


from collections import OrderedDict
from typing import Mapping

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


A__        =			logging.get_logger(__name__)

A__        =			{
    """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"""
  SCREAMING_SNAKE_CASE__				: str										=      'ernie'



  def __init__(	self    :				Optional[Any]     ,  A_    :				Union[str, Any]=3_0522     ,  A_    :				str=768     ,  A_    :				Union[str, Any]=12     ,  A_    :				Tuple=12     ,  A_    :				Union[str, Any]=3072     ,  A_    :				Optional[int]="gelu"     ,  A_    :				Any=0.1     ,  A_    :				str=0.1     ,  A_    :				Optional[Any]=512     ,  A_    :				str=2     ,  A_    :				List[Any]=3     ,  A_    :				Dict=False     ,  A_    :				List[Any]=0.02     ,  A_    :				Tuple=1E-1_2     ,  A_    :				str=0     ,  A_    :				str="absolute"     ,  A_    :				Optional[int]=True     ,  A_    :				int=None     ,  **A_    :				Optional[int]     ,  )					-> List[Any]:
      super().__init__(pad_token_id=_snake_case     ,  **_snake_case	)

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







class    UpperCamelCase_			(  a_			):


  """simple docstring"""



  @property
  def      _UpperCamelCase    (	self    :				List[str]	)					-> Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
          SCREAMING_SNAKE_CASE__					     =						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
          SCREAMING_SNAKE_CASE__					     =						{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
              ("task_type_ids", dynamic_axis),
          ]	)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__        =			logging.get_logger(__name__)

A__        =			{
    "facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class    UpperCamelCase_			(  _snake_case		,     _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[str]										=      """convnext"""



  def __init__(	self    :				Optional[Any]     ,  A_    :				Any=3     ,  A_    :				Tuple=4     ,  A_    :				Optional[int]=4     ,  A_    :				Dict=None     ,  A_    :				List[Any]=None     ,  A_    :				Union[str, Any]="gelu"     ,  A_    :				Optional[Any]=0.02     ,  A_    :				Union[str, Any]=1E-1_2     ,  A_    :				Dict=1E-6     ,  A_    :				List[str]=0.0     ,  A_    :				int=224     ,  A_    :				Optional[Any]=None     ,  A_    :				List[str]=None     ,  **A_    :				Union[str, Any]     ,  )					-> Tuple:
      super().__init__(**A_	)

      SCREAMING_SNAKE_CASE__					     =						num_channels
      SCREAMING_SNAKE_CASE__					     =						patch_size
      SCREAMING_SNAKE_CASE__					     =						num_stages
      SCREAMING_SNAKE_CASE__					     =						[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      SCREAMING_SNAKE_CASE__					     =						[3, 3, 9, 3] if depths is None else depths
      SCREAMING_SNAKE_CASE__					     =						hidden_act
      SCREAMING_SNAKE_CASE__					     =						initializer_range
      SCREAMING_SNAKE_CASE__					     =						layer_norm_eps
      SCREAMING_SNAKE_CASE__					     =						layer_scale_init_value
      SCREAMING_SNAKE_CASE__					     =						drop_path_rate
      SCREAMING_SNAKE_CASE__					     =						image_size
      SCREAMING_SNAKE_CASE__					     =						["stem"] + [f"stage{idx}" for idx in range(1     ,  len(self.depths	) + 1	)]
      SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						get_aligned_output_features_output_indices(
          out_features=A_     ,  out_indices=A_     ,  stage_names=self.stage_names	)







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: Dict										=      version.parse("""1.11"""			)



  @property
  def      _UpperCamelCase    (	self    :				str	)					-> Mapping[str, Mapping[int, str]]:
      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
          ]	)







  @property
  def      _UpperCamelCase    (	self    :				Tuple	)					-> float:
      return 1E-5


