


import torch

from transformers import AutoModel
class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_="sayef/fsner-bert-base-uncased"						)   ->			List[Any]:







								'''simple docstring'''

								super(lowercase__ ,       self						).__init__()

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






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								return self.bert(**lowercase__						).last_hidden_state






					def A      (				self ,       snake_case_						)   ->			int:







								'''simple docstring'''

								return token_embeddings.sum(2 ,       keepdim=lowercase__						)






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_=1						)   ->			Tuple:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_ ,       snake_case_						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   W_supports["""sizes"""].tolist()
								__lowercase 					=   W_supports["""start_token_id"""].item()
								__lowercase 					=   W_supports["""end_token_id"""].item()

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

								__lowercase 					=   self.BERT(**lowercase__						)
								__lowercase 					=   self.BERT(**lowercase__						)

								__lowercase 					=   None
								__lowercase 					=   None

								__lowercase 					=   W_supports["""input_ids"""] == start_token_id
								__lowercase 					=   W_supports["""input_ids"""] == end_token_id

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

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

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

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

								return p_starts, p_ends






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a : List[str]		=				{
    '''configuration_table_transformer''': [
        '''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TableTransformerConfig''',
        '''TableTransformerOnnxConfig''',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : Union[str, Any]		=				[
	    '''TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TableTransformerForObjectDetection''',
	    '''TableTransformerModel''',
	    '''TableTransformerPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_table_transformer import (
	    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TableTransformerConfig,
	    TableTransformerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_table_transformer import (
		    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TableTransformerForObjectDetection,
		    TableTransformerModel,
		    TableTransformerPreTrainedModel,
		)

else:
	import sys

	a : Any		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


