


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


snake_case__			 =						{
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

snake_case__			 =						logging.get_logger(__name__)


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'maskformer'
						lowerCamelCase_         =     {'hidden_size': 'mask_feature_size'}
						lowerCamelCase_         =     ['resnet', 'swin']
						lowerCamelCase_         =     ['detr']




						def __init__(	self						:   int   ,     __A						:   int = 256   ,     __A						:   int = 256   ,     __A						:   float = 0.1   ,     __A						:   bool = False   ,     __A						:   Optional[Dict] = None   ,     __A						:   Optional[Dict] = None   ,     __A						:   float = 0.02   ,     __A						:   float = 1.0   ,     __A						:   float = 1.0   ,     __A						:   float = 1.0   ,     __A						:   float = 20.0   ,     __A						:   Optional[bool] = None   ,     **__A						:   Optional[Any]   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									if backbone_config is None:
												# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
												a__ :List[str]						  =    SwinConfig(
												    image_size=384   ,     in_channels=3   ,     patch_size=4   ,     embed_dim=128   ,     depths=[2, 2, 18, 2]   ,     num_heads=[4, 8, 16, 32]   ,     window_size=12   ,     drop_path_rate=0.3   ,     out_features=["stage1", "stage2", "stage3", "stage4"]   ,     )

									if isinstance(__A   ,     __A						):
												a__ :Any						  =    backbone_config.pop("model_type"						)
												a__ :List[Any]						  =    CONFIG_MAPPING[backbone_model_type]
												a__ :Optional[int]						  =    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 MaskFormer. '''
												    F'''Supported model types: {','.join(self.backbones_supported						)}'''						)

									if decoder_config is None:
												# fall back to https://huggingface.co/facebook/detr-resnet-50
												a__ :Union[str, Any]						  =    DetrConfig()
									else:
												# verify that the decoder is supported
												a__ :List[Any]						  =    (
												    decoder_config.pop("model_type"						) if isinstance(__A   ,     __A						) else decoder_config.model_type
												)
												if decoder_type not in self.decoders_supported:
															raise ValueError(
															    F'''Transformer Decoder {decoder_type} not supported, please use one of'''
															    F''' {','.join(self.decoders_supported						)}'''						)
												if isinstance(__A   ,     __A						):
															a__ :str						  =    CONFIG_MAPPING[decoder_type]
															a__ :List[str]						  =    config_class.from_dict(__A						)

									a__ :List[Any]						  =    backbone_config
									a__ :List[Any]						  =    decoder_config
									# main feature dimension for the model
									a__ :List[Any]						  =    fpn_feature_size
									a__ :str						  =    mask_feature_size
									# initializer
									a__ :List[str]						  =    init_std
									a__ :Any						  =    init_xavier_std
									# Hungarian matcher && loss
									a__ :Dict						  =    cross_entropy_weight
									a__ :Optional[int]						  =    dice_weight
									a__ :List[Any]						  =    mask_weight
									a__ :Optional[int]						  =    use_auxiliary_loss
									a__ :List[str]						  =    no_object_weight
									a__ :List[Any]						  =    output_auxiliary_logits

									a__ :Any						  =    self.decoder_config.encoder_attention_heads
									a__ :Optional[Any]						  =    self.decoder_config.num_hidden_layers
									super().__init__(**__A						)




						@classmethod
						def  _snake_case					(	cls						:   Optional[Any]   ,     __A						:   PretrainedConfig   ,     __A						:   PretrainedConfig   ,     **__A						:   List[Any]						)      ->Any:






									"""simple docstring"""






									return cls(
									    backbone_config=__A   ,     decoder_config=__A   ,     **__A   ,     )






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






									"""simple docstring"""






									a__ :str						  =    copy.deepcopy(self.__dict__						)
									a__ :List[str]						  =    self.backbone_config.to_dict()
									a__ :Union[str, Any]						  =    self.decoder_config.to_dict()
									a__ :str						  =    self.__class__.model_type
									return output






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


snake_case__			 =						'''scheduler_config.json'''


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     1
						lowerCamelCase_         =     2
						lowerCamelCase_         =     3
						lowerCamelCase_         =     4
						lowerCamelCase_         =     5
						lowerCamelCase_         =     6
						lowerCamelCase_         =     7
						lowerCamelCase_         =     8
						lowerCamelCase_         =     9
						lowerCamelCase_         =     10
						lowerCamelCase_         =     11
						lowerCamelCase_         =     12
						lowerCamelCase_         =     13
						lowerCamelCase_         =     14






@dataclass
class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     42






class 					lowerCAmelCase_ :
						lowerCamelCase_         =     SCHEDULER_CONFIG_NAME
						lowerCamelCase_         =     []
						lowerCamelCase_         =     True




						@classmethod
						def  _snake_case					(	cls						:   Union[str, Any]   ,     __A						:   Dict[str, Any] = None   ,     __A						:   Optional[str] = None   ,     __A						:   Optional[int]=False   ,     **__A						:   Union[str, Any]   ,     )      ->List[Any]:






									"""simple docstring"""






									a__  ,  a__  ,  a__ :List[Any]						  =    cls.load_config(
									    pretrained_model_name_or_path=__A   ,     subfolder=__A   ,     return_unused_kwargs=__A   ,     return_commit_hash=__A   ,     **__A   ,     )
									return cls.from_config(__A   ,     return_unused_kwargs=__A   ,     **__A						)




						def  _snake_case					(	self						:   str   ,     __A						:   Union[str, os.PathLike]   ,     __A						:   bool = False   ,     **__A						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									self.save_config(save_directory=__A   ,     push_to_hub=__A   ,     **__A						)




						@property
						def  _snake_case					(	self						:   List[Any]						)      ->Dict:






									"""simple docstring"""






									return self._get_compatibles()






						@classmethod
						def  _snake_case					(	cls						:   Dict						)      ->int:






									"""simple docstring"""






									a__ :Optional[int]						  =    list(set([cls.__name__] + cls._compatibles						)						)
									a__ :Union[str, Any]						  =    importlib.import_module(__name__.split("."						)[0]						)
									a__ :Optional[int]						  =    [
									    getattr(__A   ,     __A						) for c in compatible_classes_str if hasattr(__A   ,     __A						)
									]
									return compatible_classes


