





from collections import OrderedDict
from typing import Mapping

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


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

a				:							Optional[int]					=			{
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}

class      lowerCamelCase	(  SCREAMING_SNAKE_CASE     ):
       _UpperCamelCase    		=      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       _UpperCamelCase    		=      """squeezebert"""

       def __init__(			self	,						a__=3_05_22	,						a__=7_68	,						a__=12	,						a__=12	,						a__=30_72	,						a__="gelu"	,						a__=0.1	,						a__=0.1	,						a__=5_12	,						a__=2	,						a__=0.02	,						a__=1e-12	,						a__=0	,						a__=7_68	,						a__=4	,						a__=4	,						a__=4	,						a__=1	,						a__=4	,						a__=4	,						**a__	,						):






        '''simple docstring'''






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

        snake_case   : List[Any]	  =      vocab_size
        snake_case   : Optional[Any]	  =      hidden_size
        snake_case   : str	  =      num_hidden_layers
        snake_case   : Optional[Any]	  =      num_attention_heads
        snake_case   : List[str]	  =      hidden_act
        snake_case   : Optional[int]	  =      intermediate_size
        snake_case   : Union[str, Any]	  =      hidden_dropout_prob
        snake_case   : List[Any]	  =      attention_probs_dropout_prob
        snake_case   : Optional[Any]	  =      max_position_embeddings
        snake_case   : Tuple	  =      type_vocab_size
        snake_case   : Optional[int]	  =      initializer_range
        snake_case   : Dict	  =      layer_norm_eps
        snake_case   : Tuple	  =      embedding_size
        snake_case   : Any	  =      q_groups
        snake_case   : List[Any]	  =      k_groups
        snake_case   : Optional[Any]	  =      v_groups
        snake_case   : Union[str, Any]	  =      post_attention_groups
        snake_case   : Tuple	  =      intermediate_groups
        snake_case   : Any	  =      output_groups







class      lowerCamelCase	(  SCREAMING_SNAKE_CASE     ):

       @property
       def 				UpperCAmelCase				(			self					):






        '''simple docstring'''






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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				:							str					=			{'configuration_dinat': ['DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DinatConfig']}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a				:							Union[str, Any]					=			[
      'DINAT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DinatForImageClassification',
      'DinatModel',
      'DinatPreTrainedModel',
      'DinatBackbone',
  ]

if TYPE_CHECKING:
  from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dinat import (
        DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DinatBackbone,
        DinatForImageClassification,
        DinatModel,
        DinatPreTrainedModel,
    )

else:
  import sys

  a				:							List[Any]					=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






