



'''simple docstring'''






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class 							a__( unittest.TestCase		,			lowerCamelCase__     ):







				def       lowercase_	(				self			:  Dict     ):
						a						: Optional[Any]					       =					load_tool('text-classification'     )
						self.tool.setup()
						a						: Any					       =					load_tool('text-classification'						,      remote=lowerCAmelCase__     )







				def       lowercase_	(				self			:  Optional[Any]     ):
						a						: Union[str, Any]					       =					self.tool('That\'s quite cool'						,      ['positive', 'negative']     )
						self.assertEqual(lowerCAmelCase__						,      'positive'     )







				def       lowercase_	(				self			:  int     ):
						a						: List[str]					       =					self.remote_tool('That\'s quite cool'						,      ['positive', 'negative']     )
						self.assertEqual(lowerCAmelCase__						,      'positive'     )







				def       lowercase_	(				self			:  str     ):
						a						: List[str]					       =					self.tool(text='That\'s quite cool'						,      labels=['positive', 'negative']     )
						self.assertEqual(lowerCAmelCase__						,      'positive'     )







				def       lowercase_	(				self			:  List[Any]     ):
						a						: List[Any]					       =					self.remote_tool(text='That\'s quite cool'						,      labels=['positive', 'negative']     )
						self.assertEqual(lowerCAmelCase__						,      'positive'     )




'''simple docstring'''






import torch

from transformers import AutoModel

class 							a__( torch.nn.Module     ):







    def __init__(				self			:  Any						,      __snake_case			:  str="sayef/fsner-bert-base-uncased"     ):
      super(__snake_case						,      self     ).__init__()

      a						: List[str]					       =					AutoModel.from_pretrained(__snake_case						,      return_dict=__snake_case     )
      a						: Optional[Any]					       =					torch.nn.CosineSimilarity(3						,      1e-0_8     )
      a						: Tuple					       =					torch.nn.Softmax(dim=1     )







    def       lowercase_	(				self			:  List[str]						,      **__snake_case			:  int     ):
      return self.bert(**__snake_case     ).last_hidden_state







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]     ):
      return token_embeddings.sum(2						,      keepdim=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]=1     ):
      return self.softmax(T * self.cos(__snake_case						,      __snake_case     )     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  int						,      __snake_case			:  Tuple     ):
      a						: List[Any]					       =					W_supports['sizes'].tolist()
      a						: Any					       =					W_supports['start_token_id'].item()
      a						: int					       =					W_supports['end_token_id'].item()

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

      a						: Optional[Any]					       =					self.BERT(**__snake_case     )
      a						: Tuple					       =					self.BERT(**__snake_case     )

      a						: Dict					       =					None
      a						: Optional[Any]					       =					None

      a						: Union[str, Any]					       =					W_supports['input_ids'] == start_token_id
      a						: str					       =					W_supports['input_ids'] == end_token_id

      for i, size in enumerate(__snake_case     ):
        if i == 0:
          a						: Optional[int]					       =					0
        else:
          a						: Tuple					       =					support_sizes[i - 1]

        a						: Tuple					       =					S[s : s + size][start_token_masks[s : s + size]]
        a						: int					       =					S[s : s + size][end_token_masks[s : s + size]]

        a						: Any					       =					torch.matmul(q[i]						,      s_start.T     ).sum(1     ).softmax(0     )
        a						: List[str]					       =					torch.matmul(q[i]						,      s_end.T     ).sum(1     ).softmax(0     )

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

      return p_starts, p_ends