


"""simple docstring"""



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											=	{
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}
class     __magic_name__						(	__UpperCAmelCase   ):
       __A	:					List[Any]					     =      "ernie"






       def __init__(      self     :			str     ,   snake_case__     :			int=3_0_5_2_2     ,   snake_case__     :			List[str]=7_6_8     ,   snake_case__     :			List[Any]=1_2     ,   snake_case__     :			Tuple=1_2     ,   snake_case__     :			Dict=3_0_7_2     ,   snake_case__     :			Any="gelu"     ,   snake_case__     :			Optional[int]=0.1     ,   snake_case__     :			Optional[Any]=0.1     ,   snake_case__     :			int=5_1_2     ,   snake_case__     :			Optional[int]=2     ,   snake_case__     :			Dict=3     ,   snake_case__     :			Union[str, Any]=False     ,   snake_case__     :			int=0.02     ,   snake_case__     :			Any=1e-1_2     ,   snake_case__     :			List[Any]=0     ,   snake_case__     :			List[str]="absolute"     ,   snake_case__     :			str=True     ,   snake_case__     :			str=None     ,   **snake_case__     :			List[Any]     ,   ):

              '''simple docstring'''






              super().__init__(pad_token_id=__lowercase     ,   **__lowercase )

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







class     __magic_name__						(	__UpperCAmelCase   ):






       @property
       def        __snake_case  (      self     :			List[Any] ):

              '''simple docstring'''






              if self.task == "multiple-choice":
                     lowercase						:str											= {0: 'batch', 1: 'choice', 2: 'sequence'}
              else:
                     lowercase						:List[str]											= {0: 'batch', 1: 'sequence'}
              return OrderedDict(
                  [
                      ('''input_ids''', dynamic_axis),
                      ('''attention_mask''', dynamic_axis),
                      ('''token_type_ids''', dynamic_axis),
                      ('''task_type_ids''', dynamic_axis),
                  ] )







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__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


UpperCamelCase__              =						{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		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(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
