


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							torch.FloatTensor
     __lowercase				:							torch.FloatTensor




class 		__magic_name__						(snake_case_   ,snake_case_				):





     '''simple docstring'''



     __lowercase				:							Any	   =       1

     @register_to_config
     def __init__(					self:str     ,		_a:int = 20_00     ,		_a:float = 0.15     ,		_a:float = 0.01     ,		_a:float = 1348.0     ,		_a:float = 1e-5     ,		_a:int = 1     ,		):
       # standard deviation of the initial noise distribution
       snake_case__						= sigma_max

       # setable values
       snake_case__						= None

       self.set_sigmas(_a     ,		_a     ,		_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:torch.FloatTensor     ,		_a:Optional[int] = None   ):
       return sample

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int     ,		_a:float = None     ,		_a:Union[str, torch.device] = None   ):
       snake_case__						= sampling_eps if sampling_eps is not None else self.config.sampling_eps

       snake_case__						= torch.linspace(1     ,		_a     ,		_a     ,		device=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:int     ,		_a:float = None     ,		_a:float = None     ,		_a:float = None   ):
       snake_case__						= sigma_min if sigma_min is not None else self.config.sigma_min
       snake_case__						= sigma_max if sigma_max is not None else self.config.sigma_max
       snake_case__						= sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
         self.set_timesteps(_a     ,		_a   )

       snake_case__						= sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       snake_case__						= torch.exp(torch.linspace(math.log(_a   )     ,		math.log(_a   )     ,		_a   )   )
       snake_case__						= torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:Optional[Any]     ,		_a:Optional[Any]   ):
       return torch.where(
           timesteps == 0     ,		torch.zeros_like(t.to(timesteps.device   )   )     ,		self.discrete_sigmas[timesteps - 1].to(timesteps.device   )     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple     ,		_a:torch.FloatTensor     ,		_a:int     ,		_a:torch.FloatTensor     ,		_a:Optional[torch.Generator] = None     ,		_a:bool = True     ,		):
       if self.timesteps is None:
         raise ValueError(
             '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''   )

       snake_case__						= timestep * torch.ones(
           sample.shape[0]     ,		device=sample.device   )  # torch.repeat_interleave(timestep, sample.shape[0])
       snake_case__						= (timestep * (len(self.timesteps   ) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       snake_case__						= timesteps.to(self.discrete_sigmas.device   )

       snake_case__						= self.discrete_sigmas[timesteps].to(sample.device   )
       snake_case__						= self.get_adjacent_sigma(_a     ,		_a   ).to(sample.device   )
       snake_case__						= torch.zeros_like(_a   )
       snake_case__						= (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       snake_case__						= diffusion.flatten()
       while len(diffusion.shape   ) < len(sample.shape   ):
         snake_case__						= diffusion.unsqueeze(-1   )
       snake_case__						= drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       snake_case__						= randn_tensor(
           sample.shape     ,		layout=sample.layout     ,		generator=_a     ,		device=sample.device     ,		dtype=sample.dtype   )
       snake_case__						= sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       snake_case__						= prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
         return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=_a     ,		prev_sample_mean=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:torch.FloatTensor     ,		_a:torch.FloatTensor     ,		_a:Optional[torch.Generator] = None     ,		_a:bool = True     ,		):
       if self.timesteps is None:
         raise ValueError(
             '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''   )

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       snake_case__						= randn_tensor(sample.shape     ,		layout=sample.layout     ,		generator=_a   ).to(sample.device   )

       # compute step size from the model_output, the noise, and the snr
       snake_case__						= torch.norm(model_output.reshape(model_output.shape[0]     ,		-1   )     ,		dim=-1   ).mean()
       snake_case__						= torch.norm(noise.reshape(noise.shape[0]     ,		-1   )     ,		dim=-1   ).mean()
       snake_case__						= (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       snake_case__						= step_size * torch.ones(sample.shape[0]   ).to(sample.device   )
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       snake_case__						= step_size.flatten()
       while len(step_size.shape   ) < len(sample.shape   ):
         snake_case__						= step_size.unsqueeze(-1   )
       snake_case__						= sample + step_size * model_output
       snake_case__						= prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
         return (prev_sample,)

       return SchedulerOutput(prev_sample=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:torch.FloatTensor     ,		_a:torch.FloatTensor     ,		_a:torch.FloatTensor     ,		):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       snake_case__						= timesteps.to(original_samples.device   )
       snake_case__						= self.discrete_sigmas.to(original_samples.device   )[timesteps]
       snake_case__						= (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(_a   ) * sigmas[:, None, None, None]
       )
       snake_case__						= noise + original_samples
       return noisy_samples







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





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__							:						List[Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Optional[int]							     =   {
    """facebook/data2vec-vision-base-ft""": (
        """https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
    ),
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       'data2vec-vision'

     def __init__(					self:int     ,		_a:Tuple=7_68     ,		_a:int=12     ,		_a:Any=12     ,		_a:Optional[int]=30_72     ,		_a:Optional[int]="gelu"     ,		_a:Any=0.0     ,		_a:Any=0.0     ,		_a:List[str]=0.02     ,		_a:Dict=1e-12     ,		_a:Tuple=2_24     ,		_a:Any=16     ,		_a:str=3     ,		_a:str=False     ,		_a:Union[str, Any]=False     ,		_a:Optional[int]=False     ,		_a:Any=False     ,		_a:Dict=0.1     ,		_a:Dict=0.1     ,		_a:str=True     ,		_a:str=[3, 5, 7, 11]     ,		_a:List[str]=[1, 2, 3, 6]     ,		_a:List[str]=True     ,		_a:Any=0.4     ,		_a:str=2_56     ,		_a:Union[str, Any]=1     ,		_a:int=False     ,		_a:Optional[int]=2_55     ,		**_a:Dict     ,		):
       super().__init__(**_a   )

       snake_case__						= hidden_size
       snake_case__						= num_hidden_layers
       snake_case__						= num_attention_heads
       snake_case__						= intermediate_size
       snake_case__						= hidden_act
       snake_case__						= hidden_dropout_prob
       snake_case__						= attention_probs_dropout_prob
       snake_case__						= initializer_range
       snake_case__						= layer_norm_eps

       snake_case__						= image_size
       snake_case__						= patch_size
       snake_case__						= num_channels
       snake_case__						= use_mask_token
       snake_case__						= use_absolute_position_embeddings
       snake_case__						= use_relative_position_bias
       snake_case__						= use_shared_relative_position_bias
       snake_case__						= layer_scale_init_value
       snake_case__						= drop_path_rate
       snake_case__						= use_mean_pooling
       # decode head attributes (semantic segmentation)
       snake_case__						= out_indices
       snake_case__						= pool_scales
       # auxiliary head attributes (semantic segmentation)
       snake_case__						= use_auxiliary_head
       snake_case__						= auxiliary_loss_weight
       snake_case__						= auxiliary_channels
       snake_case__						= auxiliary_num_convs
       snake_case__						= auxiliary_concat_input
       snake_case__						= semantic_loss_ignore_index




class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Any	   =       version.parse('1.11'				)

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]   )







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return 1e-4

