import copy
from typing import Dict, List, Optional

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


lowerCamelCase_    :		int							=	{
    """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
}

lowerCamelCase_    :		List[str]							=	logging.get_logger(__name__)


class 						a__							(       __snake_case		):
			A__    :       Union[str, Any]        =      'mask2former'
			A__    :       Dict        =      ['swin']
			A__    :       Dict        =      {'hidden_size': 'hidden_dim'}



			def __init__(						self				,						UpperCAmelCase = None				,						UpperCAmelCase = 2_5_6				,						UpperCAmelCase = 2_5_6				,						UpperCAmelCase = 2_5_6				,						UpperCAmelCase = 1_0_2_4				,						UpperCAmelCase = "relu"				,						UpperCAmelCase = 6				,						UpperCAmelCase = 1_0				,						UpperCAmelCase = 8				,						UpperCAmelCase = 0.0				,						UpperCAmelCase = 2_0_4_8				,						UpperCAmelCase = False				,						UpperCAmelCase = False				,						UpperCAmelCase = 4				,						UpperCAmelCase = 2_5_5				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 0.1				,						UpperCAmelCase = 2.0				,						UpperCAmelCase = 5.0				,						UpperCAmelCase = 5.0				,						UpperCAmelCase = 1_2_5_4_4				,						UpperCAmelCase = 3.0				,						UpperCAmelCase = 0.75				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1.0				,						UpperCAmelCase = True				,						UpperCAmelCase = [4, 8, 1_6, 3_2]				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   str:
								if backbone_config is None:
													logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'   )
													__a		 =       CONFIG_MAPPING['swin'](
													    image_size=2_2_4				,						in_channels=3				,						patch_size=4				,						embed_dim=9_6				,						depths=[2, 2, 1_8, 2]				,						num_heads=[3, 6, 1_2, 2_4]				,						window_size=7				,						drop_path_rate=0.3				,						use_absolute_embeddings=UpperCAmelCase				,						out_features=['stage1', 'stage2', 'stage3', 'stage4']				,						)

								if isinstance(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       backbone_config.pop('model_type'   )
													__a		 =       CONFIG_MAPPING[backbone_model_type]
													__a		 =       config_class.from_dict(UpperCAmelCase   )

								# 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   )}'''   )

								__a		 =       backbone_config
								__a		 =       feature_size
								__a		 =       mask_feature_size
								__a		 =       hidden_dim
								__a		 =       encoder_feedforward_dim
								__a		 =       activation_function
								__a		 =       encoder_layers
								__a		 =       decoder_layers
								__a		 =       num_attention_heads
								__a		 =       dropout
								__a		 =       dim_feedforward
								__a		 =       pre_norm
								__a		 =       enforce_input_projection
								__a		 =       common_stride
								__a		 =       ignore_value
								__a		 =       num_queries
								__a		 =       no_object_weight
								__a		 =       class_weight
								__a		 =       mask_weight
								__a		 =       dice_weight
								__a		 =       train_num_points
								__a		 =       oversample_ratio
								__a		 =       importance_sample_ratio
								__a		 =       init_std
								__a		 =       init_xavier_std
								__a		 =       use_auxiliary_loss
								__a		 =       feature_strides
								__a		 =       output_auxiliary_logits
								__a		 =       decoder_layers

								super().__init__(**UpperCAmelCase   )



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







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict[str, any]:
								__a		 =       copy.deepcopy(self.__dict__   )
								__a		 =       self.backbone_config.to_dict()
								__a		 =       self.__class__.model_type
								return output





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_    :		Dict							=	{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		int							=	[
	    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SwinForImageClassification""",
	    """SwinForMaskedImageModeling""",
	    """SwinModel""",
	    """SwinPreTrainedModel""",
	    """SwinBackbone""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFSwinForImageClassification""",
	    """TFSwinForMaskedImageModeling""",
	    """TFSwinModel""",
	    """TFSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swin import (
		    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwinBackbone,
		    SwinForImageClassification,
		    SwinForMaskedImageModeling,
		    SwinModel,
		    SwinPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_swin import (
		    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSwinForImageClassification,
		    TFSwinForMaskedImageModeling,
		    TFSwinModel,
		    TFSwinPreTrainedModel,
		)

else:
	import sys

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




