code: 
  
  
  """simple docstring"""
  
  
  
  
  from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
  from .base import PipelineTool
  
  
  __magic_name__	:   Optional[int]          =							'Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\''
  
  
  
  
  class  __snake_case      (lowerCamelCase_     ):
  							__a				   =					"""google/flan-t5-base"""
  							__a				   =					(
  							    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
  							    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
  							)
  							__a				   =					"""text_qa"""
  							__a				   =					AutoTokenizer
  							__a				   =					AutoModelForSeqaSeqLM
  
  							__a				   =					["""text""", """text"""]
  							__a				   =					["""text"""]
  
  
  
  
  							def      __a      (    self:						Optional[Any]    ,       A_:						int    ,       A_:						Optional[Any]      ):
  										__lowerCamelCase        =			QA_PROMPT.format(text=A_    ,       question=A_      )
  										return self.pre_processor(A_    ,       return_tensors="""pt"""      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]    ,       A_:						str      ):
  										__lowerCamelCase        =			self.model.generate(**A_      )
  
  										__lowerCamelCase        =			inputs["input_ids"].shape
  										__lowerCamelCase        =			output_ids.shape[0]
  
  										return output_ids.reshape(A_    ,       out_b // in_b    ,       *output_ids.shape[1:]      )[0][0]
  
  
  
  
  							def      __a      (    self:						Union[str, Any]    ,       A_:						Tuple      ):
  										return self.pre_processor.decode(A_    ,       skip_special_tokens=A_    ,       clean_up_tokenization_spaces=A_      )
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  
  
  
  class  __snake_case      (unittest.TestCase ,		lowerCamelCase     ):
  
  
  
  
  							def      __a      (    self:						Optional[Any]      ):
  										__lowerCamelCase        =			load_tool("""translation"""      )
  										self.tool.setup()
  										__lowerCamelCase        =			load_tool("""translation"""    ,       remote=A_      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						int      ):
  										__lowerCamelCase        =			self.remote_tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			self.tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						Any      ):
  										__lowerCamelCase        =			self.remote_tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
  										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )
  
  
  
  
  							def      __a      (    self:						List[Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										# There is a single output
  										if len(self.tool.outputs      ) == 1:
  													__lowerCamelCase        =			[outputs]
  
  										self.assertListEqual(output_types(A_      )    ,       self.tool.outputs      )
  
  
  
  
  							def      __a      (    self:						Union[str, Any]      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  										for output, output_type in zip(A_    ,       self.tool.outputs      ):
  													__lowerCamelCase        =			AGENT_TYPE_MAPPING[output_type]
  													self.assertTrue(isinstance(A_    ,       A_      )      )
  
  
  
  
  							def      __a      (    self:						Dict      ):
  										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
  
  										__lowerCamelCase        =			[]
  
  										for _input, input_type in zip(A_    ,       self.tool.inputs      ):
  													if isinstance(A_    ,       A_      ):
  																_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input      ) for _input_type in input_type]      )
  													else:
  																_inputs.append(AGENT_TYPE_MAPPING[input_type](_input      )      )
  
          # Should not raise an error
  										__lowerCamelCase        =			self.tool(*A_      )
  
  										if not isinstance(A_    ,       A_      ):
  													__lowerCamelCase        =			[outputs]
  
  										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )
  
  
  
  
  