

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

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

		__lowerCamelCase				:				Dict					       = load_tf_weights_in_transfo_xl(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)
		# Save pytorch-model
		__lowerCamelCase				:				Tuple					       = os.path.join(lowerCamelCase__	, lowerCamelCase__			)
		__lowerCamelCase				:				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__":
					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,
					)





"""simple docstring"""







from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     return {key.lstrip('-'      ): value for key, value in zip(unknown_args[::2]     ,			unknown_args[1::2]      )}



def 			lowercase__(       ):
     lowercase_	:				Any													=	ArgumentParser(
         'HuggingFace Datasets CLI tool'     ,			usage='datasets-cli <command> [<args>]'     ,			allow_abbrev=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Tuple													=	parser.add_subparsers(help='datasets-cli command helpers'      )
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     EnvironmentCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     TestCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     RunBeamCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     DummyDataCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )

     # Parse args
     lowercase_							,  lowercase_	:				Dict													=	parser.parse_known_args()
     if not hasattr(__SCREAMING_SNAKE_CASE     ,			'func'      ):
          parser.print_help()
          exit(1      )
     lowercase_	:				int													=	parse_unknown_args(__SCREAMING_SNAKE_CASE      )

     # Run
     lowercase_	:				List[Any]													=	args.func(__SCREAMING_SNAKE_CASE     ,			**__SCREAMING_SNAKE_CASE      )
     service.run()


if __name__ == "__main__":
       main()

