Spaces:
Running
Running
File size: 1,738 Bytes
a9beef1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
from typing import Dict
import numpy as np
NULL_CHAR = '\x00'
def construct_alphabet_list(alphabet_string: str) -> list[str]:
if not isinstance(alphabet_string, str):
raise TypeError("alphabet_string must be a string")
char_list = list(alphabet_string)
return [NULL_CHAR] + char_list
def get_alphabet_map(alphabet_list: list[str]) -> Dict[str, int]:
"""creates a char to index map from full alphabet list"""
return {char: idx for idx, char in enumerate(alphabet_list)}
def encode_text(text: str, char_to_index_map: Dict[str, int],
max_length: int, add_eos: bool = True, eos_char_index: int = 0
) -> tuple[np.ndarray, int]:
"""Encode a text string into a sequence of integer indices"""
encoded = [char_to_index_map.get(c, eos_char_index) for c in text]
if add_eos:
encoded.append(eos_char_index)
true_length = len(encoded)
if true_length <= max_length:
padded_encoded = np.full(max_length, eos_char_index, dtype=np.int64)
padded_encoded[:true_length] = encoded
else:
padded_encoded = np.array(encoded[:max_length], dtype=np.int64)
true_length = max_length
return np.array([padded_encoded]), true_length
def convert_offsets_to_absolute_coords(stroke_offsets: list[list[float]]) -> list[list[float]]:
if not stroke_offsets:
return []
# convert to numpy for vectorized operations
strokes_array = np.array(stroke_offsets)
# vectorized cumulative sum for x and y
strokes_array[:, 0] = np.cumsum(strokes_array[:, 0]) # cumulative dx
strokes_array[:, 1] = np.cumsum(strokes_array[:, 1]) # cumulative dy
return strokes_array.tolist() |