





A__:    Any  	=	[
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	str					=     {"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
    UpperCamelCase__:	List[str]					=     0
    UpperCamelCase__:	Tuple					=     0
    while place < len(A_):
        if (place + 1 < len(A_)) and (vals[roman[place]] < vals[roman[place + 1]]):
            total += vals[roman[place + 1]] - vals[roman[place]]
            place += 2
        else:
            total += vals[roman[place]]
            place += 1
    return total



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Tuple					=     []
    for arabic, roman in ROMAN:
        ((UpperCamelCase__)				, (UpperCamelCase__)):	List[Any]					=     divmod(A_    ,A_)
        result.append(roman * factor)
        if number == 0:
            break
    return "".join(A_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()








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__:    Tuple  	=	logging.get_logger(__name__)

A__:    List[Any]  	=	{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class        _a    (    UpperCamelCase__						,			UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       """swin"""

      UpperCamelCase__  		=       {
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }






      def __init__(		self:			Any	,    __lowerCamelCase:			Optional[Any]=224	,    __lowerCamelCase:			List[Any]=4	,    __lowerCamelCase:			Any=3	,    __lowerCamelCase:			Dict=96	,    __lowerCamelCase:			List[str]=[2, 2, 6, 2]	,    __lowerCamelCase:			int=[3, 6, 12, 24]	,    __lowerCamelCase:			str=7	,    __lowerCamelCase:			Union[str, Any]=4.0	,    __lowerCamelCase:			Union[str, Any]=True	,    __lowerCamelCase:			List[str]=0.0	,    __lowerCamelCase:			Union[str, Any]=0.0	,    __lowerCamelCase:			List[str]=0.1	,    __lowerCamelCase:			Union[str, Any]="gelu"	,    __lowerCamelCase:			List[str]=False	,    __lowerCamelCase:			Any=0.02	,    __lowerCamelCase:			Any=1e-5	,    __lowerCamelCase:			Optional[Any]=32	,    __lowerCamelCase:			Tuple=None	,    __lowerCamelCase:			int=None	,    **__lowerCamelCase:			int	,    ):
          '''simple docstring'''


          super().__init__(**__lowerCamelCase					)

          UpperCamelCase__:	List[Any]					=     image_size
          UpperCamelCase__:	str					=     patch_size
          UpperCamelCase__:	Optional[int]					=     num_channels
          UpperCamelCase__:	Dict					=     embed_dim
          UpperCamelCase__:	int					=     depths
          UpperCamelCase__:	List[str]					=     len(__lowerCamelCase					)
          UpperCamelCase__:	List[Any]					=     num_heads
          UpperCamelCase__:	Union[str, Any]					=     window_size
          UpperCamelCase__:	Dict					=     mlp_ratio
          UpperCamelCase__:	List[Any]					=     qkv_bias
          UpperCamelCase__:	Union[str, Any]					=     hidden_dropout_prob
          UpperCamelCase__:	int					=     attention_probs_dropout_prob
          UpperCamelCase__:	str					=     drop_path_rate
          UpperCamelCase__:	str					=     hidden_act
          UpperCamelCase__:	Any					=     use_absolute_embeddings
          UpperCamelCase__:	Union[str, Any]					=     layer_norm_eps
          UpperCamelCase__:	Tuple					=     initializer_range
          UpperCamelCase__:	Tuple					=     encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          UpperCamelCase__:	Any					=     int(embed_dim * 2 ** (len(__lowerCamelCase					) - 1)					)
          UpperCamelCase__:	str					=     ["stem"] + [F"stage{idx}" for idx in range(1	,    len(__lowerCamelCase					) + 1					)]
          UpperCamelCase__				, UpperCamelCase__:	List[str]					=     get_aligned_output_features_output_indices(
              out_features=__lowerCamelCase	,    out_indices=__lowerCamelCase	,    stage_names=self.stage_names					)




class        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       version.parse("""1.11""")






      @property
      def 			UpperCAmelCase_       (		self:			List[Any]					):
          '''simple docstring'''


          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]					)






      @property
      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          return 1e-4

