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

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     1







							@register_to_config
							def __init__(				self     :    str	,					__snake_case     :    Dict=2000	,					__snake_case     :    Union[str, Any]=0.1	,					__snake_case     :    Union[str, Any]=20	,					__snake_case     :    Optional[int]=1e-3		):
										lowerCamelCase						:Any						  =     None
										lowerCamelCase						:Union[str, Any]						  =     None
										lowerCamelCase						:Optional[Any]						  =     None







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Optional[Any]	,					__snake_case     :    Union[str, torch.device] = None		):
										lowerCamelCase						:List[Any]						  =     torch.linspace(1	,					self.config.sampling_eps	,					__snake_case	,					device=__snake_case		)







							def 				snake_case  (				self     :    Any	,					__snake_case     :    str	,					__snake_case     :    Any	,					__snake_case     :    Dict	,					__snake_case     :    int=None		):
										if self.timesteps is None:
													raise ValueError(
													    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''		)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										lowerCamelCase						:Any						  =     (
										    -0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										lowerCamelCase						:str						  =     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff		)		)
										lowerCamelCase						:Optional[Any]						  =     std.flatten()
										while len(std.shape		) < len(score.shape		):
													lowerCamelCase						:Any						  =     std.unsqueeze(-1		)
										lowerCamelCase						:Optional[int]						  =     -score / std

										# compute
										lowerCamelCase						:List[str]						  =     -1.0 / len(self.timesteps		)

										lowerCamelCase						:Optional[int]						  =     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										lowerCamelCase						:int						  =     beta_t.flatten()
										while len(beta_t.shape		) < len(x.shape		):
													lowerCamelCase						:Any						  =     beta_t.unsqueeze(-1		)
										lowerCamelCase						:List[Any]						  =     -0.5 * beta_t * x

										lowerCamelCase						:List[Any]						  =     torch.sqrt(__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     drift - diffusion**2 * score
										lowerCamelCase						:Optional[int]						  =     x + drift * dt

										# add noise
										lowerCamelCase						:Tuple						  =     randn_tensor(x.shape	,					layout=x.layout	,					generator=__snake_case	,					device=x.device	,					dtype=x.dtype		)
										lowerCamelCase						:str						  =     x_mean + diffusion * math.sqrt(-dt		) * noise

										return x, x_mean







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




from collections import OrderedDict
from typing import Mapping

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


A__           =							logging.get_logger(__name__)

A__           =							{
    """andreasmadsen/efficient_mlm_m0.40""": (
        """https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
    ),
}
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase						    =     'roberta-prelayernorm'







							def __init__(				self     :    str	,					__snake_case     :    List[str]=50265	,					__snake_case     :    Union[str, Any]=768	,					__snake_case     :    Tuple=12	,					__snake_case     :    int=12	,					__snake_case     :    Any=3072	,					__snake_case     :    Optional[int]="gelu"	,					__snake_case     :    List[Any]=0.1	,					__snake_case     :    int=0.1	,					__snake_case     :    Union[str, Any]=512	,					__snake_case     :    Dict=2	,					__snake_case     :    int=0.0_2	,					__snake_case     :    Any=1e-1_2	,					__snake_case     :    Optional[int]=1	,					__snake_case     :    Dict=0	,					__snake_case     :    Optional[int]=2	,					__snake_case     :    Any="absolute"	,					__snake_case     :    Union[str, Any]=True	,					__snake_case     :    List[str]=None	,					**__snake_case     :    Optional[int]	,					):
										super().__init__(pad_token_id=__snake_case	,					bos_token_id=__snake_case	,					eos_token_id=__snake_case	,					**__snake_case		)

										lowerCamelCase						:Optional[int]						  =     vocab_size
										lowerCamelCase						:Dict						  =     hidden_size
										lowerCamelCase						:Tuple						  =     num_hidden_layers
										lowerCamelCase						:Optional[int]						  =     num_attention_heads
										lowerCamelCase						:Any						  =     hidden_act
										lowerCamelCase						:List[Any]						  =     intermediate_size
										lowerCamelCase						:Union[str, Any]						  =     hidden_dropout_prob
										lowerCamelCase						:str						  =     attention_probs_dropout_prob
										lowerCamelCase						:Tuple						  =     max_position_embeddings
										lowerCamelCase						:int						  =     type_vocab_size
										lowerCamelCase						:Optional[Any]						  =     initializer_range
										lowerCamelCase						:Union[str, Any]						  =     layer_norm_eps
										lowerCamelCase						:Dict						  =     position_embedding_type
										lowerCamelCase						:List[Any]						  =     use_cache
										lowerCamelCase						:Optional[int]						  =     classifier_dropout


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@property
							def 				snake_case  (				self     :    Any		):
										if self.task == "multiple-choice":
													lowerCamelCase						:Optional[Any]						  =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
													lowerCamelCase						:List[str]						  =     {0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]		)



