





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__      =logging.get_logger(__name__)

UpperCamelCase__      ={
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}





class 			lowerCAmelCase__( __SCREAMING_SNAKE_CASE    ):






       '''simple docstring'''


       __snake_case 			=    'camembert'




       def __init__(      self    ,						__lowerCamelCase=3_0_5_2_2    ,						__lowerCamelCase=7_6_8    ,						__lowerCamelCase=1_2    ,						__lowerCamelCase=1_2    ,						__lowerCamelCase=3_0_7_2    ,						__lowerCamelCase="gelu"    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=5_1_2    ,						__lowerCamelCase=2    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=1E-12    ,						__lowerCamelCase=1    ,						__lowerCamelCase=0    ,						__lowerCamelCase=2    ,						__lowerCamelCase="absolute"    ,						__lowerCamelCase=True    ,						__lowerCamelCase=None    ,						**__lowerCamelCase    ,						)			->     Optional[int]:
            super().__init__(pad_token_id=__snake_case    ,						bos_token_id=__snake_case    ,						eos_token_id=__snake_case    ,						**__snake_case							)

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





class 			lowerCAmelCase__( __SCREAMING_SNAKE_CASE    ):






       '''simple docstring'''


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


UpperCamelCase__      ={
    'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'LILT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LiltForQuestionAnswering',
           'LiltForSequenceClassification',
           'LiltForTokenClassification',
           'LiltModel',
           'LiltPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)