"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__  :  int						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  Tuple						     =	{
    '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 				snake_case       ( _UpperCamelCase				):




					"""simple docstring"""

					snake_case__				    = "camembert"







					def __init__(				self   :	Any    ,lowerCamelCase__   :	List[str]=30_522    ,lowerCamelCase__   :	str=768    ,lowerCamelCase__   :	Union[str, Any]=12    ,lowerCamelCase__   :	List[Any]=12    ,lowerCamelCase__   :	str=3_072    ,lowerCamelCase__   :	Optional[Any]="gelu"    ,lowerCamelCase__   :	Union[str, Any]=0.1    ,lowerCamelCase__   :	List[str]=0.1    ,lowerCamelCase__   :	List[str]=512    ,lowerCamelCase__   :	List[Any]=2    ,lowerCamelCase__   :	Dict=0.0_2    ,lowerCamelCase__   :	Any=1e-12    ,lowerCamelCase__   :	int=1    ,lowerCamelCase__   :	Optional[Any]=0    ,lowerCamelCase__   :	Optional[Any]=2    ,lowerCamelCase__   :	str="absolute"    ,lowerCamelCase__   :	Optional[int]=True    ,lowerCamelCase__   :	List[Any]=None    ,**lowerCamelCase__   :	Dict    ,):
									super().__init__(pad_token_id=lowerCAmelCase_    ,bos_token_id=lowerCAmelCase_    ,eos_token_id=lowerCAmelCase_    ,**lowerCAmelCase_			)

									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							hidden_size
									UpperCAmelCase__             =							num_hidden_layers
									UpperCAmelCase__             =							num_attention_heads
									UpperCAmelCase__             =							hidden_act
									UpperCAmelCase__             =							intermediate_size
									UpperCAmelCase__             =							hidden_dropout_prob
									UpperCAmelCase__             =							attention_probs_dropout_prob
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							type_vocab_size
									UpperCAmelCase__             =							initializer_range
									UpperCAmelCase__             =							layer_norm_eps
									UpperCAmelCase__             =							position_embedding_type
									UpperCAmelCase__             =							use_cache
									UpperCAmelCase__             =							classifier_dropout






class 				snake_case       ( _UpperCamelCase				):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(				self   :	Dict			):
									if self.task == "multiple-choice":
													UpperCAmelCase__             =							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
													UpperCAmelCase__             =							{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


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _snake_case  :    List[str]						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

