





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


a__:      List[str]									=		logging.get_logger(__name__)

# General docstring
a__:      str									=		'RegNetConfig'

# Base docstring
a__:      int									=		'facebook/regnet-y-040'
a__:      List[str]									=		[1, 1_088, 7, 7]

# Image classification docstring
a__:      int									=		'facebook/regnet-y-040'
a__:      List[str]									=		'tabby, tabby cat'

a__:      Dict									=		[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = 3,__lowerCamelCase = 1,__lowerCamelCase = 1,__lowerCamelCase = "relu",):
        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 ):
        A__											=						self.convolution(__lowerCamelCase )
        A__											=						self.normalization(__lowerCamelCase )
        A__											=						self.activation(__lowerCamelCase )
        return hidden_state




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):



 def __init__(							self,__lowerCamelCase ):
        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 ):
        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     SCREAMING_SNAKE_CASE__ (						nn.Module      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = 2 ):
        super().__init__()
        A__											=						nn.Convad(__lowerCamelCase,__lowerCamelCase,kernel_size=1,stride=__lowerCamelCase,bias=__lowerCamelCase )
        A__											=						nn.BatchNormad(__lowerCamelCase )




 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.convolution(__lowerCamelCase )
        A__											=						self.normalization(__lowerCamelCase )
        return hidden_state




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase ):
        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 ):
        # b c h w -> b c 1 1
        A__											=						self.pooler(__lowerCamelCase )
        A__											=						self.attention(__lowerCamelCase )
        A__											=						hidden_state * attention
        return hidden_state




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = 1 ):
        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 ):
        A__											=						hidden_state
        A__											=						self.layer(__lowerCamelCase )
        A__											=						self.shortcut(__lowerCamelCase )
        hidden_state += residual
        A__											=						self.activation(__lowerCamelCase )
        return hidden_state




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = 1 ):
        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 ):
        A__											=						hidden_state
        A__											=						self.layer(__lowerCamelCase )
        A__											=						self.shortcut(__lowerCamelCase )
        hidden_state += residual
        A__											=						self.activation(__lowerCamelCase )
        return hidden_state




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):



 def __init__(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = 2,__lowerCamelCase = 2,):
        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 ):
        A__											=						self.layers(__lowerCamelCase )
        return hidden_state




class     SCREAMING_SNAKE_CASE__ (						nn.Module      ):



 def __init__(							self,__lowerCamelCase ):
        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 ):
        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     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	RegNetConfig
 __SCREAMING_SNAKE_CASE							       =	'''regnet'''
 __SCREAMING_SNAKE_CASE							       =	'''pixel_values'''
 __SCREAMING_SNAKE_CASE							       =	True



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        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 ):
        if isinstance(__lowerCamelCase,__lowerCamelCase ):
               A__											=						value
a__:      Optional[Any]									=		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'

a__:      Tuple									=		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.'''				,					UpperCamelCase__				,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        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 ):
        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(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''				,					UpperCamelCase__				,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def __init__(							self,__lowerCamelCase ):
        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,):
        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 )












import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       Tuple=0.999 ,     UpperCamelCase__			:       Any="cosine" ,     )->List[str]:
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(UpperCamelCase__			:       str  ):
                     return math.exp(t * -12.0  )

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

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




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	[e.name for e in KarrasDiffusionSchedulers]
 __SCREAMING_SNAKE_CASE							       =	2



 @register_to_config
 def __init__(							self,__lowerCamelCase = 1000,__lowerCamelCase = 0.00085,__lowerCamelCase = 0.012,__lowerCamelCase = "linear",__lowerCamelCase = None,__lowerCamelCase = "epsilon",__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = 1.0,__lowerCamelCase = "linspace",__lowerCamelCase = 0,):
        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,alpha_transform_type='''cosine''' )
        elif beta_schedule == "exp":
               A__											=						betas_for_alpha_bar(__lowerCamelCase,alpha_transform_type='''exp''' )
        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 )

        #  set all values
        self.set_timesteps(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
        A__											=						use_karras_sigmas



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        if schedule_timesteps is None:
               A__											=						self.timesteps

        A__											=						(schedule_timesteps == timestep).nonzero()

        # The sigma index that is taken for the **very** first `step`
        # is always the second index (or the last index if there is only 1)
        # This way we can ensure we don't accidentally skip a sigma in
        # case we start in the middle of the denoising schedule (e.g. for image-to-image)
        if len(self._index_counter ) == 0:
               A__											=						1 if len(__lowerCamelCase ) > 1 else 0
        else:
               A__											=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
               A__											=						self._index_counter[timestep_int]

        return indices[pos].item()



 @property
 def 				UpperCamelCase			(							self ):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,):
        A__											=						self.index_for_timestep(__lowerCamelCase )

        A__											=						self.sigmas[step_index]
        A__											=						sample / ((sigma**2 + 1) ** 0.5)
        return sample



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,):
        A__											=						num_inference_steps

        A__											=						num_train_timesteps or self.config.num_train_timesteps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
               A__											=						np.linspace(0,num_train_timesteps - 1,__lowerCamelCase,dtype=__lowerCamelCase )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
               A__											=						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()[::-1].copy().astype(__lowerCamelCase )
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               A__											=						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(__lowerCamelCase,0,-step_ratio )).round().copy().astype(__lowerCamelCase )
               timesteps -= 1
        else:
               raise ValueError(
                   f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )

        A__											=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
        A__											=						np.log(__lowerCamelCase )
        A__											=						np.interp(__lowerCamelCase,np.arange(0,len(__lowerCamelCase ) ),__lowerCamelCase )

        if self.config.use_karras_sigmas:
               A__											=						self._convert_to_karras(in_sigmas=__lowerCamelCase,num_inference_steps=self.num_inference_steps )
               A__											=						np.array([self._sigma_to_t(__lowerCamelCase,__lowerCamelCase ) for sigma in sigmas] )

        A__											=						np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
        A__											=						torch.from_numpy(__lowerCamelCase ).to(device=__lowerCamelCase )
        A__											=						torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

        A__											=						torch.from_numpy(__lowerCamelCase )
        A__											=						torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

        if str(__lowerCamelCase ).startswith('''mps''' ):
               # mps does not support float64
               A__											=						timesteps.to(__lowerCamelCase,dtype=torch.floataa )
        else:
               A__											=						timesteps.to(device=__lowerCamelCase )

        # empty dt and derivative
        A__											=						None
        A__											=						None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        A__											=						defaultdict(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        # get log sigma
        A__											=						np.log(__lowerCamelCase )

        # get distribution
        A__											=						log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        A__											=						np.cumsum((dists >= 0),axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
        A__											=						low_idx + 1

        A__											=						log_sigmas[low_idx]
        A__											=						log_sigmas[high_idx]

        # interpolate sigmas
        A__											=						(low - log_sigma) / (low - high)
        A__											=						np.clip(__lowerCamelCase,0,1 )

        # transform interpolation to time range
        A__											=						(1 - w) * low_idx + w * high_idx
        A__											=						t.reshape(sigma.shape )
        return t



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						in_sigmas[-1].item()
        A__											=						in_sigmas[0].item()

        A__											=						7.0  # 7.0 is the value used in the paper
        A__											=						np.linspace(0,1,__lowerCamelCase )
        A__											=						sigma_min ** (1 / rho)
        A__											=						sigma_max ** (1 / rho)
        A__											=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas



 @property
 def 				UpperCamelCase			(							self ):
        return self.dt is None



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = True,):
        A__											=						self.index_for_timestep(__lowerCamelCase )

        # advance index counter by 1
        A__											=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
               A__											=						self.sigmas[step_index]
               A__											=						self.sigmas[step_index + 1]
        else:
               # 2nd order / Heun's method
               A__											=						self.sigmas[step_index - 1]
               A__											=						self.sigmas[step_index]

        # currently only gamma=0 is supported. This usually works best anyways.
        # We can support gamma in the future but then need to scale the timestep before
        # passing it to the model which requires a change in API
        A__											=						0
        A__											=						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
               A__											=						sigma_hat if self.state_in_first_order else sigma_next
               A__											=						sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
               A__											=						sigma_hat if self.state_in_first_order else sigma_next
               A__											=						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
        elif self.config.prediction_type == "sample":
               A__											=						model_output
        else:
               raise ValueError(
                   f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )

        if self.config.clip_sample:
               A__											=						pred_original_sample.clamp(
                   -self.config.clip_sample_range,self.config.clip_sample_range )

        if self.state_in_first_order:
               # 2. Convert to an ODE derivative for 1st order
               A__											=						(sample - pred_original_sample) / sigma_hat
               # 3. delta timestep
               A__											=						sigma_next - sigma_hat

               # store for 2nd order step
               A__											=						derivative
               A__											=						dt
               A__											=						sample
        else:
               # 2. 2nd order / Heun's method
               A__											=						(sample - pred_original_sample) / sigma_next
               A__											=						(self.prev_derivative + derivative) / 2

               # 3. take prev timestep & sample
               A__											=						self.dt
               A__											=						self.sample

               # free dt and derivative
               # Note, this puts the scheduler in "first order mode"
               A__											=						None
               A__											=						None
               A__											=						None

        A__											=						sample + derivative * dt

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        A__											=						self.sigmas.to(device=original_samples.device,dtype=original_samples.dtype )
        if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase ):
               # mps does not support float64
               A__											=						self.timesteps.to(original_samples.device,dtype=torch.floataa )
               A__											=						timesteps.to(original_samples.device,dtype=torch.floataa )
        else:
               A__											=						self.timesteps.to(original_samples.device )
               A__											=						timesteps.to(original_samples.device )

        A__											=						[self.index_for_timestep(__lowerCamelCase,__lowerCamelCase ) for t in timesteps]

        A__											=						sigmas[step_indices].flatten()
        while len(sigma.shape ) < len(original_samples.shape ):
               A__											=						sigma.unsqueeze(-1 )

        A__											=						original_samples + noise * sigma
        return noisy_samples




 def __len__(							self ):
        return self.config.num_train_timesteps





