_prediction", "sample"]:
          self.check_over_configs(prediction_type=_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config(prediction_type='v_prediction')
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
          assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if str(_A).startswith('cpu'):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      elif str(_A).startswith('mps'):
          # Larger tolerance on mps
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-2
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A  ,						use_karras_sigmas=_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      assert abs(result_sum.item() - 0.0_0_0_1_5) < 1E-2
      assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07) < 1E-2


