

import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase			:				Any   =     {
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

lowercase			:				Tuple   =     logging.get_logger(__name__)







class 					__lowercase  (						lowerCamelCase__				):





							"""simple docstring"""

							UpperCAmelCase_						:		List[Any]								=	'''mask2former'''
							UpperCAmelCase_						:		List[str]								=	['''swin''']
							UpperCAmelCase_						:		str								=	{'''hidden_size''': '''hidden_dim'''}




							def __init__(					self    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 2_56    ,						__UpperCAmelCase = 2_56    ,						__UpperCAmelCase = 2_56    ,						__UpperCAmelCase = 10_24    ,						__UpperCAmelCase = "relu"    ,						__UpperCAmelCase = 6    ,						__UpperCAmelCase = 10    ,						__UpperCAmelCase = 8    ,						__UpperCAmelCase = 0.0    ,						__UpperCAmelCase = 20_48    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = False    ,						__UpperCAmelCase = 4    ,						__UpperCAmelCase = 2_55    ,						__UpperCAmelCase = 1_00    ,						__UpperCAmelCase = 0.1    ,						__UpperCAmelCase = 2.0    ,						__UpperCAmelCase = 5.0    ,						__UpperCAmelCase = 5.0    ,						__UpperCAmelCase = 1_25_44    ,						__UpperCAmelCase = 3.0    ,						__UpperCAmelCase = 0.7_5    ,						__UpperCAmelCase = 0.0_2    ,						__UpperCAmelCase = 1.0    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = [4, 8, 16, 32]    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       int:





														if backbone_config is None:
																					logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''						)
																					A      :					List[str]      		=					CONFIG_MAPPING['''swin'''](
																					    image_size=2_24    ,						in_channels=3    ,						patch_size=4    ,						embed_dim=96    ,						depths=[2, 2, 18, 2]    ,						num_heads=[3, 6, 12, 24]    ,						window_size=7    ,						drop_path_rate=0.3    ,						use_absolute_embeddings=a__    ,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    ,						)

														if isinstance(a__    ,						a__						):
																					A      :					Optional[Any]      		=					backbone_config.pop('''model_type'''						)
																					A      :					str      		=					CONFIG_MAPPING[backbone_model_type]
																					A      :					Optional[Any]      		=					config_class.from_dict(a__						)

														# verify that the backbone is supported
														if backbone_config.model_type not in self.backbones_supported:
																					logger.warning_once(
																					    f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
																					    f'Supported model types: {",".join(self.backbones_supported						)}'						)

														A      :					List[Any]      		=					backbone_config
														A      :					List[Any]      		=					feature_size
														A      :					Any      		=					mask_feature_size
														A      :					Any      		=					hidden_dim
														A      :					Optional[Any]      		=					encoder_feedforward_dim
														A      :					Optional[Any]      		=					activation_function
														A      :					Dict      		=					encoder_layers
														A      :					Optional[Any]      		=					decoder_layers
														A      :					str      		=					num_attention_heads
														A      :					List[str]      		=					dropout
														A      :					Union[str, Any]      		=					dim_feedforward
														A      :					Any      		=					pre_norm
														A      :					Optional[Any]      		=					enforce_input_projection
														A      :					str      		=					common_stride
														A      :					Tuple      		=					ignore_value
														A      :					List[str]      		=					num_queries
														A      :					Optional[Any]      		=					no_object_weight
														A      :					str      		=					class_weight
														A      :					Union[str, Any]      		=					mask_weight
														A      :					Optional[int]      		=					dice_weight
														A      :					int      		=					train_num_points
														A      :					str      		=					oversample_ratio
														A      :					Dict      		=					importance_sample_ratio
														A      :					List[Any]      		=					init_std
														A      :					Union[str, Any]      		=					init_xavier_std
														A      :					Optional[int]      		=					use_auxiliary_loss
														A      :					str      		=					feature_strides
														A      :					Optional[int]      		=					output_auxiliary_logits
														A      :					Optional[Any]      		=					decoder_layers

														super().__init__(**a__						)




							@classmethod
							def       snake_case			(					cls    ,						__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Tuple:
														return cls(
														    backbone_config=a__    ,						**a__    ,						)






							def       snake_case			(					self						)		->       List[Any]:
														A      :					Optional[int]      		=					copy.deepcopy(self.__dict__						)
														A      :					List[str]      		=					self.backbone_config.to_dict()
														A      :					Tuple      		=					self.__class__.model_type
														return output













import os




def  __UpperCAmelCase(   ):
       with open(os.path.dirname(lowercase_   ) + '''/p022_names.txt'''   ) as file:
              _lowerCamelCase				:  Optional[int]						=		str(file.readlines()[0]   )
              _lowerCamelCase				:  List[Any]						=		names.replace('''"'''			,     ''''''   ).split(''','''   )

       names.sort()

       _lowerCamelCase				:  Optional[int]						=		0
       _lowerCamelCase				:  Tuple						=		0

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

              total_score += (i + 1) * name_score
              _lowerCamelCase				:  Optional[int]						=		0
       return total_score


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

