





"""simple docstring"""






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


__UpperCAmelCase						=			{
    '''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
}

__UpperCAmelCase						=			logging.get_logger(__name__)



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				__lowerCamelCase				:	str				     =				"maskformer"
				__lowerCamelCase				:	Any				     =				{"hidden_size": "mask_feature_size"}
				__lowerCamelCase				:	str				     =				["resnet", "swin"]
				__lowerCamelCase				:	str				     =				["detr"]
				def __init__(		self    :			Dict	, a_    :			int = 2_56	, a_    :			int = 2_56	, 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_    :			str	, )	->	Any:



							'''simple docstring'''



							if backbone_config is None:
										# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
										a__  :	Dict		 =			SwinConfig(
										    image_size=3_84	, in_channels=3	, patch_size=4	, embed_dim=1_28	, 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[str]		 =			CONFIG_MAPPING[backbone_model_type]
										a__  :	Tuple		 =			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__  :	Tuple		 =			DetrConfig()
							else:
										# verify that the decoder is supported
										a__  :	Tuple		 =			(
										    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__  :	List[str]		 =			CONFIG_MAPPING[decoder_type]
													a__  :	List[Any]		 =			config_class.from_dict(a_    )

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

							a__  :	Any		 =			self.decoder_config.encoder_attention_heads
							a__  :	Tuple		 =			self.decoder_config.num_hidden_layers
							super().__init__(**a_    )
				@classmethod
				def       UpperCAmelCase      (		cls    :			Optional[int]	, a_    :			PretrainedConfig	, a_    :			PretrainedConfig	, **a_    :			Optional[Any]    )	->	Tuple:



							'''simple docstring'''



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






				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Dict[str, any]:



							'''simple docstring'''



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






"""simple docstring"""






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 					__UpperCAmelCase							(						unittest.TestCase						,  _UpperCamelCase  ):
				def       UpperCAmelCase      (		self    :			Dict    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Any		 =			load_tool("text-classification"    )
							self.tool.setup()
							a__  :	List[Any]		 =			load_tool("text-classification"	, remote=a_    )
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[Any]:



							'''simple docstring'''



							a__  :	str		 =			self.tool("That's quite cool"	, ["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Optional[int]:



							'''simple docstring'''



							a__  :	List[str]		 =			self.remote_tool("That's quite cool"	, ["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )
				def       UpperCAmelCase      (		self    :			Dict    )	->	Any:



							'''simple docstring'''



							a__  :	List[str]		 =			self.tool(text="That's quite cool"	, labels=["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )






				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	Tuple		 =			self.remote_tool(text="That's quite cool"	, labels=["positive", "negative"]    )
							self.assertEqual(a_	, "positive"    )