'''simple docstring'''






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     A__							(       _UpperCamelCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Optional[int]    				=     '''xlm-roberta'''




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







      """simple docstring"""




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

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





class     A__							(       _UpperCamelCase   ):





   """simple docstring"""





   @property
   def 			_lowerCAmelCase		( self     :		List[Any]			)       ->    Mapping[str, Mapping[int, str]]:







      """simple docstring"""




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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    Dict						    =  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MraForMaskedLM',
         'MraForMultipleChoice',
         'MraForQuestionAnswering',
         'MraForSequenceClassification',
         'MraForTokenClassification',
         'MraLayer',
         'MraModel',
         'MraPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mra import (
              MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
              MraLayer,
              MraModel,
              MraPreTrainedModel,
          )
else:
     import sys

     _snake_case  :    Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure)

