from collections import OrderedDict
from typing import Mapping

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


a_    					=       logging.get_logger(__name__)

a_    					=       {
    """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 		_lowercase						(					_lowerCamelCase       ):
 lowercase				     =   'xlm-roberta'

 def __init__( self	:							Optional[int]   ,      snake_case	:							Optional[Any]=3_0_5_2_2   ,      snake_case	:							Any=7_6_8   ,      snake_case	:							Optional[Any]=1_2   ,      snake_case	:							List[str]=1_2   ,      snake_case	:							str=3_0_7_2   ,      snake_case	:							Dict="gelu"   ,      snake_case	:							Union[str, Any]=0.1   ,      snake_case	:							Any=0.1   ,      snake_case	:							Dict=5_1_2   ,      snake_case	:							Any=2   ,      snake_case	:							Any=0.02   ,      snake_case	:							Union[str, Any]=1e-12   ,      snake_case	:							Dict=1   ,      snake_case	:							List[Any]=0   ,      snake_case	:							int=2   ,      snake_case	:							str="absolute"   ,      snake_case	:							int=True   ,      snake_case	:							Dict=None   ,      **snake_case	:							int   ,      )   ->						List[str]:

  """simple docstring"""





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

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





class 		_lowercase						(					_lowerCamelCase       ):

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

  """simple docstring"""





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








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



