
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__							: Any     =       data_utils.TransfoXLTokenizer
A__							: Tuple     =       data_utils.TransfoXLCorpus
A__							: int     =       data_utils
A__							: Union[str, Any]     =       data_utils



def   _a  ( __UpperCamelCase							:	List[str]						,__UpperCamelCase							:	List[Any]						,__UpperCamelCase							:	List[str]						,__UpperCamelCase							:	str     ):

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

						lowerCAmelCase__			:     Any					   =      corpus.__dict__
						corpus_dict_no_vocab.pop('''vocab'''						,__a     )
						lowerCAmelCase__			:     Optional[int]					   =      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[Any]					   =      os.path.abspath(__a     )
						lowerCAmelCase__			:     Any					   =      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__			:     Union[str, Any]					   =      TransfoXLConfig()
						else:
									lowerCAmelCase__			:     Any					   =      TransfoXLConfig.from_json_file(__a     )
						print(f'''Building PyTorch model from configuration: {config}'''     )
						lowerCAmelCase__			:     Optional[int]					   =      TransfoXLLMHeadModel(__a     )

						lowerCAmelCase__			:     List[Any]					   =      load_tf_weights_in_transfo_xl(__a						,__a						,__a     )
						# Save pytorch-model
						lowerCAmelCase__			:     Optional[Any]					   =      os.path.join(__a						,__a     )
						lowerCAmelCase__			:     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__							: List[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__							: 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,
							)










def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							Optional[Any]:






		"""simple docstring"""

		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )
		else:
				return a * actual_power(__a       ,	int(b / 2       )       ) * actual_power(__a       ,	int(b / 2       )       )







def   __lowerCamelCase   (				__a  :int       ,	__a  :int       )						->							float:






		"""simple docstring"""

		if b < 0:
				return 1 / actual_power(__a       ,	__a       )
		return actual_power(__a       ,	__a       )


if __name__ == "__main__":
					print(power(-2, -3))



