





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_           =      logging.get_logger(__name__)

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   'encoder-decoder'
      A_						:    Optional[int]							=   True


      def __init__(self	:				List[str]    ,      **a__	:				Any				):





         """simple docstring"""



         super().__init__(**a__				)
         assert (
             "encoder" in kwargs and "decoder" in kwargs
         ), "Config has to be initialized with encoder and decoder config"
         __snake_case								=       kwargs.pop('''encoder'''				)
         __snake_case								=       encoder_config.pop('''model_type'''				)
         __snake_case								=       kwargs.pop('''decoder'''				)
         __snake_case								=       decoder_config.pop('''model_type'''				)

         from ..auto.configuration_auto import AutoConfig

         __snake_case								=       AutoConfig.for_model(a__    ,      **a__				)
         __snake_case								=       AutoConfig.for_model(a__    ,      **a__				)
         __snake_case								=       True


      @classmethod
      def        a			(cls	:				Optional[Any]    ,      a__	:				PretrainedConfig    ,      a__	:				PretrainedConfig    ,      **a__	:				Optional[Any]				):





         """simple docstring"""



         logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''				)
         __snake_case								=       True
         __snake_case								=       True

         return cls(encoder=encoder_config.to_dict()    ,      decoder=decoder_config.to_dict()    ,      **a__				)


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       copy.deepcopy(self.__dict__				)
         __snake_case								=       self.encoder.to_dict()
         __snake_case								=       self.decoder.to_dict()
         __snake_case								=       self.__class__.model_type
         return output








import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
snake_case_           =      data_utils.TransfoXLTokenizer
snake_case_           =      data_utils.TransfoXLCorpus
snake_case_           =      data_utils
snake_case_           =      data_utils






def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  int	,				snake_case_     :  List[Any]	,				snake_case_     :  int	)				->    Dict:
   if transfo_xl_dataset_file:
      # Convert a pre-processed corpus (see original TensorFlow repo)
      with open(snake_case_	,				'''rb'''	) as fp:
         __snake_case								=       pickle.load(snake_case_	,				encoding='''latin1'''	)
      # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
      __snake_case								=       pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
      print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""	)
      __snake_case								=       corpus.vocab.__dict__
      torch.save(snake_case_	,				snake_case_	)

      __snake_case								=       corpus.__dict__
      corpus_dict_no_vocab.pop('''vocab'''	,				snake_case_	)
      __snake_case								=       pytorch_dump_folder_path + '''/''' + CORPUS_NAME
      print(f"""Save dataset to {pytorch_dataset_dump_path}"""	)
      torch.save(snake_case_	,				snake_case_	)

   if tf_checkpoint_path:
      # Convert a pre-trained TensorFlow model
      __snake_case								=       os.path.abspath(snake_case_	)
      __snake_case								=       os.path.abspath(snake_case_	)

      print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""	)
      # Initialise PyTorch model
      if transfo_xl_config_file == "":
         __snake_case								=       TransfoXLConfig()
      else:
         __snake_case								=       TransfoXLConfig.from_json_file(snake_case_	)
      print(f"""Building PyTorch model from configuration: {config}"""	)
      __snake_case								=       TransfoXLLMHeadModel(snake_case_	)

      __snake_case								=       load_tf_weights_in_transfo_xl(snake_case_	,				snake_case_	,				snake_case_	)
      # Save pytorch-model
      __snake_case								=       os.path.join(snake_case_	,				snake_case_	)
      __snake_case								=       os.path.join(snake_case_	,				snake_case_	)
      print(f"""Save PyTorch model to {os.path.abspath(snake_case_	)}"""	)
      torch.save(model.state_dict()	,				snake_case_	)
      print(f"""Save configuration file to {os.path.abspath(snake_case_	)}"""	)
      with open(snake_case_	,				'''w'''	,				encoding='''utf-8'''	) as f:
         f.write(config.to_json_string()	)


if __name__ == "__main__":
    snake_case_           =      argparse.ArgumentParser()
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default=None,
        type=str,
        required=True,
        help='Path to the folder to store the PyTorch model or dataset/vocab.',
    )
    parser.add_argument(
        '--tf_checkpoint_path',
        default='',
        type=str,
        help='An optional path to a TensorFlow checkpoint path to be converted.',
    )
    parser.add_argument(
        '--transfo_xl_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained BERT model. \n'
            'This specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--transfo_xl_dataset_file',
        default='',
        type=str,
        help='An optional dataset file to be converted in a vocabulary.',
    )
    snake_case_           =      parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )

