
"""simple docstring"""


import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


__A       						=       set()
__A       						=       get_logger(__name__)



def     _lowerCamelCase(__UpperCamelCase = None       )				-> Optional[Any]:

	def decorator(__UpperCamelCase       ):
		global _emitted_deprecation_warnings

		if inspect.isclass(__UpperCamelCase       ):
			_lowerCAmelCase								=deprecated_class_or_function.__init__
			_lowerCAmelCase								=deprecated_class_or_function.__name__
		else:
			_lowerCAmelCase								=deprecated_class_or_function
			_lowerCAmelCase								=deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			_lowerCAmelCase								=name if name != """__init__""" else deprecated_function.__qualname__.split("""."""       )[-2]

		_lowerCAmelCase								=(
		    F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
		    if help_message
		    else """"""
		)

		@wraps(__UpperCamelCase       )
		def wrapper(*__UpperCamelCase    , **__UpperCamelCase       ):
			_lowerCAmelCase								=hash(__UpperCamelCase       )
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(__UpperCamelCase    , category=__UpperCamelCase    , stacklevel=2       )
				_emitted_deprecation_warnings.add(__UpperCamelCase       )
			return deprecated_function(*__UpperCamelCase    , **__UpperCamelCase       )

		_lowerCAmelCase								="""deprecated"""

		if inspect.isclass(__UpperCamelCase       ):
			_lowerCAmelCase								=wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator







class 		lowerCamelCase__					(					enum.EnumMeta				):





			'''simple docstring'''



			def __getattribute__(							cls     ,     __UpperCAmelCase  ) ->     Optional[int]:
				_lowerCAmelCase								=super().__getattribute__(__UpperCAmelCase  )
				if isinstance(__UpperCAmelCase     ,     enum.Enum  ) and obj._on_access:
					obj._on_access()
				return obj



			def __getitem__(							cls     ,     __UpperCAmelCase  ) ->     List[str]:
				_lowerCAmelCase								=super().__getitem__(__UpperCAmelCase  )
				if member._on_access:
					member._on_access()
				return member



			def __call__(							cls     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None     ,     *, __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=1  ) ->     Optional[Any]:
				_lowerCAmelCase								=super().__call__(__UpperCAmelCase     ,     __UpperCAmelCase     ,     module=__UpperCAmelCase     ,     qualname=__UpperCAmelCase     ,     type=__UpperCAmelCase     ,     start=__UpperCAmelCase  )
				if isinstance(__UpperCAmelCase     ,     enum.Enum  ) and obj._on_access:
					obj._on_access()
				return obj







class 		lowerCamelCase__					(					enum.Enum ,	metaclass=__magic_name__				):





			'''simple docstring'''



			def __new__(							cls     ,     __UpperCAmelCase  ) ->     Optional[int]:
				_lowerCAmelCase								=object.__new__(cls  )
				_lowerCAmelCase								=value
				_lowerCAmelCase								=member.deprecate
				return member



			@property
			def 					_lowerCAmelCase		(							self  ) ->     List[Any]:
				return ""



			def 					_lowerCAmelCase		(							self  ) ->     Tuple:
				_lowerCAmelCase								=f''' {self.help_message}''' if self.help_message else """"""
				warnings.warn(
				    f'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
				    + help_message     ,     __UpperCAmelCase     ,     stacklevel=3     ,     )










"""simple docstring"""
import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 	a				(					a__   ):
 snake_case__    	=    (HeunDiscreteScheduler,)
 snake_case__    	=    1_0



 def 					UpperCamelCase__   (			self							,    **_snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      {
      'num_train_timesteps': 11_00,
      'beta_start': 0.0_001,
      'beta_end': 0.02,
      'beta_schedule': 'linear',
  }

  config.update(**_snake_case			)
  return config



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for timesteps in [10, 50, 1_00, 10_00]:
   self.check_over_configs(num_train_timesteps=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]							,    [0.0_002, 0.002, 0.02]			):
   self.check_over_configs(beta_start=_snake_case							,    beta_end=_snake_case			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






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



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config(prediction_type='v_prediction'			)
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

  scheduler.set_timesteps(self.num_inference_steps			)

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for i, t in enumerate(scheduler.timesteps			):
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if torch_device in ["cpu", "mps"]:
   assert abs(result_sum.item() - 4.69_34E-07			) < 1E-2
   assert abs(result_mean.item() - 6.11_12E-10			) < 1E-3
  else:
   # CUDA
   assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case			)

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

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma

  for t in scheduler.timesteps:
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  if str(_snake_case			).startswith('cpu'			):
   # The following sum varies between 148 and 156 on mps. Why?
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3
  elif str(_snake_case			).startswith('mps'			):
   # Larger tolerance on mps
   assert abs(result_mean.item() - 0.0_002			) < 1E-2
  else:
   # CUDA
   assert abs(result_sum.item() - 0.1_233			) < 1E-2
   assert abs(result_mean.item() - 0.0_002			) < 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.scheduler_classes[0]
  lowerCAmelCase  							=      self.get_scheduler_config()
  lowerCAmelCase  							=      scheduler_class(**_snake_case							,    use_karras_sigmas=_snake_case			)

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

  lowerCAmelCase  							=      self.dummy_model()
  lowerCAmelCase  							=      self.dummy_sample_deter.to(_snake_case			) * scheduler.init_noise_sigma
  lowerCAmelCase  							=      sample.to(_snake_case			)

  for t in scheduler.timesteps:
   lowerCAmelCase  							=      scheduler.scale_model_input(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      model(_snake_case							,    _snake_case			)

   lowerCAmelCase  							=      scheduler.step(_snake_case							,    _snake_case							,    _snake_case			)
   lowerCAmelCase  							=      output.prev_sample

  lowerCAmelCase  							=      torch.sum(torch.abs(_snake_case			)			)
  lowerCAmelCase  							=      torch.mean(torch.abs(_snake_case			)			)

  assert abs(result_sum.item() - 0.00_015			) < 1E-2
  assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95E-07			) < 1E-2





