





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					:				Any				=			logging.get_logger(__name__)

_A					:				int				=			{
    """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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[int]				=	"""swin"""

    lowerCamelCase__    :		List[Any]				=	{
        """num_attention_heads""": """num_heads""",
        """num_hidden_layers""": """num_layers""",
    }



    def __init__(       self   ,				A_=2_24   ,				A_=4   ,				A_=3   ,				A_=96   ,				A_=[2, 2, 6, 2]   ,				A_=[3, 6, 12, 24]   ,				A_=7   ,				A_=4.0   ,				A_=True   ,				A_=0.0   ,				A_=0.0   ,				A_=0.1   ,				A_="gelu"   ,				A_=False   ,				A_=0.02   ,				A_=1E-5   ,				A_=32   ,				A_=None   ,				A_=None   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(**A_     )

        SCREAMING_SNAKE_CASE__											=			image_size
        SCREAMING_SNAKE_CASE__											=			patch_size
        SCREAMING_SNAKE_CASE__											=			num_channels
        SCREAMING_SNAKE_CASE__											=			embed_dim
        SCREAMING_SNAKE_CASE__											=			depths
        SCREAMING_SNAKE_CASE__											=			len(A_     )
        SCREAMING_SNAKE_CASE__											=			num_heads
        SCREAMING_SNAKE_CASE__											=			window_size
        SCREAMING_SNAKE_CASE__											=			mlp_ratio
        SCREAMING_SNAKE_CASE__											=			qkv_bias
        SCREAMING_SNAKE_CASE__											=			hidden_dropout_prob
        SCREAMING_SNAKE_CASE__											=			attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE__											=			drop_path_rate
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			use_absolute_embeddings
        SCREAMING_SNAKE_CASE__											=			layer_norm_eps
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			int(embed_dim * 2 ** (len(A_     ) - 1)     )
        SCREAMING_SNAKE_CASE__											=			['''stem'''] + [f'''stage{idx}''' for idx in range(1   ,				len(A_     ) + 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 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		int				=	version.parse("""1.11"""    )



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

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




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return 1E-4






import os





def 						__A						(      ) ->			Dict:
	with open(os.path.dirname(__lowerCamelCase       ) + """/p022_names.txt"""       ) as file:
		a									=				str(file.readlines()[0]       )
		a									=				names.replace("""\""""				,     """"""       ).split(""","""       )

	names.sort()

	a									=				0
	a									=				0

	for i, name in enumerate(__lowerCamelCase       ):
		for letter in name:
			name_score += ord(__lowerCamelCase       ) - 64

		total_score += (i + 1) * name_score
		a									=				0
	return total_score


if __name__ == "__main__":
		print(solution())

