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from __future__ import annotations
from typing import Any
import numpy as np
import torch
from transformers import ProcessorMixin
from .preprocessing import preprocess_raw_path_to_features
class SwipeProcessor(ProcessorMixin):
"""
Processor for handling multimodal swipe inputs (path coordinates + text).
This processor combines path coordinate preprocessing with text tokenization,
creating the inputs needed for SwipeTransformer models.
Args:
tokenizer: SwipeTokenizer instance
max_path_len (int): Maximum path length. Defaults to 64.
max_char_len (int): Maximum character length. Defaults to 38.
"""
attributes = ["tokenizer"]
tokenizer_class = "AutoTokenizer" # Will use auto_map from tokenizer_config.json
def __init__(
self,
tokenizer=None,
max_path_len: int = 64,
max_char_len: int = 38,
path_input_dim: int = 6,
path_resample_mode: str = "time",
):
self.tokenizer = tokenizer
self.max_path_len = max_path_len
self.max_char_len = max_char_len
self.path_input_dim = path_input_dim
self.path_resample_mode = path_resample_mode
self.chat_template = None
def __call__(
self,
path_coords: (
list[dict[str, float]]
| list[list[dict[str, float]]]
| list[list[list[float]]]
| torch.Tensor
| np.ndarray
| None
) = None,
text: str | list[str] | None = None,
padding: bool | str = True,
truncation: bool = True,
max_length: int | None = None,
return_tensors: str | None = "pt",
**kwargs: Any,
):
"""
Process path coordinates and text into model inputs.
Args:
path_coords:
Swipe paths in one of the supported formats:
- Raw path (single example): list of dicts like `{"x": ..., "y": ..., "t": ...}`
- Raw batch: list of raw paths
- Numeric arrays/tensors: `[batch, path_len, D]` or `[path_len, D]`
If `D==3` and `path_input_dim==6`, raw `(x,y,t)` triples are converted to engineered
`(x, y, dx, dy, ds, log_dt)` features and resampled to `max_path_len`.
If omitted, the processor emits a zero path with a zero path attention mask.
text:
String or list of strings to encode.
If omitted, the processor emits padded text tokens with a zero text attention mask.
padding: Whether to pad sequences. Can be True/False or "max_length"
truncation: Whether to truncate sequences
max_length: Maximum sequence length for text (overrides max_char_len)
return_tensors: "pt" for PyTorch, "np" for NumPy, None for lists
**kwargs: Additional keyword arguments
Returns:
Dictionary with:
- path_coords: [batch, max_path_len, path_input_dim] (if path_coords provided)
Default: [batch, max_path_len, 6] for (x, y, dx, dy, ds, log_dt)
- input_ids: [batch, max_char_len] (if text provided)
- attention_mask: [batch, total_seq_len] (covers `[CLS] + path + [SEP] + text`)
"""
if path_coords is None and text is None:
raise ValueError("Must provide either path_coords or text (or both)")
batch_size, path_coords, text = self._infer_batch_size(path_coords, text)
result: dict[str, Any] = {}
path_coords_out, path_mask = self._process_path_coords(
path_coords=path_coords,
batch_size=batch_size,
truncation=truncation,
padding=padding,
return_tensors=return_tensors,
)
result["path_coords"] = path_coords_out
input_ids, char_mask = self._process_text(
text=text,
batch_size=batch_size,
padding=padding,
truncation=truncation,
max_length=max_length,
return_tensors=return_tensors,
**kwargs,
)
result["input_ids"] = input_ids
result["attention_mask"] = self._build_attention_mask(
path_mask=path_mask,
char_mask=char_mask,
batch_size=batch_size,
return_tensors=return_tensors,
)
self._convert_result_in_place(result, return_tensors=return_tensors)
return result
def _infer_batch_size(
self,
path_coords: (
list[dict[str, float]]
| list[list[dict[str, float]]]
| list[list[list[float]]]
| torch.Tensor
| np.ndarray
| None
),
text: str | list[str] | None,
) -> tuple[int, Any, str | list[str] | None]:
if path_coords is not None:
if isinstance(path_coords, (list, tuple)):
if len(path_coords) == 0:
batch_size = 1
else:
first = path_coords[0]
if isinstance(first, dict):
batch_size = 1
elif (
isinstance(first, (list, tuple))
and len(first) > 0
and isinstance(first[0], dict)
):
batch_size = len(path_coords)
elif (
isinstance(first, (list, tuple))
and len(first) > 0
and isinstance(first[0], (list, tuple))
):
path_coords = torch.tensor(path_coords, dtype=torch.float32)
batch_size = int(path_coords.shape[0])
else:
path_coords = torch.tensor([path_coords], dtype=torch.float32)
batch_size = int(path_coords.shape[0])
elif isinstance(path_coords, np.ndarray):
path_coords = torch.from_numpy(path_coords).float()
if path_coords.dim() == 2:
path_coords = path_coords.unsqueeze(0)
batch_size = int(path_coords.shape[0])
elif isinstance(path_coords, torch.Tensor):
if path_coords.dim() == 2:
path_coords = path_coords.unsqueeze(0)
batch_size = int(path_coords.shape[0])
else:
batch_size = 1
elif text is not None:
if isinstance(text, str):
batch_size = 1
text = [text]
else:
batch_size = len(text)
else:
batch_size = 1
return batch_size, path_coords, text
def _process_path_coords(
self,
*,
path_coords,
batch_size: int,
truncation: bool,
padding: bool | str,
return_tensors: str | None,
) -> tuple[Any, Any]:
if path_coords is None:
path_coords_out = torch.zeros(batch_size, self.max_path_len, self.path_input_dim)
path_mask = torch.zeros(batch_size, self.max_path_len, dtype=torch.long)
return path_coords_out, path_mask
if isinstance(path_coords, (list, tuple)) and len(path_coords) > 0:
first_elem = path_coords[0]
if isinstance(first_elem, dict) and "x" in first_elem:
path_feats, mask = preprocess_raw_path_to_features(
path_coords,
self.max_path_len,
resample_mode=self.path_resample_mode,
)
if return_tensors == "pt":
return (
torch.from_numpy(path_feats).float().unsqueeze(0),
torch.from_numpy(mask).long().unsqueeze(0),
)
return (np.expand_dims(path_feats, axis=0), np.expand_dims(mask, axis=0))
if (
isinstance(first_elem, (list, tuple))
and len(first_elem) > 0
and isinstance(first_elem[0], dict)
and "x" in first_elem[0]
):
processed_paths = []
path_masks = []
for path in path_coords:
path_feats, mask = preprocess_raw_path_to_features(
path,
self.max_path_len,
resample_mode=self.path_resample_mode,
)
processed_paths.append(path_feats)
path_masks.append(mask)
path_coords_np = np.stack(processed_paths)
path_mask_np = np.stack(path_masks)
if return_tensors == "pt":
return torch.from_numpy(path_coords_np).float(), torch.from_numpy(
path_mask_np
).long()
return path_coords_np, path_mask_np
# Numeric list input
path_tensor = torch.tensor(path_coords, dtype=torch.float32)
if path_tensor.dim() == 2:
path_tensor = path_tensor.unsqueeze(0)
current_path_len = int(path_tensor.shape[1])
if truncation and current_path_len > self.max_path_len:
path_tensor = path_tensor[:, : self.max_path_len, :]
if padding and current_path_len < self.max_path_len:
pad_len = self.max_path_len - current_path_len
pad_shape = (batch_size, pad_len, self.path_input_dim)
path_tensor = torch.cat([path_tensor, torch.zeros(pad_shape)], dim=1)
path_mask = torch.ones(batch_size, self.max_path_len, dtype=torch.long)
is_padding = (path_tensor == 0).all(dim=-1)
path_mask[is_padding] = 0
return path_tensor, path_mask
if isinstance(path_coords, np.ndarray):
path_coords = torch.from_numpy(path_coords).float()
if isinstance(path_coords, torch.Tensor):
if path_coords.dim() == 2:
path_coords = path_coords.unsqueeze(0)
if path_coords.shape[-1] == 3 and self.path_input_dim == 6:
processed_paths = []
path_masks = []
for path in path_coords.detach().cpu().numpy():
raw = [{"x": float(p[0]), "y": float(p[1]), "t": float(p[2])} for p in path]
path_feats, mask = preprocess_raw_path_to_features(
raw,
self.max_path_len,
resample_mode=self.path_resample_mode,
)
processed_paths.append(path_feats)
path_masks.append(mask)
return torch.from_numpy(np.stack(processed_paths)).float(), torch.from_numpy(
np.stack(path_masks)
).long()
if int(path_coords.shape[-1]) != int(self.path_input_dim):
raise ValueError(
f"Expected path_coords.shape[-1] == path_input_dim ({self.path_input_dim}), "
f"got {int(path_coords.shape[-1])}. If your path is (x,y,t), pass D=3."
)
path_tensor = path_coords
current_path_len = int(path_tensor.shape[1])
if truncation and current_path_len > self.max_path_len:
path_tensor = path_tensor[:, : self.max_path_len, :]
if padding and current_path_len < self.max_path_len:
pad_len = self.max_path_len - current_path_len
pad_shape = (int(path_tensor.shape[0]), pad_len, int(path_tensor.shape[-1]))
pad = torch.zeros(pad_shape, dtype=path_tensor.dtype, device=path_tensor.device)
path_tensor = torch.cat([path_tensor, pad], dim=1)
path_mask = torch.ones(
int(path_tensor.shape[0]),
int(path_tensor.shape[1]),
dtype=torch.long,
device=path_tensor.device,
)
is_padding = (path_tensor == 0).all(dim=-1)
path_mask[is_padding] = 0
return path_tensor, path_mask
# Fallback: treat unknown input as empty path.
path_coords_out = torch.zeros(batch_size, self.max_path_len, self.path_input_dim)
path_mask = torch.zeros(batch_size, self.max_path_len, dtype=torch.long)
return path_coords_out, path_mask
def _process_text(
self,
*,
text: str | list[str] | None,
batch_size: int,
padding: bool | str,
truncation: bool,
max_length: int | None,
return_tensors: str | None,
**kwargs: Any,
) -> tuple[Any, Any]:
if text is None:
if return_tensors == "pt":
char_tokens = torch.full(
(batch_size, self.max_char_len),
self.tokenizer.pad_token_id,
dtype=torch.long,
)
char_mask = torch.zeros(batch_size, self.max_char_len, dtype=torch.long)
elif return_tensors == "np":
char_tokens = np.full(
(batch_size, self.max_char_len),
self.tokenizer.pad_token_id,
dtype=np.int64,
)
char_mask = np.zeros((batch_size, self.max_char_len), dtype=np.int64)
else:
char_tokens = [
[self.tokenizer.pad_token_id] * self.max_char_len for _ in range(batch_size)
]
char_mask = [[0] * self.max_char_len for _ in range(batch_size)]
return char_tokens, char_mask
if isinstance(text, str):
text = [text]
text_max_length = max_length if max_length is not None else self.max_char_len
encoded_raw = self.tokenizer(
text,
padding=False,
truncation=False,
return_tensors=None,
**kwargs,
)
eos_id = self.tokenizer.eos_token_id
for i in range(len(encoded_raw["input_ids"])):
if encoded_raw["input_ids"][i][-1] != eos_id:
encoded_raw["input_ids"][i].append(eos_id)
max_len_needed = max(len(ids) for ids in encoded_raw["input_ids"])
if truncation and max_len_needed > text_max_length:
for i in range(len(encoded_raw["input_ids"])):
if len(encoded_raw["input_ids"][i]) > text_max_length:
encoded_raw["input_ids"][i] = encoded_raw["input_ids"][i][
: text_max_length - 1
] + [eos_id]
if padding:
pad_id = self.tokenizer.pad_token_id
for i in range(len(encoded_raw["input_ids"])):
seq_len = len(encoded_raw["input_ids"][i])
if seq_len < text_max_length:
encoded_raw["input_ids"][i].extend([pad_id] * (text_max_length - seq_len))
char_mask_list = [
[1 if token_id != self.tokenizer.pad_token_id else 0 for token_id in ids]
for ids in encoded_raw["input_ids"]
]
if return_tensors == "pt":
return (
torch.tensor(encoded_raw["input_ids"], dtype=torch.long),
torch.tensor(char_mask_list, dtype=torch.long),
)
if return_tensors == "np":
return (
np.array(encoded_raw["input_ids"], dtype=np.int64),
np.array(char_mask_list, dtype=np.int64),
)
return encoded_raw["input_ids"], char_mask_list
def _build_attention_mask(
self,
*,
path_mask,
char_mask,
batch_size: int,
return_tensors: str | None,
):
if return_tensors == "pt":
cls_mask = torch.ones(batch_size, 1, dtype=torch.long)
sep_mask = torch.ones(batch_size, 1, dtype=torch.long)
return torch.cat([cls_mask, path_mask, sep_mask, char_mask], dim=1)
if return_tensors == "np":
cls_mask = np.ones((batch_size, 1), dtype=np.int64)
sep_mask = np.ones((batch_size, 1), dtype=np.int64)
return np.concatenate([cls_mask, path_mask, sep_mask, char_mask], axis=1)
cls_mask = [[1] for _ in range(batch_size)]
sep_mask = [[1] for _ in range(batch_size)]
return [
cls + path.tolist() + sep + char
for cls, path, sep, char in zip(cls_mask, path_mask, sep_mask, char_mask, strict=False)
]
def _convert_result_in_place(
self, result: dict[str, Any], *, return_tensors: str | None
) -> None:
if return_tensors == "np":
for key, value in list(result.items()):
if isinstance(value, torch.Tensor):
result[key] = value.numpy()
elif return_tensors is None:
for key, value in list(result.items()):
if isinstance(value, torch.Tensor):
result[key] = value.tolist()
def batch_decode(self, token_ids, **kwargs):
"""
Decode token IDs to strings.
Args:
token_ids: Token IDs to decode
**kwargs: Additional arguments passed to tokenizer
Returns:
List of decoded strings
"""
return self.tokenizer.batch_decode(token_ids, **kwargs)
def decode(self, token_ids, **kwargs):
"""
Decode single sequence of token IDs to string.
Args:
token_ids: Token IDs to decode
**kwargs: Additional arguments passed to tokenizer
Returns:
Decoded string
"""
return self.tokenizer.decode(token_ids, **kwargs)
def encode_path(self, path_coords, *, return_tensors: str | None = "pt", **kwargs: Any):
"""Create model inputs from a swipe path only (no text)."""
return self(path_coords=path_coords, text=None, return_tensors=return_tensors, **kwargs)
def encode_text(self, text, *, return_tensors: str | None = "pt", **kwargs: Any):
"""Create model inputs from text only (no path)."""
return self(path_coords=None, text=text, return_tensors=return_tensors, **kwargs)
# Preprocessing methods are now imported from shared preprocessing module
# See src/swipealot/data/preprocessing.py for the implementation
def save_pretrained(
self,
save_directory,
push_to_hub=False,
**kwargs,
):
"""
Save the processor to a directory, ensuring auto_map is included.
"""
# Call parent save_pretrained
result = super().save_pretrained(
save_directory,
push_to_hub=push_to_hub,
**kwargs,
)
# Add auto_map to processor_config.json for AutoProcessor compatibility
import json
from pathlib import Path
# Try both possible config file names
for config_name in ["preprocessor_config.json", "processor_config.json"]:
processor_config_path = Path(save_directory) / config_name
if processor_config_path.exists():
with open(processor_config_path) as f:
config = json.load(f)
config["auto_map"] = {"AutoProcessor": "processing_swipe.SwipeProcessor"}
with open(processor_config_path, "w") as f:
json.dump(config, f, indent=2)
break
return result
|