



from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


_lowercase: Tuple      			=		logging.get_logger(__name__)

# General docstring
_lowercase: str      			=		"RegNetConfig"

# Base docstring
_lowercase: Union[str, Any]      			=		"facebook/regnet-y-040"
_lowercase: Optional[Any]      			=		[1, 1088, 7, 7]

# Image classification docstring
_lowercase: Dict      			=		"facebook/regnet-y-040"
_lowercase: Union[str, Any]      			=		"tabby, tabby cat"

_lowercase: str      			=		[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class 	_lowercase							(   nn.Module	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 3		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = 1		,       lowerCamelCase_ = "relu"		,       ):
						"""simple docstring"""

						super().__init__()
						a					     =   nn.Convad(
						    lowerCamelCase_		,       lowerCamelCase_		,       kernel_size=lowerCamelCase_		,       stride=lowerCamelCase_		,       padding=kernel_size // 2		,       groups=lowerCamelCase_		,       bias=lowerCamelCase_		,       )
						a					     =   nn.BatchNormad(lowerCamelCase_		)
						a					     =   ACTaFN[activation] if activation is not None else nn.Identity()




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

						a					     =   self.convolution(lowerCamelCase_		)
						a					     =   self.normalization(lowerCamelCase_		)
						a					     =   self.activation(lowerCamelCase_		)
						return hidden_state




class 	_lowercase							(   nn.Module	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__()
						a					     =   RegNetConvLayer(
						    config.num_channels		,       config.embedding_size		,       kernel_size=3		,       stride=2		,       activation=config.hidden_act		)
						a					     =   config.num_channels




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

						a					     =   pixel_values.shape[1]
						if num_channels != self.num_channels:
											raise ValueError(
											    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."		)
						a					     =   self.embedder(lowerCamelCase_		)
						return hidden_state




class 	_lowercase							(   nn.Module	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 2		):
						"""simple docstring"""

						super().__init__()
						a					     =   nn.Convad(lowerCamelCase_		,       lowerCamelCase_		,       kernel_size=1		,       stride=lowerCamelCase_		,       bias=lowerCamelCase_		)
						a					     =   nn.BatchNormad(lowerCamelCase_		)




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

						a					     =   self.convolution(lowerCamelCase_		)
						a					     =   self.normalization(lowerCamelCase_		)
						return hidden_state




class 	_lowercase							(   nn.Module	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__()

						a					     =   nn.AdaptiveAvgPoolad((1, 1)		)
						a					     =   nn.Sequential(
						    nn.Convad(lowerCamelCase_		,       lowerCamelCase_		,       kernel_size=1		)		,       nn.ReLU()		,       nn.Convad(lowerCamelCase_		,       lowerCamelCase_		,       kernel_size=1		)		,       nn.Sigmoid()		,       )




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

						a					     =   self.pooler(lowerCamelCase_		)
						a					     =   self.attention(lowerCamelCase_		)
						a					     =   hidden_state * attention
						return hidden_state




class 	_lowercase							(   nn.Module	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 1		):
						"""simple docstring"""

						super().__init__()
						a					     =   in_channels != out_channels or stride != 1
						a					     =   max(1		,       out_channels // config.groups_width		)
						a					     =   (
						    RegNetShortCut(lowerCamelCase_		,       lowerCamelCase_		,       stride=lowerCamelCase_		) if should_apply_shortcut else nn.Identity()
						)
						a					     =   nn.Sequential(
						    RegNetConvLayer(lowerCamelCase_		,       lowerCamelCase_		,       kernel_size=1		,       activation=config.hidden_act		)		,       RegNetConvLayer(lowerCamelCase_		,       lowerCamelCase_		,       stride=lowerCamelCase_		,       groups=lowerCamelCase_		,       activation=config.hidden_act		)		,       RegNetConvLayer(lowerCamelCase_		,       lowerCamelCase_		,       kernel_size=1		,       activation=lowerCamelCase_		)		,       )
						a					     =   ACTaFN[config.hidden_act]




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

						a					     =   hidden_state
						a					     =   self.layer(lowerCamelCase_		)
						a					     =   self.shortcut(lowerCamelCase_		)
						hidden_state += residual
						a					     =   self.activation(lowerCamelCase_		)
						return hidden_state




class 	_lowercase							(   nn.Module	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 1		):
						"""simple docstring"""

						super().__init__()
						a					     =   in_channels != out_channels or stride != 1
						a					     =   max(1		,       out_channels // config.groups_width		)
						a					     =   (
						    RegNetShortCut(lowerCamelCase_		,       lowerCamelCase_		,       stride=lowerCamelCase_		) if should_apply_shortcut else nn.Identity()
						)
						a					     =   nn.Sequential(
						    RegNetConvLayer(lowerCamelCase_		,       lowerCamelCase_		,       kernel_size=1		,       activation=config.hidden_act		)		,       RegNetConvLayer(lowerCamelCase_		,       lowerCamelCase_		,       stride=lowerCamelCase_		,       groups=lowerCamelCase_		,       activation=config.hidden_act		)		,       RegNetSELayer(lowerCamelCase_		,       reduced_channels=int(round(in_channels / 4		)		)		)		,       RegNetConvLayer(lowerCamelCase_		,       lowerCamelCase_		,       kernel_size=1		,       activation=lowerCamelCase_		)		,       )
						a					     =   ACTaFN[config.hidden_act]




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

						a					     =   hidden_state
						a					     =   self.layer(lowerCamelCase_		)
						a					     =   self.shortcut(lowerCamelCase_		)
						hidden_state += residual
						a					     =   self.activation(lowerCamelCase_		)
						return hidden_state




class 	_lowercase							(   nn.Module	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 2		,       lowerCamelCase_ = 2		,       ):
						"""simple docstring"""

						super().__init__()

						a					     =   RegNetXLayer if config.layer_type == "x" else RegNetYLayer

						a					     =   nn.Sequential(
						    # downsampling is done in the first layer with stride of 2
						    layer(
						        lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       stride=lowerCamelCase_		,       )		,       *[layer(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		) for _ in range(depth - 1		)]		,       )




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

						a					     =   self.layers(lowerCamelCase_		)
						return hidden_state




class 	_lowercase							(   nn.Module	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__()
						a					     =   nn.ModuleList([]		)
						# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
						self.stages.append(
						    RegNetStage(
						        lowerCamelCase_		,       config.embedding_size		,       config.hidden_sizes[0]		,       stride=2 if config.downsample_in_first_stage else 1		,       depth=config.depths[0]		,       )		)
						a					     =   zip(config.hidden_sizes		,       config.hidden_sizes[1:]		)
						for (in_channels, out_channels), depth in zip(lowerCamelCase_		,       config.depths[1:]		):
											self.stages.append(RegNetStage(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       depth=lowerCamelCase_		)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = False		,       lowerCamelCase_ = True		):
						"""simple docstring"""

						a					     =   () if output_hidden_states else None

						for stage_module in self.stages:
											if output_hidden_states:
																a					     =   hidden_states + (hidden_state,)

											a					     =   stage_module(lowerCamelCase_		)

						if output_hidden_states:
											a					     =   hidden_states + (hidden_state,)

						if not return_dict:
											return tuple(v for v in [hidden_state, hidden_states] if v is not None		)

						return BaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase_		,       hidden_states=lowerCamelCase_		)




class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							RegNetConfig
	__A							=							"regnet"
	__A							=							"pixel_values"
	__A							=							True




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

						if isinstance(lowerCamelCase_		,       nn.Convad		):
											nn.init.kaiming_normal_(module.weight		,       mode="fan_out"		,       nonlinearity="relu"		)
						elif isinstance(lowerCamelCase_		,       (nn.BatchNormad, nn.GroupNorm)		):
											nn.init.constant_(module.weight		,       1		)
											nn.init.constant_(module.bias		,       0		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		):
						"""simple docstring"""

						if isinstance(lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   value




_lowercase: str      			=		r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

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



@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top.",      lowerCAmelCase,      )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(lowerCamelCase_		)
						a					     =   config
						a					     =   RegNetEmbeddings(lowerCamelCase_		)
						a					     =   RegNetEncoder(lowerCamelCase_		)
						a					     =   nn.AdaptiveAvgPoolad((1, 1)		)
						# Initialize weights and apply final processing
						self.post_init()




	@add_start_docstrings_to_model_forward(lowerCamelCase_		)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC		,       output_type=lowerCamelCase_		,       config_class=_CONFIG_FOR_DOC		,       modality="vision"		,       expected_output=_EXPECTED_OUTPUT_SHAPE		,       )
	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						a					     =   (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						a					     =   return_dict if return_dict is not None else self.config.use_return_dict

						a					     =   self.embedder(lowerCamelCase_		)

						a					     =   self.encoder(
						    lowerCamelCase_		,       output_hidden_states=lowerCamelCase_		,       return_dict=lowerCamelCase_		)

						a					     =   encoder_outputs[0]

						a					     =   self.pooler(lowerCamelCase_		)

						if not return_dict:
											return (last_hidden_state, pooled_output) + encoder_outputs[1:]

						return BaseModelOutputWithPoolingAndNoAttention(
						    last_hidden_state=lowerCamelCase_		,       pooler_output=lowerCamelCase_		,       hidden_states=encoder_outputs.hidden_states		,       )




@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ",      lowerCAmelCase,      )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(lowerCamelCase_		)
						a					     =   config.num_labels
						a					     =   RegNetModel(lowerCamelCase_		)
						# classification head
						a					     =   nn.Sequential(
						    nn.Flatten()		,       nn.Linear(config.hidden_sizes[-1]		,       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(lowerCamelCase_		)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT		,       output_type=lowerCamelCase_		,       config_class=_CONFIG_FOR_DOC		,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,       )
	def      UpperCamelCase_       (self		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       lowerCamelCase_ = None		,       ):
						"""simple docstring"""

						a					     =   return_dict if return_dict is not None else self.config.use_return_dict

						a					     =   self.regnet(lowerCamelCase_		,       output_hidden_states=lowerCamelCase_		,       return_dict=lowerCamelCase_		)

						a					     =   outputs.pooler_output if return_dict else outputs[1]

						a					     =   self.classifier(lowerCamelCase_		)

						a					     =   None

						if labels is not None:
											if self.config.problem_type is None:
																if self.num_labels == 1:
																					a					     =   "regression"
																elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																					a					     =   "single_label_classification"
																else:
																					a					     =   "multi_label_classification"
											if self.config.problem_type == "regression":
																a					     =   MSELoss()
																if self.num_labels == 1:
																					a					     =   loss_fct(logits.squeeze()		,       labels.squeeze()		)
																else:
																					a					     =   loss_fct(lowerCamelCase_		,       lowerCamelCase_		)
											elif self.config.problem_type == "single_label_classification":
																a					     =   CrossEntropyLoss()
																a					     =   loss_fct(logits.view(-1		,       self.num_labels		)		,       labels.view(-1		)		)
											elif self.config.problem_type == "multi_label_classification":
																a					     =   BCEWithLogitsLoss()
																a					     =   loss_fct(lowerCamelCase_		,       lowerCamelCase_		)

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

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





# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							42
	__A							=							None






def     a(      A       :    Optional[Any] ,     A       :    Any=0.999 ,     A       :    Dict="cosine" ,     )		->       Optional[int]:
					"""simple docstring"""





					if alpha_transform_type == "cosine":

										def alpha_bar_fn(A       :    Optional[int]       ):
															return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(A       :    Any       ):
															return math.exp(t * -12.0       )

					else:
										raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''       )

					a					     =   []
					for i in range(A       ):
										a					     =   i / num_diffusion_timesteps
										a					     =   (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(A       ) / alpha_bar_fn(A       ) ,     A       )       )
					return torch.tensor(A ,     dtype=torch.floataa       )







class 	_lowercase							(   lowerCAmelCase,      lowerCAmelCase	):





	"""simple docstring"""
	__A							=							1




	@register_to_config
	def __init__(self		,       lowerCamelCase_ = 1000		,       lowerCamelCase_ = 0.0001		,       lowerCamelCase_ = 0.02		,       lowerCamelCase_ = "linear"		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       lowerCamelCase_ = True		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = "epsilon"		,       lowerCamelCase_ = 1.0		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						if kwargs.get("set_alpha_to_one"		,       lowerCamelCase_		) is not None:
											a					     =   (
											    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
											)
											deprecate("set_alpha_to_one"		,       "1.0.0"		,       lowerCamelCase_		,       standard_warn=lowerCamelCase_		)
											a					     =   kwargs["set_alpha_to_one"]
						if trained_betas is not None:
											a					     =   torch.tensor(lowerCamelCase_		,       dtype=torch.floataa		)
						elif beta_schedule == "linear":
											a					     =   torch.linspace(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       dtype=torch.floataa		)
						elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											a					     =   (
											    torch.linspace(beta_start**0.5		,       beta_end**0.5		,       lowerCamelCase_		,       dtype=torch.floataa		) ** 2
											)
						elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											a					     =   betas_for_alpha_bar(lowerCamelCase_		)
						else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

						a					     =   1.0 - self.betas
						a					     =   torch.cumprod(self.alphas		,       dim=0		)

						# At every step in inverted ddim, we are looking into the next alphas_cumprod
						# For the final step, there is no next alphas_cumprod, and the index is out of bounds
						# `set_alpha_to_zero` decides whether we set this parameter simply to zero
						# in this case, self.step() just output the predicted noise
						# or whether we use the final alpha of the "non-previous" one.
						a					     =   torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

						# standard deviation of the initial noise distribution
						a					     =   1.0

						# setable values
						a					     =   None
						a					     =   torch.from_numpy(np.arange(0		,       lowerCamelCase_		).copy().astype(np.intaa		)		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						return sample




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_ = None		):
						"""simple docstring"""

						if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
											    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
											    F''' maximal {self.config.num_train_timesteps} timesteps.'''		)

						a					     =   num_inference_steps
						a					     =   self.config.num_train_timesteps // self.num_inference_steps
						# creates integer timesteps by multiplying by ratio
						# casting to int to avoid issues when num_inference_step is power of 3
						a					     =   (np.arange(0		,       lowerCamelCase_		) * step_ratio).round().copy().astype(np.intaa		)
						a					     =   torch.from_numpy(lowerCamelCase_		).to(lowerCamelCase_		)
						self.timesteps += self.config.steps_offset




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_ = 0.0		,       lowerCamelCase_ = False		,       lowerCamelCase_ = None		,       lowerCamelCase_ = True		,       ):
						"""simple docstring"""

						a					     =   timestep + self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						# change original implementation to exactly match noise levels for analogous forward process
						a					     =   self.alphas_cumprod[timestep]
						a					     =   (
						    self.alphas_cumprod[prev_timestep]
						    if prev_timestep < self.config.num_train_timesteps
						    else self.final_alpha_cumprod
						)

						a					     =   1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						if self.config.prediction_type == "epsilon":
											a					     =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											a					     =   model_output
						elif self.config.prediction_type == "sample":
											a					     =   model_output
											a					     =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
						elif self.config.prediction_type == "v_prediction":
											a					     =   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											a					     =   (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
						else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
											    " `v_prediction`"		)

						# 4. Clip or threshold "predicted x_0"
						if self.config.clip_sample:
											a					     =   pred_original_sample.clamp(
											    -self.config.clip_sample_range		,       self.config.clip_sample_range		)

						# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						a					     =   (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

						# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						a					     =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if not return_dict:
											return (prev_sample, pred_original_sample)
						return DDIMSchedulerOutput(prev_sample=lowerCamelCase_		,       pred_original_sample=lowerCamelCase_		)




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

						return self.config.num_train_timesteps
