

from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


a__				=		logging.get_logger(__name__)


# General docstring
a__				=		'''MobileNetV1Config'''

# Base docstring
a__				=		'''google/mobilenet_v1_1.0_224'''
a__				=		[1, 1024, 7, 7]

# Image classification docstring
a__				=		'''google/mobilenet_v1_1.0_224'''
a__				=		'''tabby, tabby cat'''


a__				=		[
    '''google/mobilenet_v1_1.0_224''',
    '''google/mobilenet_v1_0.75_192''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]



def    __UpperCAmelCase (	__a      :			str		,__a      :			Union[str, Any]		,__a      :			int=None	)			->     Tuple:

					"""simple docstring"""

					_a	:    Optional[int]         =		{}

					if isinstance(__a		,__a	):
										_a	:    Tuple         =		model.mobilenet_va
					else:
										_a	:    Optional[Any]         =		model

					_a	:    List[Any]         =		'''MobilenetV1/Conv2d_0/'''
					_a	:    int         =		backbone.conv_stem.convolution.weight
					_a	:    Optional[int]         =		backbone.conv_stem.normalization.bias
					_a	:    List[str]         =		backbone.conv_stem.normalization.weight
					_a	:    Optional[Any]         =		backbone.conv_stem.normalization.running_mean
					_a	:    Tuple         =		backbone.conv_stem.normalization.running_var

					for i in range(13	):
										_a	:    Optional[int]         =		i + 1
										_a	:    int         =		i * 2

										_a	:    Union[str, Any]         =		backbone.layer[pt_index]
										_a	:    Dict         =		F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
										_a	:    List[Any]         =		pointer.convolution.weight
										_a	:    Tuple         =		pointer.normalization.bias
										_a	:    Union[str, Any]         =		pointer.normalization.weight
										_a	:    str         =		pointer.normalization.running_mean
										_a	:    List[Any]         =		pointer.normalization.running_var

										_a	:    Optional[int]         =		backbone.layer[pt_index + 1]
										_a	:    List[Any]         =		F"""MobilenetV1/Conv2d_{tf_index}_pointwise/"""
										_a	:    Tuple         =		pointer.convolution.weight
										_a	:    Any         =		pointer.normalization.bias
										_a	:    List[Any]         =		pointer.normalization.weight
										_a	:    int         =		pointer.normalization.running_mean
										_a	:    List[Any]         =		pointer.normalization.running_var

					if isinstance(__a		,__a	):
										_a	:    Optional[int]         =		'''MobilenetV1/Logits/Conv2d_1c_1x1/'''
										_a	:    int         =		model.classifier.weight
										_a	:    Union[str, Any]         =		model.classifier.bias

					return tf_to_pt_map



def    __UpperCAmelCase (	__a      :			Optional[Any]		,__a      :			List[str]		,__a      :			Optional[Any]	)			->     Tuple:

					"""simple docstring"""

					try:
										import numpy as np
										import tensorflow as tf
					except ImportError:
										logger.error(
										    '''Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '''
										    '''https://www.tensorflow.org/install/ for installation instructions.'''	)
										raise

					# Load weights from TF model
					_a	:    Optional[int]         =		tf.train.list_variables(__a	)
					_a	:    int         =		{}
					for name, shape in init_vars:
										logger.info(F"""Loading TF weight {name} with shape {shape}"""	)
										_a	:    str         =		tf.train.load_variable(__a		,__a	)
										_a	:    int         =		array

					# Build TF to PyTorch weights loading map
					_a	:    List[str]         =		_build_tf_to_pytorch_map(__a		,__a		,__a	)

					for name, pointer in tf_to_pt_map.items():
										logger.info(F"""Importing {name}"""	)
										if name not in tf_weights:
															logger.info(F"""{name} not in tf pre-trained weights, skipping"""	)
															continue

										_a	:    Union[str, Any]         =		tf_weights[name]

										if "depthwise_weights" in name:
															logger.info('''Transposing depthwise'''	)
															_a	:    int         =		np.transpose(__a		,(2, 3, 0, 1)	)
										elif "weights" in name:
															logger.info('''Transposing'''	)
															if len(pointer.shape	) == 2:  # copying into linear layer
																				_a	:    Optional[int]         =		array.squeeze().transpose()
															else:
																				_a	:    List[str]         =		np.transpose(__a		,(3, 2, 0, 1)	)

										if pointer.shape != array.shape:
															raise ValueError(F"""Pointer shape {pointer.shape} and array shape {array.shape} mismatched"""	)

										logger.info(F"""Initialize PyTorch weight {name} {array.shape}"""	)
										_a	:    Dict         =		torch.from_numpy(__a	)

										tf_weights.pop(__a		,__a	)
										tf_weights.pop(name + '''/RMSProp'''		,__a	)
										tf_weights.pop(name + '''/RMSProp_1'''		,__a	)
										tf_weights.pop(name + '''/ExponentialMovingAverage'''		,__a	)

					logger.info(F"""Weights not copied to PyTorch model: {', '.join(tf_weights.keys()	)}"""	)
					return model



def    __UpperCAmelCase (	__a      :			torch.Tensor		,__a      :			nn.Convad	)			->     torch.Tensor:

					"""simple docstring"""

					_a      ,					_a	:    int         =		features.shape[-2:]
					_a      ,					_a	:    Tuple         =		conv_layer.stride
					_a      ,					_a	:    Optional[int]         =		conv_layer.kernel_size

					if in_height % stride_height == 0:
										_a	:    Dict         =		max(kernel_height - stride_height		,0	)
					else:
										_a	:    int         =		max(kernel_height - (in_height % stride_height)		,0	)

					if in_width % stride_width == 0:
										_a	:    Optional[Any]         =		max(kernel_width - stride_width		,0	)
					else:
										_a	:    List[str]         =		max(kernel_width - (in_width % stride_width)		,0	)

					_a	:    Any         =		pad_along_width // 2
					_a	:    Optional[Any]         =		pad_along_width - pad_left
					_a	:    List[Any]         =		pad_along_height // 2
					_a	:    Any         =		pad_along_height - pad_top

					_a	:    List[Any]         =		(pad_left, pad_right, pad_top, pad_bottom)
					return nn.functional.pad(__a		,__a		,'''constant'''		,0.0	)

class 				UpperCAmelCase_		(			nn.Module   ):
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a = 1       ,							_a = 1       ,							_a = False       ,							_a = True       ,							_a = True       ,							)    ->    None:
												super().__init__()
												_a	:    List[str]         =		config

												if in_channels % groups != 0:
																	raise ValueError(F"""Input channels ({in_channels}) are not divisible by {groups} groups."""       )
												if out_channels % groups != 0:
																	raise ValueError(F"""Output channels ({out_channels}) are not divisible by {groups} groups."""       )

												_a	:    str         =		0 if config.tf_padding else int((kernel_size - 1) / 2       )

												_a	:    Dict         =		nn.Convad(
												    in_channels=_a       ,							out_channels=_a       ,							kernel_size=_a       ,							stride=_a       ,							padding=_a       ,							groups=_a       ,							bias=_a       ,							padding_mode='''zeros'''       ,							)

												if use_normalization:
																	_a	:    Dict         =		nn.BatchNormad(
																	    num_features=_a       ,							eps=config.layer_norm_eps       ,							momentum=0.9997       ,							affine=_a       ,							track_running_stats=_a       ,							)
												else:
																	_a	:    Optional[int]         =		None

												if use_activation:
																	if isinstance(_a       ,							_a       ):
																						_a	:    Any         =		ACTaFN[use_activation]
																	elif isinstance(config.hidden_act       ,							_a       ):
																						_a	:    str         =		ACTaFN[config.hidden_act]
																	else:
																						_a	:    Optional[Any]         =		config.hidden_act
												else:
																	_a	:    List[str]         =		None




							def 				__lowercase				(			self       ,							_a       )    ->    torch.Tensor:
												if self.config.tf_padding:
																	_a	:    int         =		apply_tf_padding(_a       ,							self.convolution       )
												_a	:    Dict         =		self.convolution(_a       )
												if self.normalization is not None:
																	_a	:    Any         =		self.normalization(_a       )
												if self.activation is not None:
																	_a	:    Any         =		self.activation(_a       )
												return features



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Dict	    =    MobileNetVaConfig
							UpperCAmelCase__       :						Dict	    =    load_tf_weights_in_mobilenet_va
							UpperCAmelCase__       :						Any	    =    "mobilenet_v1"
							UpperCAmelCase__       :						str	    =    "pixel_values"
							UpperCAmelCase__       :						Union[str, Any]	    =    False


							def 				__lowercase				(			self       ,							_a       )    ->    None:
												if isinstance(_a       ,							(nn.Linear, nn.Convad)       ):
																	module.weight.data.normal_(mean=0.0       ,							std=self.config.initializer_range       )
																	if module.bias is not None:
																						module.bias.data.zero_()
												elif isinstance(_a       ,							nn.BatchNormad       ):
																	module.bias.data.zero_()
																	module.weight.data.fill_(1.0       )



a__				=		R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

a__				=		R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''

@add_start_docstrings(
    "The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."				,       __lowercase				,       )
class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a = True       )    ->    Dict:
												super().__init__(_a       )
												_a	:    Union[str, Any]         =		config

												_a	:    Optional[int]         =		3_2
												_a	:    int         =		max(int(depth * config.depth_multiplier       )       ,							config.min_depth       )

												_a	:    Tuple         =		MobileNetVaConvLayer(
												    _a       ,							in_channels=config.num_channels       ,							out_channels=_a       ,							kernel_size=3       ,							stride=2       ,							)

												_a	:    Dict         =		[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

												_a	:    Optional[Any]         =		nn.ModuleList()
												for i in range(1_3       ):
																	_a	:    str         =		out_channels

																	if strides[i] == 2 or i == 0:
																						depth *= 2
																						_a	:    List[Any]         =		max(int(depth * config.depth_multiplier       )       ,							config.min_depth       )

																	self.layer.append(
																	    MobileNetVaConvLayer(
																	        _a       ,							in_channels=_a       ,							out_channels=_a       ,							kernel_size=3       ,							stride=strides[i]       ,							groups=_a       ,							)       )

																	self.layer.append(
																	    MobileNetVaConvLayer(
																	        _a       ,							in_channels=_a       ,							out_channels=_a       ,							kernel_size=1       ,							)       )

												_a	:    Union[str, Any]         =		nn.AdaptiveAvgPoolad((1, 1)       ) if add_pooling_layer else None

												# Initialize weights and apply final processing
												self.post_init()


							def 				__lowercase				(			self       ,							_a       )    ->    Dict:
												raise NotImplementedError




							@add_start_docstrings_to_model_forward(_a       )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC       ,							output_type=_a       ,							config_class=_CONFIG_FOR_DOC       ,							modality='''vision'''       ,							expected_output=_EXPECTED_OUTPUT_SHAPE       ,							)
							def 				__lowercase				(			self       ,							_a = None       ,							_a = None       ,							_a = None       ,							)    ->    Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
												_a	:    int         =		(
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												_a	:    List[Any]         =		return_dict if return_dict is not None else self.config.use_return_dict

												if pixel_values is None:
																	raise ValueError('''You have to specify pixel_values'''       )

												_a	:    str         =		self.conv_stem(_a       )

												_a	:    List[str]         =		() if output_hidden_states else None

												for i, layer_module in enumerate(self.layer       ):
																	_a	:    Union[str, Any]         =		layer_module(_a       )

																	if output_hidden_states:
																						_a	:    List[str]         =		all_hidden_states + (hidden_states,)

												_a	:    Any         =		hidden_states

												if self.pooler is not None:
																	_a	:    Dict         =		torch.flatten(self.pooler(_a       )       ,							start_dim=1       )
												else:
																	_a	:    str         =		None

												if not return_dict:
																	return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None       )

												return BaseModelOutputWithPoolingAndNoAttention(
												    last_hidden_state=_a       ,							pooler_output=_a       ,							hidden_states=_a       ,							)



@add_start_docstrings(
    "\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "				,       __lowercase				,       )
class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							def __init__(			self       ,							_a       )    ->    None:
												super().__init__(_a       )

												_a	:    int         =		config.num_labels
												_a	:    List[str]         =		MobileNetVaModel(_a       )

												_a	:    Union[str, Any]         =		self.mobilenet_va.layer[-1].convolution.out_channels

												# Classifier head
												_a	:    List[str]         =		nn.Dropout(config.classifier_dropout_prob       ,							inplace=_a       )
												_a	:    Tuple         =		nn.Linear(_a       ,							config.num_labels       ) if config.num_labels > 0 else nn.Identity()

												# Initialize weights and apply final processing
												self.post_init()




							@add_start_docstrings_to_model_forward(_a       )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,							output_type=_a       ,							config_class=_CONFIG_FOR_DOC       ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,							)
							def 				__lowercase				(			self       ,							_a = None       ,							_a = None       ,							_a = None       ,							_a = None       ,							)    ->    Union[tuple, ImageClassifierOutputWithNoAttention]:
												_a	:    Optional[int]         =		return_dict if return_dict is not None else self.config.use_return_dict

												_a	:    Any         =		self.mobilenet_va(_a       ,							output_hidden_states=_a       ,							return_dict=_a       )

												_a	:    Union[str, Any]         =		outputs.pooler_output if return_dict else outputs[1]

												_a	:    Dict         =		self.classifier(self.dropout(_a       )       )

												_a	:    str         =		None
												if labels is not None:
																	if self.config.problem_type is None:
																						if self.num_labels == 1:
																											_a	:    Tuple         =		'''regression'''
																						elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																											_a	:    Optional[int]         =		'''single_label_classification'''
																						else:
																											_a	:    Union[str, Any]         =		'''multi_label_classification'''

																	if self.config.problem_type == "regression":
																						_a	:    Dict         =		MSELoss()
																						if self.num_labels == 1:
																											_a	:    Tuple         =		loss_fct(logits.squeeze()       ,							labels.squeeze()       )
																						else:
																											_a	:    Union[str, Any]         =		loss_fct(_a       ,							_a       )
																	elif self.config.problem_type == "single_label_classification":
																						_a	:    Optional[Any]         =		CrossEntropyLoss()
																						_a	:    List[Any]         =		loss_fct(logits.view(-1       ,							self.num_labels       )       ,							labels.view(-1       )       )
																	elif self.config.problem_type == "multi_label_classification":
																						_a	:    Union[str, Any]         =		BCEWithLogitsLoss()
																						_a	:    List[Any]         =		loss_fct(_a       ,							_a       )

												if not return_dict:
																	_a	:    List[str]         =		(logits,) + outputs[2:]
																	return ((loss,) + output) if loss is not None else output

												return ImageClassifierOutputWithNoAttention(
												    loss=_a       ,							logits=_a       ,							hidden_states=outputs.hidden_states       ,							)






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						List[Any]	    =    (DDPMScheduler,)


							def 				__lowercase				(			self       ,							**_a       )    ->    Any:
												_a	:    List[Any]         =		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''variance_type''': '''fixed_small''',
												    '''clip_sample''': True,
												}

												config.update(**_a       )
												return config


							def 				__lowercase				(			self       )    ->    Any:
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=_a       )


							def 				__lowercase				(			self       )    ->    List[Any]:
												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=_a       ,							beta_end=_a       )


							def 				__lowercase				(			self       )    ->    List[str]:
												for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=_a       )


							def 				__lowercase				(			self       )    ->    str:
												for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												self.check_over_configs(thresholding=_a       )
												for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=_a       ,							prediction_type=_a       ,							sample_max_value=_a       ,							)


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(prediction_type=_a       )


							def 				__lowercase				(			self       )    ->    int:
												for t in [0, 5_0_0, 9_9_9]:
																	self.check_over_forward(time_step=_a       )


							def 				__lowercase				(			self       )    ->    int:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    List[Any]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.0_0979       )       ) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1e-5


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    int         =		self.scheduler_classes[0]
												_a	:    int         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    Optional[int]         =		len(_a       )

												_a	:    Optional[Any]         =		self.dummy_model()
												_a	:    str         =		self.dummy_sample_deter
												_a	:    Union[str, Any]         =		torch.manual_seed(0       )

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

																	# 2. predict previous mean of sample x_t-1
																	_a	:    Optional[int]         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	_a	:    List[Any]         =		pred_prev_sample

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Optional[Any]         =		torch.mean(torch.abs(_a       )       )

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


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Optional[int]         =		self.scheduler_classes[0]
												_a	:    Optional[Any]         =		self.get_scheduler_config(prediction_type='''v_prediction'''       )
												_a	:    Union[str, Any]         =		scheduler_class(**_a       )

												_a	:    Dict         =		len(_a       )

												_a	:    int         =		self.dummy_model()
												_a	:    Tuple         =		self.dummy_sample_deter
												_a	:    List[Any]         =		torch.manual_seed(0       )

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

																	# 2. predict previous mean of sample x_t-1
																	_a	:    int         =		scheduler.step(_a       ,							_a       ,							_a       ,							generator=_a       ).prev_sample

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

												_a	:    str         =		torch.sum(torch.abs(_a       )       )
												_a	:    Tuple         =		torch.mean(torch.abs(_a       )       )

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


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    Union[str, Any]         =		self.scheduler_classes[0]
												_a	:    Tuple         =		self.get_scheduler_config()
												_a	:    Any         =		scheduler_class(**_a       )

												_a	:    Optional[Any]         =		[1_0_0, 8_7, 5_0, 1, 0]

												scheduler.set_timesteps(timesteps=_a       )

												_a	:    Optional[int]         =		scheduler.timesteps

												for i, timestep in enumerate(_a       ):
																	if i == len(_a       ) - 1:
																						_a	:    Dict         =		-1
																	else:
																						_a	:    Tuple         =		timesteps[i + 1]

																	_a	:    Optional[Any]         =		scheduler.previous_timestep(_a       )
																	_a	:    Optional[Any]         =		prev_t.item()

																	self.assertEqual(_a       ,							_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Tuple         =		scheduler_class(**_a       )

												_a	:    str         =		[1_0_0, 8_7, 5_0, 5_1, 0]

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


							def 				__lowercase				(			self       )    ->    str:
												_a	:    List[str]         =		self.scheduler_classes[0]
												_a	:    List[str]         =		self.get_scheduler_config()
												_a	:    Dict         =		scheduler_class(**_a       )

												_a	:    Union[str, Any]         =		[1_0_0, 8_7, 5_0, 1, 0]
												_a	:    Optional[Any]         =		len(_a       )

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




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Dict         =		self.scheduler_classes[0]
												_a	:    Union[str, Any]         =		self.get_scheduler_config()
												_a	:    int         =		scheduler_class(**_a       )

												_a	:    str         =		[scheduler.config.num_train_timesteps]

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



