
"""simple docstring"""


import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
						import jax.numpy as jnp




class  __lowerCamelCase  ( lowerCAmelCase			):




				def __get__(				self							,						UpperCAmelCase							,						UpperCAmelCase=None   ):
							# See docs.python.org/3/howto/descriptor.html#properties
							if obj is None:
										return self
							if self.fget is None:
										raise AttributeError('''unreadable attribute'''   )
							lowerCamelCase_					=  '''__cached_''' + self.fget.__name__
							lowerCamelCase_					=  getattr(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							if cached is None:
										lowerCamelCase_					=  self.fget(UpperCAmelCase   )
										setattr(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
							return cached






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  val.lower()
			if val in {"y", "yes", "t", "true", "on", "1"}:
						return 1
			if val in {"n", "no", "f", "false", "off", "0"}:
						return 0
			raise ValueError(f"invalid truth value {val!r}"						)






def  lowercase						(	lowerCAmelCase__						):
			if is_torch_fx_proxy(lowerCAmelCase__						):
						return True
			if is_torch_available():
						import torch

						if isinstance(lowerCAmelCase__	,torch.Tensor						):
									return True
			if is_tf_available():
						import tensorflow as tf

						if isinstance(lowerCAmelCase__	,tf.Tensor						):
									return True

			if is_flax_available():
						import jax.numpy as jnp
						from jax.core import Tracer

						if isinstance(lowerCAmelCase__	,(jnp.ndarray, Tracer)						):
									return True

			return isinstance(lowerCAmelCase__	,np.ndarray						)






def  lowercase						(	lowerCAmelCase__						):
			return isinstance(lowerCAmelCase__	,np.ndarray						)






def  lowercase						(	lowerCAmelCase__						):
			return _is_numpy(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import torch

			return isinstance(lowerCAmelCase__	,torch.Tensor						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_torch_available() else _is_torch(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import torch

			return isinstance(lowerCAmelCase__	,torch.device						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_torch_available() else _is_torch_device(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import torch

			if isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
						if hasattr(lowerCAmelCase__	,lowerCAmelCase__						):
									lowerCamelCase_					=  getattr(lowerCAmelCase__	,lowerCAmelCase__						)
						else:
									return False
			return isinstance(lowerCAmelCase__	,torch.dtype						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_torch_available() else _is_torch_dtype(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import tensorflow as tf

			return isinstance(lowerCAmelCase__	,tf.Tensor						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_tf_available() else _is_tensorflow(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import tensorflow as tf

			# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
			if hasattr(lowerCAmelCase__	,'''is_symbolic_tensor'''						):
						return tf.is_symbolic_tensor(lowerCAmelCase__						)
			return type(lowerCAmelCase__						) == tf.Tensor






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			import jax.numpy as jnp  # noqa: F811

			return isinstance(lowerCAmelCase__	,jnp.ndarray						)






def  lowercase						(	lowerCAmelCase__						):
			return False if not is_flax_available() else _is_jax(lowerCAmelCase__						)






def  lowercase						(	lowerCAmelCase__						):
			if isinstance(lowerCAmelCase__	,(dict, UserDict)						):
						return {k: to_py_obj(lowerCAmelCase__						) for k, v in obj.items()}
			elif isinstance(lowerCAmelCase__	,(list, tuple)						):
						return [to_py_obj(lowerCAmelCase__						) for o in obj]
			elif is_tf_tensor(lowerCAmelCase__						):
						return obj.numpy().tolist()
			elif is_torch_tensor(lowerCAmelCase__						):
						return obj.detach().cpu().tolist()
			elif is_jax_tensor(lowerCAmelCase__						):
						return np.asarray(lowerCAmelCase__						).tolist()
			elif isinstance(lowerCAmelCase__	,(np.ndarray, np.number)						):  # tolist also works on 0d np arrays
						return obj.tolist()
			else:
						return obj






def  lowercase						(	lowerCAmelCase__						):
			if isinstance(lowerCAmelCase__	,(dict, UserDict)						):
						return {k: to_numpy(lowerCAmelCase__						) for k, v in obj.items()}
			elif isinstance(lowerCAmelCase__	,(list, tuple)						):
						return np.array(lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						return obj.numpy()
			elif is_torch_tensor(lowerCAmelCase__						):
						return obj.detach().cpu().numpy()
			elif is_jax_tensor(lowerCAmelCase__						):
						return np.asarray(lowerCAmelCase__						)
			else:
						return obj




class  __lowerCamelCase  ( lowerCAmelCase			):




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  fields(self   )

							# Safety and consistency checks
							if not len(UpperCAmelCase   ):
										raise ValueError(f"{self.__class__.__name__} has no fields."   )
							if not all(field.default is None for field in class_fields[1:]   ):
										raise ValueError(f"{self.__class__.__name__} should not have more than one required field."   )

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

							if other_fields_are_none and not is_tensor(UpperCAmelCase   ):
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  first_field.items()
													lowerCamelCase_					=  True
										else:
													try:
																lowerCamelCase_					=  iter(UpperCAmelCase   )
																lowerCamelCase_					=  True
													except TypeError:
																lowerCamelCase_					=  False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
										if first_field_iterator:
													for idx, element in enumerate(UpperCAmelCase   ):
																if (
																    not isinstance(UpperCAmelCase							,						(list, tuple)   )
																    or not len(UpperCAmelCase   ) == 2
																    or not isinstance(element[0]							,						UpperCAmelCase   )
																):
																			if idx == 0:
																						# If we do not have an iterator of key/values, set it as attribute
																						lowerCamelCase_					=  first_field
																			else:
																						# If we have a mixed iterator, raise an error
																						raise ValueError(
																						    f"Cannot set key/value for {element}. It needs to be a tuple (key, value)."   )
																			break
																setattr(self							,						element[0]							,						element[1]   )
																if element[1] is not None:
																			lowerCamelCase_					=  element[1]
										elif first_field is not None:
													lowerCamelCase_					=  first_field
							else:
										for field in class_fields:
													lowerCamelCase_					=  getattr(self							,						field.name   )
													if v is not None:
																lowerCamelCase_					=  v




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




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




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




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




				def __getitem__(				self							,						UpperCAmelCase   ):
							if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
										lowerCamelCase_					=  dict(self.items()   )
										return inner_dict[k]
							else:
										return self.to_tuple()[k]




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




				def __setitem__(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							# Will raise a KeyException if needed
							super().__setitem__(UpperCAmelCase							,						UpperCAmelCase   )
							# Don't call self.__setattr__ to avoid recursion errors
							super().__setattr__(UpperCAmelCase							,						UpperCAmelCase   )





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




class  __lowerCamelCase  ( lowerCAmelCase						,	lowerCAmelCase			):




				@classmethod
				def        UpperCAmelCase__						(				cls							,						UpperCAmelCase   ):
							raise ValueError(
							    f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys()   )}"   )




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Tuple          =    'longest'
				a__:      Dict          =    'max_length'
				a__:      Optional[int]          =    'do_not_pad'




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    'pt'
				a__:      Union[str, Any]          =    'tf'
				a__:      int          =    'np'
				a__:      List[str]          =    'jax'




class  __lowerCamelCase  :




				def __init__(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  context_managers
							lowerCamelCase_					=  ExitStack()




				def __enter__(				self   ):
							for context_manager in self.context_managers:
										self.stack.enter_context(UpperCAmelCase   )





				def __exit__(				self							,						*UpperCAmelCase							,						**UpperCAmelCase   ):
							self.stack.__exit__(*UpperCAmelCase							,						**UpperCAmelCase   )






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  infer_framework(lowerCAmelCase__						)
			if framework == "tf":
						lowerCamelCase_					=  inspect.signature(model_class.call						)  # TensorFlow models
			elif framework == "pt":
						lowerCamelCase_					=  inspect.signature(model_class.forward						)  # PyTorch models
			else:
						lowerCamelCase_					=  inspect.signature(model_class.__call__						)  # Flax models

			for p in signature.parameters:
						if p == "return_loss" and signature.parameters[p].default is True:
									return True

			return False






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  model_class.__name__
			lowerCamelCase_					=  infer_framework(lowerCAmelCase__						)
			if framework == "tf":
						lowerCamelCase_					=  inspect.signature(model_class.call						)  # TensorFlow models
			elif framework == "pt":
						lowerCamelCase_					=  inspect.signature(model_class.forward						)  # PyTorch models
			else:
						lowerCamelCase_					=  inspect.signature(model_class.__call__						)  # Flax models

			if "QuestionAnswering" in model_name:
						return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
			else:
						return [p for p in signature.parameters if "label" in p]






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__ = ""	,lowerCAmelCase__ = "."						):

			def _flatten_dict(lowerCAmelCase__	,lowerCAmelCase__=""	,lowerCAmelCase__="."						):
						for k, v in d.items():
									lowerCamelCase_					=  str(lowerCAmelCase__						) + delimiter + str(lowerCAmelCase__						) if parent_key else k
									if v and isinstance(lowerCAmelCase__	,lowerCAmelCase__						):
												yield from flatten_dict(lowerCAmelCase__	,lowerCAmelCase__	,delimiter=lowerCAmelCase__						).items()
									else:
												yield key, v

			return dict(_flatten_dict(lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)						)






@contextmanager
def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__ = False						):
			if use_temp_dir:
						with tempfile.TemporaryDirectory() as tmp_dir:
									yield tmp_dir
			else:
						yield working_dir






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.transpose(lowerCAmelCase__	,axes=lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.T if axes is None else array.permute(*lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.transpose(lowerCAmelCase__	,perm=lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return jnp.transpose(lowerCAmelCase__	,axes=lowerCAmelCase__						)
			else:
						raise ValueError(f"Type not supported for transpose: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.reshape(lowerCAmelCase__	,lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.reshape(*lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.reshape(lowerCAmelCase__	,lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return jnp.reshape(lowerCAmelCase__	,lowerCAmelCase__						)
			else:
						raise ValueError(f"Type not supported for reshape: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__=None						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.squeeze(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.squeeze() if axis is None else array.squeeze(dim=lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.squeeze(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return jnp.squeeze(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			else:
						raise ValueError(f"Type not supported for squeeze: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.expand_dims(lowerCAmelCase__	,lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.unsqueeze(dim=lowerCAmelCase__						)
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.expand_dims(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return jnp.expand_dims(lowerCAmelCase__	,axis=lowerCAmelCase__						)
			else:
						raise ValueError(f"Type not supported for expand_dims: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__						):
			if is_numpy_array(lowerCAmelCase__						):
						return np.size(lowerCAmelCase__						)
			elif is_torch_tensor(lowerCAmelCase__						):
						return array.numel()
			elif is_tf_tensor(lowerCAmelCase__						):
						import tensorflow as tf

						return tf.size(lowerCAmelCase__						)
			elif is_jax_tensor(lowerCAmelCase__						):
						return array.size
			else:
						raise ValueError(f"Type not supported for expand_dims: {type(lowerCAmelCase__						)}."						)






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			for key, value in auto_map.items():
						if isinstance(lowerCAmelCase__	,(tuple, list)						):
									lowerCamelCase_					=  [f"{repo_id}--{v}" if (v is not None and '''--''' not in v) else v for v in value]
						elif value is not None and "--" not in value:
									lowerCamelCase_					=  f"{repo_id}--{value}"

			return auto_map






def  lowercase						(	lowerCAmelCase__						):
			for base_class in inspect.getmro(lowerCAmelCase__						):
						lowerCamelCase_					=  base_class.__module__
						lowerCamelCase_					=  base_class.__name__
						if module.startswith('''tensorflow'''						) or module.startswith('''keras'''						) or name == "TFPreTrainedModel":
									return "tf"
						elif module.startswith('''torch'''						) or name == "PreTrainedModel":
									return "pt"
						elif module.startswith('''flax'''						) or module.startswith('''jax'''						) or name == "FlaxPreTrainedModel":
									return "flax"
			else:
						raise TypeError(f"Could not infer framework from class {model_class}."						)





"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Any          =    (DDPMScheduler,)




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  {
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCAmelCase   )
							return config




				def        UpperCAmelCase__						(				self   ):
							for timesteps in [1, 5, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,						[0.0_0_2, 0.0_2, 0.2, 2]   ):
										self.check_over_configs(beta_start=UpperCAmelCase							,						beta_end=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for schedule in ["linear", "squaredcos_cap_v2"]:
										self.check_over_configs(beta_schedule=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for variance in ["fixed_small", "fixed_large", "other"]:
										self.check_over_configs(variance_type=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							for clip_sample in [True, False]:
										self.check_over_configs(clip_sample=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							self.check_over_configs(thresholding=UpperCAmelCase   )
							for threshold in [0.5, 1.0, 2.0]:
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=UpperCAmelCase							,						prediction_type=UpperCAmelCase							,						sample_max_value=UpperCAmelCase							,						)




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




				def        UpperCAmelCase__						(				self   ):
							for t in [0, 500, 999]:
										self.check_over_forward(time_step=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0_9_7_9   )   ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.0_2   )   ) < 1e-5




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter
							lowerCamelCase_					=  torch.manual_seed(0   )

							for t in reversed(range(UpperCAmelCase   )   ):
										# 1. predict noise residual
										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										# 2. predict previous mean of sample x_t-1
										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						generator=UpperCAmelCase   ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										lowerCamelCase_					=  pred_prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_5_8.9_6_0_6   ) < 1e-2
							assert abs(result_mean.item() - 0.3_3_7_2   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config(prediction_type='''v_prediction'''   )
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  len(UpperCAmelCase   )

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter
							lowerCamelCase_					=  torch.manual_seed(0   )

							for t in reversed(range(UpperCAmelCase   )   ):
										# 1. predict noise residual
										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										# 2. predict previous mean of sample x_t-1
										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase							,						generator=UpperCAmelCase   ).prev_sample

										# if t > 0:
										#     noise = self.dummy_sample_deter
										#     variance = scheduler.get_variance(t) ** (0.5) * noise
										#
										# sample = pred_prev_sample + variance
										lowerCamelCase_					=  pred_prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							assert abs(result_sum.item() - 2_0_2.0_2_9_6   ) < 1e-2
							assert abs(result_mean.item() - 0.2_6_3_1   ) < 1e-3




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=UpperCAmelCase   )

							lowerCamelCase_					=  scheduler.timesteps

							for i, timestep in enumerate(UpperCAmelCase   ):
										if i == len(UpperCAmelCase   ) - 1:
													lowerCamelCase_					=  -1
										else:
													lowerCamelCase_					=  timesteps[i + 1]

										lowerCamelCase_					=  scheduler.previous_timestep(UpperCAmelCase   )
										lowerCamelCase_					=  prev_t.item()

										self.assertEqual(UpperCAmelCase							,						UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 51, 0]

							with self.assertRaises(UpperCAmelCase							,						msg='''`custom_timesteps` must be in descending order.'''   ):
										scheduler.set_timesteps(timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [100, 87, 50, 1, 0]
							lowerCamelCase_					=  len(UpperCAmelCase   )

							with self.assertRaises(UpperCAmelCase							,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''   ):
										scheduler.set_timesteps(num_inference_steps=UpperCAmelCase							,						timesteps=UpperCAmelCase   )





				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							lowerCamelCase_					=  [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    UpperCAmelCase							,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,						):
										scheduler.set_timesteps(timesteps=UpperCAmelCase   )



