




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        =    data_utils.TransfoXLTokenizer
__A        =    data_utils.TransfoXLCorpus
__A        =    data_utils
__A        =    data_utils







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


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

								__lowerCamelCase		       =    corpus.__dict__
								corpus_dict_no_vocab.pop('vocab' ,     UpperCamelCase__      )
								__lowerCamelCase		       =    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		       =    os.path.abspath(UpperCamelCase__      )
								__lowerCamelCase		       =    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		       =    TransfoXLConfig()
								else:
												__lowerCamelCase		       =    TransfoXLConfig.from_json_file(UpperCamelCase__      )
								print(F"""Building PyTorch model from configuration: {config}"""      )
								__lowerCamelCase		       =    TransfoXLLMHeadModel(UpperCamelCase__      )

								__lowerCamelCase		       =    load_tf_weights_in_transfo_xl(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								# Save pytorch-model
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    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__":
					__A        =    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        =    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,
					)







import math
import sys



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	''''''
				try:
								with open(__lowerCAmelCase  ,  '''rb'''      ) as binary_file:
												snake_case__	:      int     		=	binary_file.read()
								for dat in data:
												snake_case__	:      Any     		=	f"""{dat:08b}"""
												result += curr_byte
								return result
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	{'''0''': '''0''', '''1''': '''1'''}
				snake_case__		, snake_case__	:      List[Any]     		=	'''''', ''''''
				snake_case__	:      Tuple     		=	len(__lowerCAmelCase      )

				for i in range(len(__lowerCAmelCase      )      ):
								curr_string += data_bits[i]
								if curr_string not in lexicon:
												continue

								snake_case__	:      Tuple     		=	lexicon[curr_string]
								result += last_match_id
								snake_case__	:      Any     		=	last_match_id + '''0'''

								if math.loga(__lowerCAmelCase      ).is_integer():
												snake_case__	:      Tuple     		=	{}
												for curr_key in list(__lowerCAmelCase      ):
																snake_case__	:      Union[str, Any]     		=	lexicon.pop(__lowerCAmelCase      )
												snake_case__	:      Optional[Any]     		=	new_lex

								snake_case__	:      Tuple     		=	last_match_id + '''1'''
								index += 1
								snake_case__	:      Dict     		=	''''''
				return result



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Dict     		=	8
				try:
								with open(__lowerCAmelCase  ,  '''wb'''      ) as opened_file:
												snake_case__	:      Union[str, Any]     		=	[
												    to_write[i : i + byte_length]
												    for i in range(0  ,  len(__lowerCAmelCase      )  ,  __lowerCAmelCase      )
												]

												if len(result_byte_array[-1]      ) % byte_length == 0:
																result_byte_array.append('''10000000'''      )
												else:
																result_byte_array[-1] += "1" + "0" * (
																    byte_length - len(result_byte_array[-1]      ) - 1
																)

												for elem in result_byte_array[:-1]:
																opened_file.write(int(__lowerCAmelCase  ,  2      ).to_bytes(1  ,  byteorder='''big'''      )      )
				except OSError:
								print('''File not accessible'''      )
								sys.exit()



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      Any     		=	0
				for letter in data_bits:
								if letter == "1":
												break
								counter += 1

				snake_case__	:      Optional[int]     		=	data_bits[counter:]
				snake_case__	:      int     		=	data_bits[counter + 1 :]
				return data_bits



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					None:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	read_file_binary(__lowerCAmelCase      )
				snake_case__	:      List[str]     		=	remove_prefix(__lowerCAmelCase      )
				snake_case__	:      Any     		=	decompress_data(__lowerCAmelCase      )
				write_file_binary(__lowerCAmelCase  ,  __lowerCAmelCase      )


if __name__ == "__main__":
				compress(sys.argv[1], sys.argv[2])

