





import copy
from typing import Dict, List, Optional

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


_a				:	str           =       {
    """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_							(       _a		):







 """simple docstring"""

 A			 =       "mask2former"
 A			 =       ["swin"]
 A			 =       {"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  ,				):
       if backbone_config is None:
             logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""							)
             __lowerCamelCase				   =       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=__lowerCamelCase  ,				out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]  ,				)

       if isinstance(__lowerCamelCase  ,				__lowerCamelCase							):
             __lowerCamelCase				   =       backbone_config.pop("""model_type"""							)
             __lowerCamelCase				   =       CONFIG_MAPPING[backbone_model_type]
             __lowerCamelCase				   =       config_class.from_dict(__lowerCamelCase							)

       # 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							)}'''							)

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

       super().__init__(**__lowerCamelCase							)


 @classmethod
 def 		lowerCamelCase_ (   cls  ,				UpperCAmelCase  ,				**UpperCAmelCase							):
       return cls(
           backbone_config=__lowerCamelCase  ,				**__lowerCamelCase  ,				)



 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       copy.deepcopy(self.__dict__							)
       __lowerCamelCase				   =       self.backbone_config.to_dict()
       __lowerCamelCase				   =       self.__class__.model_type
       return output












from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__magic_name__  :  Any					=       TypeVar("""KEY""")
__magic_name__  :  Dict					=       TypeVar("""VAL""")





@dataclass(frozen=_a    ,					slots=_a			)
class     SCREAMING_SNAKE_CASE__		(Generic[KEY, VAL]			):
     lowercase_ :      KEY
     lowercase_ :      VAL





class     SCREAMING_SNAKE_CASE__		(_Item			):
     def __init__(						self	:		Tuple						):



       """simple docstring"""
       super().__init__(__lowerCamelCase       ,						__lowerCamelCase						)



     def __bool__(						self	:		Tuple						):



       """simple docstring"""
       return False



__magic_name__  :  str					=       _DeletedItem()





class     SCREAMING_SNAKE_CASE__		(MutableMapping[KEY, VAL]			):
     def __init__(						self	:		Any       ,						__lowerCamelCase	:		int = 8       ,						__lowerCamelCase	:		float = 0.75						):



       """simple docstring"""
       lowerCAmelCase__										=      initial_block_size
       lowerCAmelCase__										=      [None] * initial_block_size
       assert 0.0 < capacity_factor < 1.0
       lowerCAmelCase__										=      capacity_factor
       lowerCAmelCase__										=      0
     def        A__      (						self	:		Union[str, Any]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       return hash(__lowerCamelCase						) % len(self._buckets						)
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       return (ind + 1) % len(self._buckets						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets[ind]
       if not stored:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         self._len += 1
         return True
       elif stored.key == key:
         lowerCAmelCase__										=      _Item(__lowerCamelCase       ,						__lowerCamelCase						)
         return True
       else:
         return False
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor
       return len(self						) >= int(__lowerCamelCase						)
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       if len(self._buckets						) <= self._initial_block_size:
         return False
       lowerCAmelCase__										=      len(self._buckets						) * self._capacity_factor / 2
       return len(self						) < limit
     def        A__      (						self	:		List[Any]       ,						__lowerCamelCase	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      self._buckets
       lowerCAmelCase__										=      [None] * new_size
       lowerCAmelCase__										=      0
       for item in old_buckets:
         if item:
           self._add_item(item.key       ,						item.val						)
     def        A__      (						self	:		Any						):



       """simple docstring"""
       self._resize(len(self._buckets						) * 2						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       self._resize(len(self._buckets						) // 2						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       lowerCAmelCase__										=      self._get_bucket_index(__lowerCamelCase						)
       for _ in range(len(self._buckets						)						):
         yield ind
         lowerCAmelCase__										=      self._get_next_ind(__lowerCamelCase						)
     def        A__      (						self	:		Dict       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         if self._try_set(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase						):
           break
     def __setitem__(						self	:		str       ,						__lowerCamelCase	:		KEY       ,						__lowerCamelCase	:		VAL						):



       """simple docstring"""
       if self._is_full():
         self._size_up()

       self._add_item(__lowerCamelCase       ,						__lowerCamelCase						)
     def __delitem__(						self	:		Tuple       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           raise KeyError(__lowerCamelCase						)
         if item is _deleted:
           continue
         if item.key == key:
           lowerCAmelCase__										=      _deleted
           self._len -= 1
           break
       if self._is_sparse():
         self._size_down()
     def __getitem__(						self	:		Optional[int]       ,						__lowerCamelCase	:		KEY						):



       """simple docstring"""
       for ind in self._iterate_buckets(__lowerCamelCase						):
         lowerCAmelCase__										=      self._buckets[ind]
         if item is None:
           break
         if item is _deleted:
           continue
         if item.key == key:
           return item.val
       raise KeyError(__lowerCamelCase						)
     def __len__(						self	:		int						):



       """simple docstring"""
       return self._len
     def __iter__(						self	:		Union[str, Any]						):



       """simple docstring"""
       yield from (item.key for item in self._buckets if item)



     def __repr__(						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      ''' ,'''.join(
           F"""{item.key}: {item.val}""" for item in self._buckets if item						)
       return F"""HashMap({val_string})"""

