



"""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_			   =					{
    '''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_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							List[Any]							       =		"""mask2former"""
						_A		:							List[Any]							       =		["""swin"""]
						_A		:							Any							       =		{"""hidden_size""": """hidden_dim"""}







						def __init__(self   ,		lowercase__ = None   ,		lowercase__ = 2_56   ,		lowercase__ = 2_56   ,		lowercase__ = 2_56   ,		lowercase__ = 10_24   ,		lowercase__ = "relu"   ,		lowercase__ = 6   ,		lowercase__ = 10   ,		lowercase__ = 8   ,		lowercase__ = 0.0   ,		lowercase__ = 20_48   ,		lowercase__ = False   ,		lowercase__ = False   ,		lowercase__ = 4   ,		lowercase__ = 2_55   ,		lowercase__ = 1_00   ,		lowercase__ = 0.1   ,		lowercase__ = 2.0   ,		lowercase__ = 5.0   ,		lowercase__ = 5.0   ,		lowercase__ = 1_25_44   ,		lowercase__ = 3.0   ,		lowercase__ = 0.75   ,		lowercase__ = 0.02   ,		lowercase__ = 1.0   ,		lowercase__ = True   ,		lowercase__ = [4, 8, 16, 32]   ,		lowercase__ = None   ,		**lowercase__   ,		):
											if backbone_config is None:
																logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""       )
																snake_case_					:		Dict 					=					CONFIG_MAPPING["""swin"""](
																    image_size=2_24   ,		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=lowercase__   ,		out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   ,		)

											if isinstance(lowercase__   ,		lowercase__       ):
																snake_case_					:		int 					=					backbone_config.pop("""model_type"""       )
																snake_case_					:		Optional[int] 					=					CONFIG_MAPPING[backbone_model_type]
																snake_case_					:		int 					=					config_class.from_dict(lowercase__       )

											# 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       )}'       )

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

											super().__init__(**lowercase__       )







						@classmethod
						def 						__UpperCamelCase							(cls   ,		lowercase__   ,		**lowercase__       ):
											return cls(
											    backbone_config=lowercase__   ,		**lowercase__   ,		)







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Tuple 					=					copy.deepcopy(self.__dict__       )
											snake_case_					:		int 					=					self.backbone_config.to_dict()
											snake_case_					:		str 					=					self.__class__.model_type
											return output









"""simple docstring"""

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
			from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Dict							       =		"""deberta-v2"""







						def __init__(self   ,		lowercase__=12_81_00   ,		lowercase__=15_36   ,		lowercase__=24   ,		lowercase__=24   ,		lowercase__=61_44   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=0   ,		lowercase__=0.02   ,		lowercase__=1e-7   ,		lowercase__=False   ,		lowercase__=-1   ,		lowercase__=0   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=0   ,		lowercase__="gelu"   ,		**lowercase__   ,		):
											super().__init__(**lowercase__       )

											snake_case_					:		Union[str, Any] 					=					hidden_size
											snake_case_					:		str 					=					num_hidden_layers
											snake_case_					:		Tuple 					=					num_attention_heads
											snake_case_					:		Dict 					=					intermediate_size
											snake_case_					:		Optional[int] 					=					hidden_act
											snake_case_					:		Union[str, Any] 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		List[Any] 					=					max_position_embeddings
											snake_case_					:		Union[str, Any] 					=					type_vocab_size
											snake_case_					:		Union[str, Any] 					=					initializer_range
											snake_case_					:		List[Any] 					=					relative_attention
											snake_case_					:		Dict 					=					max_relative_positions
											snake_case_					:		Optional[int] 					=					pad_token_id
											snake_case_					:		List[str] 					=					position_biased_input

											# Backwards compatibility
											if type(lowercase__       ) == str:
																snake_case_					:		Union[str, Any] 					=					[x.strip() for x in pos_att_type.lower().split("""|"""       )]

											snake_case_					:		Optional[int] 					=					pos_att_type
											snake_case_					:		List[str] 					=					vocab_size
											snake_case_					:		Tuple 					=					layer_norm_eps

											snake_case_					:		List[Any] 					=					kwargs.get("""pooler_hidden_size"""   ,		lowercase__       )
											snake_case_					:		List[str] 					=					pooler_dropout
											snake_case_					:		int 					=					pooler_hidden_act







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						@property
						def 						__UpperCamelCase							(self       ):
											if self.task == "multiple-choice":
																snake_case_					:		List[Any] 					=					{0: """batch""", 1: """choice""", 2: """sequence"""}
											else:
																snake_case_					:		int 					=					{0: """batch""", 1: """sequence"""}
											if self._config.type_vocab_size > 0:
																return OrderedDict(
																    [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]       )
											else:
																return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]       )







						@property
						def 						__UpperCamelCase							(self       ):
											return 12







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = -1   ,		lowercase__ = -1   ,		lowercase__ = -1   ,		lowercase__ = False   ,		lowercase__ = None   ,		lowercase__ = 3   ,		lowercase__ = 40   ,		lowercase__ = 40   ,		lowercase__ = None   ,		):
											snake_case_					:		str 					=					super().generate_dummy_inputs(preprocessor=lowercase__   ,		framework=lowercase__       )
											if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																del dummy_inputs["token_type_ids"]
											return dummy_inputs




