


'''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


__lowercase      :		Tuple			=      {
    '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
}

__lowercase      :		Dict			=      logging.get_logger(__name__)





class 				__UpperCamelCase							(						UpperCamelCase_   ):
	A_           =       "maskformer"
	A_           =       {"hidden_size": "mask_feature_size"}
	A_           =       ["resnet", "swin"]
	A_           =       ["detr"]




	def __init__(  self		,							__a = 256		,							__a = 256		,							__a = 0.1		,							__a = False		,							__a = None		,							__a = None		,							__a = 0.02		,							__a = 1.0		,							__a = 1.0		,							__a = 1.0		,							__a = 20.0		,							__a = None		,							**__a		,							):

						'''simple docstring'''





						if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											__a     :			Any          =    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(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE				):
											__a     :			List[str]          =    backbone_config.pop('model_type'				)
											__a     :			Any          =    CONFIG_MAPPING[backbone_model_type]
											__a     :			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
											__a     :			Any          =    DetrConfig()
						else:
											# verify that the decoder is supported
											__a     :			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				):
																__a     :			List[Any]          =    CONFIG_MAPPING[decoder_type]
																__a     :			int          =    config_class.from_dict(__SCREAMING_SNAKE_CASE				)

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

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




	@classmethod
	def  __UpperCAmelCase				(  cls		,							__a		,							__a		,							**__a				):

						'''simple docstring'''





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





	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						__a     :			str          =    copy.deepcopy(self.__dict__				)
						__a     :			Union[str, Any]          =    self.backbone_config.to_dict()
						__a     :			List[str]          =    self.decoder_config.to_dict()
						__a     :			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,
)


