from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  str      ,   lowercase__ :  Distribution      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Dict=None      ,   lowercase__ :  int=0):







        '''simple docstring'''



        lowerCAmelCase__					     = 1.0 if scale is None else scale
        lowerCAmelCase__					     = 0.0 if loc is None else loc

        super().__init__(lowercase__      ,   [AffineTransform(loc=self.loc      ,   scale=self.scale      ,   event_dim=lowercase__)])




 @property
 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        return self.base_dist.mean * self.scale + self.loc




 @property
 def 		__snake_case						(      self :  Any):







        '''simple docstring'''



        return self.base_dist.variance * self.scale**2




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







        '''simple docstring'''



        return self.variance.sqrt()






class     a_			(						nn.Module						):






 '''simple docstring'''






 def __init__(      self :  List[Any]      ,   lowercase__ :  int      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  Callable[..., Tuple[torch.Tensor]]      ,   **lowercase__ :  Optional[int]):







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__					     = args_dim
        lowerCAmelCase__					     = nn.ModuleList([nn.Linear(lowercase__      ,   lowercase__) for dim in args_dim.values()])
        lowerCAmelCase__					     = domain_map




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  torch.Tensor):







        '''simple docstring'''



        lowerCAmelCase__					     = [proj(lowercase__) for proj in self.proj]

        return self.domain_map(*lowercase__)






class     a_			(						nn.Module						):






 '''simple docstring'''






 def __init__(      self :  Any      ,   lowercase__ :  str):







        '''simple docstring'''



        super().__init__()
        lowerCAmelCase__					     = function




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  List[str]      ,   *lowercase__ :  Optional[Any]):







        '''simple docstring'''



        return self.function(lowercase__      ,   *lowercase__)






class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= 42
 UpperCAmelCase_     			= 42
 UpperCAmelCase_     			= 42




 def __init__(      self :  Dict      ,   lowercase__ :  int = 1):







        '''simple docstring'''



        lowerCAmelCase__					     = dim
        lowerCAmelCase__					     = {k: dim * self.args_dim[k] for k in self.args_dim}




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Tuple):







        '''simple docstring'''



        if self.dim == 1:
               return self.distribution_class(*lowercase__)
        else:
               return Independent(self.distribution_class(*lowercase__)      ,   1)




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  Tuple      ,   lowercase__ :  Optional[torch.Tensor] = None      ,   lowercase__ :  Optional[torch.Tensor] = None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = self._base_distribution(lowercase__)
        if loc is None and scale is None:
               return distr
        else:
               return AffineTransformed(lowercase__      ,   loc=lowercase__      ,   scale=lowercase__      ,   event_dim=self.event_dim)




 @property
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        return () if self.dim == 1 else (self.dim,)




 @property
 def 		__snake_case						(      self :  Tuple):







        '''simple docstring'''



        return len(self.event_shape)




 @property
 def 		__snake_case						(      self :  List[str]):







        '''simple docstring'''



        return 0.0




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  int):







        '''simple docstring'''



        return ParameterProjection(
            in_features=lowercase__      ,   args_dim=self.args_dim      ,   domain_map=LambdaLayer(self.domain_map)      ,   )




 def 		__snake_case						(      self :  Optional[int]      ,   *lowercase__ :  torch.Tensor):







        '''simple docstring'''



        raise NotImplementedError()




 @staticmethod
 def 		__snake_case						(      lowercase__ :  torch.Tensor):







        '''simple docstring'''



        return (x + torch.sqrt(torch.square(lowercase__) + 4.0)) / 2.0






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= {"df": 1, "loc": 1, "scale": 1}
 UpperCAmelCase_     			= StudentT




 @classmethod
 def 		__snake_case						(      cls :  Tuple      ,   lowercase__ :  torch.Tensor      ,   lowercase__ :  torch.Tensor      ,   lowercase__ :  torch.Tensor):







        '''simple docstring'''



        lowerCAmelCase__					     = cls.squareplus(lowercase__).clamp_min(torch.finfo(scale.dtype).eps)
        lowerCAmelCase__					     = 2.0 + cls.squareplus(lowercase__)
        return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= {"loc": 1, "scale": 1}
 UpperCAmelCase_     			= Normal




 @classmethod
 def 		__snake_case						(      cls :  int      ,   lowercase__ :  torch.Tensor      ,   lowercase__ :  torch.Tensor):







        '''simple docstring'''



        lowerCAmelCase__					     = cls.squareplus(lowercase__).clamp_min(torch.finfo(scale.dtype).eps)
        return loc.squeeze(-1), scale.squeeze(-1)






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= {"total_count": 1, "logits": 1}
 UpperCAmelCase_     			= NegativeBinomial




 @classmethod
 def 		__snake_case						(      cls :  Optional[int]      ,   lowercase__ :  torch.Tensor      ,   lowercase__ :  torch.Tensor):







        '''simple docstring'''



        lowerCAmelCase__					     = cls.squareplus(lowercase__)
        return total_count.squeeze(-1), logits.squeeze(-1)




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = distr_args
        if self.dim == 1:
               return self.distribution_class(total_count=lowercase__      ,   logits=lowercase__)
        else:
               return Independent(self.distribution_class(total_count=lowercase__      ,   logits=lowercase__)      ,   1)




 def 		__snake_case						(      self :  str      ,   lowercase__ :  Dict      ,   lowercase__ :  Optional[torch.Tensor] = None      ,   lowercase__ :  Optional[torch.Tensor] = None):







        '''simple docstring'''



        lowerCAmelCase__     , lowerCAmelCase__					     = distr_args

        if scale is not None:
               # See scaling property of Gamma.
               logits += scale.log()

        return self._base_distribution((total_count, logits))

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     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 'xlm-roberta-xl'




 def __init__(      self :  int      ,   lowercase__ :  Optional[int]=250_880      ,   lowercase__ :  Union[str, Any]=2_560      ,   lowercase__ :  int=36      ,   lowercase__ :  int=32      ,   lowercase__ :  List[Any]=10_240      ,   lowercase__ :  Union[str, Any]="gelu"      ,   lowercase__ :  Dict=0.1      ,   lowercase__ :  Any=0.1      ,   lowercase__ :  Any=514      ,   lowercase__ :  Optional[int]=1      ,   lowercase__ :  Any=0.02      ,   lowercase__ :  List[str]=1e-05      ,   lowercase__ :  Dict=1      ,   lowercase__ :  Union[str, Any]=0      ,   lowercase__ :  Dict=2      ,   lowercase__ :  str="absolute"      ,   lowercase__ :  Dict=True      ,   lowercase__ :  List[Any]=None      ,   **lowercase__ :  Optional[Any]      ,   ):







        '''simple docstring'''



        super().__init__(pad_token_id=lowercase__      ,   bos_token_id=lowercase__      ,   eos_token_id=lowercase__      ,   **lowercase__)
        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = hidden_size
        lowerCAmelCase__					     = num_hidden_layers
        lowerCAmelCase__					     = num_attention_heads
        lowerCAmelCase__					     = hidden_act
        lowerCAmelCase__					     = intermediate_size
        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = max_position_embeddings
        lowerCAmelCase__					     = type_vocab_size
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = layer_norm_eps
        lowerCAmelCase__					     = position_embedding_type
        lowerCAmelCase__					     = use_cache
        lowerCAmelCase__					     = classifier_dropout






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 @property
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



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