/usr/local/lib/python3.6/site-packages/transformers
Edit: /usr/local/lib/python3.6/site-packages/transformers/convert_slow_tokenizers_checkpoints_to_fast.py (4954B)
# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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.
""" Convert slow tokenizers checkpoints in fast (serialization format of the `tokenizers` library)"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
TOKENIZER_CLASSES = {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}
def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path, force_download):
if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys())}.")
if tokenizer_name is None:
tokenizer_names = TOKENIZER_CLASSES
else:
tokenizer_names = {tokenizer_name: getattr(transformers, tokenizer_name + "Fast")}
logger.info(f"Loading tokenizer classes: {tokenizer_names}")
for tokenizer_name in tokenizer_names:
tokenizer_class = TOKENIZER_CLASSES[tokenizer_name]
add_prefix = True
if checkpoint_name is None:
checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys())
else:
checkpoint_names = [checkpoint_name]
logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}")
for checkpoint in checkpoint_names:
logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}")
# Load tokenizer
tokenizer = tokenizer_class.from_pretrained(checkpoint, force_download=force_download)
# Save fast tokenizer
logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}")
# For organization names we create sub-directories
if "/" in checkpoint:
checkpoint_directory, checkpoint_prefix_name = checkpoint.split("/")
dump_path_full = os.path.join(dump_path, checkpoint_directory)
elif add_prefix:
checkpoint_prefix_name = checkpoint
dump_path_full = dump_path
else:
checkpoint_prefix_name = None
dump_path_full = dump_path
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]:
file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint]
next_char = file_path.split(checkpoint)[-1][0]
if next_char == "/":
dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name)
checkpoint_prefix_name = None
logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}")
file_names = tokenizer.save_pretrained(
dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name
)
logger.info(f"=> File names {file_names}")
for file_name in file_names:
if not file_name.endswith("tokenizer.json"):
os.remove(file_name)
logger.info(f"=> removing {file_name}")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help=f"Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will "
"download and convert all the checkpoints from AWS.",
)
parser.add_argument(
"--checkpoint_name",
default=None,
type=str,
help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
)
parser.add_argument(
"--force_download",
action="store_true",
help="Re-download checkpoints.",
)
args = parser.parse_args()
convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)