'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase :				Dict														= logging.get_logger(__name__)

_lowerCamelCase :				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    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""ernie"""



   def __init__(    self					,      lowerCAmelCase__=30_522					,      lowerCAmelCase__=768					,      lowerCAmelCase__=12					,      lowerCAmelCase__=12					,      lowerCAmelCase__=3_072					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=512					,      lowerCAmelCase__=2					,      lowerCAmelCase__=3					,      lowerCAmelCase__=False					,      lowerCAmelCase__=0.02					,      lowerCAmelCase__=1e-12					,      lowerCAmelCase__=0					,      lowerCAmelCase__="absolute"					,      lowerCAmelCase__=True					,      lowerCAmelCase__=None					,      **lowerCAmelCase__					,      )				->     Any:
        """simple docstring"""





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

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




class    lowerCamelCase__  (   __snake_case     ):



   @property
   def  _UpperCamelCase		(    self     )				->     Mapping[str, Mapping[int, str]]:
        """simple docstring"""





        if self.task == "multiple-choice":
             _UpperCamelCase  :int       ={0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
             _UpperCamelCase  :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),
            ]     )







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase					=     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""LEDTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LEDForConditionalGeneration""",
      """LEDForQuestionAnswering""",
      """LEDForSequenceClassification""",
      """LEDModel""",
      """LEDPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
  from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
  from .tokenization_led import LEDTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_led_fast import LEDTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
  import sys

  UpperCamelCase					=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






