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import base64
import functools
import gzip
import json
import os
import random
import time
from typing import Any, cast
import blobfile
import tiktoken
def benchmark_batch(documents: list[str]) -> None:
num_threads = int(os.environ["RAYON_NUM_THREADS"])
num_bytes = sum(map(len, map(str.encode, documents)))
print(f"num_threads: {num_threads}, num_bytes: {num_bytes}")
enc = tiktoken.get_encoding("gpt2")
enc.encode("warmup")
start = time.perf_counter_ns()
enc.encode_ordinary_batch(documents, num_threads=num_threads)
end = time.perf_counter_ns()
print(f"tiktoken \t{num_bytes / (end - start) * 1e9} bytes / s")
import transformers
hf_enc = cast(Any, transformers).GPT2TokenizerFast.from_pretrained("gpt2")
hf_enc.model_max_length = 1e30 # silence!
hf_enc.encode("warmup")
start = time.perf_counter_ns()
hf_enc(documents)
end = time.perf_counter_ns()
print(f"huggingface \t{num_bytes / (end - start) * 1e9} bytes / s")
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