






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


snake_case   :   Union[str, Any]    		=		logging.get_logger(__name__)

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 					UpperCamelCase__   (							a_):







    """simple docstring"""
    __UpperCAmelCase											=   """ernie"""







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





           '''simple docstring'''

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

           __magic_name__										=						vocab_size
           __magic_name__										=						hidden_size
           __magic_name__										=						num_hidden_layers
           __magic_name__										=						num_attention_heads
           __magic_name__										=						hidden_act
           __magic_name__										=						intermediate_size
           __magic_name__										=						hidden_dropout_prob
           __magic_name__										=						attention_probs_dropout_prob
           __magic_name__										=						max_position_embeddings
           __magic_name__										=						type_vocab_size
           __magic_name__										=						task_type_vocab_size
           __magic_name__										=						use_task_id
           __magic_name__										=						initializer_range
           __magic_name__										=						layer_norm_eps
           __magic_name__										=						position_embedding_type
           __magic_name__										=						use_cache
           __magic_name__										=						classifier_dropout



class 					UpperCamelCase__   (							a_):







    """simple docstring"""







    @property
    def 							a__ (		self   :  Any   ):





           '''simple docstring'''

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









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case   :   Optional[int]    		=		{
    """configuration_splinter""": ["""SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SplinterConfig"""],
    """tokenization_splinter""": ["""SplinterTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Dict    		=		["""SplinterTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case   :   Dict    		=		[
     """SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SplinterForQuestionAnswering""",
     """SplinterForPreTraining""",
     """SplinterLayer""",
     """SplinterModel""",
     """SplinterPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
 from .tokenization_splinter import SplinterTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_splinter_fast import SplinterTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_splinter import (
      SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
      SplinterForPreTraining,
      SplinterForQuestionAnswering,
      SplinterLayer,
      SplinterModel,
      SplinterPreTrainedModel,
  )


else:
 import sys

 snake_case   :   List[str]    		=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)