"""simple docstring"""






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
       import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class 					_A		(			unittest.TestCase    ,       lowerCAmelCase      ):

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      load_tool("""text-to-speech"""		)
            self.tool.setup()

      def 							A__			(		self		):







            """simple docstring"""



            torch.manual_seed(0		)
            lowercase								=      self.tool("""hey"""		)
            lowercase								=      result.to_raw()
            self.assertTrue(
                torch.allclose(
                    resulting_tensor[:3]						,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]		)						,			)		)







      def 							A__			(		self		):







            """simple docstring"""



            torch.manual_seed(0		)
            lowercase								=      self.tool("""hey"""		)
            lowercase								=      result.to_raw()
            self.assertTrue(
                torch.allclose(
                    resulting_tensor[:3]						,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]		)						,			)		)




"""simple docstring"""






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate





@dataclass(frozen=lowerCAmelCase      )
class 					_A		(			lowerCAmelCase      ):
      # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
      snake_case__     :  str				    =      field(default='question-answering-extractive'    ,       metadata={'include_in_asdict_even_if_is_default': True}      )
      snake_case__     :  ClassVar[Features]				    =      Features({'question': Value('string'      ), 'context': Value('string'      )}      )
      snake_case__     :  ClassVar[Features]				    =      Features(
          {
              'answers': Sequence(
                  {
                      'text': Value('string'      ),
                      'answer_start': Value('int32'      ),
                  }      )
          }      )
      snake_case__     :  str				    =      "question"
      snake_case__     :  str				    =      "context"
      snake_case__     :  str				    =      "answers"

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}



