
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


lowerCamelCase  ={
    "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
}

lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	'''maskformer'''
     SCREAMING_SNAKE_CASE_		 =	{'''hidden_size''': '''mask_feature_size'''}
     SCREAMING_SNAKE_CASE_		 =	['''resnet''', '''swin''']
     SCREAMING_SNAKE_CASE_		 =	['''detr''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = 2_5_6    ,    __SCREAMING_SNAKE_CASE = 2_5_6    ,    __SCREAMING_SNAKE_CASE = 0.1    ,    __SCREAMING_SNAKE_CASE = False    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = 0.02    ,    __SCREAMING_SNAKE_CASE = 1.0    ,    __SCREAMING_SNAKE_CASE = 1.0    ,    __SCREAMING_SNAKE_CASE = 1.0    ,    __SCREAMING_SNAKE_CASE = 20.0    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> str:




           """simple docstring"""







           if backbone_config is None:
                 # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
                 UpperCamelCase__     : Any											=					SwinConfig(
                     image_size=3_8_4    ,    in_channels=3    ,    patch_size=4    ,    embed_dim=1_2_8    ,    depths=[2, 2, 1_8, 2]    ,    num_heads=[4, 8, 1_6, 3_2]    ,    window_size=1_2    ,    drop_path_rate=0.3    ,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    ,    )

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

           # 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
                 UpperCamelCase__     : Any											=					DetrConfig()
           else:
                 # verify that the decoder is supported
                 UpperCamelCase__     : Optional[Any]											=					(
                     decoder_config.pop('''model_type'''	) if isinstance(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) 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(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	):
                       UpperCamelCase__     : List[Any]											=					CONFIG_MAPPING[decoder_type]
                       UpperCamelCase__     : int											=					config_class.from_dict(__SCREAMING_SNAKE_CASE	)

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

           UpperCamelCase__     : int											=					self.decoder_config.encoder_attention_heads
           UpperCamelCase__     : str											=					self.decoder_config.num_hidden_layers
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     @classmethod
     def  __SCREAMING_SNAKE_CASE      (				cls    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







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






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




           """simple docstring"""







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




# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)


