



"""simple docstring"""

# 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  _UpperCAmelCase (    lowerCAmelCase__					,  lowerCAmelCase__):
 _lowerCAmelCase     :    List[Any]	      =	1

 @register_to_config
 def __init__(  self	: Tuple						,  lowercase_	: Dict=2000						,  lowercase_	: Union[str, Any]=0.1						,  lowercase_	: List[Any]=20						,  lowercase_	: Optional[int]=1E-3       ):
     snake_case_				:  Optional[Any]       =			None
     snake_case_				:  int       =			None
     snake_case_				:  Dict       =			None

 def 		_snake_case      (  self	: Any						,  lowercase_	: Dict						,  lowercase_	: Union[str, torch.device] = None       ):
     snake_case_				:  str       =			torch.linspace(1						,  self.config.sampling_eps						,  lowercase_						,  device=lowercase_       )

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Union[str, Any]						,  lowercase_	: Tuple						,  lowercase_	: Optional[Any]						,  lowercase_	: Tuple=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
     snake_case_				:  Union[str, Any]       =			(
         -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
     )
     snake_case_				:  str       =			torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
     snake_case_				:  Optional[Any]       =			std.flatten()
     while len(std.shape       ) < len(score.shape       ):
         snake_case_				:  str       =			std.unsqueeze(-1       )
     snake_case_				:  Any       =			-score / std

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

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

     snake_case_				:  List[Any]       =			torch.sqrt(lowercase_       )
     snake_case_				:  Optional[Any]       =			drift - diffusion**2 * score
     snake_case_				:  str       =			x + drift * dt

     # add noise
     snake_case_				:  Optional[int]       =			randn_tensor(x.shape						,  layout=x.layout						,  generator=lowercase_						,  device=x.device						,  dtype=x.dtype       )
     snake_case_				:  Tuple       =			x_mean + diffusion * math.sqrt(-dt       ) * noise

     return x, x_mean






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










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Any=50265						,  lowercase_	: Union[str, Any]=768						,  lowercase_	: Optional[int]=12						,  lowercase_	: List[str]=12						,  lowercase_	: Dict=3072						,  lowercase_	: Tuple="gelu"						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Tuple=0.1						,  lowercase_	: List[str]=512						,  lowercase_	: List[Any]=2						,  lowercase_	: Union[str, Any]=0.02						,  lowercase_	: List[Any]=1E-12						,  lowercase_	: Optional[int]=1						,  lowercase_	: Tuple=0						,  lowercase_	: str=2						,  lowercase_	: int="absolute"						,  lowercase_	: str=True						,  lowercase_	: Tuple=None						,  **lowercase_	: str						,  ):
     super().__init__(pad_token_id=lowercase_						,  bos_token_id=lowercase_						,  eos_token_id=lowercase_						,  **lowercase_       )

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





class  _UpperCAmelCase (    lowerCAmelCase__):

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





