


'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


__snake_case							      =     logging.get_logger(__name__)


def 			a      (			__a ,					__a ,					__a ,					__a=False   )  ->	Dict:



					'''simple docstring'''






					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
										    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
										    ''' instructions.'''   )
										raise

					if not is_sharded:
										UpperCamelCase__   :Optional[Any]												= os.path.abspath(__a   )
										logger.info(f'''Loading PyTorch weights from {pt_path}'''   )

										UpperCamelCase__   :Tuple												= torch.load(__a ,					map_location='''cpu'''   )
										logger.info(f'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters.'''   )

										UpperCamelCase__   :List[Any]												= convert_pytorch_state_dict_to_flax(__a ,					__a   )
					else:
										# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
										UpperCamelCase__   :Union[str, Any]												= convert_pytorch_sharded_state_dict_to_flax(__a ,					__a   )
					return flax_state_dict


def 			a      (			__a ,					__a ,					__a ,					__a ,					)  ->	(Tuple[str], np.ndarray):



					'''simple docstring'''






					def is_key_or_prefix_key_in_dict(__a   ) -> bool:
										return len(set(__a   ) & {key, (model_prefix,) + key}   ) > 0

					# layer norm
					UpperCamelCase__   :Dict												= pt_tuple_key[:-1] + ('''scale''',)
					if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__a   ):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer mean
					UpperCamelCase__   :Dict												= pt_tuple_key[:-1] + ('''mean''',)
					if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__a   ):
										return renamed_pt_tuple_key, pt_tensor

					# batch norm layer var
					UpperCamelCase__   :Optional[Any]												= pt_tuple_key[:-1] + ('''var''',)
					if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__a   ):
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					UpperCamelCase__   :Optional[int]												= pt_tuple_key[:-1] + ('''embedding''',)
					if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__a   ):
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					UpperCamelCase__   :Tuple												= pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__a   ):
										UpperCamelCase__   :int												= pt_tensor.transpose(2 ,					3 ,					1 ,					0   )
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					UpperCamelCase__   :Tuple												= pt_tuple_key[:-1] + ('''kernel''',)
					if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__a   ):
										UpperCamelCase__   :str												= pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					UpperCamelCase__   :List[str]												= pt_tuple_key[:-1] + ('''weight''',)
					if pt_tuple_key[-1] == "gamma":
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm bias
					UpperCamelCase__   :List[Any]												= pt_tuple_key[:-1] + ('''bias''',)
					if pt_tuple_key[-1] == "beta":
										return renamed_pt_tuple_key, pt_tensor

					# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
					UpperCamelCase__   :Tuple												= None
					if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
										UpperCamelCase__   :Tuple												= pt_tuple_key[-2] + '''_g'''
					elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
										UpperCamelCase__   :Union[str, Any]												= pt_tuple_key[-2] + '''_v'''
					if name is not None:
										UpperCamelCase__   :Tuple												= pt_tuple_key[:-3] + (name,)
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor


def 			a      (			__a ,					__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :Optional[Any]												= {k: v.numpy() for k, v in pt_state_dict.items()}

					UpperCamelCase__   :List[str]												= flax_model.base_model_prefix

					# use params dict if the model contains batch norm layers
					if "params" in flax_model.params:
										UpperCamelCase__   :Optional[Any]												= flax_model.params['''params''']
					else:
										UpperCamelCase__   :Optional[Any]												= flax_model.params
					UpperCamelCase__   :Union[str, Any]												= flatten_dict(__a   )

					# add batch_stats keys,values to dict
					if "batch_stats" in flax_model.params:
										UpperCamelCase__   :List[str]												= flatten_dict(flax_model.params['''batch_stats''']   )
										random_flax_state_dict.update(__a   )

					UpperCamelCase__   :Any												= {}

					UpperCamelCase__   :Optional[int]												= (model_prefix not in flax_model_params) and (
					    model_prefix in {k.split('''.'''   )[0] for k in pt_state_dict.keys()}
					)
					UpperCamelCase__   :Union[str, Any]												= (model_prefix in flax_model_params) and (
					    model_prefix not in {k.split('''.'''   )[0] for k in pt_state_dict.keys()}
					)

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										UpperCamelCase__   :int												= tuple(pt_key.split('''.'''   )   )

										# remove base model prefix if necessary
										UpperCamelCase__   :Union[str, Any]												= pt_tuple_key[0] == model_prefix
										if load_model_with_head_into_base_model and has_base_model_prefix:
															UpperCamelCase__   :List[str]												= pt_tuple_key[1:]

										# Correctly rename weight parameters
										UpperCamelCase__					,				UpperCamelCase__   :int												= rename_key_and_reshape_tensor(
										    __a ,					__a ,					__a ,					__a   )

										# add model prefix if necessary
										UpperCamelCase__   :int												= (model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															UpperCamelCase__   :Optional[Any]												= (model_prefix,) + flax_key

										if flax_key in random_flax_state_dict:
															if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																				    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )

        # add batch stats if the model contains batchnorm layers
										if "batch_stats" in flax_model.params:
															if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																				UpperCamelCase__   :Union[str, Any]												= jnp.asarray(__a   )
																				continue
															# remove num_batches_tracked key
															if "num_batches_tracked" in flax_key[-1]:
																				flax_state_dict.pop(__a ,					__a   )
																				continue

															# also add unexpected weight so that warning is thrown
															UpperCamelCase__   :Tuple												= jnp.asarray(__a   )

										else:
															# also add unexpected weight so that warning is thrown
															UpperCamelCase__   :Union[str, Any]												= jnp.asarray(__a   )

					return unflatten_dict(__a   )


def 			a      (			__a ,					__a   )  ->	List[str]:



					'''simple docstring'''






					import torch

					# Load the index
					UpperCamelCase__   :int												= {}
					for shard_file in shard_filenames:
										# load using msgpack utils
										UpperCamelCase__   :List[Any]												= torch.load(__a   )
										UpperCamelCase__   :List[Any]												= {k: v.numpy() for k, v in pt_state_dict.items()}

										UpperCamelCase__   :List[Any]												= flax_model.base_model_prefix

										# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
										if "batch_stats" in flax_model.params:
															UpperCamelCase__   :int												= flax_model.params['''params''']

															UpperCamelCase__   :Union[str, Any]												= flatten_dict(__a   )
															random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']   )   )
										else:
															UpperCamelCase__   :Dict												= flax_model.params
															UpperCamelCase__   :int												= flatten_dict(__a   )

										UpperCamelCase__   :List[str]												= (model_prefix not in flax_model_params) and (
										    model_prefix in {k.split('''.'''   )[0] for k in pt_state_dict.keys()}
										)
										UpperCamelCase__   :int												= (model_prefix in flax_model_params) and (
										    model_prefix not in {k.split('''.'''   )[0] for k in pt_state_dict.keys()}
										)
										# Need to change some parameters name to match Flax names
										for pt_key, pt_tensor in pt_state_dict.items():
															UpperCamelCase__   :str												= tuple(pt_key.split('''.'''   )   )

															# remove base model prefix if necessary
															UpperCamelCase__   :Dict												= pt_tuple_key[0] == model_prefix
															if load_model_with_head_into_base_model and has_base_model_prefix:
																				UpperCamelCase__   :Dict												= pt_tuple_key[1:]

															# Correctly rename weight parameters
															UpperCamelCase__					,				UpperCamelCase__   :Optional[Any]												= rename_key_and_reshape_tensor(
															    __a ,					__a ,					__a ,					__a   )
															# add model prefix if necessary
															UpperCamelCase__   :Any												= (model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				UpperCamelCase__   :List[str]												= (model_prefix,) + flax_key

															if flax_key in random_flax_state_dict:
																				if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																									raise ValueError(
																									    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																									    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )

            # add batch stats if the model contains batchnorm layers
															if "batch_stats" in flax_model.params:
																				if "mean" in flax_key[-1]:
																									UpperCamelCase__   :List[Any]												= jnp.asarray(__a   )
																									continue
																				if "var" in flax_key[-1]:
																									UpperCamelCase__   :List[Any]												= jnp.asarray(__a   )
																									continue
																				# remove num_batches_tracked key
																				if "num_batches_tracked" in flax_key[-1]:
																									flax_state_dict.pop(__a ,					__a   )
																									continue

																				# also add unexpected weight so that warning is thrown
																				UpperCamelCase__   :str												= jnp.asarray(__a   )

															else:
																				# also add unexpected weight so that warning is thrown
																				UpperCamelCase__   :List[Any]												= jnp.asarray(__a   )
					return unflatten_dict(__a   )


def 			a      (			__a ,					__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :Tuple												= os.path.abspath(__a   )
					logger.info(f'''Loading Flax weights from {flax_checkpoint_path}'''   )

					# import correct flax class
					UpperCamelCase__   :str												= getattr(__a ,					'''Flax''' + model.__class__.__name__   )

					# load flax weight dict
					with open(__a ,					'''rb'''   ) as state_f:
										try:
															UpperCamelCase__   :Tuple												= from_bytes(__a ,					state_f.read()   )
										except UnpicklingError:
															raise EnvironmentError(f'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''   )

					return load_flax_weights_in_pytorch_model(__a ,					__a   )







def 			a      (			__a ,					__a   )  ->	int:



					'''simple docstring'''






					try:
										import torch  # noqa: F401
					except ImportError:
										logger.error(
										    '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
										    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
										    ''' instructions.'''   )
										raise

					# check if we have bf16 weights
					UpperCamelCase__   :List[Any]												= flatten_dict(jax.tree_util.tree_map(lambda __a   : x.dtype == jnp.bfloataa ,					__a   )   ).values()
					if any(__a   ):
										# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
										# and bf16 is not fully supported in PT yet.
										logger.warning(
										    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
										    '''before loading those in PyTorch model.'''   )
										UpperCamelCase__   :str												= jax.tree_util.tree_map(
										    lambda __a   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params ,					__a   )

					UpperCamelCase__   :Optional[Any]												= flatten_dict(__a   )
					UpperCamelCase__   :str												= pt_model.state_dict()

					UpperCamelCase__   :List[Any]												= (pt_model.base_model_prefix in flax_state) and (
					    pt_model.base_model_prefix not in {k.split('''.'''   )[0] for k in pt_model_dict.keys()}
					)
					UpperCamelCase__   :Optional[int]												= (pt_model.base_model_prefix not in flax_state) and (
					    pt_model.base_model_prefix in {k.split('''.'''   )[0] for k in pt_model_dict.keys()}
					)

					# keep track of unexpected & missing keys
					UpperCamelCase__   :Optional[Any]												= []
					UpperCamelCase__   :str												= set(pt_model_dict.keys()   )

					for flax_key_tuple, flax_tensor in flax_state_dict.items():
										UpperCamelCase__   :Tuple												= flax_key_tuple[0] == pt_model.base_model_prefix
										UpperCamelCase__   :int												= '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple   ) in pt_model_dict

										# adapt flax_key to prepare for loading from/to base model only
										if load_model_with_head_into_base_model and has_base_model_prefix:
															UpperCamelCase__   :str												= flax_key_tuple[1:]
										elif load_base_model_into_model_with_head and require_base_model_prefix:
															UpperCamelCase__   :List[str]												= (pt_model.base_model_prefix,) + flax_key_tuple

										# rename flax weights to PyTorch format
										if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__a   ) not in pt_model_dict:
															# conv layer
															UpperCamelCase__   :Any												= flax_key_tuple[:-1] + ('''weight''',)
															UpperCamelCase__   :Tuple												= jnp.transpose(__a ,					(3, 2, 0, 1)   )
										elif flax_key_tuple[-1] == "kernel" and ".".join(__a   ) not in pt_model_dict:
															# linear layer
															UpperCamelCase__   :Dict												= flax_key_tuple[:-1] + ('''weight''',)
															UpperCamelCase__   :Tuple												= flax_tensor.T
										elif flax_key_tuple[-1] in ["scale", "embedding"]:
															UpperCamelCase__   :Union[str, Any]												= flax_key_tuple[:-1] + ('''weight''',)

										# adding batch stats from flax batch norm to pt
										elif "mean" in flax_key_tuple[-1]:
															UpperCamelCase__   :Optional[Any]												= flax_key_tuple[:-1] + ('''running_mean''',)
										elif "var" in flax_key_tuple[-1]:
															UpperCamelCase__   :List[Any]												= flax_key_tuple[:-1] + ('''running_var''',)

										if "batch_stats" in flax_state:
															UpperCamelCase__   :Optional[int]												= '''.'''.join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
										else:
															UpperCamelCase__   :Any												= '''.'''.join(__a   )

										# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
										UpperCamelCase__   :Dict												= {}
										# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
										for key in pt_model_dict:
															UpperCamelCase__   :Dict												= key.split('''.'''   )
															UpperCamelCase__   :Union[str, Any]												= None
															if key_components[-3::2] == ["parametrizations", "original0"]:
																				UpperCamelCase__   :Union[str, Any]												= key_components[-2] + '''_g'''
															elif key_components[-3::2] == ["parametrizations", "original1"]:
																				UpperCamelCase__   :List[Any]												= key_components[-2] + '''_v'''
															if name is not None:
																				UpperCamelCase__   :Dict												= key_components[:-3] + [name]
																				UpperCamelCase__   :Optional[int]												= '''.'''.join(__a   )
																				UpperCamelCase__   :Any												= key

										if flax_key in special_pt_names:
															UpperCamelCase__   :Tuple												= special_pt_names[flax_key]

										if flax_key in pt_model_dict:
															if flax_tensor.shape != pt_model_dict[flax_key].shape:
																				raise ValueError(
																				    f'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
																				    f'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''   )
															else:
																				# add weight to pytorch dict
																				UpperCamelCase__   :Dict												= np.asarray(__a   ) if not isinstance(__a ,					np.ndarray   ) else flax_tensor
																				UpperCamelCase__   :Optional[int]												= torch.from_numpy(__a   )
																				# remove from missing keys
																				missing_keys.remove(__a   )
										else:
															# weight is not expected by PyTorch model
															unexpected_keys.append(__a   )

					pt_model.load_state_dict(__a   )

					# re-transform missing_keys to list
					UpperCamelCase__   :List[str]												= list(__a   )

					if len(__a   ) > 0:
										logger.warning(
										    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
										    f''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
										    f''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
										    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
										    f''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
										    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
										    ''' FlaxBertForSequenceClassification model).'''   )
					else:
										logger.warning(f'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''   )
					if len(__a   ) > 0:
										logger.warning(
										    f'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
										    f''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
										    ''' use it for predictions and inference.'''   )
					else:
										logger.warning(
										    f'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
										    '''If your task is similar to the task the model of the checkpoint was trained on, '''
										    f'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''   )

					return pt_model




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							(DDPMScheduler,)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {
						    "num_train_timesteps": 1000,
						    "beta_start": 0.0001,
						    "beta_end": 0.02,
						    "beta_schedule": "linear",
						    "variance_type": "fixed_small",
						    "clip_sample": True,
						}

						config.update(**lowerCamelCase_		)
						return config




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,       [0.002, 0.02, 0.2, 2]		):
											self.check_over_configs(beta_start=lowerCamelCase_		,       beta_end=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_0979		)		) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

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

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

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

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

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

						assert abs(result_sum.item() - 258.9606		) < 1E-2
						assert abs(result_mean.item() - 0.3372		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config(prediction_type="v_prediction"		)
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   len(lowerCamelCase_		)

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

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

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

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

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

						assert abs(result_sum.item() - 202.0296		) < 1E-2
						assert abs(result_mean.item() - 0.2631		) < 1E-3




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

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

						scheduler.set_timesteps(timesteps=lowerCamelCase_		)

						a					     =   scheduler.timesteps

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

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

											self.assertEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.scheduler_classes[0]
						a					     =   self.get_scheduler_config()
						a					     =   scheduler_class(**lowerCamelCase_		)

						a					     =   [scheduler.config.num_train_timesteps]

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