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@ -16,7 +16,7 @@ repos:
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- id: auto-walrus
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.7.2
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rev: v0.7.4
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hooks:
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- id: ruff
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- id: ruff-format
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@ -42,7 +42,7 @@ repos:
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pass_filenames: false
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- repo: https://github.com/abravalheri/validate-pyproject
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rev: v0.22
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rev: v0.23
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hooks:
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- id: validate-pyproject
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@ -1280,6 +1280,7 @@
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* [Barcode Validator](strings/barcode_validator.py)
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* [Bitap String Match](strings/bitap_string_match.py)
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* [Boyer Moore Search](strings/boyer_moore_search.py)
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* [Bpe Tokenizer](strings/bpe_tokenizer.py)
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* [Camel Case To Snake Case](strings/camel_case_to_snake_case.py)
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* [Can String Be Rearranged As Palindrome](strings/can_string_be_rearranged_as_palindrome.py)
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* [Capitalize](strings/capitalize.py)
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@ -172,7 +172,7 @@ def solved(values):
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def from_file(filename, sep="\n"):
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"Parse a file into a list of strings, separated by sep."
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return open(filename).read().strip().split(sep) # noqa: SIM115
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return open(filename).read().strip().split(sep)
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def random_puzzle(assignments=17):
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@ -1,4 +1,4 @@
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#!/usr/bin/env python3
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#!python
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import os
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try:
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129
strings/bpe_tokenizer.py
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129
strings/bpe_tokenizer.py
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@ -0,0 +1,129 @@
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"""Byte-Pair Encoding: Subword-based tokenization algorithm used
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by state-of-the-art language models.
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Wikipedia: https://en.wikipedia.org/wiki/Byte_pair_encoding"""
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import itertools
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from collections import OrderedDict
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def get_byte_pair_counts(ids: list[int]) -> dict:
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"""Count consecutive byte-pairs of an encoded string.
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>>> ids = [73, 32, 97, 109, 32, 74, 111, 110, 83, 110, 111, 119, 46]
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>>> get_byte_pair_counts(ids)
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{(73, 32): 1, (32, 97): 1, (97, 109): 1, (109, 32): 1, (32, 74): 1, (74, 111): 1, (111, 110): 1, (110, 83): 1, (83, 110): 1, (110, 111): 1, (111, 119): 1, (119, 46): 1}
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>>> ids = [2, 3, 6, 2, 3, 6, 2, 5]
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>>> get_byte_pair_counts(ids)
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{(2, 3): 2, (3, 6): 2, (6, 2): 2, (2, 5): 1}
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""" # noqa: E501
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counts: dict = {}
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for pair in itertools.pairwise(ids):
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counts[pair] = counts.get(pair, 0) + 1
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return counts
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def merge(ids: list[int], pair: tuple, idx: int) -> list[int]:
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"""Replace most occurring byte pair with new byte that is not used
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in the data. For utf-8 encoding, we start with 256 as the new byte
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>>> ids = [2, 3, 6, 2, 3, 6, 2, 5]
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>>> pair = (2, 3)
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>>> idx = 256
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>>> merge(ids, pair, idx)
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[256, 6, 256, 6, 2, 5]
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"""
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new_ids = []
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i = 0
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while i < len(ids):
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if i < len(ids) - 1 and (ids[i] == pair[0] and ids[i + 1] == pair[1]):
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new_ids.append(idx)
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i += 2
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else:
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new_ids.append(ids[i])
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i += 1
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return new_ids
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class Tokenizer:
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"""Tokenize a string using the byte-pair encoding algorithm"""
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def __init__(self, num_merges: int = 20, verbose: bool = False) -> None:
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self.num_merges = num_merges
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self.merges: dict = {}
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self.verbose = verbose
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def encode(self, text: str) -> list[int]:
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"""Convert a string to tokens (bytes)
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>>> t = Tokenizer()
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>>> text = "I am JonSnow."
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>>> t.encode(text)
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[73, 32, 97, 109, 32, 74, 111, 110, 83, 110, 111, 119, 46]
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>>> t = Tokenizer()
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>>> text = ""
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>>> t.encode(text)
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[]
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"""
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text_b = text.encode("utf-8") # raw bytes
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tokens = list(map(int, text_b)) # convert to list of integers
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if self.verbose:
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print(f"Input text: {text}")
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print(f"Tokens: {tokens}")
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ids = list(tokens) # create a copy of tokens
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self.merges = OrderedDict() # store a mapping of merges (int, int) -> int
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max_merges = len(tokens) - 1
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num_merges = min(self.num_merges, max_merges)
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# start merging most frequently occurring byte pairs
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for i in range(num_merges):
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counts = get_byte_pair_counts(ids)
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pair = max(counts, key=counts.__getitem__)
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if counts[pair] == 1:
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continue
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idx = 256 + i # create new token for every merge step
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if self.verbose:
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print(f"Merging {pair} into a new token {idx}")
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ids = merge(ids, pair, idx)
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self.merges[pair] = idx
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return ids
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def decode(self, ids: list[int]) -> str:
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"""Convert a list of tokens to the original string
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>>> t = Tokenizer()
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>>> ids = [73, 32, 97, 109, 32, 74, 111, 110, 83, 110, 111, 119, 46]
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>>> t.decode(ids)
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'I am JonSnow.'
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>>> t = Tokenizer()
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>>> ids = []
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>>> t.decode(ids)
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''
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"""
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vocab = {idx: bytes([idx]) for idx in range(256)} # original vocabulary
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# The iteration of items should be in the order of
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# their insertion. This is the default behavior in Python 3
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# but we use an OrderedDict explicitly here
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for (p0, p1), idx in self.merges.items():
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vocab[idx] = vocab[p0] + vocab[p1]
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if self.verbose:
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print("Vocabulary (after merging): {vocab}")
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tokens = b"".join(vocab[idx] for idx in ids)
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# handle UnicodeDecodeError by replacing the invalid
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# start byte to conform to utf-8 format
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text = tokens.decode("utf-8", errors="replace")
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return text
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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