380 lines
15 KiB
Python
380 lines
15 KiB
Python
# coding=utf-8
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# Copyright 2024
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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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"""Tokenization class for model MyT5."""
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import json
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import os
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import warnings
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from collections import defaultdict
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from typing import Optional, Union
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from ...tokenization_utils import AddedToken, PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {"vocab_file": "byte_maps.json"}
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class ByteRewriter:
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"""
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Byte rewriter class for MyT5 tokenizer.
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This class is used to rewrite bytes using a hash tree. The hash tree is constructed from a set of rewriting rules.
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Args:
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rewriting_rules (`str` or `dict[str, str]`):
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A path to a json file containing the rewriting rules or a dictionary containing the rewriting rules.
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"""
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LEAF = "[LEAF]"
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def __init__(self, rewriting_rules: Union[str, dict[str, str]]):
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if isinstance(rewriting_rules, str):
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with open(rewriting_rules, "r") as f:
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rewriting_rules = json.load(f)
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elif not isinstance(rewriting_rules, dict):
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raise TypeError(
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f"rewriting_rules should be either a path to json file or a dict, got {type(rewriting_rules)}"
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)
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self.hash_tree = self.construct_hash_tree(rewriting_rules)
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reverse_rewriting_rules = {v: k for k, v in rewriting_rules.items()}
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self.reverse_hash_tree = self.construct_hash_tree(reverse_rewriting_rules)
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def add_leaf(self, hash_tree: dict[str, Union[dict, list[str]]], byte_in_sequence: str, byte_out_sequence: str):
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"""
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Add a leaf with the output byte sequence to the hash tree.
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"""
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byte_in_list = byte_in_sequence.split(" ")
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byte_out_list = byte_out_sequence.split(" ")
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tree_pointer = hash_tree
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for b in byte_in_list:
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if b not in tree_pointer:
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tree_pointer[b] = {}
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tree_pointer = tree_pointer[b]
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tree_pointer[self.LEAF] = byte_out_list
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def construct_hash_tree(self, rewriting_rules: dict[str, str]) -> dict[str, Union[dict, list[str]]]:
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"""
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Construct a hash tree for rewritten byte sequences.
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"""
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hash_tree = defaultdict(dict)
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for b in (f"{x:02x}" for x in range(256)):
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hash_tree[b][self.LEAF] = [b]
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for in_sequence, out_sequence in rewriting_rules.items():
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self.add_leaf(hash_tree, in_sequence, out_sequence)
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return hash_tree
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def search_hash_tree(self, byte_sequence: list[str]) -> Union[None, list[str]]:
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"""
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Search the hash tree and return the rewritten byte sequence if found.
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"""
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tree_pointer = self.hash_tree
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for b in byte_sequence:
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if b in tree_pointer:
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tree_pointer = tree_pointer[b]
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else:
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return None
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return tree_pointer[self.LEAF]
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def rewrite_bytes(self, in_bytes: list[str], reverse=False) -> list[str]:
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"""
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Rewrite a sequence of bytes using the hash tree.
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Args:
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in_bytes (`list[str]`): A list of bytes to be rewritten.
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reverse (`bool`): If True, decoding is performed with the reverse hash tree.
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Returns:
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`list[str]`: The rewritten byte sequence.
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"""
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out_bytes = []
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b_start = 0
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b_end = 0
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while b_start < len(in_bytes):
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tree_pointer = self.hash_tree if not reverse else self.reverse_hash_tree
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for j in range(b_start, len(in_bytes)):
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b = in_bytes[j]
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if b in tree_pointer:
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tree_pointer = tree_pointer[b]
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elif j == b_start:
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cur_leaf = [b]
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b_end = j
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break
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else:
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break
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if self.LEAF in tree_pointer:
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cur_leaf = tree_pointer[self.LEAF]
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b_end = j
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out_bytes.extend(cur_leaf)
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b_start = b_end + 1
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return out_bytes
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class MyT5Tokenizer(PreTrainedTokenizer):
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"""
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Construct a MyT5 tokenizer.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`): The file containing the byte rewriting rules.
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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extra_ids (`int`, *optional*, defaults to 125):
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Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
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accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
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indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
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like in ByT5 preprocessing see
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[here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)).
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additional_special_tokens (`list[str]`, *optional*):
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Additional special tokens used by the tokenizer.
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"""
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model_input_names = ["input_ids", "attention_mask"]
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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eos_token="</s>",
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unk_token="<unk>",
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pad_token="<pad>",
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extra_ids=125,
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additional_special_tokens=None,
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**kwargs,
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) -> None:
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# Add extra_ids to the special token list
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if extra_ids > 0 and additional_special_tokens is None:
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additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
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elif extra_ids > 0 and additional_special_tokens is not None and len(additional_special_tokens) > 0:
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# Check that we have the right number of extra_id special tokens
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extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
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if extra_tokens != extra_ids:
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raise ValueError(
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f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
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" provided to MyT5Tokenizer. In this case the additional_special_tokens must include the"
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" extra_ids tokens"
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)
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pad_token = AddedToken(pad_token, lstrip=True, rstrip=True) if isinstance(pad_token, str) else pad_token
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eos_token = AddedToken(eos_token, lstrip=True, rstrip=True) if isinstance(eos_token, str) else eos_token
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unk_token = AddedToken(unk_token, lstrip=True, rstrip=True) if isinstance(unk_token, str) else unk_token
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# unk token needs to be in the vocab with correct index
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self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token}
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self.offset = len(self._added_tokens_decoder)
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self._utf_vocab_size = 2**8 # utf is 8 bits
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# Load byte maps
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self.byte_maps = json.load(open(vocab_file, "r"))
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self.decompose_rewriter = ByteRewriter(self.byte_maps["decompose_map"])
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self.merge_rewriter = ByteRewriter(self.byte_maps["merge_map"])
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super().__init__(
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eos_token=eos_token,
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unk_token=unk_token,
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pad_token=pad_token,
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extra_ids=0,
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additional_special_tokens=additional_special_tokens,
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**kwargs,
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)
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@property
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def vocab_size(self):
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return self._utf_vocab_size
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# Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.get_vocab
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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# Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.get_special_tokens_mask
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def get_special_tokens_mask(
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self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
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) -> list[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`list[int]`):
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List of IDs.
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token_ids_1 (`list[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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# normal case: some special tokens
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if token_ids_1 is None:
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return ([0] * len(token_ids_0)) + [1]
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return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
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def _add_eos_if_not_present(self, token_ids: list[int]) -> list[int]:
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"""Do not add eos again if user already added it."""
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if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
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warnings.warn(
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f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
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" eos tokens being added."
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)
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return token_ids
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else:
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return token_ids + [self.eos_token_id]
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def create_token_type_ids_from_sequences(
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self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
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) -> list[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. MyT5 does not
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make use of token type ids, therefore a list of zeros is returned.
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Args:
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token_ids_0 (`list[int]`):
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List of IDs.
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token_ids_1 (`list[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`list[int]`: List of zeros.
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"""
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eos = [self.eos_token_id]
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if token_ids_1 is None:
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return len(token_ids_0 + eos) * [0]
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
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# Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.build_inputs_with_special_tokens
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def build_inputs_with_special_tokens(
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self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
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) -> list[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A sequence has the following format:
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- single sequence: `X </s>`
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- pair of sequences: `A </s> B </s>`
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Args:
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token_ids_0 (`list[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`list[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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token_ids_0 = self._add_eos_if_not_present(token_ids_0)
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if token_ids_1 is None:
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return token_ids_0
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else:
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token_ids_1 = self._add_eos_if_not_present(token_ids_1)
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return token_ids_0 + token_ids_1
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def _tokenize(self, text: str, **kwargs) -> list[str]:
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"""Take as input a string and return a list of strings (tokens) for words/sub-words.
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Represents tokens in two character hex format"""
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tokens = [f"{i:02x}" for i in text.encode("utf-8")]
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tokens = self.morphological_encode(tokens)
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return tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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if len(token) != 2:
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token_id = None
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else:
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token_id = int(token, 16) + self.offset
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return token_id
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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token = f"{index - self.offset:02x}"
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return token
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def morphological_encode(self, indices: list[str]) -> list[str]:
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# Decompose and merge morphological sequences
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indices = self.decompose_rewriter.rewrite_bytes(indices, reverse=False)
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indices = self.merge_rewriter.rewrite_bytes(indices, reverse=False)
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return indices
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def morphological_decode(self, indices: list[str]) -> list[str]:
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# Demerge and compose morphological sequences
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indices = self.merge_rewriter.rewrite_bytes(indices, reverse=True)
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indices = self.decompose_rewriter.rewrite_bytes(indices, reverse=True)
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return indices
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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bstring = b""
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out_tokens = []
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for token in tokens:
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if token in self.added_tokens_decoder:
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out_tokens.append(self.added_tokens_decoder[token])
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elif token in self.added_tokens_encoder:
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out_tokens.append(token)
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else:
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out_tokens.append(token)
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out_tokens = self.morphological_decode(out_tokens)
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_added_tokens = set(self.added_tokens_decoder.values()) | set(self.added_tokens_encoder)
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for token in out_tokens:
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if token in _added_tokens:
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bstring += bytes(token, "utf-8")
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else:
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bstring += bytes.fromhex(token)
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string = bstring.decode("utf-8", errors="ignore")
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return string
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
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if os.path.isdir(save_directory):
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vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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else:
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vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
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with open(vocab_file, "w", encoding="utf-8") as writer:
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writer.write(json.dumps(self.byte_maps, indent=2, ensure_ascii=False))
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return (vocab_file,)
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__all__ = ["MyT5Tokenizer"]
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