


import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE			: 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
}

SCREAMING_SNAKE_CASE			: Optional[int]				   =					logging.get_logger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      int       					=     """mask2former"""
       lowercase_					:      List[Any]       					=     ["""swin"""]
       lowercase_					:      Dict       					=     {"""hidden_size""": """hidden_dim"""}
       def __init__(   self, lowerCamelCase = None, lowerCamelCase = 2_56, lowerCamelCase = 2_56, lowerCamelCase = 2_56, lowerCamelCase = 10_24, lowerCamelCase = "relu", lowerCamelCase = 6, lowerCamelCase = 10, lowerCamelCase = 8, lowerCamelCase = 0.0, lowerCamelCase = 20_48, lowerCamelCase = False, lowerCamelCase = False, lowerCamelCase = 4, lowerCamelCase = 2_55, lowerCamelCase = 1_00, lowerCamelCase = 0.1, lowerCamelCase = 2.0, lowerCamelCase = 5.0, lowerCamelCase = 5.0, lowerCamelCase = 1_25_44, lowerCamelCase = 3.0, lowerCamelCase = 0.7_5, lowerCamelCase = 0.0_2, lowerCamelCase = 1.0, lowerCamelCase = True, lowerCamelCase = [4, 8, 16, 32], lowerCamelCase = None, **lowerCamelCase, )			->  Tuple:




           """simple docstring"""

           if backbone_config is None:
               logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.')
               _lowercase			:				int 						=						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=lowerCamelCase, out_features=['stage1', 'stage2', 'stage3', 'stage4'], )

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

           # 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)}''')

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

           super().__init__(**lowerCamelCase)
       @classmethod
       def      UpperCamelCase	(   cls, lowerCamelCase, **lowerCamelCase)			->  str:




           """simple docstring"""

           return cls(
               backbone_config=lowerCamelCase, **lowerCamelCase, )




       def      UpperCamelCase	(   self)			->  Dict[str, any]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						copy.deepcopy(self.__dict__)
           _lowercase			:				str 						=						self.backbone_config.to_dict()
           _lowercase			:				Union[str, Any] 						=						self.__class__.model_type
           return output






from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					1.0_5457_1817E-34  # unit of ℏ : J * s

SCREAMING_SNAKE_CASE			: int				   =					3E8  # unit of c : m * s^-1



def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		dict[str, float]:

    if (force, area, distance).count(0					) != 1:
        raise ValueError('One and only one argument must be 0'					)
    if force < 0:
        raise ValueError('Magnitude of force can not be negative'					)
    if distance < 0:
        raise ValueError('Distance can not be negative'					)
    if area < 0:
        raise ValueError('Area can not be negative'					)
    if force == 0:
        _lowercase			:				int 						=						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
            240 * (distance) ** 4
        )
        return {"force": force}
    elif area == 0:
        _lowercase			:				List[Any] 						=						(240 * force * (distance) ** 4) / (
            REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
        )
        return {"area": area}
    elif distance == 0:
        _lowercase			:				List[Any] 						=						(
            (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
        ) ** (1 / 4)
        return {"distance": distance}
    raise ValueError('One and only one argument must be 0'					)


# Run doctest
if __name__ == "__main__":
 import doctest

 doctest.testmod()


