

import copy
from typing import Dict, List, Optional

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


UpperCAmelCase_							:					Dict		 =   {
    '''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
}

UpperCAmelCase_							:					Dict		 =   logging.get_logger(__name__)






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Tuple     					=				'''mask2former'''
	_lowercase		:   Optional[Any]     					=				['''swin''']
	_lowercase		:   Dict     					=				{'''hidden_size''': '''hidden_dim'''}




	def __init__(     self						:     Optional[Any]					,      __A						:     Optional[Dict] = None					,      __A						:     int = 256					,      __A						:     int = 256					,      __A						:     int = 256					,      __A						:     int = 1024					,      __A						:     str = "relu"					,      __A						:     int = 6					,      __A						:     int = 10					,      __A						:     int = 8					,      __A						:     float = 0.0					,      __A						:     int = 2048					,      __A						:     bool = False					,      __A						:     bool = False					,      __A						:     int = 4					,      __A						:     int = 255					,      __A						:     int = 100					,      __A						:     float = 0.1					,      __A						:     float = 2.0					,      __A						:     float = 5.0					,      __A						:     float = 5.0					,      __A						:     int = 1_2544					,      __A						:     float = 3.0					,      __A						:     float = 0.7_5					,      __A						:     float = 0.0_2					,      __A						:     float = 1.0					,      __A						:     bool = True					,      __A						:     List[int] = [4, 8, 16, 32]					,      __A						:     bool = None					,      **__A						:     Dict					,      ):
		if backbone_config is None:
			logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""							)
			__A :			List[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=__A					,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]					,      )

		if isinstance(__A					,      __A							):
			__A :			Any		    =			backbone_config.pop("""model_type"""							)
			__A :			Union[str, Any]		    =			CONFIG_MAPPING[backbone_model_type]
			__A :			str		    =			config_class.from_dict(__A							)

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

		super().__init__(**__A							)




	@classmethod
	def        lowerCAmelCase_       (     cls						:     str					,      __A						:     PretrainedConfig					,      **__A						:     int							):
		return cls(
		    backbone_config=__A					,      **__A					,      )




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Optional[int]		    =			copy.deepcopy(self.__dict__							)
		__A :			Any		    =			self.backbone_config.to_dict()
		__A :			Optional[int]		    =			self.__class__.model_type
		return output








from __future__ import annotations
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   Dict			,a__    :   Union[str, Any]			,a__    :   Any       )					->				Optional[int]:  # noqa: E741
	while r - l > 1:
		__A :			Any		    =			(l + r) // 2
		if v[m] >= key:
			__A :			Optional[int]		    =			m
		else:
			__A :			List[Any]		    =			m  # noqa: E741
	return r
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]       )					->				int:
	if len(a__       ) == 0:
		return 0

	__A :			str		    =			[0] * len(a__       )
	__A :			List[str]		    =			1

	__A :			List[Any]		    =			v[0]

	for i in range(1			,len(a__       )       ):
		if v[i] < tail[0]:
			__A :			int		    =			v[i]
		elif v[i] > tail[length - 1]:
			__A :			Union[str, Any]		    =			v[i]
			length += 1
		else:
			__A :			Any		    =			v[i]

	return length


if __name__ == "__main__":
			import doctest

			doctest.testmod()





