

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      		=     {
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Optional[int]										=    "ibert"



      def __init__(      self				:    Optional[Any]      , A__				:    List[Any]=30522      , A__				:    Optional[int]=768      , A__				:    List[str]=12      , A__				:    Tuple=12      , A__				:    Optional[int]=3072      , A__				:    List[Any]="gelu"      , A__				:    int=0.1      , A__				:    Optional[int]=0.1      , A__				:    Optional[int]=512      , A__				:    List[Any]=2      , A__				:    Optional[int]=0.02      , A__				:    List[str]=1e-12      , A__				:    Optional[int]=1      , A__				:    List[str]=0      , A__				:    Optional[Any]=2      , A__				:    Union[str, Any]="absolute"      , A__				:    Union[str, Any]=False      , A__				:    List[str]="none"      , **A__				:    int      , )   ->					Union[str, Any]:
        super().__init__(pad_token_id=A__      , bos_token_id=A__      , eos_token_id=A__      , **A__		)

        _snake_case						=							vocab_size
        _snake_case						=							hidden_size
        _snake_case						=							num_hidden_layers
        _snake_case						=							num_attention_heads
        _snake_case						=							hidden_act
        _snake_case						=							intermediate_size
        _snake_case						=							hidden_dropout_prob
        _snake_case						=							attention_probs_dropout_prob
        _snake_case						=							max_position_embeddings
        _snake_case						=							type_vocab_size
        _snake_case						=							initializer_range
        _snake_case						=							layer_norm_eps
        _snake_case						=							position_embedding_type
        _snake_case						=							quant_mode
        _snake_case						=							force_dequant



class   lowercase_   (  __lowercase					):



      @property
      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
          _snake_case						=							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
          _snake_case						=							{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


__A      		=     {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''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

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

