






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__							=     {
    """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 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Any 				= "xlm-roberta"



       def __init__(							self			,						a__=3_05_22			,						a__=7_68			,						a__=12			,						a__=12			,						a__=30_72			,						a__="gelu"			,						a__=0.1			,						a__=0.1			,						a__=5_12			,						a__=2			,						a__=0.02			,						a__=1e-12			,						a__=1			,						a__=0			,						a__=2			,						a__="absolute"			,						a__=True			,						a__=None			,						**a__			,						) ->      Optional[Any]:






            '''simple docstring'''





            super().__init__(pad_token_id=a__			,						bos_token_id=a__			,						eos_token_id=a__			,						**a__    )

            __snake_case			:Dict          =						vocab_size
            __snake_case			:str          =						hidden_size
            __snake_case			:List[Any]          =						num_hidden_layers
            __snake_case			:str          =						num_attention_heads
            __snake_case			:str          =						hidden_act
            __snake_case			:Optional[Any]          =						intermediate_size
            __snake_case			:Optional[Any]          =						hidden_dropout_prob
            __snake_case			:Union[str, Any]          =						attention_probs_dropout_prob
            __snake_case			:Tuple          =						max_position_embeddings
            __snake_case			:Dict          =						type_vocab_size
            __snake_case			:int          =						initializer_range
            __snake_case			:str          =						layer_norm_eps
            __snake_case			:str          =						position_embedding_type
            __snake_case			:List[str]          =						use_cache
            __snake_case			:Optional[Any]          =						classifier_dropout
class 			snake_case__   (							lowercase_):

       '''simple docstring'''



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






            '''simple docstring'''





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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase__							=     {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__							=     [
         """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """CanineForMultipleChoice""",
         """CanineForQuestionAnswering""",
         """CanineForSequenceClassification""",
         """CanineForTokenClassification""",
         """CanineLayer""",
         """CanineModel""",
         """CaninePreTrainedModel""",
         """load_tf_weights_in_canine""",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

     lowerCamelCase__							=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


