from typing import Callable
def 					snake_case						(__lowercase						)       ->       Tuple:

						'''simple docstring'''



						def wrapper(__lowercase						):
												_snake_case						:     List[Any]				=    function_with_docstring.__doc__
												return target_function

						return wrapper
from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE						:    str     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    Union[str, Any]     = {
    '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    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		'ernie'



							def __init__(						self      ,				lowercase_=30_522      ,				lowercase_=768      ,				lowercase_=12      ,				lowercase_=12      ,				lowercase_=3_072      ,				lowercase_="gelu"      ,				lowercase_=0.1      ,				lowercase_=0.1      ,				lowercase_=512      ,				lowercase_=2      ,				lowercase_=3      ,				lowercase_=False      ,				lowercase_=0.02      ,				lowercase_=1e-12      ,				lowercase_=0      ,				lowercase_="absolute"      ,				lowercase_=True      ,				lowercase_=None      ,				**lowercase_      ,				):
													super().__init__(pad_token_id=lowercase_      ,				**lowercase_				)

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




class    lowercase_    (     __snake_case		):



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