




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 			_UpperCamelCase      (				unittest.TestCase	,       lowerCAmelCase		):
      def 	UpperCAmelCase_						(			self   :int      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						load_tool("text-classification"      )
       self.tool.setup()
       UpperCAmelCase__							 =						load_tool("text-classification" ,      remote=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.tool("That's quite cool" ,      ["positive", "negative"]      )
       self.assertEqual(lowerCamelCase ,      "positive"      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			int:
       UpperCAmelCase__							 =						self.remote_tool("That's quite cool" ,      ["positive", "negative"]      )
       self.assertEqual(lowerCamelCase ,      "positive"      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			str:
       UpperCAmelCase__							 =						self.tool(text="That's quite cool" ,      labels=["positive", "negative"]      )
       self.assertEqual(lowerCamelCase ,      "positive"      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Any:
       UpperCAmelCase__							 =						self.remote_tool(text="That's quite cool" ,      labels=["positive", "negative"]      )
       self.assertEqual(lowerCamelCase ,      "positive"      )











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

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

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

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

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

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

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

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

       return p_starts, p_ends





