import random
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from random import randint
from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
import numpy as np
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.utils import PaddingStrategy
@dataclass
class MyDataCollatorForSeq2Seq:
"""
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`]):
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 >=
7.5 (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`):
The type of Tensor to return. Allowable values are "np", "pt" and "tf".
"""
tokenizer: PreTrainedTokenizerBase
model: Optional[Any] = None
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = 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
labels = [feature["labels"] for feature in features] if "labels" in features[0].keys() else None
# We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
# same length to return tensors.
if labels is not None:
max_label_length = max(len(l) for l in labels)
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
for feature in features:
remainder = [self.label_pad_token_id] * (max_label_length - len(feature["labels"]))
if isinstance(feature["labels"], list):
feature["labels"] = (
feature["labels"] + remainder if padding_side == "right" else remainder + feature["labels"]
)
elif padding_side == "right":
feature["labels"] = np.concatenate([feature["labels"], remainder]).astype(np.int64)
else:
feature["labels"] = np.concatenate([remainder, feature["labels"]]).astype(np.int64)
feature_pt = [{k: v for k, v in d.items() if k not in ['input', 'dicom', 'instruction', 'output']} for d in features]
feature_pt = self.tokenizer.pad(
feature_pt,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors=return_tensors,
)
# add "dicom" back in the dicts
feature_pt.data['dicom'] = [elem['dicom'] for elem in features]
features = feature_pt
# 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=features["labels"])
features["decoder_input_ids"] = decoder_input_ids
return features
