






"""simple docstring"""







import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a			:    Tuple				 =		{
    """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
}

a			:    Optional[int]				 =		logging.get_logger(__name__)


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase    	= "mask2former"
						__lowerCamelCase    	= ["swin"]
						__lowerCamelCase    	= {"hidden_size": "hidden_dim"}




						def __init__(       self	,  snake_case__ = None	,  snake_case__ = 256	,  snake_case__ = 256	,  snake_case__ = 256	,  snake_case__ = 1024	,  snake_case__ = "relu"	,  snake_case__ = 6	,  snake_case__ = 10	,  snake_case__ = 8	,  snake_case__ = 0.0	,  snake_case__ = 2048	,  snake_case__ = False	,  snake_case__ = False	,  snake_case__ = 4	,  snake_case__ = 255	,  snake_case__ = 100	,  snake_case__ = 0.1	,  snake_case__ = 2.0	,  snake_case__ = 5.0	,  snake_case__ = 5.0	,  snake_case__ = 12544	,  snake_case__ = 3.0	,  snake_case__ = 0.75	,  snake_case__ = 0.02	,  snake_case__ = 1.0	,  snake_case__ = True	,  snake_case__ = [4, 8, 16, 32]	,  snake_case__ = None	,  **snake_case__	,  ):
												'''simple docstring'''
												if backbone_config is None:
																		logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."      )
																		lowercase__    :				Union[str, Any]=	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=snake_case__	,  out_features=["stage1", "stage2", "stage3", "stage4"]	,  )

												if isinstance(snake_case__	,  snake_case__      ):
																		lowercase__    :				Dict=	backbone_config.pop("model_type"      )
																		lowercase__    :				Tuple=	CONFIG_MAPPING[backbone_model_type]
																		lowercase__    :				Any=	config_class.from_dict(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 Mask2Former. '''
																		    F'''Supported model types: {','.join(self.backbones_supported      )}'''      )

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

												super().__init__(**snake_case__      )




						@classmethod
						def 		UpperCAmelCase_	(       cls	,  snake_case__	,  **snake_case__      ):
												'''simple docstring'''
												return cls(
												    backbone_config=snake_case__	,  **snake_case__	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	copy.deepcopy(self.__dict__      )
												lowercase__    :				List[Any]=	self.backbone_config.to_dict()
												lowercase__    :				str=	self.__class__.model_type
												return output














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    Union[str, Any]				 =		{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    int				 =		[
			    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """PegasusXForConditionalGeneration""",
			    """PegasusXModel""",
			    """PegasusXPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_pegasus_x import (
						    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PegasusXForConditionalGeneration,
						    PegasusXModel,
						    PegasusXPreTrainedModel,
						)


else:
			import sys

			a			:    Optional[Any]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






