


'''simple docstring'''






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
__A   :    Dict											= data_utils.TransfoXLTokenizer
__A   :    Tuple											= data_utils.TransfoXLCorpus
__A   :    int											= data_utils
__A   :    Optional[Any]											= data_utils







def 							UpperCamelCase_     (       A__   :  Optional[Any]				,       A__   :  Dict				,       A__   :  Optional[Any]				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





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

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

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    lowerCAmelCase_     : List[str]     =  os.path.abspath(A__   )
    lowerCAmelCase_     : int     =  os.path.abspath(A__   )

    print(f'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'   )
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      lowerCAmelCase_     : Dict     =  TransfoXLConfig()
    else:
      lowerCAmelCase_     : Optional[Any]     =  TransfoXLConfig.from_json_file(A__   )
    print(f'Building PyTorch model from configuration: {config}'   )
    lowerCAmelCase_     : Dict     =  TransfoXLLMHeadModel(A__   )

    lowerCAmelCase_     : Optional[int]     =  load_tf_weights_in_transfo_xl(A__				,       A__				,       A__   )
    # Save pytorch-model
    lowerCAmelCase_     : Any     =  os.path.join(A__				,       A__   )
    lowerCAmelCase_     : List[Any]     =  os.path.join(A__				,       A__   )
    print(f'Save PyTorch model to {os.path.abspath(A__   )}'   )
    torch.save(model.state_dict()				,       A__   )
    print(f'Save configuration file to {os.path.abspath(A__   )}'   )
    with open(A__				,       """w"""				,       encoding="""utf-8"""   ) as f:
      f.write(config.to_json_string()   )


if __name__ == "__main__":
  __A   :    str											= 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.",
  )
  __A   :    Tuple											= 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,
  )







'''simple docstring'''






from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
  from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



