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

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


A__   :		Union[str, Any] 		=logging.get_logger(__name__)

A__   :		Optional[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  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''xlm-roberta-xl'''
 def __init__(   self			:    str				,   lowerCamelCase			:    Any=25_08_80				,   lowerCamelCase			:    Optional[int]=25_60				,   lowerCamelCase			:    List[str]=36				,   lowerCamelCase			:    Dict=32				,   lowerCamelCase			:    Any=1_02_40				,   lowerCamelCase			:    Optional[int]="gelu"				,   lowerCamelCase			:    str=0.1				,   lowerCamelCase			:    str=0.1				,   lowerCamelCase			:    int=5_14				,   lowerCamelCase			:    Tuple=1				,   lowerCamelCase			:    Any=0.02				,   lowerCamelCase			:    Optional[int]=1e-0_5				,   lowerCamelCase			:    Optional[int]=1				,   lowerCamelCase			:    str=0				,   lowerCamelCase			:    Any=2				,   lowerCamelCase			:    List[Any]="absolute"				,   lowerCamelCase			:    List[str]=True				,   lowerCamelCase			:    Optional[Any]=None				,   **lowerCamelCase			:    Optional[int]				,   ):



   """simple docstring"""





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







class  __A				( _SCREAMING_SNAKE_CASE			):
 @property
 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





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

'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__   :		int 		=logging.get_logger(__name__)

A__   :		Union[str, Any] 		={
    'facebook/dpr-ctx_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-single-nq-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-single-nq-base': (
        'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'
    ),
    'facebook/dpr-ctx_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-question_encoder-multiset-base': (
        'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'
    ),
    'facebook/dpr-reader-multiset-base': (
        'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'
    ),
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''dpr'''
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    Tuple=3_05_22				,   lowerCamelCase			:    List[str]=7_68				,   lowerCamelCase			:    Union[str, Any]=12				,   lowerCamelCase			:    Tuple=12				,   lowerCamelCase			:    Dict=30_72				,   lowerCamelCase			:    List[Any]="gelu"				,   lowerCamelCase			:    List[Any]=0.1				,   lowerCamelCase			:    Union[str, Any]=0.1				,   lowerCamelCase			:    Optional[Any]=5_12				,   lowerCamelCase			:    List[Any]=2				,   lowerCamelCase			:    int=0.02				,   lowerCamelCase			:    Union[str, Any]=1e-1_2				,   lowerCamelCase			:    int=0				,   lowerCamelCase			:    List[str]="absolute"				,   lowerCamelCase			:    int = 0				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   super().__init__(pad_token_id=lowerCamelCase				,   **lowerCamelCase       )

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



