






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						str:
       # Initialise PyTorch model
       _snake_case      :							List[Any]     				=						TaConfig.from_json_file(lowercase_  )
       print(f'''Building PyTorch model from configuration: {config}'''  )
       _snake_case      :							Dict     				=						TaForConditionalGeneration(lowercase_  )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(lowercase_      ,		lowercase_      ,		lowercase_  )

       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''  )
       model.save_pretrained(lowercase_  )


if __name__ == "__main__":
   lowerCAmelCase_   =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   lowerCAmelCase_   =   parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)













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 				A    (unittest.TestCase       ,__UpperCAmelCase				):



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						load_tool('''translation'''					)
             self.tool.setup()
             _snake_case      :							Any     				=						load_tool('''translation'''     ,  remote=lowercase_					)



      def    __a (					self					)      ->   Optional[int]:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
             self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)



      def    __a (					self					)      ->   Union[str, Any]:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						self.remote_tool('''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
             self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Dict     				=						self.tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
             self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self.remote_tool(text='''Hey, what\'s up?'''     ,  src_lang='''English'''     ,  tgt_lang='''French'''					)
             self.assertEqual(lowercase_     ,  '''- Hé, comment ça va?'''					)



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							Optional[int]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
             _snake_case      :							Dict     				=						self.tool(*lowercase_					)

             # There is a single output
             if len(self.tool.outputs					) == 1:
                    _snake_case      :							Optional[Any]     				=						[outputs]

             self.assertListEqual(output_types(lowercase_					)     ,  self.tool.outputs					)



      def    __a (					self					)      ->   Tuple:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']
             _snake_case      :							Dict     				=						self.tool(*lowercase_					)

             if not isinstance(lowercase_     ,  lowercase_					):
                    _snake_case      :							Optional[Any]     				=						[outputs]

             self.assertEqual(len(lowercase_					)     ,  len(self.tool.outputs					)					)

             for output, output_type in zip(lowercase_     ,  self.tool.outputs					):
                    _snake_case      :							List[str]     				=						AGENT_TYPE_MAPPING[output_type]
                    self.assertTrue(isinstance(lowercase_     ,  lowercase_					)					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							Dict     				=						['''Hey, what\'s up?''', '''English''', '''Spanish''']

             _snake_case      :							Any     				=						[]

             for _input, input_type in zip(lowercase_     ,  self.tool.inputs					):
                    if isinstance(lowercase_     ,  lowercase_					):
                           _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
             _snake_case      :							List[Any]     				=						self.tool(*lowercase_					)

             if not isinstance(lowercase_     ,  lowercase_					):
                    _snake_case      :							Any     				=						[outputs]

             self.assertEqual(len(lowercase_					)     ,  len(self.tool.outputs					)					)






