
from collections import OrderedDict
from typing import Mapping

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


snake_case				:	List[Any]							   =						logging.get_logger(__name__)

snake_case				:	Optional[int]							   =						{
    "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    _snake_case      (  snake_case       ):
			UpperCamelCase__					   =					'ernie'







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

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







class    _snake_case      (  snake_case       ):







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


def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:







	'''simple docstring'''


	if not isinstance(_snake_case   ,     _snake_case     ) or number < 0:
		raise ValueError("Input must be a non-negative integer"     )

	__magic_name__	:	Dict			     =  number & ~(number - 1)
	__magic_name__	:	Optional[Any]			     =  0
	while intermediate:
		intermediate >>= 1
		index += 1
	return index - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
