





from collections import OrderedDict
from typing import Mapping

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


__UpperCAmelCase			:  Tuple										=				logging.get_logger(__name__)

__UpperCAmelCase			:  List[str]										=				{
    "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        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
       lowerCAmelCase__ 						=	"""squeezebert"""




       def __init__(    self					:       Union[str, Any]				,							A					:       List[Any]=30_522				,							A					:       str=768				,							A					:       Optional[Any]=12				,							A					:       List[Any]=12				,							A					:       Optional[int]=3_072				,							A					:       List[str]="gelu"				,							A					:       str=0.1				,							A					:       int=0.1				,							A					:       Tuple=512				,							A					:       int=2				,							A					:       Union[str, Any]=0.02				,							A					:       Tuple=1E-12				,							A					:       Any=0				,							A					:       Optional[Any]=768				,							A					:       List[str]=4				,							A					:       Dict=4				,							A					:       List[Any]=4				,							A					:       Optional[Any]=1				,							A					:       int=4				,							A					:       int=4				,							**A					:       List[Any]				,							):
         super().__init__(pad_token_id=A				,							**A						)

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







class        __snake_case			(				__lowerCamelCase     ):





       '''simple docstring'''







       @property
       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         if self.task == "multiple-choice":
           __snake_case:		Union[str, Any]					    =		{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
           __snake_case:		List[str]					    =		{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


__UpperCAmelCase			:  Optional[Any]										=				{"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase			:  str										=				[
          "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
          "SEWDForCTC",
          "SEWDForSequenceClassification",
          "SEWDModel",
          "SEWDPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_sew_d import (
                SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
                SEWDForCTC,
                SEWDForSequenceClassification,
                SEWDModel,
                SEWDPreTrainedModel,
            )


else:
      import sys

      __UpperCAmelCase			:  Union[str, Any]										=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






