
import torch

from transformers import AutoModel





class  snake_case     (							torch.nn.Module   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_="sayef/fsner-bert-base-uncased"    ):

									"""simple docstring"""







									super(SCREAMING_SNAKE_CASE_       ,       self    ).__init__()

									SCREAMING_SNAKE_CASE_						 = AutoModel.from_pretrained(SCREAMING_SNAKE_CASE_       ,       return_dict=SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = torch.nn.CosineSimilarity(3       ,       1e-0_8    )
									SCREAMING_SNAKE_CASE_						 = torch.nn.Softmax(dim=1    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return self.bert(**SCREAMING_SNAKE_CASE_    ).last_hidden_state

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return token_embeddings.sum(2       ,       keepdim=SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=1    ):

									"""simple docstring"""







									return self.softmax(T * self.cos(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )    )







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = W_supports['''sizes'''].tolist()
									SCREAMING_SNAKE_CASE_						 = W_supports['''start_token_id'''].item()
									SCREAMING_SNAKE_CASE_						 = W_supports['''end_token_id'''].item()

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

									SCREAMING_SNAKE_CASE_						 = self.BERT(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = self.BERT(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = None
									SCREAMING_SNAKE_CASE_						 = None

									SCREAMING_SNAKE_CASE_						 = W_supports['''input_ids'''] == start_token_id
									SCREAMING_SNAKE_CASE_						 = W_supports['''input_ids'''] == end_token_id

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

											SCREAMING_SNAKE_CASE_						 = S[s : s + size][start_token_masks[s : s + size]]
											SCREAMING_SNAKE_CASE_						 = S[s : s + size][end_token_masks[s : s + size]]

											SCREAMING_SNAKE_CASE_						 = torch.matmul(q[i]       ,       s_start.T    ).sum(1    ).softmax(0    )
											SCREAMING_SNAKE_CASE_						 = torch.matmul(q[i]       ,       s_end.T    ).sum(1    ).softmax(0    )

											if p_starts is not None:
													SCREAMING_SNAKE_CASE_						 = torch.vstack((p_starts, p_start)    )
													SCREAMING_SNAKE_CASE_						 = torch.vstack((p_ends, p_end)    )
											else:
													SCREAMING_SNAKE_CASE_						 = p_start
													SCREAMING_SNAKE_CASE_						 = p_end

									return p_starts, p_ends

"""simple docstring"""




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase__							=				{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase__							=				logging.get_logger(__name__)





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''maskformer'''
							UpperCAmelCase__						     =      {'''hidden_size''': '''mask_feature_size'''}
							UpperCAmelCase__						     =      ['''resnet''', '''swin''']
							UpperCAmelCase__						     =      ['''detr''']

							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 0.1       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = 0.02       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 20.0       ,       SCREAMING_SNAKE_CASE_ = None       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											SCREAMING_SNAKE_CASE_						 = SwinConfig(
											    image_size=3_84       ,       in_channels=3       ,       patch_size=4       ,       embed_dim=1_28       ,       depths=[2, 2, 18, 2]       ,       num_heads=[4, 8, 16, 32]       ,       window_size=12       ,       drop_path_rate=0.3       ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       ,       )

									if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = backbone_config.pop('''model_type'''    )
											SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[backbone_model_type]
											SCREAMING_SNAKE_CASE_						 = 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 MaskFormer. '
											    f'Supported model types: {",".join(self.backbones_supported    )}'    )

									if decoder_config is None:
											# fall back to https://huggingface.co/facebook/detr-resnet-50
											SCREAMING_SNAKE_CASE_						 = DetrConfig()
									else:
											# verify that the decoder is supported
											SCREAMING_SNAKE_CASE_						 = (
											    decoder_config.pop('''model_type'''    ) if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else decoder_config.model_type
											)
											if decoder_type not in self.decoders_supported:
													raise ValueError(
													    f'Transformer Decoder {decoder_type} not supported, please use one of'
													    f' {",".join(self.decoders_supported    )}'    )
											if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
													SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[decoder_type]
													SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = backbone_config
									SCREAMING_SNAKE_CASE_						 = decoder_config
									# main feature dimension for the model
									SCREAMING_SNAKE_CASE_						 = fpn_feature_size
									SCREAMING_SNAKE_CASE_						 = mask_feature_size
									# initializer
									SCREAMING_SNAKE_CASE_						 = init_std
									SCREAMING_SNAKE_CASE_						 = init_xavier_std
									# Hungarian matcher && loss
									SCREAMING_SNAKE_CASE_						 = cross_entropy_weight
									SCREAMING_SNAKE_CASE_						 = dice_weight
									SCREAMING_SNAKE_CASE_						 = mask_weight
									SCREAMING_SNAKE_CASE_						 = use_auxiliary_loss
									SCREAMING_SNAKE_CASE_						 = no_object_weight
									SCREAMING_SNAKE_CASE_						 = output_auxiliary_logits

									SCREAMING_SNAKE_CASE_						 = self.decoder_config.encoder_attention_heads
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.num_hidden_layers
									super().__init__(**SCREAMING_SNAKE_CASE_    )

							@classmethod
							def _lowercase					(cls       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return cls(
									    backbone_config=SCREAMING_SNAKE_CASE_       ,       decoder_config=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_       ,       )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = copy.deepcopy(self.__dict__    )
									SCREAMING_SNAKE_CASE_						 = self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.to_dict()
									SCREAMING_SNAKE_CASE_						 = self.__class__.model_type
									return output