import copy
from typing import Dict, List, Optional

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


lowerCamelCase_    :		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
}

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


class 						a__							(       lowerCamelCase__		):
			A__    :       Optional[Any]        =      'mask2former'
			A__    :       Optional[Any]        =      ['swin']
			A__    :       str        =      {'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				,						)			->   Optional[Any]:




								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   )			->   Dict:
								return cls(
								    backbone_config=UpperCAmelCase				,						**UpperCAmelCase				,						)







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    :		Optional[int]							=	{"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		str							=	["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	[
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[int]							=	[
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

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




