





import os
import sys

__lowerCamelCase :  Union[str, Any]										=  os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__lowerCamelCase :  List[Any]										=  [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]



@add_start_docstrings(AutoConfig.__doc__  )
def 	A_       (  *_lowerCAmelCase							,     **_lowerCAmelCase  )		->				int:
 return AutoConfig.from_pretrained(*_lowerCAmelCase							,     **_lowerCAmelCase  )



@add_start_docstrings(AutoTokenizer.__doc__  )
def 	A_       (  *_lowerCAmelCase							,     **_lowerCAmelCase  )		->				Optional[int]:
 return AutoTokenizer.from_pretrained(*_lowerCAmelCase							,     **_lowerCAmelCase  )



@add_start_docstrings(AutoModel.__doc__  )
def 	A_       (  *_lowerCAmelCase							,     **_lowerCAmelCase  )		->				Dict:
 return AutoModel.from_pretrained(*_lowerCAmelCase							,     **_lowerCAmelCase  )



@add_start_docstrings(AutoModelForCausalLM.__doc__  )
def 	A_       (  *_lowerCAmelCase							,     **_lowerCAmelCase  )		->				str:
 return AutoModelForCausalLM.from_pretrained(*_lowerCAmelCase							,     **_lowerCAmelCase  )



@add_start_docstrings(AutoModelForMaskedLM.__doc__  )
def 	A_       (  *_lowerCAmelCase							,     **_lowerCAmelCase  )		->				List[Any]:
 return AutoModelForMaskedLM.from_pretrained(*_lowerCAmelCase							,     **_lowerCAmelCase  )



@add_start_docstrings(AutoModelForSequenceClassification.__doc__  )
def 	A_       (  *_lowerCAmelCase							,     **_lowerCAmelCase  )		->				List[str]:
 return AutoModelForSequenceClassification.from_pretrained(*_lowerCAmelCase							,     **_lowerCAmelCase  )



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__  )
def 	A_       (  *_lowerCAmelCase							,     **_lowerCAmelCase  )		->				Optional[Any]:
 return AutoModelForQuestionAnswering.from_pretrained(*_lowerCAmelCase							,     **_lowerCAmelCase  )







from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase :  Tuple										=  logging.get_logger(__name__)

__lowerCamelCase :  str										=  {
    """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       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     'camembert'





      def __init__(				self ,							A_=3_0522 ,							A_=768 ,							A_=12 ,							A_=12 ,							A_=3072 ,							A_="gelu" ,							A_=0.1 ,							A_=0.1 ,							A_=512 ,							A_=2 ,							A_=0.02 ,							A_=1e-12 ,							A_=1 ,							A_=0 ,							A_=2 ,							A_="absolute" ,							A_=True ,							A_=None ,							**A_ ,							):
       '''simple docstring'''




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

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



class       A__     ( __snake_case    ):





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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