
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :      str       =				logging.get_logger(__name__)

_a   :      Union[str, Any]       =				{
    """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json""",
}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       """mpnet"""
    def __init__(     self    ,     snake_case_=3_05_27    ,     snake_case_=7_68    ,     snake_case_=12    ,     snake_case_=12    ,     snake_case_=30_72    ,     snake_case_="gelu"    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=5_12    ,     snake_case_=0.02    ,     snake_case_=1E-12    ,     snake_case_=32    ,     snake_case_=1    ,     snake_case_=0    ,     snake_case_=2    ,     **snake_case_    ,     ):
      super().__init__(pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     **snake_case_  )

      _snake_case  :    Tuple            = vocab_size
      _snake_case  :    int            = hidden_size
      _snake_case  :    str            = num_hidden_layers
      _snake_case  :    Any            = num_attention_heads
      _snake_case  :    Optional[Any]            = hidden_act
      _snake_case  :    Union[str, Any]            = intermediate_size
      _snake_case  :    Any            = hidden_dropout_prob
      _snake_case  :    Any            = attention_probs_dropout_prob
      _snake_case  :    Any            = max_position_embeddings
      _snake_case  :    int            = initializer_range
      _snake_case  :    Any            = layer_norm_eps
      _snake_case  :    Optional[int]            = relative_attention_num_buckets







from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCAmelCase__    :							Tuple       		=     [
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCAmelCase__    :							Tuple       		=     get_logger(__name__)


UpperCAmelCase__    :							List[Any]       		=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Optional[TaskTemplate]:





  """simple docstring"""





  _lowercase:			Union[str, Any]      =     task_template_dict.get('task'				)
  if task_name is None:
    logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(UpperCamelCase				)}'				)
    return None
  _lowercase:			int      =     NAMEaTEMPLATE.get(UpperCamelCase				)
  return template.from_dict(UpperCamelCase				)
