code: 
  
  
  
  
  
  '''simple docstring'''
  
  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
  
  
  __lowerCAmelCase							:			Any				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Dict				={
      "MBZUAI/swiftformer-xs": "https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json",
  }
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       """swiftformer"""
   def __init__(       self   :Any		, lowercase_   :Union[str, Any]=3		, lowercase_   :str=[3, 3, 6, 4]		, lowercase_   :List[Any]=[48, 56, 1_12, 2_20]		, lowercase_   :Optional[int]=4		, lowercase_   :str=[True, True, True, True]		, lowercase_   :Tuple="gelu"		, lowercase_   :int=3		, lowercase_   :List[Any]=2		, lowercase_   :Optional[Any]=1		, lowercase_   :List[str]=0.0		, lowercase_   :List[Any]=True		, lowercase_   :Any=1E-5		, lowercase_   :Optional[int]=1E-5		, **lowercase_   :List[str]		, )->							Dict:
       super().__init__(**lowercase_							)
       A__       =    num_channels
       A__       =    depths
       A__       =    embed_dims
       A__       =    mlp_ratio
       A__       =    downsamples
       A__       =    hidden_act
       A__       =    down_patch_size
       A__       =    down_stride
       A__       =    down_pad
       A__       =    drop_path_rate
       A__       =    use_layer_scale
       A__       =    layer_scale_init_value
       A__       =    batch_norm_eps
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       version.parse("""1.11"""       )
   @property
   def   UpperCAmelCase_							(       self   :List[Any]							)->							Mapping[str, Mapping[int, str]]:
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ]							)
  
  
  
   @property
   def   UpperCAmelCase_							(       self   :str							)->							float:
       return 1E-4
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  