





"""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 					_UpperCamelCase		( unittest.TestCase						, __snake_case):






    def  A    (self   ):


      """simple docstring"""
      A__											=							load_tool("""translation"""   )
      self.tool.setup()
      A__											=							load_tool("""translation"""						,      remote=lowerCamelCase__   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.tool("""Hey, what's up?"""						,      src_lang="""English"""						,      tgt_lang="""French"""   )
      self.assertEqual(lowerCamelCase__						,      """- Hé, comment ça va?"""   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.remote_tool("""Hey, what's up?"""						,      src_lang="""English"""						,      tgt_lang="""French"""   )
      self.assertEqual(lowerCamelCase__						,      """- Hé, comment ça va?"""   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.tool(text="""Hey, what's up?"""						,      src_lang="""English"""						,      tgt_lang="""French"""   )
      self.assertEqual(lowerCamelCase__						,      """- Hé, comment ça va?"""   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							self.remote_tool(text="""Hey, what's up?"""						,      src_lang="""English"""						,      tgt_lang="""French"""   )
      self.assertEqual(lowerCamelCase__						,      """- Hé, comment ça va?"""   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							["""Hey, what's up?""", """English""", """Spanish"""]
      A__											=							self.tool(*lowerCamelCase__   )

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

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






    def  A    (self   ):


      """simple docstring"""
      A__											=							["""Hey, what's up?""", """English""", """Spanish"""]
      A__											=							self.tool(*lowerCamelCase__   )

      if not isinstance(lowerCamelCase__						,      lowerCamelCase__   ):
        A__											=							[outputs]

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

      for output, output_type in zip(lowerCamelCase__						,      self.tool.outputs   ):
        A__											=							AGENT_TYPE_MAPPING[output_type]
        self.assertTrue(isinstance(lowerCamelCase__						,      lowerCamelCase__   )   )






    def  A    (self   ):


      """simple docstring"""
      A__											=							["""Hey, what's up?""", """English""", """Spanish"""]

      A__											=							[]

      for _input, input_type in zip(lowerCamelCase__						,      self.tool.inputs   ):
        if isinstance(lowerCamelCase__						,      lowerCamelCase__   ):
          _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
      A__											=							self.tool(*lowerCamelCase__   )

      if not isinstance(lowerCamelCase__						,      lowerCamelCase__   ):
        A__											=							[outputs]

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












"""simple docstring"""




import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	List[str]		,				UpperCamelCase     :	Tuple		,				UpperCamelCase     :	Optional[int]		,				UpperCamelCase     :	Union[str, Any]		,				UpperCamelCase     :	Optional[int]    ):
  # Initialise PyTorch model
  A__											=							BertConfig.from_pretrained(
      """bert-large-cased"""		,				vocab_size=UpperCamelCase		,				max_position_embeddings=512		,				is_decoder=UpperCamelCase		,				add_cross_attention=UpperCamelCase		,				)
  A__											=							bert_config.to_dict()
  del bert_config_dict["type_vocab_size"]
  A__											=							BertGenerationConfig(**UpperCamelCase    )
  if is_encoder:
    A__											=							BertGenerationEncoder(UpperCamelCase    )
  else:
    A__											=							BertGenerationDecoder(UpperCamelCase    )
  print(F"""Building PyTorch model from configuration: {config}"""    )

  # Load weights from tf checkpoint
  load_tf_weights_in_bert_generation(
      UpperCamelCase		,				UpperCamelCase		,				model_class="""bert"""		,				is_encoder_named_decoder=UpperCamelCase		,				is_encoder=UpperCamelCase		,				)

  # Save pytorch-model
  print(F"""Save PyTorch model and config to {pytorch_dump_path}"""    )
  model.save_pretrained(UpperCamelCase    )


if __name__ == "__main__":
      lowerCamelCase__			=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
      lowerCamelCase__			=   parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )




