image-match/python/py/Lib/site-packages/transformers/data/datasets/squad.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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.

import os
import time
from dataclasses import dataclass, field
from enum import Enum

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_python import PreTrainedTokenizer
from ...utils import check_torch_load_is_safe, logging
from ..processors.squad import SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features


logger = logging.get_logger(__name__)

MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class SquadDataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    model_type: str = field(
        default=None, metadata={"help": "Model type selected in the list: " + ", ".join(MODEL_TYPES)}
    )
    data_dir: str = field(
        default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
    )
    max_seq_length: int = field(
        default=128,
        metadata={
            "help": (
                "The maximum total input sequence length after tokenization. Sequences longer "
                "than this will be truncated, sequences shorter will be padded."
            )
        },
    )
    doc_stride: int = field(
        default=128,
        metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
    )
    max_query_length: int = field(
        default=64,
        metadata={
            "help": (
                "The maximum number of tokens for the question. Questions longer than this will "
                "be truncated to this length."
            )
        },
    )
    max_answer_length: int = field(
        default=30,
        metadata={
            "help": (
                "The maximum length of an answer that can be generated. This is needed because the start "
                "and end predictions are not conditioned on one another."
            )
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    version_2_with_negative: bool = field(
        default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}
    )
    null_score_diff_threshold: float = field(
        default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
    )
    n_best_size: int = field(
        default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
    )
    lang_id: int = field(
        default=0,
        metadata={
            "help": (
                "language id of input for language-specific xlm models (see"
                " tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
            )
        },
    )
    threads: int = field(default=1, metadata={"help": "multiple threads for converting example to features"})


class Split(Enum):
    train = "train"
    dev = "dev"


class SquadDataset(Dataset):
    args: SquadDataTrainingArguments
    features: list[SquadFeatures]
    mode: Split
    is_language_sensitive: bool

    def __init__(
        self,
        args: SquadDataTrainingArguments,
        tokenizer: PreTrainedTokenizer,
        limit_length: int | None = None,
        mode: str | Split = Split.train,
        is_language_sensitive: bool = False,
        cache_dir: str | None = None,
        dataset_format: str = "pt",
    ):
        self.args = args
        self.is_language_sensitive = is_language_sensitive
        self.processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
        if isinstance(mode, str):
            try:
                mode = Split[mode]
            except KeyError:
                raise KeyError("mode is not a valid split name")
        self.mode = mode
        # Load data features from cache or dataset file
        version_tag = "v2" if args.version_2_with_negative else "v1"
        cached_features_file = os.path.join(
            cache_dir if cache_dir is not None else args.data_dir,
            f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}",
        )

        # Make sure only the first process in distributed training processes the dataset,
        # and the others will use the cache.
        lock_path = cached_features_file + ".lock"
        with FileLock(lock_path):
            if os.path.exists(cached_features_file) and not args.overwrite_cache:
                start = time.time()
                check_torch_load_is_safe()
                self.old_features = torch.load(cached_features_file, weights_only=True)

                # Legacy cache files have only features, while new cache files
                # will have dataset and examples also.
                self.features = self.old_features["features"]
                self.dataset = self.old_features.get("dataset", None)
                self.examples = self.old_features.get("examples", None)
                logger.info(
                    f"Loading features from cached file {cached_features_file} [took %.3f s]", time.time() - start
                )

                if self.dataset is None or self.examples is None:
                    logger.warning(
                        f"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
                        " future run"
                    )
            else:
                if mode == Split.dev:
                    self.examples = self.processor.get_dev_examples(args.data_dir)
                else:
                    self.examples = self.processor.get_train_examples(args.data_dir)

                self.features, self.dataset = squad_convert_examples_to_features(
                    examples=self.examples,
                    tokenizer=tokenizer,
                    max_seq_length=args.max_seq_length,
                    doc_stride=args.doc_stride,
                    max_query_length=args.max_query_length,
                    is_training=mode == Split.train,
                    threads=args.threads,
                    return_dataset=dataset_format,
                )

                start = time.time()
                torch.save(
                    {"features": self.features, "dataset": self.dataset, "examples": self.examples},
                    cached_features_file,
                )
                # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                logger.info(
                    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"
                )

    def __len__(self):
        return len(self.features)

    def __getitem__(self, i) -> dict[str, torch.Tensor]:
        # Convert to Tensors and build dataset
        feature = self.features[i]

        input_ids = torch.tensor(feature.input_ids, dtype=torch.long)
        attention_mask = torch.tensor(feature.attention_mask, dtype=torch.long)
        token_type_ids = torch.tensor(feature.token_type_ids, dtype=torch.long)
        cls_index = torch.tensor(feature.cls_index, dtype=torch.long)
        p_mask = torch.tensor(feature.p_mask, dtype=torch.float)
        is_impossible = torch.tensor(feature.is_impossible, dtype=torch.float)

        inputs = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "token_type_ids": token_type_ids,
        }

        if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
            del inputs["token_type_ids"]

        if self.args.model_type in ["xlnet", "xlm"]:
            inputs.update({"cls_index": cls_index, "p_mask": p_mask})
            if self.args.version_2_with_negative:
                inputs.update({"is_impossible": is_impossible})
            if self.is_language_sensitive:
                inputs.update({"langs": (torch.ones(input_ids.shape, dtype=torch.int64) * self.args.lang_id)})

        if self.mode == Split.train:
            start_positions = torch.tensor(feature.start_position, dtype=torch.long)
            end_positions = torch.tensor(feature.end_position, dtype=torch.long)
            inputs.update({"start_positions": start_positions, "end_positions": end_positions})

        return inputs