
"""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
lowerCamelCase__   					=		data_utils.TransfoXLTokenizer
lowerCamelCase__   					=		data_utils.TransfoXLCorpus
lowerCamelCase__   					=		data_utils
lowerCamelCase__   					=		data_utils
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase		):
	if transfo_xl_dataset_file:
		# Convert a pre-processed corpus (see original TensorFlow repo)
		with open(_UpperCamelCase       ,  'rb'		) as fp:
			__lowerCAmelCase			:						Dict									=      pickle.load(_UpperCamelCase       ,  encoding='latin1'		)
		# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
		__lowerCAmelCase			:						Union[str, Any]									=      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(_UpperCamelCase       ,  _UpperCamelCase		)

		__lowerCAmelCase			:						Tuple									=      corpus.__dict__
		corpus_dict_no_vocab.pop('vocab'       ,  _UpperCamelCase		)
		__lowerCAmelCase			:						Tuple									=      pytorch_dump_folder_path + '/' + CORPUS_NAME
		print(F"Save dataset to {pytorch_dataset_dump_path}"		)
		torch.save(_UpperCamelCase       ,  _UpperCamelCase		)

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		__lowerCAmelCase			:						str									=      os.path.abspath(_UpperCamelCase		)
		__lowerCAmelCase			:						Any									=      os.path.abspath(_UpperCamelCase		)

		print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."		)
		# Initialise PyTorch model
		if transfo_xl_config_file == "":
			__lowerCAmelCase			:						List[Any]									=      TransfoXLConfig()
		else:
			__lowerCAmelCase			:						List[Any]									=      TransfoXLConfig.from_json_file(_UpperCamelCase		)
		print(F"Building PyTorch model from configuration: {config}"		)
		__lowerCAmelCase			:						Union[str, Any]									=      TransfoXLLMHeadModel(_UpperCamelCase		)

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


if __name__ == "__main__":
							lowerCamelCase__   					=		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.""",
							)
							lowerCamelCase__   					=		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,
							)






def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Optional[int]       	=   int(_lowerCAmelCase     )
 if decimal in (0, 1):  # Exit cases for the recursion
  return str(_lowerCAmelCase     )
 UpperCamelCase	,    UpperCamelCase	:					Dict       	=   divmod(_lowerCAmelCase							,   2     )
 return binary_recursive(_lowerCAmelCase     ) + str(_lowerCAmelCase     )



def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Tuple       	=   str(_lowerCAmelCase     ).strip()
 if not number:
  raise ValueError("No input value was provided"     )
 UpperCamelCase	:					Optional[int]       	=   "-" if number.startswith("-"     ) else ""
 UpperCamelCase	:					Any       	=   number.lstrip("-"     )
 if not number.isnumeric():
  raise ValueError("Input value is not an integer"     )
 return F"""{negative}0b{binary_recursive(int(_lowerCAmelCase     )     )}"""


if __name__ == "__main__":
    from doctest import testmod

    testmod()
