



'''simple docstring'''
import math
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 SchedulerMixin, SchedulerOutput






class 							lowercase__     (							lowercase						, lowercase       ):
	lowercase__      =      1





	@register_to_config
	def __init__(			self :    Optional[Any]			,lowerCamelCase__ :    int = 1000			,lowerCamelCase__ :    Optional[Union[np.ndarray, List[float]]] = None			):







							'''simple docstring'''
							self.set_timesteps(lowerCamelCase__			)

							# standard deviation of the initial noise distribution
							_UpperCamelCase :		Dict			    =       1.0

							# For now we only support F-PNDM, i.e. the runge-kutta method
							# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
							# mainly at formula (9), (12), (13) and the Algorithm 2.
							_UpperCamelCase :		str			    =       4

							# running values
							_UpperCamelCase :		List[Any]			    =       []





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    int			,lowerCamelCase__ :    Union[str, torch.device] = None			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       num_inference_steps
							_UpperCamelCase :		Optional[Any]			    =       torch.linspace(1			,0			,num_inference_steps + 1			)[:-1]
							_UpperCamelCase :		Optional[Any]			    =       torch.cat([steps, torch.tensor([0.0]			)]			)

							if self.config.trained_betas is not None:
													_UpperCamelCase :		str			    =       torch.tensor(self.config.trained_betas			,dtype=torch.floataa			)
							else:
													_UpperCamelCase :		Union[str, Any]			    =       torch.sin(steps * math.pi / 2			) ** 2

							_UpperCamelCase :		List[str]			    =       (1.0 - self.betas**2) ** 0.5

							_UpperCamelCase :		List[Any]			    =       (torch.atana(self.betas			,self.alphas			) / math.pi * 2)[:-1]
							_UpperCamelCase :		Optional[Any]			    =       timesteps.to(lowerCamelCase__			)

							_UpperCamelCase :		Tuple			    =       []





	def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    torch.FloatTensor			,lowerCamelCase__ :    int			,lowerCamelCase__ :    torch.FloatTensor			,lowerCamelCase__ :    bool = True			,):







							'''simple docstring'''
							if self.num_inference_steps is None:
													raise ValueError(
													    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'			)

							_UpperCamelCase :		int			    =       (self.timesteps == timestep).nonzero().item()
							_UpperCamelCase :		int			    =       timestep_index + 1

							_UpperCamelCase :		Any			    =       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
							self.ets.append(lowerCamelCase__			)

							if len(self.ets			) == 1:
													_UpperCamelCase :		Optional[Any]			    =       self.ets[-1]
							elif len(self.ets			) == 2:
													_UpperCamelCase :		List[str]			    =       (3 * self.ets[-1] - self.ets[-2]) / 2
							elif len(self.ets			) == 3:
													_UpperCamelCase :		Optional[Any]			    =       (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
							else:
													_UpperCamelCase :		Any			    =       (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

							_UpperCamelCase :		Any			    =       self._get_prev_sample(lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			)

							if not return_dict:
													return (prev_sample,)

							return SchedulerOutput(prev_sample=lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    torch.FloatTensor			,*lowerCamelCase__ :    str			,**lowerCamelCase__ :    Tuple			):







							'''simple docstring'''
							return sample





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    int			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       self.alphas[timestep_index]
							_UpperCamelCase :		Dict			    =       self.betas[timestep_index]

							_UpperCamelCase :		Optional[Any]			    =       self.alphas[prev_timestep_index]
							_UpperCamelCase :		Tuple			    =       self.betas[prev_timestep_index]

							_UpperCamelCase :		Optional[Any]			    =       (sample - sigma * ets) / max(lowerCamelCase__			,1E-8			)
							_UpperCamelCase :		Dict			    =       next_alpha * pred + ets * next_sigma

							return prev_sample





	def __len__(			self :    Tuple			):







							'''simple docstring'''
							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


snake_case_			:     Optional[int]  	=  logging.get_logger(__name__)

snake_case_			:     List[Any]  	=  {
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """xlm-roberta-xl"""





	def __init__(			self :    Optional[int]			,lowerCamelCase__ :    Optional[Any]=250880			,lowerCamelCase__ :    Tuple=2560			,lowerCamelCase__ :    Union[str, Any]=36			,lowerCamelCase__ :    List[str]=32			,lowerCamelCase__ :    Optional[Any]=10240			,lowerCamelCase__ :    Tuple="gelu"			,lowerCamelCase__ :    int=0.1			,lowerCamelCase__ :    int=0.1			,lowerCamelCase__ :    Optional[int]=514			,lowerCamelCase__ :    List[str]=1			,lowerCamelCase__ :    Dict=0.0_2			,lowerCamelCase__ :    Any=1E-05			,lowerCamelCase__ :    Union[str, Any]=1			,lowerCamelCase__ :    str=0			,lowerCamelCase__ :    Tuple=2			,lowerCamelCase__ :    Union[str, Any]="absolute"			,lowerCamelCase__ :    Optional[Any]=True			,lowerCamelCase__ :    List[str]=None			,**lowerCamelCase__ :    Dict			,):







							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__			,bos_token_id=lowerCamelCase__			,eos_token_id=lowerCamelCase__			,**lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       vocab_size
							_UpperCamelCase :		Optional[Any]			    =       hidden_size
							_UpperCamelCase :		str			    =       num_hidden_layers
							_UpperCamelCase :		str			    =       num_attention_heads
							_UpperCamelCase :		Any			    =       hidden_act
							_UpperCamelCase :		Dict			    =       intermediate_size
							_UpperCamelCase :		Optional[int]			    =       hidden_dropout_prob
							_UpperCamelCase :		Any			    =       attention_probs_dropout_prob
							_UpperCamelCase :		List[Any]			    =       max_position_embeddings
							_UpperCamelCase :		str			    =       type_vocab_size
							_UpperCamelCase :		Optional[Any]			    =       initializer_range
							_UpperCamelCase :		Optional[int]			    =       layer_norm_eps
							_UpperCamelCase :		Optional[int]			    =       position_embedding_type
							_UpperCamelCase :		Optional[Any]			    =       use_cache
							_UpperCamelCase :		Optional[Any]			    =       classifier_dropout






class 							lowercase__     (							lowercase       ):





	@property
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							if self.task == "multiple-choice":
													_UpperCamelCase :		Union[str, Any]			    =       {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
													_UpperCamelCase :		Optional[Any]			    =       {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]			)


