
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
lowercase_      							=       data_utils.TransfoXLTokenizer
lowercase_      							=       data_utils.TransfoXLCorpus
lowercase_      							=       data_utils
lowercase_      							=       data_utils


def 				a__ (				snake_case    ,       snake_case    ,       snake_case    ,       snake_case				):





  """simple docstring"""







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

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

    __SCREAMING_SNAKE_CASE	:     List[str] =						load_tf_weights_in_transfo_xl(snake_case    ,       snake_case    ,       snake_case				)
    # Save pytorch-model
    __SCREAMING_SNAKE_CASE	:     List[str] =						os.path.join(snake_case    ,       snake_case				)
    __SCREAMING_SNAKE_CASE	:     int =						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__":
   lowercase_      							=       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.""",
   )
   lowercase_      							=       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,
   )

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    ArgumentParser("Accelerate CLI tool"   , usage="accelerate <command> [<args>]"   , allow_abbrev=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    parser.add_subparsers(help="accelerate command helpers"	)

		# Register commands
		get_config_parser(subparsers=lowerCAmelCase	)
		env_command_parser(subparsers=lowerCAmelCase	)
		launch_command_parser(subparsers=lowerCAmelCase	)
		tpu_command_parser(subparsers=lowerCAmelCase	)
		test_command_parser(subparsers=lowerCAmelCase	)

		# Let's go
		SCREAMING_SNAKE_CASE_  :		Dict				      =    parser.parse_args()

		if not hasattr(lowerCAmelCase   , "func"	):
				parser.print_help()
				exit(1	)

		# Run
		args.func(lowerCAmelCase	)


if __name__ == "__main__":
		main()
