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import json
import os
import tempfile
import pytest
import torch
from config import (
AUDIO_END_TOKEN,
AUDIO_START_TOKEN,
NUM_SPEECH_TOKENS,
THINK_END_TOKEN,
THINK_START_TOKEN,
USER_PROMPT_TOKEN,
get_all_special_tokens,
)
from dataset import MambaCoTDataCollator
class SimpleTokenizer:
def __init__(self):
self.vocab = {}
self._next_id = 0
self.eos_token_id = self._add("[EOS]")
for token in get_all_special_tokens():
self._add(token)
def _add(self, token):
if token not in self.vocab:
self.vocab[token] = self._next_id
self._next_id += 1
return self.vocab[token]
def encode(self, text, add_special_tokens=False, return_tensors=None):
tokens = text.split()
ids = [self.vocab.get(t, self._add(t)) for t in tokens]
if return_tensors == "pt":
return torch.tensor([ids], dtype=torch.long)
return ids
def convert_tokens_to_ids(self, tokens):
return [self.vocab.get(t, self._add(t)) for t in tokens]
def convert_ids_to_tokens(self, ids):
inv = {v: k for k, v in self.vocab.items()}
return [inv.get(i, f"[UNK_{i}]") for i in ids]
def decode(self, ids, skip_special_tokens=False):
return " ".join(self.convert_ids_to_tokens(ids))
def __len__(self):
return len(self.vocab)
def add_special_tokens(self, special_tokens_dict):
count = 0
for tokens in special_tokens_dict.values():
if isinstance(tokens, list):
for t in tokens:
if t not in self.vocab:
self._add(t)
count += 1
return count
class TestJSONLParsing:
def test_valid_jsonl(self):
data = [
{
"prompt": "Hello world",
"reasoning": "Neutral greeting",
"audio_file": "test.wav",
},
{
"prompt": "Oh great",
"reasoning": "Sarcastic [EMO:sarcastic]",
"audio_file": "test2.wav",
"emotion_label": "sarcastic",
},
]
with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f:
for item in data:
f.write(json.dumps(item) + "\n")
tmpfile = f.name
try:
parsed = []
with open(tmpfile, "r") as f:
for line in f:
if line.strip():
parsed.append(json.loads(line))
assert len(parsed) == 2
assert parsed[0]["prompt"] == "Hello world"
assert parsed[1]["emotion_label"] == "sarcastic"
finally:
os.unlink(tmpfile)
def test_empty_lines_skipped(self):
with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f:
f.write(json.dumps({"prompt": "a", "reasoning": "", "audio_file": "x.wav"}) + "\n")
f.write("\n")
f.write(" \n")
f.write(json.dumps({"prompt": "b", "reasoning": "", "audio_file": "y.wav"}) + "\n")
tmpfile = f.name
try:
parsed = []
with open(tmpfile, "r") as f:
for line in f:
if line.strip():
parsed.append(json.loads(line))
assert len(parsed) == 2
finally:
os.unlink(tmpfile)
def test_optional_fields(self):
data = {"prompt": "Test", "reasoning": "", "audio_file": "x.wav",
"speech_tokens": [42, 100, 200]}
with tempfile.NamedTemporaryFile(mode="w", suffix=".jsonl", delete=False) as f:
f.write(json.dumps(data) + "\n")
tmpfile = f.name
try:
with open(tmpfile, "r") as f:
item = json.loads(f.readline())
assert item["speech_tokens"] == [42, 100, 200]
finally:
os.unlink(tmpfile)
class TestSequenceConstruction:
def setup_method(self):
self.tokenizer = SimpleTokenizer()
def test_stage1_no_reasoning(self):
prompt = "Hello world"
prompt_text = f"{USER_PROMPT_TOKEN} {prompt}"
prompt_ids = self.tokenizer.encode(prompt_text)
audio_start_ids = self.tokenizer.encode(AUDIO_START_TOKEN)
speech_ids = self.tokenizer.convert_tokens_to_ids(["[SPEECH_42]", "[SPEECH_100]"])
audio_end_ids = self.tokenizer.encode(AUDIO_END_TOKEN)
input_ids = prompt_ids + audio_start_ids + speech_ids + audio_end_ids
input_ids.append(self.tokenizer.eos_token_id)
tokens = self.tokenizer.convert_ids_to_tokens(input_ids)
assert USER_PROMPT_TOKEN in tokens
assert AUDIO_START_TOKEN in tokens
assert AUDIO_END_TOKEN in tokens
assert THINK_START_TOKEN not in tokens
assert THINK_END_TOKEN not in tokens
def test_stage2_with_reasoning(self):
prompt = "Great Monday"
reasoning = "[EMO:sarcastic] [PACE:slow]"
prompt_ids = self.tokenizer.encode(f"{USER_PROMPT_TOKEN} {prompt}")
reasoning_ids = self.tokenizer.encode(f"{THINK_START_TOKEN} {reasoning} {THINK_END_TOKEN}")
audio_start_ids = self.tokenizer.encode(AUDIO_START_TOKEN)
speech_ids = self.tokenizer.convert_tokens_to_ids(["[SPEECH_0]"])
audio_end_ids = self.tokenizer.encode(AUDIO_END_TOKEN)
input_ids = prompt_ids + reasoning_ids + audio_start_ids + speech_ids + audio_end_ids
input_ids.append(self.tokenizer.eos_token_id)
tokens = self.tokenizer.convert_ids_to_tokens(input_ids)
assert THINK_START_TOKEN in tokens
assert THINK_END_TOKEN in tokens
assert "[EMO:sarcastic]" in tokens
def test_speech_token_range(self):
speech_tokens = [f"[SPEECH_{i}]" for i in [0, 512, NUM_SPEECH_TOKENS - 1]]
ids = self.tokenizer.convert_tokens_to_ids(speech_tokens)
assert len(ids) == 3
assert all(isinstance(i, int) for i in ids)
@pytest.fixture(scope="module")
def real_registry():
from tokenizer import load_base_tokenizer, expand_tokenizer, TokenRegistry
tok = load_base_tokenizer()
expand_tokenizer(tok)
return TokenRegistry(tok)
class TestCollator:
def _make_item(self, registry, input_ids, prompt_end,
reasoning_start=0, reasoning_end=0,
speech_start=0, speech_end=0,
residual_codes=None, speaker_id=None):
return {
"input_ids": torch.tensor(input_ids, dtype=torch.long),
"prompt_end": prompt_end,
"reasoning_start": reasoning_start,
"reasoning_end": reasoning_end,
"speech_start": speech_start,
"speech_end": speech_end,
"residual_codes": residual_codes,
"speaker_id": speaker_id,
}
def test_padding(self, real_registry):
collator = MambaCoTDataCollator(
pad_token_id=real_registry.pad_id,
registry=real_registry,
stage=1,
)
batch = [
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, 101, real_registry.eos_id],
prompt_end=2, speech_start=2, speech_end=4,
),
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, 101, 102, 103, real_registry.eos_id],
prompt_end=2, speech_start=2, speech_end=6,
),
]
result = collator(batch)
assert result["input_ids"].shape == (2, 6)
assert result["input_ids"][0, 4].item() == real_registry.pad_id
assert result["input_ids"][0, 5].item() == real_registry.pad_id
def test_prompt_masked_in_labels(self, real_registry):
collator = MambaCoTDataCollator(
pad_token_id=real_registry.pad_id,
registry=real_registry,
stage=1,
)
batch = [
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, 101, 102, real_registry.eos_id],
prompt_end=3, speech_start=3, speech_end=5,
),
]
result = collator(batch)
labels = result["labels"]
assert labels[0, 0].item() == -100
assert labels[0, 1].item() == -100
assert labels[0, 2].item() == -100
assert labels[0, 3].item() != -100
def test_padding_masked_in_labels(self, real_registry):
collator = MambaCoTDataCollator(
pad_token_id=real_registry.pad_id,
registry=real_registry,
stage=1,
)
batch = [
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, real_registry.eos_id],
prompt_end=1, speech_start=1, speech_end=3,
),
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, 101, 102, real_registry.eos_id],
prompt_end=1, speech_start=1, speech_end=5,
),
]
result = collator(batch)
labels = result["labels"]
assert labels[0, 3].item() == -100
assert labels[0, 4].item() == -100
def test_attention_mask(self, real_registry):
collator = MambaCoTDataCollator(
pad_token_id=real_registry.pad_id,
registry=real_registry,
stage=1,
)
batch = [
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, real_registry.eos_id],
prompt_end=1, speech_start=1, speech_end=3,
),
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, 101, 102, real_registry.eos_id],
prompt_end=1, speech_start=1, speech_end=5,
),
]
result = collator(batch)
mask = result["attention_mask"]
assert mask[0, 0].item() == 1
assert mask[0, 2].item() == 1
assert mask[0, 3].item() == 0
assert mask[0, 4].item() == 0
assert mask[1].sum().item() == 5
def test_loss_weights_stage1(self, real_registry):
collator = MambaCoTDataCollator(
pad_token_id=real_registry.pad_id,
registry=real_registry,
stage=1,
)
batch = [
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, 101, 102, real_registry.eos_id],
prompt_end=2,
reasoning_start=2, reasoning_end=2,
speech_start=2, speech_end=5,
),
]
result = collator(batch)
weights = result["loss_weights"]
assert weights[0, 0].item() == pytest.approx(0.0)
assert weights[0, 1].item() == pytest.approx(0.0)
assert weights[0, 2].item() == pytest.approx(1.0)
assert weights[0, 3].item() == pytest.approx(1.0)
def test_loss_weights_stage2(self, real_registry):
lambda_r = 0.3
lambda_s = 1.0
collator = MambaCoTDataCollator(
pad_token_id=real_registry.pad_id,
registry=real_registry,
stage=2,
lambda_r=lambda_r,
lambda_s=lambda_s,
)
batch = [
self._make_item(
real_registry,
input_ids=[
real_registry.bos_id, real_registry.user_prompt_id, 100,
real_registry.think_start_id, 200, 201, real_registry.think_end_id,
real_registry.audio_start_id, 300, real_registry.audio_end_id,
real_registry.eos_id,
],
prompt_end=3,
reasoning_start=4, reasoning_end=6,
speech_start=8, speech_end=9,
),
]
result = collator(batch)
weights = result["loss_weights"]
assert weights[0, 0].item() == pytest.approx(0.0)
assert weights[0, 1].item() == pytest.approx(0.0)
assert weights[0, 2].item() == pytest.approx(0.0)
assert weights[0, 3].item() == pytest.approx(lambda_r)
assert weights[0, 4].item() == pytest.approx(lambda_r)
assert weights[0, 5].item() == pytest.approx(lambda_r)
assert weights[0, 7].item() == pytest.approx(lambda_s)
assert weights[0, 8].item() == pytest.approx(lambda_s)
def test_residual_codes_none(self, real_registry):
collator = MambaCoTDataCollator(
pad_token_id=real_registry.pad_id,
registry=real_registry,
stage=1,
)
batch = [
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, real_registry.eos_id],
prompt_end=1, speech_start=1, speech_end=3,
residual_codes=None,
),
]
result = collator(batch)
assert result["residual_codes"] is None
def test_speaker_ids_none(self, real_registry):
collator = MambaCoTDataCollator(
pad_token_id=real_registry.pad_id,
registry=real_registry,
stage=1,
)
batch = [
self._make_item(
real_registry,
input_ids=[real_registry.bos_id, 100, real_registry.eos_id],
prompt_end=1, speech_start=1, speech_end=3,
speaker_id=None,
),
]
result = collator(batch)
assert result["speaker_ids"] is None
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