wehub-resource-sync
130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2022 The Salesforce authors, The Open AI Team Authors and The HuggingFace Inc. team
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.utils import try_import
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from ..gpt.tokenizer import GPTTokenizer
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__all__ = ["CodeGenTokenizer"]
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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class CodeGenTokenizer(GPTTokenizer):
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resource_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}
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pretrained_resource_files_map = {"vocab_file": {}, "merges_file": {}}
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pretrained_init_configuration = {}
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def __init__(
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self,
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vocab_file,
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merges_file,
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errors="replace",
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max_len=None,
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pad_token="<|endoftext|>",
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eos_token="<|endoftext|>",
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unk_token="<|endoftext|>",
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eol_token="\u010a",
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**kwargs
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):
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super().__init__(
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vocab_file=vocab_file,
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merges_file=merges_file,
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errors=errors,
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max_len=max_len,
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pad_token=pad_token,
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eos_token=eos_token,
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unk_token=unk_token,
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eol_token=eol_token,
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**kwargs,
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)
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def decode(
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self,
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token_ids,
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skip_special_tokens=False,
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clean_up_tokenization_spaces=True,
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truncate_before_pattern=None,
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**kwargs
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):
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"""
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Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
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tokens and clean up tokenization spaces.
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Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
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Args:
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token_ids (`Union[int, List[int], np.ndarray, paddle.Tensor]`):
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List of tokenized input ids. Can be obtained using the `__call__` method.
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skip_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not to remove special tokens in the decoding.
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clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
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Whether or not to clean up the tokenization spaces.
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truncate_before_pattern (`List[str]`, *optional*, defaults to `None`):
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A list of regular expression strings that will be used to truncate the returned string. This can be
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used to remove extra pieces of code (e.g. truncate if observing a comment symbol "#" at the beginning
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of a new line). An example pattern could be `["^#", re.escape("<|endoftext|>"), "^'''", "\n\n\n"]`.
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kwargs (additional keyword arguments, *optional*):
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Will be passed to the underlying model specific decode method.
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Returns:
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`str`: The decoded sentence.
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"""
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decoded_text = super()._decode(
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token_ids=token_ids,
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skip_special_tokens=skip_special_tokens,
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clean_up_tokenization_spaces=clean_up_tokenization_spaces,
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**kwargs,
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)
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if truncate_before_pattern is not None and len(truncate_before_pattern) > 0:
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decoded_text = self.truncate(decoded_text, truncate_before_pattern)
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return decoded_text
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def truncate(self, completion, truncate_before_pattern):
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def find_re(string, pattern, start_pos):
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m = pattern.search(string, start_pos)
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return m.start() if m else -1
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re = try_import("regex")
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terminals = [re.compile(pattern, re.MULTILINE) for pattern in truncate_before_pattern]
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prints = list(re.finditer("^print", completion, re.MULTILINE))
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if len(prints) > 1:
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completion = completion[: prints[1].start()]
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defs = list(re.finditer("^def", completion, re.MULTILINE))
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if len(defs) > 1:
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completion = completion[: defs[1].start()]
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start_pos = 0
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terminals_pos = [
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pos for pos in [find_re(completion, terminal, start_pos) for terminal in terminals] if pos != -1
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]
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if len(terminals_pos) > 0:
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return completion[: min(terminals_pos)]
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else:
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return completion
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