



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''encoder-decoder'''
     UpperCamelCase__		     =					True




     def __init__(					self			,    **a__):
         super().__init__(**a__)
         assert (
             "encoder" in kwargs and "decoder" in kwargs
         ), "Config has to be initialized with encoder and decoder config"
         A__					=       kwargs.pop('''encoder''')
         A__					=       encoder_config.pop('''model_type''')
         A__					=       kwargs.pop('''decoder''')
         A__					=       decoder_config.pop('''model_type''')

         from ..auto.configuration_auto import AutoConfig

         A__					=       AutoConfig.for_model(a__			,    **a__)
         A__					=       AutoConfig.for_model(a__			,    **a__)
         A__					=       True




     @classmethod
     def 		snake_case_		(					cls			,    a__			,    a__			,    **a__):
         logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config''')
         A__					=       True
         A__					=       True

         return cls(encoder=encoder_config.to_dict()			,    decoder=decoder_config.to_dict()			,    **a__)




     def 		snake_case_		(					self):
         A__					=       copy.deepcopy(self.__dict__)
         A__					=       self.encoder.to_dict()
         A__					=       self.decoder.to_dict()
         A__					=       self.__class__.model_type
         return output





from collections import OrderedDict
from typing import Mapping

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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''xlm-roberta'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=2			,    a__=0.0_2			,    a__=1e-12			,    a__=1			,    a__=0			,    a__=2			,    a__="absolute"			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    bos_token_id=a__			,    eos_token_id=a__			,    **a__)

         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       hidden_act
         A__					=       intermediate_size
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       initializer_range
         A__					=       layer_norm_eps
         A__					=       position_embedding_type
         A__					=       use_cache
         A__					=       classifier_dropout







class      _UpperCAmelCase      (							A__  ):




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