geneformer/collator_for_cell_classification.py

"""
Geneformer collator for cell classification.

Huggingface data collator modified to accommodate single-cell transcriptomics data for cell classification.
"""
import numpy as np
import torch
import warnings
from enum import Enum
from typing import Dict, List, Optional, Union

from transformers import (
    DataCollatorForTokenClassification,
    SpecialTokensMixin,
    BatchEncoding,
)
from transformers.utils import is_tf_available, is_torch_available, logging, to_py_obj
from transformers.utils.generic import _is_tensorflow, _is_torch

from .pretrainer import token_dictionary

EncodedInput = List[int]
logger = logging.get_logger(__name__)
VERY_LARGE_INTEGER = int(
    1e30
)  # This is used to set the max input length for a model with infinite size input
LARGE_INTEGER = int(
    1e20
)  # This is used when we need something big but slightly smaller than VERY_LARGE_INTEGER

# precollator functions

def run_once(f):
    def wrapper(*args, **kwargs):
        if not wrapper.has_run:
            wrapper.has_run = True
            return f(*args, **kwargs)
    wrapper.has_run = False
    return wrapper

@run_once
def check_output_once(output):
    return print(output)

class ExplicitEnum(Enum):
    """
    Enum with more explicit error message for missing values.
    """

    @classmethod
    def _missing_(cls, value):
        raise ValueError(
            "%r is not a valid %s, please select one of %s"
            % (value, cls.__name__, str(list(cls._value2member_map_.keys())))
        )

class TruncationStrategy(ExplicitEnum):
    """
    Possible values for the ``truncation`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for
    tab-completion in an IDE.
    """

    ONLY_FIRST = "only_first"
    ONLY_SECOND = "only_second"
    LONGEST_FIRST = "longest_first"
    DO_NOT_TRUNCATE = "do_not_truncate"



class PaddingStrategy(ExplicitEnum):
    """
    Possible values for the ``padding`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for tab-completion
    in an IDE.
    """

    LONGEST = "longest"
    MAX_LENGTH = "max_length"
    DO_NOT_PAD = "do_not_pad"



class TensorType(ExplicitEnum):
    """
    Possible values for the ``return_tensors`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for
    tab-completion in an IDE.
    """

    PYTORCH = "pt"
    TENSORFLOW = "tf"
    NUMPY = "np"
    JAX = "jax"

    
class PrecollatorForCellClassification(SpecialTokensMixin):
    mask_token = "<mask>"
    mask_token_id = token_dictionary.get("<mask>")
    pad_token = "<pad>"
    pad_token_id = token_dictionary.get("<pad>")
    padding_side = "right"
    all_special_ids = [
        token_dictionary.get("<mask>"),
        token_dictionary.get("<pad>")
    ]
    model_input_names = ["input_ids"]

    def _get_padding_truncation_strategies(
        self, padding=True, truncation=False, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs
    ):
        """
        Find the correct padding/truncation strategy with backward compatibility for old arguments (truncation_strategy
        and pad_to_max_length) and behaviors.
        """
        old_truncation_strategy = kwargs.pop("truncation_strategy", "do_not_truncate")
        old_pad_to_max_length = kwargs.pop("pad_to_max_length", False)

        # Backward compatibility for previous behavior, maybe we should deprecate it:
        # If you only set max_length, it activates truncation for max_length
        if max_length is not None and padding is False and truncation is False:
            if verbose:
                if not self.deprecation_warnings.get("Truncation-not-explicitly-activated", False):
                    logger.warning(
                        "Truncation was not explicitly activated but `max_length` is provided a specific value, "
                        "please use `truncation=True` to explicitly truncate examples to max length. "
                        "Defaulting to 'longest_first' truncation strategy. "
                        "If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy "
                        "more precisely by providing a specific strategy to `truncation`."
                    )
                self.deprecation_warnings["Truncation-not-explicitly-activated"] = True
            truncation = "longest_first"

        # Get padding strategy
        if padding is False and old_pad_to_max_length:
            if verbose:
                warnings.warn(
                    "The `pad_to_max_length` argument is deprecated and will be removed in a future version, "
                    "use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or "
                    "use `padding='max_length'` to pad to a max length. In this case, you can give a specific "
                    "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the "
                    "maximal input size of the model (e.g. 512 for Bert).",
                    FutureWarning,
                )
            if max_length is None:
                padding_strategy = PaddingStrategy.LONGEST
            else:
                padding_strategy = PaddingStrategy.MAX_LENGTH
        elif padding is not False:
            if padding is True:
                padding_strategy = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
            elif not isinstance(padding, PaddingStrategy):
                padding_strategy = PaddingStrategy(padding)
            elif isinstance(padding, PaddingStrategy):
                padding_strategy = padding
        else:
            padding_strategy = PaddingStrategy.DO_NOT_PAD

        # Get truncation strategy
        if truncation is False and old_truncation_strategy != "do_not_truncate":
            if verbose:
                warnings.warn(
                    "The `truncation_strategy` argument is deprecated and will be removed in a future version, "
                    "use `truncation=True` to truncate examples to a max length. You can give a specific "
                    "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the "
                    "maximal input size of the model (e.g. 512 for Bert). "
                    " If you have pairs of inputs, you can give a specific truncation strategy selected among "
                    "`truncation='only_first'` (will only truncate the first sentence in the pairs) "
                    "`truncation='only_second'` (will only truncate the second sentence in the pairs) "
                    "or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).",
                    FutureWarning,
                )
            truncation_strategy = TruncationStrategy(old_truncation_strategy)
        elif truncation is not False:
            if truncation is True:
                truncation_strategy = (
                    TruncationStrategy.LONGEST_FIRST
                )  # Default to truncate the longest sequences in pairs of inputs
            elif not isinstance(truncation, TruncationStrategy):
                truncation_strategy = TruncationStrategy(truncation)
            elif isinstance(truncation, TruncationStrategy):
                truncation_strategy = truncation
        else:
            truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE

        # Set max length if needed
        if max_length is None:
            if padding_strategy == PaddingStrategy.MAX_LENGTH:
                if self.model_max_length > LARGE_INTEGER:
                    if verbose:
                        if not self.deprecation_warnings.get("Asking-to-pad-to-max_length", False):
                            logger.warning(
                                "Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. "
                                "Default to no padding."
                            )
                        self.deprecation_warnings["Asking-to-pad-to-max_length"] = True
                    padding_strategy = PaddingStrategy.DO_NOT_PAD
                else:
                    max_length = self.model_max_length

            if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
                if self.model_max_length > LARGE_INTEGER:
                    if verbose:
                        if not self.deprecation_warnings.get("Asking-to-truncate-to-max_length", False):
                            logger.warning(
                                "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. "
                                "Default to no truncation."
                            )
                        self.deprecation_warnings["Asking-to-truncate-to-max_length"] = True
                    truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
                else:
                    max_length = self.model_max_length

        # Test if we have a padding token
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (not self.pad_token or self.pad_token_id < 0):
            raise ValueError(
                "Asking to pad but the tokenizer does not have a padding token. "
                "Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` "
                "or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`."
            )

        # Check that we will truncate to a multiple of pad_to_multiple_of if both are provided
        if (
            truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
            and padding_strategy != PaddingStrategy.DO_NOT_PAD
            and pad_to_multiple_of is not None
            and max_length is not None
            and (max_length % pad_to_multiple_of != 0)
        ):
            raise ValueError(
                f"Truncation and padding are both activated but "
                f"truncation length ({max_length}) is not a multiple of pad_to_multiple_of ({pad_to_multiple_of})."
            )

        return padding_strategy, truncation_strategy, max_length, kwargs

    def pad(
        self,
        encoded_inputs: Union[
            BatchEncoding,
            List[BatchEncoding],
            Dict[str, EncodedInput],
            Dict[str, List[EncodedInput]],
            List[Dict[str, EncodedInput]],
        ],
        padding: Union[bool, str, PaddingStrategy] = True,
        max_length: Optional[int] = None,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        verbose: bool = True,
    ) -> BatchEncoding:
        """
        Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
        in the batch.

        Padding side (left/right) padding token ids are defined at the tokenizer level (with ``self.padding_side``,
        ``self.pad_token_id`` and ``self.pad_token_type_id``)

        .. note::

            If the ``encoded_inputs`` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
            result will use the same type unless you provide a different tensor type with ``return_tensors``. In the
            case of PyTorch tensors, you will lose the specific device of your tensors however.

        Args:
            encoded_inputs (:class:`~transformers.BatchEncoding`, list of :class:`~transformers.BatchEncoding`, :obj:`Dict[str, List[int]]`, :obj:`Dict[str, List[List[int]]` or :obj:`List[Dict[str, List[int]]]`):
                Tokenized inputs. Can represent one input (:class:`~transformers.BatchEncoding` or :obj:`Dict[str,
                List[int]]`) or a batch of tokenized inputs (list of :class:`~transformers.BatchEncoding`, `Dict[str,
                List[List[int]]]` or `List[Dict[str, List[int]]]`) so you can use this method during preprocessing as
                well as in a PyTorch Dataloader collate function.

                Instead of :obj:`List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors),
                see the note above for the return type.
            padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
                 index) among:

                * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
                  single sequence if provided).
                * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
                  maximum acceptable input length for the model if that argument is not provided.
                * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
                  different lengths).
            max_length (:obj:`int`, `optional`):
                Maximum length of the returned list and optionally padding length (see above).
            pad_to_multiple_of (:obj:`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
                >= 7.5 (Volta).
            return_attention_mask (:obj:`bool`, `optional`):
                Whether to return the attention mask. If left to the default, will return the attention mask according
                to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.

                `What are attention masks? <../glossary.html#attention-mask>`__
            return_tensors (:obj:`str` or :class:`~transformers.tokenization_utils_base.TensorType`, `optional`):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
            verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to print more information and warnings.
        """
        # If we have a list of dicts, let's convert it in a dict of lists
        # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
        if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], (dict, BatchEncoding)):
            encoded_inputs = {key: [example[key] for example in encoded_inputs] for key in encoded_inputs[0].keys()}

        # The model's main input name, usually `input_ids`, has be passed for padding
        if self.model_input_names[0] not in encoded_inputs:
            raise ValueError(
                "You should supply an encoding or a list of encodings to this method"
                f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
            )

        required_input = encoded_inputs[self.model_input_names[0]]

        if not required_input:
            if return_attention_mask:
                encoded_inputs["attention_mask"] = []
            return encoded_inputs

        # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        first_element = required_input[0]
        if isinstance(first_element, (list, tuple)):
            # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
            index = 0
            while len(required_input[index]) == 0:
                index += 1
            if index < len(required_input):
                first_element = required_input[index][0]
        # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
        if not isinstance(first_element, (int, list, tuple)):
            if is_tf_available() and _is_tensorflow(first_element):
                return_tensors = "tf" if return_tensors is None else return_tensors
            elif is_torch_available() and _is_torch(first_element):
                return_tensors = "pt" if return_tensors is None else return_tensors
            elif isinstance(first_element, np.ndarray):
                return_tensors = "np" if return_tensors is None else return_tensors
            else:
                raise ValueError(
                    f"type of {first_element} unknown: {type(first_element)}. "
                    f"Should be one of a python, numpy, pytorch or tensorflow object."
                )

            for key, value in encoded_inputs.items():
                encoded_inputs[key] = to_py_obj(value)

        # Convert padding_strategy in PaddingStrategy
        padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
            padding=padding, max_length=max_length, verbose=verbose
        )

        required_input = encoded_inputs[self.model_input_names[0]]
        if required_input and not isinstance(required_input[0], (list, tuple)):
            encoded_inputs = self._pad(
                encoded_inputs,
                max_length=max_length,
                padding_strategy=padding_strategy,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
            )
            return BatchEncoding(encoded_inputs, tensor_type=return_tensors)

        batch_size = len(required_input)
        assert all(
            len(v) == batch_size for v in encoded_inputs.values()
        ), "Some items in the output dictionary have a different batch size than others."

        if padding_strategy == PaddingStrategy.LONGEST:
            max_length = max(len(inputs) for inputs in required_input)
            padding_strategy = PaddingStrategy.MAX_LENGTH

        batch_outputs = {}
        for i in range(batch_size):
            inputs = dict((k, v[i]) for k, v in encoded_inputs.items())
            outputs = self._pad(
                inputs,
                max_length=max_length,
                padding_strategy=padding_strategy,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
            )

            for key, value in outputs.items():
                if key not in batch_outputs:
                    batch_outputs[key] = []
                batch_outputs[key].append(value)
        del batch_outputs["label"]
        return BatchEncoding(batch_outputs, tensor_type=return_tensors)

    def _pad(
        self,
        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
        max_length: Optional[int] = None,
        padding_strategy: PaddingStrategy = PaddingStrategy.LONGEST,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = True,
    ) -> dict:
        """
        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)

        Args:
            encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
            max_length: maximum length of the returned list and optionally padding length (see below).
                Will truncate by taking into account the special tokens.
            padding_strategy: PaddingStrategy to use for padding.

                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
                - PaddingStrategy.DO_NOT_PAD: Do not pad
                The tokenizer padding sides are defined in self.padding_side:

                    - 'left': pads on the left of the sequences
                    - 'right': pads on the right of the sequences
            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
                >= 7.5 (Volta).
            return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
        """
        # Load from model defaults
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        required_input = encoded_inputs[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
            max_length = len(required_input)

        if max_length is not None and 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

        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length

        if needs_to_be_padded:
            difference = max_length - len(required_input)
            if self.padding_side == "right":
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [1] * len(required_input) + [0] * difference
                if "token_type_ids" in encoded_inputs:
                    encoded_inputs["token_type_ids"] = (
                        encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference
                    )
                if "special_tokens_mask" in encoded_inputs:
                    encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
                encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
            elif self.padding_side == "left":
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [0] * difference + [1] * len(required_input)
                if "token_type_ids" in encoded_inputs:
                    encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
                        "token_type_ids"
                    ]
                if "special_tokens_mask" in encoded_inputs:
                    encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
                encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
            else:
                raise ValueError("Invalid padding strategy:" + str(self.padding_side))
        elif return_attention_mask and "attention_mask" not in encoded_inputs:
            encoded_inputs["attention_mask"] = [1] * len(required_input)
        
#         check_output_once(encoded_inputs)
        
        return encoded_inputs

    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        """
        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.
        Args:
            token_ids_0 (:obj:`List[int]`):
                List of ids of the first sequence.
            token_ids_1 (:obj:`List[int]`, `optional`):
                List of ids of the second sequence.
            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not the token list is already formatted with special tokens for the model.
        Returns:
            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        assert already_has_special_tokens and token_ids_1 is None, (
            "You cannot use ``already_has_special_tokens=False`` with this tokenizer. "
            "Please use a slow (full python) tokenizer to activate this argument."
            "Or set `return_special_tokens_mask=True` when calling the encoding method "
            "to get the special tokens mask in any tokenizer. "
        )

        all_special_ids = self.all_special_ids  # cache the property

        special_tokens_mask = [1 if token in all_special_ids else 0 for token in token_ids_0]

        return special_tokens_mask

    def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
        """
        Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
        vocabulary.
        Args:
            tokens (:obj:`str` or :obj:`List[str]`): One or several token(s) to convert to token id(s).
        Returns:
            :obj:`int` or :obj:`List[int]`: The token id or list of token ids.
        """
        if tokens is None:
            return None

        if isinstance(tokens, str):
            return self._convert_token_to_id_with_added_voc(tokens)

        ids = []
        for token in tokens:
            ids.append(self._convert_token_to_id_with_added_voc(token))
        return ids

    def _convert_token_to_id_with_added_voc(self, token):
        if token is None:
            return None

        return token_dictionary.get(token)

    def __len__(self):
        return len(token_dictionary)


# collator functions

class DataCollatorForCellClassification(DataCollatorForTokenClassification):
    """
    Data collator that will dynamically pad the inputs received, as well as the labels.
    Args:
        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
            The tokenizer used for encoding the data.
        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
            among:
            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
              sequence if provided).
            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
              maximum acceptable input length for the model if that argument is not provided.
            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
              different lengths).
        max_length (:obj:`int`, `optional`):
            Maximum length of the returned list and optionally padding length (see above).
        pad_to_multiple_of (:obj:`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 >=
            7.5 (Volta).
        label_pad_token_id (:obj:`int`, `optional`, defaults to -100):
            The id to use when padding the labels (-100 will be automatically ignore by PyTorch loss functions).
    """

    tokenizer: PrecollatorForCellClassification()
    padding: Union[bool, str, PaddingStrategy] = True
    max_length: Optional[int] = None
    pad_to_multiple_of: Optional[int] = None
    label_pad_token_id: int = -100

    def __call__(self, features):
        label_name = "label" if "label" in features[0].keys() else "labels"
        labels = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
        batch = self.tokenizer.pad(
            features,
            padding=self.padding,
            max_length=self.max_length,
            pad_to_multiple_of=self.pad_to_multiple_of,
            return_tensors="pt",
        )
        
        # 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.)
        first = features[0]
        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)

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