
'''simple docstring'''
A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_000,
    "atomic-mass-unit": 6.0_2_2_1_3_6_6_5_2E2_6,
}

A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_6_0_5_4_0_1_9_9E-2_7,
}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	float			)					->							float:





  """simple docstring"""

  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    __A		:  Tuple       				=		(
        F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
        F"Supported values are: {', '.join(__SCREAMING_SNAKE_CASE			)}"
    )
    raise ValueError(__SCREAMING_SNAKE_CASE			)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
     import doctest

     doctest.testmod()





'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__ :		str					=logging.get_logger(__name__)

A__ :		Optional[int]					={
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Tuple					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Union[str, Any]=10_24					, lowerCamelCase      :     List[str]=16					, lowerCamelCase      :     str=16					, lowerCamelCase      :     Union[str, Any]=4					, lowerCamelCase      :     Optional[Any]="gelu"					, lowerCamelCase      :     Union[str, Any]=1e-6					, **lowerCamelCase      :     Union[str, Any]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[str]       				=		hidden_size
   __A		:  Any       				=		image_size
   __A		:  Dict       				=		patch_size
   __A		:  Union[str, Any]       				=		image_size // patch_size
   __A		:  List[Any]       				=		mask_input_channels
   __A		:  str       				=		num_point_embeddings
   __A		:  Tuple       				=		hidden_act
   __A		:  List[str]       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     int=2_56					, lowerCamelCase      :     Tuple="relu"					, lowerCamelCase      :     Optional[Any]=20_48					, lowerCamelCase      :     int=2					, lowerCamelCase      :     Tuple=8					, lowerCamelCase      :     int=2					, lowerCamelCase      :     List[str]=3					, lowerCamelCase      :     Dict=3					, lowerCamelCase      :     Any=2_56					, lowerCamelCase      :     int=1e-6					, **lowerCamelCase      :     Tuple					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[Any]       				=		hidden_size
   __A		:  Union[str, Any]       				=		hidden_act
   __A		:  Tuple       				=		mlp_dim
   __A		:  Any       				=		num_hidden_layers
   __A		:  Dict       				=		num_attention_heads
   __A		:  Optional[Any]       				=		attention_downsample_rate
   __A		:  List[Any]       				=		num_multimask_outputs
   __A		:  Optional[Any]       				=		iou_head_depth
   __A		:  Optional[int]       				=		iou_head_hidden_dim
   __A		:  Any       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     List[str]=7_68					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Optional[Any]=12					, lowerCamelCase      :     Tuple=12					, lowerCamelCase      :     int=3					, lowerCamelCase      :     Dict=10_24					, lowerCamelCase      :     int=16					, lowerCamelCase      :     Any="gelu"					, lowerCamelCase      :     Tuple=1e-0_6					, lowerCamelCase      :     Optional[int]=0.0					, lowerCamelCase      :     List[str]=1e-1_0					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     Union[str, Any]=4.0					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Optional[Any]=True					, lowerCamelCase      :     str=14					, lowerCamelCase      :     str=[2, 5, 8, 11]					, lowerCamelCase      :     Dict=1_28					, lowerCamelCase      :     Any=None					, **lowerCamelCase      :     Optional[int]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)

   __A		:  Any       				=		hidden_size
   __A		:  Union[str, Any]       				=		output_channels
   __A		:  int       				=		num_hidden_layers
   __A		:  Any       				=		num_attention_heads
   __A		:  Tuple       				=		num_channels
   __A		:  Tuple       				=		image_size
   __A		:  Optional[Any]       				=		patch_size
   __A		:  List[str]       				=		hidden_act
   __A		:  Dict       				=		layer_norm_eps
   __A		:  Optional[int]       				=		attention_dropout
   __A		:  Dict       				=		initializer_range
   __A		:  Union[str, Any]       				=		qkv_bias
   __A		:  Optional[Any]       				=		mlp_ratio
   __A		:  Optional[int]       				=		use_abs_pos
   __A		:  int       				=		use_rel_pos
   __A		:  str       				=		window_size
   __A		:  Union[str, Any]       				=		global_attn_indexes
   __A		:  Dict       				=		num_pos_feats
   __A		:  int       				=		int(hidden_size * mlp_ratio							) if mlp_dim is None else mlp_dim







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												='''sam'''
 lowerCamelCase												=True
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Optional[Any]=None					, lowerCamelCase      :     List[Any]=0.02					, **lowerCamelCase      :     Any					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  str       				=		vision_config if vision_config is not None else {}
   __A		:  int       				=		prompt_encoder_config if prompt_encoder_config is not None else {}
   __A		:  Dict       				=		mask_decoder_config if mask_decoder_config is not None else {}

   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  List[str]       				=		vision_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		prompt_encoder_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		mask_decoder_config.to_dict()

   __A		:  Any       				=		SamVisionConfig(**lowerCamelCase							)
   __A		:  List[Any]       				=		SamPromptEncoderConfig(**lowerCamelCase							)
   __A		:  Union[str, Any]       				=		SamMaskDecoderConfig(**lowerCamelCase							)
   __A		:  Any       				=		initializer_range






 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  str       				=		copy.deepcopy(self.__dict__							)
   __A		:  Dict       				=		self.vision_config.to_dict()
   __A		:  Any       				=		self.prompt_encoder_config.to_dict()
   __A		:  str       				=		self.mask_decoder_config.to_dict()
   __A		:  Optional[int]       				=		self.__class__.model_type
   return output



