





"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "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 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''xlm-roberta-xl'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=25_08_80		,SCREAMING_SNAKE_CASE__=25_60		,SCREAMING_SNAKE_CASE__=36		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=1_02_40		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_14		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-05		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__="absolute"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__		,)			->      str:





             """simple docstring"""






             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__		,bos_token_id=SCREAMING_SNAKE_CASE__		,eos_token_id=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Tuple						=  vocab_size
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  hidden_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Any						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Any						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :Tuple						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Dict						=  max_position_embeddings
             __SCREAMING_SNAKE_CASE   :int						=  type_vocab_size
             __SCREAMING_SNAKE_CASE   :str						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Tuple						=  layer_norm_eps
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  position_embedding_type
             __SCREAMING_SNAKE_CASE   :str						=  use_cache
             __SCREAMING_SNAKE_CASE   :List[str]						=  classifier_dropout






class 					_SCREAMING_SNAKE_CASE(   A  ):




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE   :Dict						=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[int]						=  {0: '''batch''', 1: '''sequence'''}
             return OrderedDict(
                 [
                     ('''input_ids''', dynamic_axis),
                     ('''attention_mask''', dynamic_axis),
                 ]							)






"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      Optional[torch.FloatTensor]							=  None






class 					_SCREAMING_SNAKE_CASE(   A    ,	A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  2




       @register_to_config
       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = 1_00		,SCREAMING_SNAKE_CASE__ = 1.0_0_7		,SCREAMING_SNAKE_CASE__ = 80		,SCREAMING_SNAKE_CASE__ = 0.0_5		,SCREAMING_SNAKE_CASE__ = 50		,)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sigma_max

             # setable values
             __SCREAMING_SNAKE_CASE   :int						=  None
             __SCREAMING_SNAKE_CASE   :np.IntTensor						=  None
             __SCREAMING_SNAKE_CASE   :torch.FloatTensor						=  None  # sigma(t_i)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      torch.FloatTensor:





             """simple docstring"""






             return sample




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  num_inference_steps
             __SCREAMING_SNAKE_CASE   :int						=  np.arange(0		,self.num_inference_steps							)[::-1].copy()
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.from_numpy(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  [
                 (
                     self.config.sigma_max**2
                     * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                 )
                 for i in self.timesteps
             ]
             __SCREAMING_SNAKE_CASE   :List[str]						=  torch.tensor(SCREAMING_SNAKE_CASE__		,dtype=torch.floataa		,device=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      Tuple[torch.FloatTensor, float]:





             """simple docstring"""






             if self.config.s_min <= sigma <= self.config.s_max:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  min(self.config.s_churn / self.num_inference_steps		,2**0.5 - 1							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  0

             # sample eps ~ N(0, S_noise^2 * I)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.config.s_noise * randn_tensor(sample.shape		,generator=SCREAMING_SNAKE_CASE__							).to(sample.device							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  sigma + gamma * sigma
             __SCREAMING_SNAKE_CASE   :str						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

             return sample_hat, sigma_hat




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  sample_hat + sigma_hat * model_output
             __SCREAMING_SNAKE_CASE   :Tuple						=  (sample_hat - pred_original_sample) / sigma_hat
             __SCREAMING_SNAKE_CASE   :List[Any]						=  sample_hat + (sigma_prev - sigma_hat) * derivative

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sample_prev + sigma_prev * model_output
             __SCREAMING_SNAKE_CASE   :List[Any]						=  (sample_prev - pred_original_sample) / sigma_prev
             __SCREAMING_SNAKE_CASE   :Dict						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      Union[str, Any]:





             """simple docstring"""






             raise NotImplementedError()