"""simple docstring"""
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__   :      Union[str, 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__   :      str							=logging.get_logger(__name__)



class      _UpperCAmelCase						( A__						):






    """simple docstring"""
    __snake_case    =       """mask2former"""
    __snake_case    =       ["""swin"""]
    __snake_case    =       {"""hidden_size""": """hidden_dim"""}







    def __init__(			self    , _lowercase = None    , _lowercase = 256    , _lowercase = 256    , _lowercase = 256    , _lowercase = 1024    , _lowercase = "relu"    , _lowercase = 6    , _lowercase = 10    , _lowercase = 8    , _lowercase = 0.0    , _lowercase = 2048    , _lowercase = False    , _lowercase = False    , _lowercase = 4    , _lowercase = 255    , _lowercase = 100    , _lowercase = 0.1    , _lowercase = 2.0    , _lowercase = 5.0    , _lowercase = 5.0    , _lowercase = 12544    , _lowercase = 3.0    , _lowercase = 0.75    , _lowercase = 0.02    , _lowercase = 1.0    , _lowercase = True    , _lowercase = [4, 8, 16, 32]    , _lowercase = None    , **_lowercase    , )	->	List[Any]:
        if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''					)
            _lowerCamelCase	: int       =       CONFIG_MAPPING['''swin'''](
                image_size=224    , 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=_lowercase    , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    , )

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

        # 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					)}'''					)

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

        super().__init__(**_lowercase					)







    @classmethod
    def 				a__					(			cls    , _lowercase    , **_lowercase					)	->	str:
        return cls(
            backbone_config=_lowercase    , **_lowercase    , )







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










def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for data in source_data:
								for i, el in enumerate(lowerCamelCase_							):
												if len(lowerCamelCase_							) < i + 1:
																data_lists.append([]							)
												data_lists[i].append(float(lowerCamelCase_							)							)
				return data_lists






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for dlist, weight in zip(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     min(lowerCamelCase_							)
								lowercase__  					=     max(lowerCamelCase_							)

								lowercase__  					=     []
								# for weight 0 score is 1 - actual score
								if weight == 0:
												for item in dlist:
																try:
																				score.append(1 - ((item - mind) / (maxd - mind))							)
																except ZeroDivisionError:
																				score.append(1							)

								elif weight == 1:
												for item in dlist:
																try:
																				score.append((item - mind) / (maxd - mind)							)
																except ZeroDivisionError:
																				score.append(0							)

        # weight not 0 or 1
								else:
												lowercase__  					=     F"""Invalid weight of {weight:f} provided"""
												raise ValueError(lowerCamelCase_							)

								score_lists.append(lowerCamelCase_							)

				return score_lists






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     [0 for i in range(len(score_lists[0]							)							)]

				for slist in score_lists:
								for j, ele in enumerate(lowerCamelCase_							):
												lowercase__  					=     final_scores[j] + ele

				return final_scores






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_data(lowerCamelCase_							)
				lowercase__  					=     calculate_each_score(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     generate_final_scores(lowerCamelCase_							)

				# append scores to source data
				for i, ele in enumerate(lowerCamelCase_							):
								source_data[i].append(lowerCamelCase_							)

				return source_data




