



"""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
__snake_case							:     int   =					data_utils.TransfoXLTokenizer
__snake_case							:     str   =					data_utils.TransfoXLCorpus
__snake_case							:     Dict   =					data_utils
__snake_case							:     List[str]   =					data_utils


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

						__lowerCAmelCase   :						List[Any]				= corpus.__dict__
						corpus_dict_no_vocab.pop("vocab"      ,__snake_case			)
						__lowerCAmelCase   :						List[str]				= 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
						__lowerCAmelCase   :						List[str]				= os.path.abspath(__snake_case			)
						__lowerCAmelCase   :						Optional[Any]				= 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 == "":
									__lowerCAmelCase   :						List[str]				= TransfoXLConfig()
						else:
									__lowerCAmelCase   :						Optional[int]				= TransfoXLConfig.from_json_file(__snake_case			)
						print(F"""Building PyTorch model from configuration: {config}"""			)
						__lowerCAmelCase   :						List[str]				= TransfoXLLMHeadModel(__snake_case			)

						__lowerCAmelCase   :						Tuple				= load_tf_weights_in_transfo_xl(__snake_case      ,__snake_case      ,__snake_case			)
						# Save pytorch-model
						__lowerCAmelCase   :						Union[str, Any]				= os.path.join(__snake_case      ,__snake_case			)
						__lowerCAmelCase   :						Union[str, Any]				= 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							:     Any   =					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							:     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"""






import re

from filelock import FileLock


try:
	import nltk

	__snake_case							:     Any   =					True
except (ImportError, ModuleNotFoundError):
	__snake_case							:     Optional[int]   =					False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)


def     _lowercase       (						__snake_case			)		->     str:
			re.sub("<n>"      ,""      ,__snake_case			)  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(__snake_case			)			)