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- #!/usr/bin/env python
- # Copyright (c) Facebook, Inc. and its affiliates.
- # All rights reserved.
- #
- # This source code is licensed under the license found in
- # https://github.com/pytorch/fairseq/blob/master/LICENSE
- import argparse
- import contextlib
- import sys
- import sentencepiece as spm
- def main():
- parser = argparse.ArgumentParser()
- parser.add_argument("--model", required=True,
- help="sentencepiece model to use for encoding")
- parser.add_argument("--inputs", nargs="+", default=['-'],
- help="input files to filter/encode")
- parser.add_argument("--outputs", nargs="+", default=['-'],
- help="path to save encoded outputs")
- parser.add_argument("--output_format", choices=["piece", "id"], default="piece")
- parser.add_argument("--min-len", type=int, metavar="N",
- help="filter sentence pairs with fewer than N tokens")
- parser.add_argument("--max-len", type=int, metavar="N",
- help="filter sentence pairs with more than N tokens")
- args = parser.parse_args()
- assert len(args.inputs) == len(args.outputs), \
- "number of input and output paths should match"
- sp = spm.SentencePieceProcessor()
- sp.Load(args.model)
- if args.output_format == "piece":
- def encode(l):
- return sp.EncodeAsPieces(l)
- elif args.output_format == "id":
- def encode(l):
- return list(map(str, sp.EncodeAsIds(l)))
- else:
- raise NotImplementedError
- if args.min_len is not None or args.max_len is not None:
- def valid(line):
- return (
- (args.min_len is None or len(line) >= args.min_len) and
- (args.max_len is None or len(line) <= args.max_len)
- )
- else:
- def valid(lines):
- return True
- with contextlib.ExitStack() as stack:
- inputs = [
- stack.enter_context(open(input, "r", encoding="utf-8"))
- if input != "-" else sys.stdin
- for input in args.inputs
- ]
- outputs = [
- stack.enter_context(open(output, "w", encoding="utf-8"))
- if output != "-" else sys.stdout
- for output in args.outputs
- ]
- stats = {
- "num_empty": 0,
- "num_filtered": 0,
- }
- def encode_line(line):
- line = line.strip()
- if len(line) > 0:
- line = encode(line)
- if valid(line):
- return line
- else:
- stats["num_filtered"] += 1
- else:
- stats["num_empty"] += 1
- return None
- for i, lines in enumerate(zip(*inputs), start=1):
- enc_lines = list(map(encode_line, lines))
- if not any(enc_line is None for enc_line in enc_lines):
- for enc_line, output_h in zip(enc_lines, outputs):
- print(" ".join(enc_line), file=output_h)
- if i % 10000 == 0:
- print("processed {} lines".format(i), file=sys.stderr)
- print("skipped {} empty lines".format(stats["num_empty"]), file=sys.stderr)
- print("filtered {} lines".format(stats["num_filtered"]), file=sys.stderr)
- if __name__ == "__main__":
- main()
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