image-match/python/py/Lib/site-packages/transformers/data/data_collator.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 multiprocessing as mp
import warnings
from collections.abc import Callable, Mapping
from dataclasses import dataclass
from random import randint
from typing import Any

import numpy as np

from ..tokenization_utils_base import PreTrainedTokenizerBase
from ..utils import PaddingStrategy


InputDataClass = Any

"""
A DataCollator is a function that takes a list of samples from a Dataset and collate them into a batch, as a dictionary
of PyTorch tensors or NumPy arrays.
"""
DataCollator = Callable[[list[InputDataClass]], dict[str, Any]]


class DataCollatorMixin:
    def __call__(self, features, return_tensors: str | None = None):
        if return_tensors is None:
            return_tensors = self.return_tensors
        if return_tensors == "pt":
            return self.torch_call(features)
        elif return_tensors == "np":
            return self.numpy_call(features)
        else:
            raise ValueError(f"Framework '{return_tensors}' not recognized!")


def pad_without_fast_tokenizer_warning(tokenizer, *pad_args, **pad_kwargs):
    """
    Pads without triggering the warning about how using the pad function is sub-optimal when using a fast tokenizer.
    """

    # To avoid errors when using Feature extractors
    if not hasattr(tokenizer, "deprecation_warnings"):
        return tokenizer.pad(*pad_args, **pad_kwargs)

    # Save the state of the warning, then disable it
    warning_state = tokenizer.deprecation_warnings.get("Asking-to-pad-a-fast-tokenizer", False)
    tokenizer.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True

    try:
        padded = tokenizer.pad(*pad_args, **pad_kwargs)
    finally:
        # Restore the state of the warning.
        tokenizer.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = warning_state

    return padded


def default_data_collator(features: list[InputDataClass], return_tensors="pt") -> dict[str, Any]:
    """
    Very simple data collator that simply collates batches of dict-like objects and performs special handling for
    potential keys named:

        - `label`: handles a single value (int or float) per object
        - `label_ids`: handles a list of values per object

    Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
    to the model. See glue and ner for example of how it's useful.
    """

    # In this function we'll make the assumption that all `features` in the batch
    # have the same attributes.
    # So we will look at the first element as a proxy for what attributes exist
    # on the whole batch.

    if return_tensors == "pt":
        return torch_default_data_collator(features)
    elif return_tensors == "np":
        return numpy_default_data_collator(features)


@dataclass
class DefaultDataCollator(DataCollatorMixin):
    """
    Very simple data collator that simply collates batches of dict-like objects and performs special handling for
    potential keys named:

        - `label`: handles a single value (int or float) per object
        - `label_ids`: handles a list of values per object

    Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
    to the model. See glue and ner for example of how it's useful.

    This is an object (like other data collators) rather than a pure function like default_data_collator. This can be
    helpful if you need to set a return_tensors value at initialization.

    Args:
        return_tensors (`str`, *optional*, defaults to `"pt"`):
            The type of Tensor to return. Allowable values are "np", or "pt".
    """

    return_tensors: str = "pt"

    def __call__(self, features: list[dict[str, Any]], return_tensors=None) -> dict[str, Any]:
        if return_tensors is None:
            return_tensors = self.return_tensors
        return default_data_collator(features, return_tensors)


def torch_default_data_collator(features: list[InputDataClass]) -> dict[str, Any]:
    import torch

    if not isinstance(features[0], Mapping):
        features = [vars(f) for f in features]
    first = features[0]
    batch = {}

    # Special handling for labels.
    # Ensure that tensor is created with the correct type
    # (it should be automatically the case, but let's make sure of it.)
    if "label" in first and first["label"] is not None:
        label = first["label"].item() if isinstance(first["label"], torch.Tensor) else first["label"]
        dtype = torch.long if isinstance(label, int) else torch.float
        batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype)
    elif "label_ids" in first and first["label_ids"] is not None:
        if isinstance(first["label_ids"], torch.Tensor):
            batch["labels"] = torch.stack([f["label_ids"] for f in features])
        else:
            dtype = torch.long if isinstance(first["label_ids"][0], int) else torch.float
            batch["labels"] = torch.tensor([f["label_ids"] for f in features], dtype=dtype)

    # Handling of all other possible keys.
    # Again, we will use the first element to figure out which key/values are not None for this model.
    for k, v in first.items():
        if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
            if isinstance(v, torch.Tensor):
                batch[k] = torch.stack([f[k] for f in features])
            elif isinstance(v, np.ndarray):
                batch[k] = torch.from_numpy(np.stack([f[k] for f in features]))
            else:
                batch[k] = torch.tensor([f[k] for f in features])

    return batch


def numpy_default_data_collator(features: list[InputDataClass]) -> dict[str, Any]:
    if not isinstance(features[0], Mapping):
        features = [vars(f) for f in features]
    first = features[0]
    batch = {}

    # Special handling for labels.
    # Ensure that tensor is created with the correct type
    # (it should be automatically the case, but let's make sure of it.)
    if "label" in first and first["label"] is not None:
        label = first["label"].item() if isinstance(first["label"], np.ndarray) else first["label"]
        dtype = np.int64 if isinstance(label, int) else np.float32
        batch["labels"] = np.array([f["label"] for f in features], dtype=dtype)
    elif "label_ids" in first and first["label_ids"] is not None:
        if isinstance(first["label_ids"], np.ndarray):
            batch["labels"] = np.stack([f["label_ids"] for f in features])
        else:
            dtype = np.int64 if isinstance(first["label_ids"][0], int) else np.float32
            batch["labels"] = np.array([f["label_ids"] for f in features], dtype=dtype)

    # Handling of all other possible keys.
    # Again, we will use the first element to figure out which key/values are not None for this model.
    for k, v in first.items():
        if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
            if isinstance(v, np.ndarray):
                batch[k] = np.stack([f[k] for f in features])
            else:
                batch[k] = np.array([f[k] for f in features])

    return batch


@dataclass
class DataCollatorWithPadding:
    """
    Data collator that will dynamically pad the inputs received.

    Args:
        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
            The tokenizer used for encoding the data.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:

            - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
              sequence is provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
        max_length (`int`, *optional*):
            Maximum length of the returned list and optionally padding length (see above).
        pad_to_multiple_of (`int`, *optional*):
            If set will pad the sequence to a multiple of the provided value.

            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.0 (Volta).
        return_tensors (`str`, *optional*, defaults to `"pt"`):
            The type of Tensor to return. Allowable values are "np", or "pt".
    """

    tokenizer: PreTrainedTokenizerBase
    padding: bool | str | PaddingStrategy = True
    max_length: int | None = None
    pad_to_multiple_of: int | None = None
    return_tensors: str = "pt"

    def __call__(self, features: list[dict[str, Any]]) -> dict[str, Any]:
        batch = pad_without_fast_tokenizer_warning(
            self.tokenizer,
            features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=self.return_tensors,
        )
        if "label" in batch:
            batch["labels"] = batch["label"]
            del batch["label"]
        if "label_ids" in batch:
            batch["labels"] = batch["label_ids"]
            del batch["label_ids"]
        return batch


@dataclass
class DataCollatorForTokenClassification(DataCollatorMixin):
    """
    Data collator that will dynamically pad the inputs received, as well as the labels.

    Args:
        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
            The tokenizer used for encoding the data.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:

            - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
              sequence is provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
        max_length (`int`, *optional*):
            Maximum length of the returned list and optionally padding length (see above).
        pad_to_multiple_of (`int`, *optional*):
            If set will pad the sequence to a multiple of the provided value.

            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.0 (Volta).
        label_pad_token_id (`int`, *optional*, defaults to -100):
            The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
        return_tensors (`str`, *optional*, defaults to `"pt"`):
            The type of Tensor to return. Allowable values are "np", or "pt".
    """

    tokenizer: PreTrainedTokenizerBase
    padding: bool | str | PaddingStrategy = True
    max_length: int | None = None
    pad_to_multiple_of: int | None = None
    label_pad_token_id: int = -100
    return_tensors: str = "pt"

    def torch_call(self, features):
        import torch

        label_name = "label" if "label" in features[0] else "labels"
        labels = [feature[label_name] for feature in features] if label_name in features[0] else None

        no_labels_features = [{k: v for k, v in feature.items() if k != label_name} for feature in features]

        batch = pad_without_fast_tokenizer_warning(
            self.tokenizer,
            no_labels_features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )

        if labels is None:
            return batch

        sequence_length = batch["input_ids"].shape[1]
        padding_side = self.tokenizer.padding_side

        def to_list(tensor_or_iterable):
            if isinstance(tensor_or_iterable, torch.Tensor):
                return tensor_or_iterable.tolist()
            return list(tensor_or_iterable)

        if padding_side == "right":
            batch[label_name] = [
                to_list(label) + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels
            ]
        else:
            batch[label_name] = [
                [self.label_pad_token_id] * (sequence_length - len(label)) + to_list(label) for label in labels
            ]

        batch[label_name] = torch.tensor(batch[label_name], dtype=torch.int64)
        return batch

    def numpy_call(self, features):
        label_name = "label" if "label" in features[0] else "labels"
        labels = [feature[label_name] for feature in features] if label_name in features[0] else None
        batch = pad_without_fast_tokenizer_warning(
            self.tokenizer,
            features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            # Conversion to tensors will fail if we have labels as they are not of the same length yet.
            return_tensors="np" if labels is None else None,
        )

        if labels is None:
            return batch

        sequence_length = np.array(batch["input_ids"]).shape[1]
        padding_side = self.tokenizer.padding_side
        if padding_side == "right":
            batch["labels"] = [
                list(label) + [self.label_pad_token_id] * (sequence_length - len(label)) for label in labels
            ]
        else:
            batch["labels"] = [
                [self.label_pad_token_id] * (sequence_length - len(label)) + list(label) for label in labels
            ]

        batch = {k: np.array(v, dtype=np.int64) for k, v in batch.items()}
        return batch


def _torch_collate_batch(examples, tokenizer, pad_to_multiple_of: int | None = None):
    """Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
    import torch

    # Tensorize if necessary.
    if isinstance(examples[0], (list, tuple, np.ndarray)):
        examples = [torch.tensor(e, dtype=torch.long) for e in examples]

    length_of_first = examples[0].size(0)

    # Check if padding is necessary.

    are_tensors_same_length = all(x.size(0) == length_of_first for x in examples)
    if are_tensors_same_length and (pad_to_multiple_of is None or length_of_first % pad_to_multiple_of == 0):
        if not isinstance(examples, torch.Tensor):
            return torch.stack(examples, dim=0)

    # If yes, check if we have a `pad_token`.
    if tokenizer.pad_token is None:
        raise ValueError(
            "You are attempting to pad samples but the tokenizer you are using"
            f" ({tokenizer.__class__.__name__}) does not have a pad token."
        )

    # Creating the full tensor and filling it with our data.
    max_length = max(x.size(0) for x in examples)
    if pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
    result = examples[0].new_full([len(examples), max_length], tokenizer.pad_token_id)
    for i, example in enumerate(examples):
        if tokenizer.padding_side == "right":
            result[i, : example.shape[0]] = example
        else:
            result[i, -example.shape[0] :] = example
    return result


def _numpy_collate_batch(examples, tokenizer, pad_to_multiple_of: int | None = None):
    """Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary."""
    # Tensorize if necessary.
    if isinstance(examples[0], (list, tuple)):
        examples = [np.array(e, dtype=np.int64) for e in examples]

    # Check if padding is necessary.
    length_of_first = len(examples[0])
    are_tensors_same_length = all(len(x) == length_of_first for x in examples)
    if are_tensors_same_length and (pad_to_multiple_of is None or length_of_first % pad_to_multiple_of == 0):
        return np.stack(examples, axis=0)

    # If yes, check if we have a `pad_token`.
    if tokenizer.pad_token is None:
        raise ValueError(
            "You are attempting to pad samples but the tokenizer you are using"
            f" ({tokenizer.__class__.__name__}) does not have a pad token."
        )

    # Creating the full tensor and filling it with our data.
    max_length = max(len(x) for x in examples)
    if pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
    result = np.full(shape=(len(examples), max_length), fill_value=tokenizer.pad_token_id, dtype=examples[0].dtype)
    for i, example in enumerate(examples):
        if tokenizer.padding_side == "right":
            result[i, : example.shape[0]] = example
        else:
            result[i, -example.shape[0] :] = example
    return result


@dataclass
class DataCollatorForMultipleChoice(DataCollatorMixin):
    """
    Data collator that dynamically pads a batch of nested examples for multiple choice, so that all choices
    of all examples have the same length.

    Args:
        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
            The tokenizer used for encoding the data.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
            Select a strategy to pad the returned sequences according to the model's padding side and padding index
            among:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              is provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        max_length (`int`, *optional*):
            Maximum length of the returned list and optionally padding length (see above).
        pad_to_multiple_of (`int`, *optional*):
            Pad the sequence to a multiple of the provided value.

            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.5 (Volta).
        return_tensors (`str`, *optional*, defaults to `"pt"`):
            The type of Tensor to return. Allowable values are "np", or "pt".
    """

    tokenizer: PreTrainedTokenizerBase
    padding: bool | str | PaddingStrategy = True
    max_length: int | None = None
    pad_to_multiple_of: int | None = None
    return_tensors: str = "pt"

    def torch_call(self, examples: list[dict[str, Any]]):  # Refactored implementation from the docs.
        import torch

        # Take labels out of the examples beforehand, because they aren't nested.
        label_name = "label" if "label" in examples[0] else "labels"
        labels = [example.pop(label_name) for example in examples]

        batch_size = len(examples)
        num_choices = len(examples[0]["input_ids"])

        # Go from e.g. 2 examples of 2 choices [{input_ids: [[1], [2]]}, {input_ids: [[3], [4]]}]
        # to 4 examples [{input_ids: [1]}, {input_ids: [2]}] + [{input_ids: [3]}, {input_ids: [4]}]
        flat_examples = sum(
            ([{k: v[i] for k, v in example.items()} for i in range(num_choices)] for example in examples), start=[]
        )

        # Pad all choices of all examples as if you're padding any other batch of examples.
        batch = self.tokenizer.pad(
            flat_examples,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )

        # Reshape from B*C x L into B x C x L, and add the labels back in.
        batch = {k: v.view(batch_size, num_choices, -1) for k, v in batch.items()}
        batch["labels"] = torch.tensor(labels, dtype=torch.int64)
        return batch


@dataclass
class DataCollatorForSeq2Seq:
    """
    Data collator that will dynamically pad the inputs received, as well as the labels.

    Args:
        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
            The tokenizer used for encoding the data.
        model ([`PreTrainedModel`], *optional*):
            The model that is being trained. If set and has the *prepare_decoder_input_ids_from_labels*, use it to
            prepare the *decoder_input_ids*

            This is useful when using *label_smoothing* to avoid calculating loss twice.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:

            - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
              sequence is provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
        max_length (`int`, *optional*):
            Maximum length of the returned list and optionally padding length (see above).
        pad_to_multiple_of (`int`, *optional*):
            If set will pad the sequence to a multiple of the provided value.

            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
            7.0 (Volta).
        label_pad_token_id (`int`, *optional*, defaults to -100):
            The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
        return_tensors (`str`, *optional*, defaults to `"pt"`):
            The type of Tensor to return. Allowable values are "np", or "pt".
    """

    tokenizer: PreTrainedTokenizerBase
    model: Any | None = None
    padding: bool | str | PaddingStrategy = True
    max_length: int | None = None
    pad_to_multiple_of: int | None = None
    label_pad_token_id: int = -100
    return_tensors: str = "pt"

    def __call__(self, features, return_tensors=None):
        if return_tensors is None:
            return_tensors = self.return_tensors

        label_name = "label" if "label" in features[0] else "labels"
        labels = [feature[label_name] for feature in features] if label_name in features[0] else None
        # reconvert list[None] to None if necessary
        # this might occur when we pass {..., "labels": None}
        if labels is not None and all(label is None for label in labels):
            labels = None
        non_labels_features = [{k: v for k, v in feature.items() if k != label_name} for feature in features]

        # run through tokenizer without labels to ensure no side effects
        batch = pad_without_fast_tokenizer_warning(
            self.tokenizer,
            non_labels_features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors=return_tensors,
        )

        # we have to pad the labels manually as we cannot rely on `tokenizer.pad` and we need them to be of the same length to return tensors
        no_padding = self.padding is False or self.padding == PaddingStrategy.DO_NOT_PAD
        if labels is not None:
            if no_padding:
                if isinstance(features[0][label_name], list):
                    batch["labels"] = list(labels)
                else:
                    batch["labels"] = [np.concatenate([label, []]) for label in labels]
            else:
                max_padding = self.padding == PaddingStrategy.MAX_LENGTH and self.max_length is not None
                max_label_length = max(len(l) for l in labels) if not max_padding else self.max_length
                if self.pad_to_multiple_of is not None:
                    max_label_length = (
                        (max_label_length + self.pad_to_multiple_of - 1)
                        // self.pad_to_multiple_of
                        * self.pad_to_multiple_of
                    )

                padding_side = self.tokenizer.padding_side
                if isinstance(features[0][label_name], list):
                    batch["labels"] = [
                        label + [self.label_pad_token_id] * (max_label_length - len(label))
                        if padding_side == "right"
                        else [self.label_pad_token_id] * (max_label_length - len(label)) + label
                        for label in labels
                    ]
                else:
                    batch["labels"] = [
                        np.concatenate(
                            [
                                label,
                                np.array([self.label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),
                            ]
                        )
                        if padding_side == "right"
                        else np.concatenate(
                            [
                                np.array([self.label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),
                                label,
                            ]
                        )
                        for label in labels
                    ]

        # reintroduce side effects via tokenizer that return respective datatypes for the `return_tensors` argument
        if batch.get("labels", None) is not None:
            if return_tensors == "pt":
                import torch

                batch["labels"] = torch.tensor(batch["labels"], dtype=torch.int64)
            else:
                batch["labels"] = np.array(batch["labels"], dtype=np.int64)
        else:
            batch["labels"] = None

        # prepare decoder_input_ids
        if (
            labels is not None
            and self.model is not None
            and hasattr(self.model, "prepare_decoder_input_ids_from_labels")
        ):
            decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=batch["labels"])
            batch["decoder_input_ids"] = decoder_input_ids

        return batch


@dataclass
class DataCollatorForLanguageModeling(DataCollatorMixin):
    """
    Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they
    are not all of the same length.

    Args:
        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
            The tokenizer used for encoding the data.
        mlm (`bool`, *optional*, defaults to `True`):
            Whether or not to use masked language modeling. If set to `False`, the labels are the same as the inputs
            with the padding tokens ignored (by setting them to -100). Otherwise, the labels are -100 for non-masked
            tokens and the value to predict for the masked token.
        whole_word_mask (`bool`, *optional*, defaults to `False`):
            Whether or not to mask whole words instead of individual tokens.
        mlm_probability (`float`, *optional*, defaults to 0.15):
            The probability with which to (randomly) mask tokens in the input, when `mlm` is set to `True`.
        mask_replace_prob (`float`, *optional*, defaults to 0.8):
            The probability with which masked tokens are replaced by the tokenizer's mask token (e.g., `[MASK]`).
            Defaults to 0.8, meaning 80% of the masked tokens will be replaced with `[MASK]`.
            Only works when `mlm` is set to `True`.
        random_replace_prob (`float`, *optional*, defaults to 0.1):
            The probability with which masked tokens are replaced by random tokens from the tokenizer's vocabulary.
            Defaults to 0.1, meaning 10% of the masked tokens will be replaced with random tokens. The remaining
            masked tokens (1 - mask_replace_prob - random_replace_prob) are left unchanged.
            Only works when `mlm` is set to `True`.
        pad_to_multiple_of (`int`, *optional*):
            If set, will pad the sequence to a multiple of the provided value.
        return_tensors (`str`):
            The type of Tensor to return. Allowable values are "np", or "pt".
        seed (`int`, *optional*):
            The seed to use for the random number generator for masking. If not provided, the global RNG will be used.

    <Tip>

    For best performance, this data collator should be used with a dataset having items that are dictionaries or
    BatchEncoding, with the `"special_tokens_mask"` key, as returned by a [`PreTrainedTokenizer`] or a
    [`PreTrainedTokenizerFast`] with the argument `return_special_tokens_mask=True`.

    <Example Options and Expectations>

    1. Default Behavior:
        - `mask_replace_prob=0.8`, `random_replace_prob=0.1`.
        - Expect 80% of masked tokens replaced with `[MASK]`, 10% replaced with random tokens, and 10% left unchanged.

    2. All masked tokens replaced by `[MASK]`:
        - `mask_replace_prob=1.0`, `random_replace_prob=0.0`.
        - Expect all masked tokens to be replaced with `[MASK]`. No tokens are left unchanged or replaced with random tokens.

    3. No `[MASK]` replacement, only random tokens:
        - `mask_replace_prob=0.0`, `random_replace_prob=1.0`.
        - Expect all masked tokens to be replaced with random tokens. No `[MASK]` replacements or unchanged tokens.

    4. Balanced replacement:
        - `mask_replace_prob=0.5`, `random_replace_prob=0.4`.
        - Expect 50% of masked tokens replaced with `[MASK]`, 40% replaced with random tokens, and 10% left unchanged.

    Note:
        The sum of `mask_replace_prob` and `random_replace_prob` must not exceed 1. If their sum is less than 1, the
        remaining proportion will consist of masked tokens left unchanged.

    </Tip>
    """

    tokenizer: PreTrainedTokenizerBase
    mlm: bool = True
    whole_word_mask: bool = False
    mlm_probability: float | None = 0.15
    mask_replace_prob: float = 0.8
    random_replace_prob: float = 0.1
    pad_to_multiple_of: int | None = None
    return_tensors: str = "pt"
    seed: int | None = None

    def __post_init__(self):
        if self.mlm:
            if self.tokenizer.mask_token is None:
                raise ValueError(
                    "This tokenizer does not have a mask token which is necessary for masked language modeling. "
                    "You should pass `mlm=False` to train on causal language modeling instead."
                )
            if self.mlm_probability is None or self.mlm_probability < 0 or self.mlm_probability > 1:
                raise ValueError("mlm_probability should be between 0 and 1.")
            self.mlm_probability = float(self.mlm_probability)
        elif self.whole_word_mask:
            raise ValueError(
                "Whole word masking can only be used with mlm=True."
                "If you want to use whole word masking, please set mlm=True."
            )
        if self.mask_replace_prob + self.random_replace_prob > 1:
            raise ValueError("The sum of mask_replace_prob and random_replace_prob should not exceed 1")
        if self.mask_replace_prob < 0 or self.mask_replace_prob > 1:
            raise ValueError("mask_replace_prob should be between 0 and 1.")
        if self.random_replace_prob < 0 or self.random_replace_prob > 1:
            raise ValueError("random_replace_prob should be between 0 and 1.")

        if self.whole_word_mask:
            if not self.tokenizer.is_fast:
                warnings.warn(
                    "Whole word masking depends on offset mapping which is only natively available with fast tokenizers.",
                    UserWarning,
                )

            if self.mask_replace_prob < 1:
                warnings.warn(
                    "Random token replacement is not supported with whole word masking. "
                    "Setting mask_replace_prob to 1.",
                )
                self.mask_replace_prob = 1
                self.random_replace_prob = 0

        self.mask_replace_prob = float(self.mask_replace_prob)
        self.random_replace_prob = float(self.random_replace_prob)

        self.generator = None

    def get_generator(self, seed):
        if self.return_tensors == "pt":
            import torch

            return torch.Generator().manual_seed(seed)
        else:
            return np.random.default_rng(seed)

    def create_rng(self):
        if mp.current_process().name == "MainProcess":
            # If we are in the main process, we create a generator object with the seed
            self.generator = self.get_generator(self.seed)
        else:
            # If we are in a worker process (i.e using multiprocessing), we need to set a unique seed for each
            # worker's generator, generated as the main seed + the worker's ID.
            # (https://pytorch.org/docs/stable/data.html#randomness-in-multi-process-data-loading)
            # Only PyTorch DataLoader allows us to access the worker ID, and so we check for this.
            import torch

            worker_info = torch.utils.data.get_worker_info()
            if worker_info is None:
                error_string = (
                    "Worker process information is not available for seeding the generator. This may be because",
                    "you are using multiprocessing without using a PyTorch DataLoader. The `seed` parameter can",
                    "only be used when using multiprocessing with a PyTorch DataLoader. Please either use a",
                    "single process or use a PyTorch DataLoader with multiple workers.",
                )
                raise ValueError(error_string)

            self.generator = self.get_generator(self.seed + worker_info.id)

    def torch_call(self, examples: list[list[int] | Any | dict[str, Any]]) -> dict[str, Any]:
        # Handle dict or lists with proper padding and conversion to tensor.

        if self.seed and self.generator is None:
            # If we have a seed, we need to create a generator object. Subsequent calls to this function will use the same generator.
            # If no seed supplied, we will use the global RNG
            self.create_rng()

        if isinstance(examples[0], Mapping):
            batch = pad_without_fast_tokenizer_warning(
                self.tokenizer, examples, return_tensors="pt", pad_to_multiple_of=self.pad_to_multiple_of
            )
        else:
            batch = {
                "input_ids": _torch_collate_batch(examples, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)
            }

        # If special token mask has been preprocessed, pop it from the dict.
        special_tokens_mask = batch.pop("special_tokens_mask", None)
        offset_mapping = batch.pop("offset_mapping", None)
        if self.mlm:
            batch["input_ids"], batch["labels"] = self.torch_mask_tokens(
                batch["input_ids"], special_tokens_mask=special_tokens_mask, offset_mapping=offset_mapping
            )
        else:
            labels = batch["input_ids"].clone()
            if self.tokenizer.pad_token_id is not None:
                labels[labels == self.tokenizer.pad_token_id] = -100
            batch["labels"] = labels
        return batch

    def torch_mask_tokens(
        self, inputs: Any, special_tokens_mask: Any | None = None, offset_mapping: Any | None = None
    ) -> tuple[Any, Any]:
        """
        Prepare masked tokens inputs/labels for masked language modeling.
        """
        import torch

        labels = inputs.clone()
        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
        probability_matrix = torch.full(labels.shape, self.mlm_probability)
        if special_tokens_mask is None:
            special_tokens_mask = [
                self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
            ]

        if self.whole_word_mask:
            word_ids, no_mask_mask = self._calc_word_ids_and_prob_mask(
                to_numpy(offset_mapping), to_numpy(special_tokens_mask)
            )
            no_mask_mask = torch.tensor(no_mask_mask, dtype=torch.bool)
        else:
            no_mask_mask = (
                special_tokens_mask.bool()
                if isinstance(special_tokens_mask, torch.Tensor)
                else torch.tensor(special_tokens_mask, dtype=torch.bool)
            )

        probability_matrix.masked_fill_(no_mask_mask, value=0.0)
        masked_indices = torch.bernoulli(probability_matrix, generator=self.generator).bool()
        if self.whole_word_mask:
            masked_indices = torch.BoolTensor(self._whole_word_mask(word_ids, masked_indices))

        labels[~masked_indices] = -100  # We only compute loss on masked tokens

        # mask_replace_prob% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
        indices_replaced = (
            torch.bernoulli(torch.full(labels.shape, self.mask_replace_prob), generator=self.generator).bool()
            & masked_indices
        )
        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)

        if self.mask_replace_prob == 1 or self.random_replace_prob == 0:
            return inputs, labels

        remaining_prob = 1 - self.mask_replace_prob
        # scaling the random_replace_prob to the remaining probability for example if
        # mask_replace_prob = 0.8 and random_replace_prob = 0.1,
        # then random_replace_prob_scaled = 0.1 / 0.2 = 0.5
        random_replace_prob_scaled = self.random_replace_prob / remaining_prob

        # random_replace_prob% of the time, we replace masked input tokens with random word
        indices_random = (
            torch.bernoulli(torch.full(labels.shape, random_replace_prob_scaled), generator=self.generator).bool()
            & masked_indices
            & ~indices_replaced
        )
        random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long, generator=self.generator)
        inputs[indices_random] = random_words[indices_random]

        # The rest of the time ((1-random_replace_prob-mask_replace_prob)% of the time) we keep the masked input tokens unchanged
        return inputs, labels

    def numpy_call(self, examples: list[list[int] | Any | dict[str, Any]]) -> dict[str, Any]:
        # Handle dict or lists with proper padding and conversion to tensor.

        if self.seed and self.generator is None:
            # If we have a seed, we need to create a generator object. Subsequent calls to this function will use the same generator.
            # If no seed supplied, we will use the global RNG
            self.create_rng()

        if isinstance(examples[0], Mapping):
            batch = pad_without_fast_tokenizer_warning(
                self.tokenizer, examples, return_tensors="np", pad_to_multiple_of=self.pad_to_multiple_of
            )
        else:
            batch = {
                "input_ids": _numpy_collate_batch(examples, self.tokenizer, pad_to_multiple_of=self.pad_to_multiple_of)
            }

        # If special token mask has been preprocessed, pop it from the dict.
        special_tokens_mask = batch.pop("special_tokens_mask", None)
        offset_mapping = batch.pop("offset_mapping", None)
        if self.mlm:
            batch["input_ids"], batch["labels"] = self.numpy_mask_tokens(
                batch["input_ids"], special_tokens_mask=special_tokens_mask, offset_mapping=offset_mapping
            )
        else:
            labels = np.copy(batch["input_ids"])
            if self.tokenizer.pad_token_id is not None:
                labels[labels == self.tokenizer.pad_token_id] = -100
            batch["labels"] = labels
        return batch

    def numpy_mask_tokens(
        self,
        inputs: Any,
        special_tokens_mask: Any | None = None,
        offset_mapping: Any | None = None,
    ) -> tuple[Any, Any]:
        """
        Prepare masked tokens inputs/labels for masked language modeling.
        """
        labels = np.copy(inputs)
        # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
        probability_matrix = np.full(labels.shape, self.mlm_probability)
        if special_tokens_mask is None:
            special_tokens_mask = [
                self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
            ]

        if self.whole_word_mask:
            word_ids, no_mask_mask = self._calc_word_ids_and_prob_mask(
                to_numpy(offset_mapping), to_numpy(special_tokens_mask)
            )
        else:
            no_mask_mask = (
                special_tokens_mask.astype(bool)
                if isinstance(special_tokens_mask, np.ndarray)
                else np.array(special_tokens_mask, dtype=bool)
            )

        probability_matrix[no_mask_mask] = 0
        # Numpy doesn't have bernoulli, so we use a binomial with 1 trial
        if self.generator:
            masked_indices = self.generator.binomial(1, probability_matrix, size=probability_matrix.shape).astype(bool)
        else:
            masked_indices = np.random.binomial(1, probability_matrix, size=probability_matrix.shape).astype(bool)

        if self.whole_word_mask:
            masked_indices = self._whole_word_mask(word_ids, masked_indices)

        labels[~masked_indices] = -100  # We only compute loss on masked tokens

        # mask_replace_prob% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
        if self.generator:
            indices_replaced = (
                self.generator.binomial(1, self.mask_replace_prob, size=labels.shape).astype(bool) & masked_indices
            )
        else:
            indices_replaced = (
                np.random.binomial(1, self.mask_replace_prob, size=labels.shape).astype(bool) & masked_indices
            )
        inputs[indices_replaced] = self.tokenizer.mask_token_id

        if self.mask_replace_prob == 1 or self.random_replace_prob == 0:
            return inputs, labels

        remaining_prob = 1 - self.mask_replace_prob
        # scaling the random_replace_prob to the remaining probability for example if
        # mask_replace_prob = 0.8 and random_replace_prob = 0.1,
        # then random_replace_prob_scaled = 0.1 / 0.2 = 0.5
        random_replace_prob_scaled = self.random_replace_prob / remaining_prob
        if self.generator:
            indices_random = (
                self.generator.binomial(1, random_replace_prob_scaled, size=labels.shape).astype(bool)
                & masked_indices
                & ~indices_replaced
            )
            random_words = self.generator.integers(
                low=0, high=len(self.tokenizer), size=np.count_nonzero(indices_random), dtype=np.int64
            )
        else:
            indices_random = (
                np.random.binomial(1, random_replace_prob_scaled, size=labels.shape).astype(bool)
                & masked_indices
                & ~indices_replaced
            )
            random_words = np.random.randint(
                low=0, high=len(self.tokenizer), size=np.count_nonzero(indices_random), dtype=np.int64
            )
        inputs[indices_random] = random_words

        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
        return inputs, labels

    @staticmethod
    def _calc_word_ids_and_prob_mask(
        offsets: np.ndarray[np.ndarray[tuple[int, int]]], special_tokens_mask: np.ndarray[np.ndarray[int]]
    ) -> tuple[np.ndarray[np.ndarray[int]], np.ndarray[np.ndarray[int]]]:
        """
        Map tokens to word ids and create mask of tokens to not mask.
        Tokens that are part of the same word will have the same word id and we will only
        set a mask probability for the first token of each word.
        """

        token_starts = offsets[:, :, 0]
        token_ends = offsets[:, :, 1]

        prev_token_ends = np.roll(token_ends, 1, axis=1)
        prev_token_ends[:, 0] = -1  # First token has no previous token

        prev_token_special = np.roll(special_tokens_mask, 1, axis=1)
        prev_token_special[:, 0] = 0

        # Not special token AND (gap from previous or previous token was special)
        special_tokens_mask = special_tokens_mask.astype(bool)
        is_new_word = (~special_tokens_mask) & ((token_starts != prev_token_ends) | (prev_token_special == 1))

        word_ids = np.cumsum(is_new_word, axis=1)
        word_ids[special_tokens_mask] = -1

        prob_mask = ~is_new_word

        return word_ids, prob_mask

    @staticmethod
    def _whole_word_mask(word_ids: np.ndarray[np.ndarray[int]], mask: Any) -> Any:
        """
        Mask whole words based on word ids and mask.
        """
        mask = to_numpy(mask)

        valid_ids = word_ids != -1

        # Create 3D mask where [batch, token_i, token_j] is True if token_i and token_j are the same word
        same_word = (word_ids[:, :, None] == word_ids[:, None, :]) & valid_ids[:, :, None] & valid_ids[:, None, :]

        # For each token, set True if any token in the same word is masked
        return np.any(same_word & mask[:, None, :], axis=2)


@dataclass
class DataCollatorForWholeWordMask(DataCollatorForLanguageModeling):
    """
    Data collator used for language modeling that masks entire words.

    - collates batches of tensors, honoring their tokenizer's pad_token
    - preprocesses batches for masked language modeling
    """

    def __init__(self, *args, **kwargs):
        warnings.warn(
            "DataCollatorForWholeWordMask is deprecated and will be removed in a future version, you can now use "
            "DataCollatorForLanguageModeling with whole_word_mask=True instead.",
            FutureWarning,
        )
        super().__init__(*args, **kwargs)
        self.mlm = True  # Force masked language modeling
        self.whole_word_mask = True  # Force whole word masking


def tolist(x) -> list[Any]:
    if isinstance(x, list):
        return x
    elif hasattr(x, "numpy"):
        x = x.numpy()
    return x.tolist()


def to_numpy(x) -> np.ndarray[Any]:
    if isinstance(x, np.ndarray):
        return x
    elif hasattr(x, "detach"):
        return x.detach().cpu().numpy()
    else:
        return np.array(x)


@dataclass
class DataCollatorForSOP(DataCollatorForLanguageModeling):
    """
    Data collator used for sentence order prediction task.

    - collates batches of tensors, honoring their tokenizer's pad_token
    - preprocesses batches for both masked language modeling and sentence order prediction
    """

    def __init__(self, *args, **kwargs):
        warnings.warn(
            "DataCollatorForSOP is deprecated and will be removed in a future version, you can now use "
            "DataCollatorForLanguageModeling instead.",
            FutureWarning,
        )

    def __call__(self, examples: list[dict[str, Any]]) -> dict[str, Any]:
        import torch
        from torch.nn.utils.rnn import pad_sequence

        input_ids = [example["input_ids"] for example in examples]
        input_ids = _torch_collate_batch(input_ids, self.tokenizer)
        input_ids, labels, attention_mask = self.mask_tokens(input_ids)

        token_type_ids = [example["token_type_ids"] for example in examples]
        # size of segment_ids varied because randomness, padding zero to the end as the original implementation
        token_type_ids = pad_sequence(token_type_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id)

        sop_label_list = [example["sentence_order_label"] for example in examples]
        sentence_order_label = torch.stack(sop_label_list)

        return {
            "input_ids": input_ids,
            "labels": labels,
            "attention_mask": attention_mask,
            "token_type_ids": token_type_ids,
            "sentence_order_label": sentence_order_label,
        }

    def mask_tokens(self, inputs: Any) -> tuple[Any, Any, Any]:
        """
        Prepare masked tokens inputs/labels/attention_mask for masked language modeling: 80% MASK, 10% random, 10%
        original. N-gram not applied yet.
        """
        import torch

        if self.tokenizer.mask_token is None:
            raise ValueError(
                "This tokenizer does not have a mask token which is necessary for masked language modeling. Remove the"
                " --mlm flag if you want to use this tokenizer."
            )

        labels = inputs.clone()
        # We sample a few tokens in each sequence for masked-LM training (with probability args.mlm_probability defaults to 0.15 in Bert/RoBERTa)
        probability_matrix = torch.full(labels.shape, self.mlm_probability)
        special_tokens_mask = [
            self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
        ]
        probability_matrix.masked_fill_(torch.tensor(special_tokens_mask, dtype=torch.bool), value=0.0)
        if self.tokenizer.pad_token is not None:
            padding_mask = labels.eq(self.tokenizer.pad_token_id)
            probability_matrix.masked_fill_(padding_mask, value=0.0)
        masked_indices = torch.bernoulli(probability_matrix).bool()
        # probability be `1` (masked), however in albert model attention mask `0` means masked, revert the value
        attention_mask = (~masked_indices).float()
        if self.tokenizer.pad_token is not None:
            attention_padding_mask = labels.eq(self.tokenizer.pad_token_id)
            attention_mask.masked_fill_(attention_padding_mask, value=1.0)
        labels[~masked_indices] = -100  # We only compute loss on masked tokens, -100 is default for CE compute

        # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
        indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
        inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)

        # 10% of the time, we replace masked input tokens with random word
        indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
        random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long)
        inputs[indices_random] = random_words[indices_random]

        # The rest of the time (10% of the time) we keep the masked input tokens unchanged
        return inputs, labels, attention_mask


@dataclass
class DataCollatorForPermutationLanguageModeling(DataCollatorMixin):
    """
    Data collator used for permutation language modeling.

    - collates batches of tensors, honoring their tokenizer's pad_token
    - preprocesses batches for permutation language modeling with procedures specific to XLNet
    """

    tokenizer: PreTrainedTokenizerBase
    plm_probability: float = 1 / 6
    max_span_length: int = 5  # maximum length of a span of masked tokens
    return_tensors: str = "pt"

    def torch_call(self, examples: list[list[int] | Any | dict[str, Any]]) -> dict[str, Any]:
        if isinstance(examples[0], Mapping):
            examples = [e["input_ids"] for e in examples]
        batch = _torch_collate_batch(examples, self.tokenizer)
        inputs, perm_mask, target_mapping, labels = self.torch_mask_tokens(batch)
        return {"input_ids": inputs, "perm_mask": perm_mask, "target_mapping": target_mapping, "labels": labels}

    def numpy_call(self, examples: list[list[int] | Any | dict[str, Any]]) -> dict[str, Any]:
        if isinstance(examples[0], Mapping):
            examples = [e["input_ids"] for e in examples]
        batch = _numpy_collate_batch(examples, self.tokenizer)
        inputs, perm_mask, target_mapping, labels = self.numpy_mask_tokens(batch)
        return {"input_ids": inputs, "perm_mask": perm_mask, "target_mapping": target_mapping, "labels": labels}

    def torch_mask_tokens(self, inputs: Any) -> tuple[Any, Any, Any, Any]:
        """
        The masked tokens to be predicted for a particular sequence are determined by the following algorithm:

            0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
            1. Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
            2. Reserve a context of length `context_length = span_length / plm_probability` to surround span to be
               masked
            3. Sample a starting point `start_index` from the interval `[cur_len, cur_len + context_length -
               span_length]` and mask tokens `start_index:start_index + span_length`
            4. Set `cur_len = cur_len + context_length`. If `cur_len < max_len` (i.e. there are tokens remaining in the
               sequence to be processed), repeat from Step 1.
        """
        import torch

        if self.tokenizer.mask_token is None:
            raise ValueError(
                "This tokenizer does not have a mask token which is necessary for permutation language modeling."
                " Please add a mask token if you want to use this tokenizer."
            )

        if inputs.size(1) % 2 != 0:
            raise ValueError(
                "This collator requires that sequence lengths be even to create a leakage-free perm_mask. Please see"
                " relevant comments in source code for details."
            )

        labels = inputs.clone()
        # Creating the mask and target_mapping tensors
        masked_indices = torch.full(labels.shape, 0, dtype=torch.bool)
        target_mapping = torch.zeros((labels.size(0), labels.size(1), labels.size(1)), dtype=torch.float32)

        for i in range(labels.size(0)):
            # Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
            cur_len = 0
            max_len = labels.size(1)

            while cur_len < max_len:
                # Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
                span_length = torch.randint(1, self.max_span_length + 1, (1,)).item()
                # Reserve a context of length `context_length = span_length / plm_probability` to surround the span to be masked
                context_length = int(span_length / self.plm_probability)
                # Sample a starting point `start_index` from the interval `[cur_len, cur_len + context_length - span_length]` and mask tokens `start_index:start_index + span_length`
                start_index = cur_len + torch.randint(context_length - span_length + 1, (1,)).item()
                masked_indices[i, start_index : start_index + span_length] = 1
                # Set `cur_len = cur_len + context_length`
                cur_len += context_length

            # Since we're replacing non-masked tokens with -100 in the labels tensor instead of skipping them altogether,
            # the i-th predict corresponds to the i-th token.
            target_mapping[i] = torch.eye(labels.size(1))

        special_tokens_mask = torch.tensor(
            [self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()],
            dtype=torch.bool,
        )
        masked_indices.masked_fill_(special_tokens_mask, value=0.0)
        if self.tokenizer.pad_token is not None:
            padding_mask = labels.eq(self.tokenizer.pad_token_id)
            masked_indices.masked_fill_(padding_mask, value=0.0)

        # Mask indicating non-functional tokens, where functional tokens are [SEP], [CLS], padding, etc.
        non_func_mask = ~(padding_mask | special_tokens_mask)

        inputs[masked_indices] = self.tokenizer.mask_token_id
        labels[~masked_indices] = -100  # We only compute loss on masked tokens

        perm_mask = torch.zeros((labels.size(0), labels.size(1), labels.size(1)), dtype=torch.float32)

        for i in range(labels.size(0)):
            # Generate permutation indices i.e. sample a random factorisation order for the sequence. This will
            # determine which tokens a given token can attend to (encoded in `perm_mask`).
            # Note: Length of token sequence being permuted has to be less than or equal to reused sequence length
            # (see documentation for `mems`), otherwise information may leak through due to reuse. In this implementation,
            # we assume that reused length is half of sequence length and permutation length is equal to reused length.
            # This requires that the sequence length be even.

            # Create a linear factorisation order
            perm_index = torch.arange(labels.size(1))
            # Split this into two halves, assuming that half the sequence is reused each time
            perm_index = perm_index.reshape((-1, labels.size(1) // 2)).transpose(0, 1)
            # Permute the two halves such that they do not cross over
            perm_index = perm_index[torch.randperm(labels.size(1) // 2)]
            # Flatten this out into the desired permuted factorisation order
            perm_index = torch.flatten(perm_index.transpose(0, 1))
            # Set the permutation indices of non-masked (non-functional) tokens to the
            # smallest index (-1) so that:
            # (1) They can be seen by all other positions
            # (2) They cannot see masked positions, so there won't be information leak
            perm_index.masked_fill_(~masked_indices[i] & non_func_mask[i], -1)
            # The logic for whether the i-th token can attend on the j-th token based on the factorisation order:
            # 0 (can attend): If perm_index[i] > perm_index[j] or j is neither masked nor a functional token
            # 1 (cannot attend): If perm_index[i] <= perm_index[j] and j is either masked or a functional token
            perm_mask[i] = (
                perm_index.reshape((labels.size(1), 1)) <= perm_index.reshape((1, labels.size(1)))
            ) & masked_indices[i]

        return inputs.long(), perm_mask, target_mapping, labels.long()

    def numpy_mask_tokens(self, inputs: Any) -> tuple[Any, Any, Any, Any]:
        """
        The masked tokens to be predicted for a particular sequence are determined by the following algorithm:

            0. Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
            1. Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
            2. Reserve a context of length `context_length = span_length / plm_probability` to surround span to be
               masked
            3. Sample a starting point `start_index` from the interval `[cur_len, cur_len + context_length -
               span_length]` and mask tokens `start_index:start_index + span_length`
            4. Set `cur_len = cur_len + context_length`. If `cur_len < max_len` (i.e. there are tokens remaining in the
               sequence to be processed), repeat from Step 1.
        """
        if self.tokenizer.mask_token is None:
            raise ValueError(
                "This tokenizer does not have a mask token which is necessary for permutation language modeling."
                " Please add a mask token if you want to use this tokenizer."
            )

        if inputs.shape[1] % 2 != 0:
            raise ValueError(
                "This collator requires that sequence lengths be even to create a leakage-free perm_mask. Please see"
                " relevant comments in source code for details."
            )

        labels = np.copy(inputs)
        # Creating the mask and target_mapping tensors
        masked_indices = np.full(labels.shape, 0, dtype=bool)
        target_mapping = np.zeros((labels.shape[0], labels.shape[1], labels.shape[1]), dtype=np.float32)

        for i in range(labels.shape[0]):
            # Start from the beginning of the sequence by setting `cur_len = 0` (number of tokens processed so far).
            cur_len = 0
            max_len = labels.shape[1]

            while cur_len < max_len:
                # Sample a `span_length` from the interval `[1, max_span_length]` (length of span of tokens to be masked)
                span_length = randint(1, self.max_span_length + 1)
                # Reserve a context of length `context_length = span_length / plm_probability` to surround the span to be masked
                context_length = int(span_length / self.plm_probability)
                # Sample a starting point `start_index` from the interval `[cur_len, cur_len + context_length - span_length]` and mask tokens `start_index:start_index + span_length`
                start_index = cur_len + randint(0, context_length - span_length + 1)
                masked_indices[i, start_index : start_index + span_length] = 1
                # Set `cur_len = cur_len + context_length`
                cur_len += context_length

            # Since we're replacing non-masked tokens with -100 in the labels tensor instead of skipping them altogether,
            # the i-th predict corresponds to the i-th token.
            target_mapping[i] = np.eye(labels.shape[1])

        special_tokens_mask = np.array(
            [self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()],
            dtype=bool,
        )
        masked_indices[special_tokens_mask] = 0
        if self.tokenizer.pad_token is not None:
            padding_mask = labels == self.tokenizer.pad_token_id
            masked_indices[padding_mask] = 0.0

        # Mask indicating non-functional tokens, where functional tokens are [SEP], [CLS], padding, etc.
        non_func_mask = ~(padding_mask | special_tokens_mask)

        inputs[masked_indices] = self.tokenizer.mask_token_id
        labels[~masked_indices] = -100  # We only compute loss on masked tokens

        perm_mask = np.zeros((labels.shape[0], labels.shape[1], labels.shape[1]), dtype=np.float32)

        for i in range(labels.shape[0]):
            # Generate permutation indices i.e. sample a random factorisation order for the sequence. This will
            # determine which tokens a given token can attend to (encoded in `perm_mask`).
            # Note: Length of token sequence being permuted has to be less than or equal to reused sequence length
            # (see documentation for `mems`), otherwise information may leak through due to reuse. In this implementation,
            # we assume that reused length is half of sequence length and permutation length is equal to reused length.
            # This requires that the sequence length be even.

            # Create a linear factorisation order
            perm_index = np.arange(labels.shape[1])
            # Split this into two halves, assuming that half the sequence is reused each time
            perm_index = perm_index.reshape((-1, labels.shape[1] // 2)).T
            # Permute the two halves such that they do not cross over
            np.random.shuffle(perm_index)
            # Flatten this out into the desired permuted factorisation order
            perm_index = perm_index.T.flatten()
            # Set the permutation indices of non-masked (non-functional) tokens to the
            # smallest index (-1) so that:
            # (1) They can be seen by all other positions
            # (2) They cannot see masked positions, so there won't be information leak
            perm_index[~masked_indices[i] & non_func_mask[i]] = -1
            # The logic for whether the i-th token can attend on the j-th token based on the factorisation order:
            # 0 (can attend): If perm_index[i] > perm_index[j] or j is neither masked nor a functional token
            # 1 (cannot attend): If perm_index[i] <= perm_index[j] and j is either masked or a functional token
            perm_mask[i] = (
                perm_index.reshape((labels.shape[1], 1)) <= perm_index.reshape((1, labels.shape[1]))
            ) & masked_indices[i]

        return inputs.astype(np.int64), perm_mask, target_mapping, labels.astype(np.int64)


@dataclass
class DataCollatorWithFlattening(DefaultDataCollator):
    """
    Data collator used for padding free approach. Does the following:

    - concatenates the entire mini batch into single long sequence of shape [1, total_tokens]
    - uses `separator_id` to separate sequences within the concatenated `labels`, default value is -100
    - no padding will be added, returns `input_ids`, `labels` and `position_ids` by default
    - optionally returns the kwargs contained in FlashAttentionKwargs
    - optionally returns seq_idx indicating which sequence each token belongs to

    <Tip warning={true}>

    Using `DataCollatorWithFlattening` will flatten the entire mini batch into single long sequence.
    Make sure your attention computation is able to handle it!

    </Tip>
    """

    def __init__(
        self,
        *args,
        return_position_ids=True,
        separator_id=-100,
        return_flash_attn_kwargs=False,
        return_seq_idx=False,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        self.return_position_ids = return_position_ids
        self.separator_id = separator_id
        self.return_flash_attn_kwargs = return_flash_attn_kwargs
        self.return_seq_idx = return_seq_idx
        self._int_64_keys = {"labels", "position_ids", "input_ids"}
        self._batch_dim_keys = {"labels", "position_ids", "input_ids", "seq_idx"}
        self._py_int_keys = {"max_length_q", "max_length_k"}

    def __call__(self, features, return_tensors=None, separator_id=None):
        if return_tensors is None:
            return_tensors = self.return_tensors
        if separator_id is None:
            separator_id = self.separator_id
        is_labels_provided = "labels" in features[0]
        batch = {"input_ids": [], "labels": []}
        if self.return_position_ids:
            batch.update({"position_ids": []})
        if self.return_seq_idx:
            batch.update({"seq_idx": []})
        if self.return_flash_attn_kwargs:
            cu_seq_lens = [0]
            max_length = 0
        for seq_idx, sample in enumerate(features):
            input_ids = sample["input_ids"]
            # Convert to list if tensor
            if hasattr(input_ids, "tolist"):
                input_ids = input_ids.tolist()
            batch["input_ids"] += input_ids

            if is_labels_provided:
                labels = sample["labels"]
                # Convert to list if tensor
                if hasattr(labels, "tolist"):
                    labels = labels.tolist()
                batch["labels"] += [separator_id] + labels[1:]
            else:
                batch["labels"] += [separator_id] + input_ids[1:]
            if self.return_position_ids:
                batch["position_ids"] += list(range(len(input_ids)))
            if self.return_seq_idx:
                batch["seq_idx"] += [seq_idx for _ in range(len(input_ids))]
            if self.return_flash_attn_kwargs:
                cu_seq_lens.append(cu_seq_lens[-1] + len(input_ids))
                max_length = max(max_length, len(input_ids))

        if self.return_flash_attn_kwargs:
            batch["cu_seq_lens_q"] = batch["cu_seq_lens_k"] = cu_seq_lens
            batch["max_length_q"] = batch["max_length_k"] = max_length

        # FlashAttentionKwargs and seq_idx are expected to be int32s.
        if return_tensors == "pt":
            import torch

            data_cls = torch.tensor
            dtype_64 = torch.int64
            dtype_32 = torch.int32
        elif return_tensors == "np":
            data_cls = np.array
            dtype_64 = np.int64
            dtype_32 = np.int32
        else:
            raise ValueError(f'return_tensors must be one of ("pt", "np"), {return_tensors=} not supported')

        for k, v in batch.items():
            if k in self._batch_dim_keys:
                v = [v]
            # Flash attention max_len_{q,k} are python ints
            if k not in self._py_int_keys:
                batch[k] = data_cls(v, dtype=dtype_64 if k in self._int_64_keys else dtype_32)

        return batch