





from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_   =   logging.get_logger(__name__)

lowerCAmelCase_   =   {
    "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 				A    (__UpperCAmelCase				):
      _SCREAMING_SNAKE_CASE					=							"""ernie"""



      def __init__(					self     ,  lowercase_=3_0522     ,  lowercase_=768     ,  lowercase_=12     ,  lowercase_=12     ,  lowercase_=3072     ,  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_     ,  )      ->   str:



             '''simple docstring'''

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

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


class 				A    (__UpperCAmelCase				):



      @property
      def    __a (					self					)      ->   Mapping[str, Mapping[int, str]]:



             '''simple docstring'''

             if self.task == "multiple-choice":
                    _snake_case      :							Tuple     				=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                    _snake_case      :							Optional[Any]     				=						{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 						A_			(  lowercase_  )			->						list:
       if len(lowercase_  ) <= 1:
              return data
       else:
              return [
                  *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                  data[0],
                  *quick_sort([e for e in data[1:] if e > data[0]]  ),
              ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






