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import copy
from typing import NamedTuple, Union, Literal
import numpy as np
from transformers import CLIPTokenizer
def dropout(tokens: list[int], dropout: float):
if dropout != 0:
tokens = [token for token in tokens if np.random.random() > dropout]
return tokens
def shuffle_all(tokens: list[int]):
if len(tokens) >= 2:
tokens = copy.copy(tokens)
np.random.shuffle(tokens)
return tokens
def shuffle_leading(tokens: list[int]):
if len(tokens) >= 3:
subtokens = tokens[:-1]
np.random.shuffle(subtokens)
tokens = subtokens + tokens[-1:]
return tokens
def shuffle_trailing(tokens: list[int]):
if len(tokens) >= 3:
subtokens = tokens[1:]
np.random.shuffle(subtokens)
tokens = tokens[:1] + subtokens
return tokens
def shuffle_between(tokens: list[int]):
if len(tokens) >= 4:
subtokens = tokens[1:-1]
np.random.shuffle(subtokens)
tokens = tokens[:1] + subtokens + tokens[-1:]
return tokens
def shuffle_none(tokens: list[int]):
return tokens
def shuffle_auto(tokens: list[int]):
if len(tokens) >= 5:
return shuffle_between(tokens)
if len(tokens) >= 3:
return shuffle_trailing(tokens)
return shuffle_all(tokens)
class MultiCLIPTokenizer(CLIPTokenizer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.token_map: dict[int, list[int]] = {}
self.is_training = False
self.vector_shuffle = shuffle_auto
self.dropout = 0
def train(self):
self.is_training = True
def eval(self):
self.is_training = False
def set_dropout(self, dropout: float):
self.dropout = dropout
def set_use_vector_shuffle(self, algorithm: Union[bool, Literal["all", "trailing", "leading", "between", "off"]]):
if algorithm == "leading":
self.vector_shuffle = shuffle_leading
elif algorithm == "trailing":
self.vector_shuffle = shuffle_trailing
elif algorithm == "between":
self.vector_shuffle = shuffle_between
elif algorithm == "auto":
self.vector_shuffle = shuffle_auto
elif algorithm == True or algorithm == "all":
self.vector_shuffle = shuffle_all
else:
self.vector_shuffle = shuffle_none
def add_multi_tokens(
self,
new_tokens: Union[str, list[str]],
num_vectors: Union[int, list[int]] = 1
) -> Union[list[int], list[list[int]]]:
if isinstance(new_tokens, list):
if isinstance(num_vectors, int):
num_vectors = [num_vectors] * len(new_tokens)
if len(num_vectors) != len(new_tokens):
raise ValueError("Expected new_tokens and num_vectors to have the same len")
return [self.add_multi_tokens(new_token, vecs) for new_token, vecs in zip(new_tokens, num_vectors)]
if isinstance(num_vectors, list):
raise ValueError("Expected num_vectors to be int for single token")
if num_vectors < 1:
raise ValueError("Expected num_vectors to be >= 1")
tokens = [new_tokens] + [f"{new_tokens}_{i}" for i in range(1, num_vectors)]
super().add_tokens(tokens)
ids = super().convert_tokens_to_ids(tokens)
self.token_map[ids[0]] = ids
return ids
def expand_id(self, id: int):
if id in self.token_map:
ids = self.token_map[id]
if self.is_training:
ids = dropout(self.vector_shuffle(ids), self.dropout)
return ids
else:
return [id]
def expand_ids(self, ids: list[int]):
return [
new_id
for id in ids
for new_id in self.expand_id(id)
]
def expand_batched_ids(self, input_ids: Union[list[int], list[list[int]], tuple[list[int]]]):
if isinstance(input_ids, (list, tuple)) and isinstance(input_ids[0], list):
return [self.expand_ids(batch) for batch in input_ids]
else:
return self.expand_ids(input_ids)
def _call_one(self, *args, **kwargs):
result = super()._call_one(*args, **kwargs)
result.input_ids = self.expand_batched_ids(result.input_ids)
return result
def encode(self, *args, **kwargs):
result = super().encode(*args, **kwargs)
result = self.expand_batched_ids(result)
return result
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