"""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
_UpperCamelCase     :		Union[str, Any]											=					data_utils.TransfoXLTokenizer
_UpperCamelCase     :		List[str]											=					data_utils.TransfoXLCorpus
_UpperCamelCase     :		str											=					data_utils
_UpperCamelCase     :		Dict											=					data_utils





def        a_  (   _lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				Optional[int]  ):





							'''simple docstring'''

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

														lowercase__    :	Optional[Any]     					=			corpus.__dict__
														corpus_dict_no_vocab.pop('vocab'     ,				lowerCamelCase_  )
														lowercase__    :	Tuple     					=			pytorch_dump_folder_path + '/' + CORPUS_NAME
														print(f"""Save dataset to {pytorch_dataset_dump_path}"""  )
														torch.save(lowerCamelCase_     ,				lowerCamelCase_  )

							if tf_checkpoint_path:
														# Convert a pre-trained TensorFlow model
														lowercase__    :	Optional[int]     					=			os.path.abspath(lowerCamelCase_  )
														lowercase__    :	Optional[int]     					=			os.path.abspath(lowerCamelCase_  )

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

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


if __name__ == "__main__":
							_UpperCamelCase     :		Optional[int]											=					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.",
							)
							_UpperCamelCase     :		List[str]											=					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 .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

