
"""simple docstring"""




import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							BioGptTokenizer
     _A								=							False



     def  lowerCAmelCase__       (      self  )->					Tuple:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCAmelCase__							: Dict 							=						[
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "w</w>",
              "r</w>",
              "t</w>",
              "lo",
              "low",
              "er</w>",
              "low</w>",
              "lowest</w>",
              "newer</w>",
              "wider</w>",
              "<unk>",
          ]
          UpperCAmelCase__							: int 							=						dict(zip(__UpperCamelCase ,			range(len(__UpperCamelCase  )  )  )  )
          UpperCAmelCase__							: Optional[Any] 							=						["l o 123", "lo w 1456", "e r</w> 1789", ""]

          UpperCAmelCase__							: List[str] 							=						os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES["vocab_file"]  )
          UpperCAmelCase__							: Union[str, Any] 							=						os.path.join(self.tmpdirname ,			VOCAB_FILES_NAMES["merges_file"]  )
          with open(self.vocab_file ,			"w"  ) as fp:
               fp.write(json.dumps(__UpperCamelCase  )  )
          with open(self.merges_file ,			"w"  ) as fp:
               fp.write("\n".join(__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						"lower newer"
          UpperCAmelCase__							: List[Any] 							=						"lower newer"
          return input_text, output_text



     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          UpperCAmelCase__							: str 							=						BioGptTokenizer(self.vocab_file ,			self.merges_file  )

          UpperCAmelCase__							: Any 							=						"lower"
          UpperCAmelCase__							: Optional[int] 							=						["low", "er</w>"]
          UpperCAmelCase__							: List[Any] 							=						tokenizer.tokenize(__UpperCamelCase  )
          self.assertListEqual(__UpperCamelCase ,			__UpperCamelCase  )

          UpperCAmelCase__							: Optional[int] 							=						tokens + ["<unk>"]
          UpperCAmelCase__							: int 							=						[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase  ) ,			__UpperCamelCase  )



     @slow
     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          UpperCAmelCase__							: str 							=						BioGptTokenizer.from_pretrained("microsoft/biogpt"  )

          UpperCAmelCase__							: Any 							=						tokenizer.encode("sequence builders" ,			add_special_tokens=__UpperCamelCase  )
          UpperCAmelCase__							: List[str] 							=						tokenizer.encode("multi-sequence build" ,			add_special_tokens=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						tokenizer.build_inputs_with_special_tokens(__UpperCamelCase  )
          UpperCAmelCase__							: Optional[Any] 							=						tokenizer.build_inputs_with_special_tokens(__UpperCamelCase ,			__UpperCamelCase  )

          self.assertTrue(encoded_sentence == [2] + text  )
          self.assertTrue(encoded_pair == [2] + text + [2] + text_a  )





"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

     # Load weights from numpy
     load_tf_weights_in_gpta(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # Save pytorch-model
     UpperCAmelCase__							: Optional[int] 							=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__							: Any 							=						pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(F"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,    lowerCAmelCase		)
     print(F"Save configuration file to {pytorch_config_dump_path}"		)
     with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
    A__      :	Optional[int]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



