






'''simple docstring'''







import copy
from typing import Dict, List, Optional

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


__lowerCAmelCase       		=      {
    '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       		=      logging.get_logger(__name__)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"mask2former"
			snake_case__  =				["swin"]
			snake_case__  =				{"hidden_size": "hidden_dim"}






			def __init__( self      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Dict] = None  ,						__SCREAMING_SNAKE_CASE      :			int = 256  ,						__SCREAMING_SNAKE_CASE      :			int = 256  ,						__SCREAMING_SNAKE_CASE      :			int = 256  ,						__SCREAMING_SNAKE_CASE      :			int = 1024  ,						__SCREAMING_SNAKE_CASE      :			str = "relu"  ,						__SCREAMING_SNAKE_CASE      :			int = 6  ,						__SCREAMING_SNAKE_CASE      :			int = 10  ,						__SCREAMING_SNAKE_CASE      :			int = 8  ,						__SCREAMING_SNAKE_CASE      :			float = 0.0  ,						__SCREAMING_SNAKE_CASE      :			int = 2048  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			bool = False  ,						__SCREAMING_SNAKE_CASE      :			int = 4  ,						__SCREAMING_SNAKE_CASE      :			int = 255  ,						__SCREAMING_SNAKE_CASE      :			int = 100  ,						__SCREAMING_SNAKE_CASE      :			float = 0.1  ,						__SCREAMING_SNAKE_CASE      :			float = 2.0  ,						__SCREAMING_SNAKE_CASE      :			float = 5.0  ,						__SCREAMING_SNAKE_CASE      :			float = 5.0  ,						__SCREAMING_SNAKE_CASE      :			int = 1_2544  ,						__SCREAMING_SNAKE_CASE      :			float = 3.0  ,						__SCREAMING_SNAKE_CASE      :			float = 0.75  ,						__SCREAMING_SNAKE_CASE      :			float = 0.02  ,						__SCREAMING_SNAKE_CASE      :			float = 1.0  ,						__SCREAMING_SNAKE_CASE      :			bool = True  ,						__SCREAMING_SNAKE_CASE      :			List[int] = [4, 8, 16, 32]  ,						__SCREAMING_SNAKE_CASE      :			bool = None  ,						**__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						)			->			int:
							if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''					)
											a_		:  Union[str, 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=__SCREAMING_SNAKE_CASE  ,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']  ,						)

							if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					):
											a_		:  List[Any]											=      backbone_config.pop('''model_type'''					)
											a_		:  Any											=      CONFIG_MAPPING[backbone_model_type]
											a_		:  Any											=      config_class.from_dict(__SCREAMING_SNAKE_CASE					)

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

							super().__init__(**__SCREAMING_SNAKE_CASE					)






			@classmethod
			def 			SCREAMING_SNAKE_CASE      ( cls      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			PretrainedConfig  ,						**__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Any:
							return cls(
							    backbone_config=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)







			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Dict[str, any]:
							a_		:  List[str]											=      copy.deepcopy(self.__dict__					)
							a_		:  Optional[Any]											=      self.backbone_config.to_dict()
							a_		:  List[str]											=      self.__class__.model_type
							return output












'''simple docstring'''







import torch

from transformers import AutoModel





class 	SCREAMING_SNAKE_CASE					(					torch.nn.Module       ):






			def __init__( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			int="sayef/fsner-bert-base-uncased"					)			->			str:
							super(__SCREAMING_SNAKE_CASE  ,						self					).__init__()

							a_		:  str											=      AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE  ,						return_dict=__SCREAMING_SNAKE_CASE					)
							a_		:  Optional[int]											=      torch.nn.CosineSimilarity(3  ,						1e-08					)
							a_		:  Dict											=      torch.nn.Softmax(dim=1					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						**__SCREAMING_SNAKE_CASE      :			int					)			->			str:
							return self.bert(**__SCREAMING_SNAKE_CASE					).last_hidden_state






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Optional[int]:
							return token_embeddings.sum(2  ,						keepdim=__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			int=1					)			->			Dict:
							return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]					)			->			Any:
							a_		:  Dict											=      W_supports['''sizes'''].tolist()
							a_		:  Tuple											=      W_supports['''start_token_id'''].item()
							a_		:  List[Any]											=      W_supports['''end_token_id'''].item()

							del W_supports["sizes"]
							del W_supports["start_token_id"]
							del W_supports["end_token_id"]

							a_		:  int											=      self.BERT(**__SCREAMING_SNAKE_CASE					)
							a_		:  Any											=      self.BERT(**__SCREAMING_SNAKE_CASE					)

							a_		:  Optional[Any]											=      None
							a_		:  Tuple											=      None

							a_		:  List[str]											=      W_supports['''input_ids'''] == start_token_id
							a_		:  Dict											=      W_supports['''input_ids'''] == end_token_id

							for i, size in enumerate(__SCREAMING_SNAKE_CASE					):
											if i == 0:
															a_		:  str											=      0
											else:
															a_		:  str											=      support_sizes[i - 1]

											a_		:  Union[str, Any]											=      S[s : s + size][start_token_masks[s : s + size]]
											a_		:  Tuple											=      S[s : s + size][end_token_masks[s : s + size]]

											a_		:  Tuple											=      torch.matmul(q[i]  ,						s_start.T					).sum(1					).softmax(0					)
											a_		:  Optional[Any]											=      torch.matmul(q[i]  ,						s_end.T					).sum(1					).softmax(0					)

											if p_starts is not None:
															a_		:  Any											=      torch.vstack((p_starts, p_start)					)
															a_		:  Dict											=      torch.vstack((p_ends, p_end)					)
											else:
															a_		:  Optional[int]											=      p_start
															a_		:  List[Any]											=      p_end

							return p_starts, p_ends




