FLMR / tokenization_flmr.py

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	# coding=utf-8
	# Copyright 2024 The HuggingFace Inc. team, The Hugging Face Team.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# 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.
	"""Tokenization classes for FLMR."""


	from typing import List, Optional, Union

	from transformers.utils import TensorType, logging
	from transformers.models.bert.tokenization_bert import BertTokenizer


	logger = logging.get_logger(__name__)

	VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer_config.json"}

	CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP = {
	"vocab_file": {
	"LinWeizheDragon/PreFLMR_ViT-L": (
	"https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-L/resolve/main/context_tokenizer/vocab.txt"
	),
	"LinWeizheDragon/FLMR": (
	"https://huggingface.co/LinWeizheDragon/FLMR/resolve/main/context_tokenizer/vocab.txt"
	),
	},
	"tokenizer_file": {
	"LinWeizheDragon/PreFLMR_ViT-L": (
	"https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-L/resolve/main/context_tokenizer/tokenizer_config.json"
	),
	"LinWeizheDragon/FLMR": (
	"https://huggingface.co/LinWeizheDragon/FLMR/resolve/main/context_tokenizer/tokenizer_config.json"
	),
	},
	}
	QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP = {
	"vocab_file": {
	"LinWeizheDragon/PreFLMR_ViT-L": (
	"https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-L/resolve/main/query_tokenizer/vocab.txt"
	),
	"LinWeizheDragon/FLMR": ("https://huggingface.co/LinWeizheDragon/FLMR/resolve/main/query_tokenizer/vocab.txt"),
	},
	"tokenizer_file": {
	"LinWeizheDragon/PreFLMR_ViT-L": (
	"https://huggingface.co/LinWeizheDragon/PreFLMR_ViT-L/resolve/main/query_tokenizer/tokenizer_config.json"
	),
	"LinWeizheDragon/FLMR": (
	"https://huggingface.co/LinWeizheDragon/FLMR/resolve/main/query_tokenizer/tokenizer_config.json"
	),
	},
	}


	CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
	"LinWeizheDragon/PreFLMR_ViT-L": 512,
	"LinWeizheDragon/FLMR": 512,
	}
	QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
	"LinWeizheDragon/PreFLMR_ViT-L": 512,
	"LinWeizheDragon/FLMR": 512,
	}


	CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
	"LinWeizheDragon/PreFLMR_ViT-L": {"do_lower_case": True},
	"LinWeizheDragon/FLMR": {"do_lower_case": True},
	}
	QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION = {
	"LinWeizheDragon/PreFLMR_ViT-L": {"do_lower_case": True},
	"LinWeizheDragon/FLMR": {"do_lower_case": True},
	}


	# Modified from colbert.modeling.tokenization
	class FLMRContextEncoderTokenizer(BertTokenizer):
	r"""
	Construct a FLMRContextEncoder tokenizer.

	[`FLMRContextEncoderTokenizer`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation
	splitting and wordpiece.

	Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters.
	"""

	vocab_files_names = VOCAB_FILES_NAMES
	pretrained_vocab_files_map = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	max_model_input_sizes = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	pretrained_init_configuration = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION

	def __init__(
	self,
	doc_maxlen: Optional[int] = 512,
	**kwargs,
	):
	super().__init__(
	doc_maxlen=doc_maxlen,
	**kwargs,
	)

	self.doc_maxlen = doc_maxlen
	self.D_marker_token, self.D_marker_token_id = "[D]", self.convert_tokens_to_ids("[unused1]")

	def __call__(
	self,
	text: List[str],
	padding: Optional[Union[str, bool]] = "max_length",
	truncation: Optional[Union[bool, str]] = "longest_first",
	max_length: Optional[int] = 512,
	return_tensors: Optional[Union[str, TensorType]] = "pt",
	**kwargs,
	):
	# add placehold for the [D] marker
	text = [". " + x for x in text]

	if max_length > self.doc_maxlen:
	# can not exceed the pre-set length
	max_length = self.doc_maxlen

	encoding = super().__call__(
	text,
	padding=padding,
	truncation=truncation,
	return_tensors=return_tensors,
	max_length=max_length,
	**kwargs,
	)

	ids, mask = encoding["input_ids"], encoding["attention_mask"]

	# postprocess for the [D] marker
	ids[:, 1] = self.D_marker_token_id

	# if bsize:
	# # This bsize function is used in the original ColBERT codebase to split inputs into multiple batches
	# if image_features is not None:
	# ids, mask, image_features, reverse_indices = _sort_by_length(ids, mask, bsize, image_features=image_features)
	# batches = _split_into_batches(ids, mask, bsize, image_features=image_features)
	# else:
	# ids, mask, reverse_indices = _sort_by_length(ids, mask, bsize)
	# batches = _split_into_batches(ids, mask, bsize)

	# return batches, reverse_indices

	encoding["input_ids"] = ids
	encoding["attention_mask"] = mask

	return encoding


	# Modified from colbert.modeling.tokenization
	class FLMRQueryEncoderTokenizer(BertTokenizer):
	r"""
	Constructs a FLMRQueryEncoder tokenizer.

	[`FLMRQueryEncoder`] is identical to [`BertTokenizer`] and runs end-to-end tokenization: punctuation
	splitting and wordpiece.

	Refer to superclass [`BertTokenizer`] for usage examples and documentation concerning parameters.
	"""

	vocab_files_names = VOCAB_FILES_NAMES
	pretrained_vocab_files_map = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
	max_model_input_sizes = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	pretrained_init_configuration = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION

	def __init__(
	self,
	*args,
	query_maxlen: Optional[int] = 32,
	attend_to_mask_tokens: Optional[bool] = False,
	**kwargs,
	):
	super().__init__(
	*args,
	query_maxlen=query_maxlen,
	attend_to_mask_tokens=attend_to_mask_tokens,
	**kwargs,
	)

	self.query_maxlen = query_maxlen
	self.background_maxlen = 512 - self.query_maxlen + 1 # FIXME: Make this configurable
	self.attend_to_mask_tokens = attend_to_mask_tokens

	self.Q_marker_token, self.Q_marker_token_id = "[Q]", self.convert_tokens_to_ids("[unused0]")

	def __call__(
	self,
	text: Union[str, List[str]],
	padding: Optional[Union[str, bool]] = "max_length",
	truncation: Optional[Union[bool, str]] = True,
	max_length: Optional[int] = None,
	return_tensors: Optional[Union[str, TensorType]] = "pt",
	**kwargs,
	):
	if isinstance(text, str):
	# convert to list if input is a single string
	text = [text]

	# add placehold for the [Q] marker
	text = [". " + x for x in text]

	if max_length is not None:
	# use user specified max_length
	pass
	else:
	# use default max length
	max_length = self.query_maxlen

	encoding = super().__call__(
	text,
	padding=padding,
	truncation=truncation,
	return_tensors=return_tensors,
	max_length=max_length,
	**kwargs,
	)

	ids, mask = encoding["input_ids"], encoding["attention_mask"]

	# postprocess for the [Q] marker and the [MASK] augmentation
	ids[:, 1] = self.Q_marker_token_id
	ids[ids == self.pad_token_id] = self.mask_token_id

	if self.attend_to_mask_tokens:
	# When attend_to_mask_tokens is True, we want to attend to the [MASK] tokens
	mask[ids == self.mask_token_id] = 1
	assert mask.sum().item() == mask.size(0) * mask.size(1), mask

	return {"input_ids": ids, "attention_mask": mask}