def __init__(self, max_output_token_length=MAX_OUTPUT_TOKEN_LENGTH, reserved=()): self.types_to_skip = () self.reserved = reserved self.mappings: Dict[str, str] self.update_mappings({ # By default, replace \n and \r. This is meant primarily for literals. '\n': unified_tokenizer.quote_special('NLCHAR'), '\r': unified_tokenizer.quote_special('CR'), unified_tokenizer.SENTINEL: unified_tokenizer.quote_special(unified_tokenizer.SENTINEL_ESCAPE), }) self.max_output_token_length = max_output_token_length
def untokenize(self, token_list): """Untokenizes via `untokenize_abstract`.""" # Untokenize agnostic. if (not token_list or token_list[-1] != unified_tokenizer.quote_special( unified_tokenizer.TokenKind.EOS.name)): raise ValueError( 'Token list %r should end with the EOS token %r.' % (token_list, unified_tokenizer.quote_special( unified_tokenizer.TokenKind.EOS.name))) whole_tokens = unified_tokenizer.reconstitute_full_unsanitary_tokens( token_list, sanitization_mapping=self.mappings, sentinel=unified_tokenizer.SENTINEL) return self.untokenize_abstract(whole_tokens)
def untokenize_abstract(self, whole_tokens): tokens: List[str] = [] for token in whole_tokens[: -1]: # Skip EOS. The caller checked it's there. if token == unified_tokenizer.quote_special( unified_tokenizer.TokenKind.NEWLINE.name): tokens.append('\n') else: tokens.append(token) return ''.join(tokens)
def tokenize_and_abstract(self, source_code): """As per the superclass.""" agnostic_tokens: List[unified_tokenizer.AbstractToken] = [] try: java_tokens = list( extended_javalang_tokenizer.tokenize_extended(source_code)) except (javalang.LexerError, TypeError) as e: # Sometimes, javalang returns a TypeError when reading a number. # See # https://github.com/c2nes/javalang/blob/0664afb7f4d40254312693f2e833c1ed4ac551c7/javalang/tokenizer.py#L370 logging.warning( 'The tokenizer raised exception `%r` while parsing %s', e, source_code) # We don't try to do recovery from errors quite yet. Mark the error as # occurring at whatever position we are in and terminate agnostic_tokens.append( unified_tokenizer.AbstractToken( unified_tokenizer.quote_special( unified_tokenizer.TokenKind.ERROR.name), unified_tokenizer.TokenKind.ERROR, unified_tokenizer.TokenMetadata( start=unified_tokenizer.Position(line=0, column=0), end=unified_tokenizer.Position(line=0, column=0)))) agnostic_tokens.append( unified_tokenizer.AbstractToken( unified_tokenizer.quote_special( unified_tokenizer.TokenKind.EOS.name), unified_tokenizer.TokenKind.EOS, unified_tokenizer.TokenMetadata( start=unified_tokenizer.Position(line=0, column=0), end=unified_tokenizer.Position(line=0, column=0)))) else: start_line = 0 start_column = 0 for token in java_tokens: # The token kind is the subclass type of the token. token_type = type(token) if token_type not in JavaTokenizer._TOKEN_TYPE_MAP: raise ValueError( 'Received Java token type %s, but it was unexpected, ' 'while tokenizing \n%s\n' % (token_type, source_code)) # JavaTokenizer counts lines and columns from 1. start_line = token.position.line - 1 start_column = token.position.column - 1 # The tokenizer seems to take some liberties with Unicode, returning # invalid characters. This cleans spellings up. spelling = token.value.encode('utf-8', errors='replace').decode('utf-8') agnostic_tokens.append( unified_tokenizer.AbstractToken( spelling, JavaTokenizer._TOKEN_TYPE_MAP[token_type], unified_tokenizer.TokenMetadata( start=unified_tokenizer.Position( line=start_line, column=start_column)))) # At this point, we have all the tokens, either as produced and abstracted, # or a placeholder error and eos in case of an exception. However, the # tokens only have start positions. Since the extended tokenizer guarantees # that tokens abut, we take a second pass, backwards, setting the end # position of a token from the start position of token following it. The # final token, `EOS` already has an end position, so we don't modify it. eos = agnostic_tokens[-1] if not eos.metadata.start: # This should be there. Raise an exception raise AssertionError( 'The end of input token is missing positioning ' 'information: %s' % eos) later_token_start: unified_tokenizer.Position = eos.metadata.start # The EOS token has an empty extent, so the end and the start are set to be # the same. filled_agnostic_tokens = [ dataclasses.replace(eos, metadata=dataclasses.replace( eos.metadata, end=eos.metadata.start)) ] # Go backwards, from the element before `eos` to the beginning. for token in (agnostic_tokens[i] for i in range(len(agnostic_tokens) - 2, -1, -1)): filled_token = dataclasses.replace(token, metadata=dataclasses.replace( token.metadata, end=later_token_start)) filled_agnostic_tokens.append(filled_token) later_token_start = token.metadata.start # Now we have the tokens, including end position, but they're reversed. # The final step is to break down whitespace tokens into primitive # WHITESPACE tokens and NEWLINE tokens. with_broken_whitespace = [] for token in filled_agnostic_tokens[::-1]: if token.kind is not unified_tokenizer.TokenKind.WHITESPACE: with_broken_whitespace.append(token) else: # This is whitespace. Replace it with primitive tokens. with_broken_whitespace.extend( unified_tokenizer.fill_range_with_whitespace( token.metadata.start, token.metadata.end)) return with_broken_whitespace
# 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. """Tests for java_tokenizer.""" from typing import Sequence, Tuple from absl.testing import absltest from absl.testing import parameterized import java_tokenizer import unified_tokenizer _NEWLINE_NAME = unified_tokenizer.quote_special( unified_tokenizer.TokenKind.NEWLINE.name) class JavaTokenizerTest(parameterized.TestCase): @parameterized.named_parameters( ( 'nothing', '', (), ), ( 'same_line', """TokenA TokenB""", # 0 67 (
def token_from_token_type(token_type): """Turns a token type into a reserved token string.""" # We use the tok_name dict from tokenize, not token. The former has # NL and COMMENT and such, whereas the latter doesn't. return unified_tokenizer.quote_special(tokenize.tok_name[token_type])
def tokenize_and_abstract(self, source_code): """Produces a language-agnostic tokenization of the input code.""" agnostic_tokens: List[unified_tokenizer.AbstractToken] = [] try: token_tuples = unified_tokenizer.code_to_tokens(source_code) except (tokenize.TokenError, IndentationError) as e: logging.warning( 'The tokenizer raised exception `%s` while parsing %s', e, source_code) # We don't try to do recovery from errors quite yet. Emit just an # error and end-of-sequence and return. agnostic_tokens.append( unified_tokenizer.AbstractToken( unified_tokenizer.quote_special( unified_tokenizer.TokenKind.ERROR.name), unified_tokenizer.TokenKind.ERROR, unified_tokenizer.TokenMetadata( start=unified_tokenizer.Position(line=0, column=0), end=unified_tokenizer.Position(line=0, column=0)))) agnostic_tokens.append( unified_tokenizer.AbstractToken( unified_tokenizer.quote_special( unified_tokenizer.TokenKind.EOS.name), unified_tokenizer.TokenKind.EOS, unified_tokenizer.TokenMetadata( start=unified_tokenizer.Position(line=0, column=0), end=unified_tokenizer.Position(line=0, column=0)))) return agnostic_tokens for token_tuple in token_tuples: spelling = token_tuple.string kind = token_tuple.type # We'll adjust the spelling of some tokens, e.g., those that we # tokenize by their type rather than their original spelling. Indentation # and dedentation tokens are like that. adjusted_spelling = spelling token_kind = unified_tokenizer.TokenKind.NONE if kind == tokenize.NAME: # Disambiguate identifiers from keywords. if keyword.iskeyword(spelling): token_kind = unified_tokenizer.TokenKind.KEYWORD else: token_kind = unified_tokenizer.TokenKind.IDENTIFIER else: if kind in PythonTokenizer._TOKEN_TYPES_TO_TOKENIZE_BY_TYPE: # Replace spelling with type. adjusted_spelling = cubert_tokenizer.token_from_token_type( kind) elif kind is tokenize.INDENT: # For INDENT, in particular, we also record the actual spelling too. adjusted_spelling = '{indent}{spelling}'.format( indent=cubert_tokenizer.token_from_token_type(kind), spelling=spelling) elif kind == tokenize.ENDMARKER: adjusted_spelling = unified_tokenizer.quote_special( unified_tokenizer.TokenKind.EOS.name) # Map everything according to table. try: token_kind = PythonTokenizer._TOKEN_TYPE_MAP[kind] except KeyError as ke: # It's possible we're here because of async/await. Those kept being # turned into keywords and then removed from keywords, so we can't # rely on knowing which they are. We'll check by spelling. # See: https://bugs.python.org/issue30406 # and https://bugs.python.org/issue33260 # and https://bugs.python.org/issue35975 if spelling in ('async', 'await'): token_kind = unified_tokenizer.TokenKind.KEYWORD else: raise ValueError( 'While trying to turn Python token %r into an ' 'agnostic one, raised %r.' % ((spelling, kind), ke)) start_line, start_column = token_tuple.start end_line, end_column = token_tuple.end # Unlike other languages, NEWLINE tokens are reported as ending on the # same line as where they started. We adjust that here, to stick to the # same convention as other tokenizers. if ((token_kind == unified_tokenizer.TokenKind.NEWLINE) or (kind == tokenize.NL)): end_line = start_line + 1 end_column = 0 agnostic_tokens.append( unified_tokenizer.AbstractToken( spelling=adjusted_spelling, kind=token_kind, metadata=unified_tokenizer.TokenMetadata( # Python's tokenizer counts lines starting from 1, so we # have to offset what we read from the `TokenInfo` tuple. start=unified_tokenizer.Position(line=start_line - 1, column=start_column), end=unified_tokenizer.Position(line=end_line - 1, column=end_column)))) return agnostic_tokens
class PythonTokenizer(cubert_tokenizer.CuBertTokenizer): """Tokenizer that extracts Python's lexical elements preserving strings.""" _TOKEN_TYPE_MAP = { tokenize.COMMENT: unified_tokenizer.TokenKind.COMMENT, tokenize.DEDENT: unified_tokenizer.TokenKind.KEYWORD, tokenize.ENDMARKER: unified_tokenizer.TokenKind.EOS, tokenize.ERRORTOKEN: unified_tokenizer.TokenKind.ERROR, tokenize.INDENT: unified_tokenizer.TokenKind.KEYWORD, tokenize.NEWLINE: unified_tokenizer.TokenKind.NEWLINE, tokenize.NL: unified_tokenizer.TokenKind.PUNCTUATION, tokenize.NUMBER: unified_tokenizer.TokenKind.NUMBER, tokenize.OP: unified_tokenizer.TokenKind.PUNCTUATION, tokenize.STRING: unified_tokenizer.TokenKind.STRING, } _REVERSE_TOKEN_MAP = { cubert_tokenizer.token_from_token_type(tokenize.INDENT): tokenize.INDENT, cubert_tokenizer.token_from_token_type(tokenize.DEDENT): tokenize.DEDENT, unified_tokenizer.quote_special(unified_tokenizer.TokenKind.EOS.name): tokenize.ENDMARKER, unified_tokenizer.quote_special(unified_tokenizer.TokenKind.ERROR.name): tokenize.ERRORTOKEN, unified_tokenizer.quote_special(unified_tokenizer.TokenKind.NEWLINE.name): tokenize.NEWLINE, cubert_tokenizer.token_from_token_type(tokenize.NL): tokenize.NL, } # Adding the end-of-string anchor \Z below, since re.fullmatch wasn't # available in Python2. # pytype: disable=module-attr _NUMBERS = re.compile('(' + tokenize.Number + r')\Z') # pytype: disable=module-attr _SINGLE_STRINGS = re.compile('(' + tokenize.String + r')\Z') _TRIPLE_STRING_BEGINNINGS = re.compile(tokenize.Triple) # pytype: disable=module-attr # pytype: disable=module-attr _COMMENTS = re.compile('(' + tokenize.Comment + r')\Z') _EXACT_TOKEN_TYPES = tokenize.EXACT_TOKEN_TYPES.keys() # pytype: disable=module-attr # Token types that CubertTokenizer will tokenize by their type and not # content. _TOKEN_TYPES_TO_TOKENIZE_BY_TYPE = [ tokenize.NEWLINE, tokenize.DEDENT, tokenize.NL ] def tokenize_and_abstract(self, source_code): """Produces a language-agnostic tokenization of the input code.""" agnostic_tokens: List[unified_tokenizer.AbstractToken] = [] try: token_tuples = unified_tokenizer.code_to_tokens(source_code) except (tokenize.TokenError, IndentationError) as e: logging.warning( 'The tokenizer raised exception `%s` while parsing %s', e, source_code) # We don't try to do recovery from errors quite yet. Emit just an # error and end-of-sequence and return. agnostic_tokens.append( unified_tokenizer.AbstractToken( unified_tokenizer.quote_special( unified_tokenizer.TokenKind.ERROR.name), unified_tokenizer.TokenKind.ERROR, unified_tokenizer.TokenMetadata( start=unified_tokenizer.Position(line=0, column=0), end=unified_tokenizer.Position(line=0, column=0)))) agnostic_tokens.append( unified_tokenizer.AbstractToken( unified_tokenizer.quote_special( unified_tokenizer.TokenKind.EOS.name), unified_tokenizer.TokenKind.EOS, unified_tokenizer.TokenMetadata( start=unified_tokenizer.Position(line=0, column=0), end=unified_tokenizer.Position(line=0, column=0)))) return agnostic_tokens for token_tuple in token_tuples: spelling = token_tuple.string kind = token_tuple.type # We'll adjust the spelling of some tokens, e.g., those that we # tokenize by their type rather than their original spelling. Indentation # and dedentation tokens are like that. adjusted_spelling = spelling token_kind = unified_tokenizer.TokenKind.NONE if kind == tokenize.NAME: # Disambiguate identifiers from keywords. if keyword.iskeyword(spelling): token_kind = unified_tokenizer.TokenKind.KEYWORD else: token_kind = unified_tokenizer.TokenKind.IDENTIFIER else: if kind in PythonTokenizer._TOKEN_TYPES_TO_TOKENIZE_BY_TYPE: # Replace spelling with type. adjusted_spelling = cubert_tokenizer.token_from_token_type( kind) elif kind is tokenize.INDENT: # For INDENT, in particular, we also record the actual spelling too. adjusted_spelling = '{indent}{spelling}'.format( indent=cubert_tokenizer.token_from_token_type(kind), spelling=spelling) elif kind == tokenize.ENDMARKER: adjusted_spelling = unified_tokenizer.quote_special( unified_tokenizer.TokenKind.EOS.name) # Map everything according to table. try: token_kind = PythonTokenizer._TOKEN_TYPE_MAP[kind] except KeyError as ke: # It's possible we're here because of async/await. Those kept being # turned into keywords and then removed from keywords, so we can't # rely on knowing which they are. We'll check by spelling. # See: https://bugs.python.org/issue30406 # and https://bugs.python.org/issue33260 # and https://bugs.python.org/issue35975 if spelling in ('async', 'await'): token_kind = unified_tokenizer.TokenKind.KEYWORD else: raise ValueError( 'While trying to turn Python token %r into an ' 'agnostic one, raised %r.' % ((spelling, kind), ke)) start_line, start_column = token_tuple.start end_line, end_column = token_tuple.end # Unlike other languages, NEWLINE tokens are reported as ending on the # same line as where they started. We adjust that here, to stick to the # same convention as other tokenizers. if ((token_kind == unified_tokenizer.TokenKind.NEWLINE) or (kind == tokenize.NL)): end_line = start_line + 1 end_column = 0 agnostic_tokens.append( unified_tokenizer.AbstractToken( spelling=adjusted_spelling, kind=token_kind, metadata=unified_tokenizer.TokenMetadata( # Python's tokenizer counts lines starting from 1, so we # have to offset what we read from the `TokenInfo` tuple. start=unified_tokenizer.Position(line=start_line - 1, column=start_column), end=unified_tokenizer.Position(line=end_line - 1, column=end_column)))) return agnostic_tokens def untokenize_abstract(self, whole_tokens): # Reconstruct Python tokenizer tuples, so that Python's untokenize can be # invoked. token_tuples: List[Tuple[int, str]] = [] for whole_token in whole_tokens: if whole_token in PythonTokenizer._EXACT_TOKEN_TYPES: token_tuples.append((tokenize.OP, whole_token)) elif cubert_tokenizer.token_from_token_type( tokenize.INDENT) in whole_token: # We baked the type and spelling into one token. Break them up. spelling = whole_token.replace( cubert_tokenizer.token_from_token_type(tokenize.INDENT), '') token_tuples.append((tokenize.INDENT, spelling)) elif whole_token in PythonTokenizer._REVERSE_TOKEN_MAP: python_kind = PythonTokenizer._REVERSE_TOKEN_MAP[whole_token] if python_kind in (tokenize.DEDENT, tokenize.ENDMARKER, tokenize.ERRORTOKEN): spelling = '' else: # python_kind in (tokenize.NEWLINE, tokenize.NL) spelling = '\n' token_tuples.append((python_kind, spelling)) elif keyword.iskeyword(whole_token): token_tuples.append((tokenize.NAME, whole_token)) elif PythonTokenizer._NUMBERS.match(whole_token): token_tuples.append((tokenize.NUMBER, whole_token)) elif PythonTokenizer._SINGLE_STRINGS.match(whole_token): token_tuples.append((tokenize.STRING, whole_token)) elif PythonTokenizer._TRIPLE_STRING_BEGINNINGS.match(whole_token): token_tuples.append((tokenize.STRING, whole_token)) elif PythonTokenizer._COMMENTS.match(whole_token): token_tuples.append((tokenize.COMMENT, whole_token)) else: # Everything else we map back to NAME. token_tuples.append((tokenize.NAME, whole_token)) reconstructed = tokenize.untokenize(typing.cast(Any, token_tuples)) return reconstructed