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test(cost): move off-peak tests beside the related cost tests

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They sat at the end of the file, which is where everyone else appends
too, so this branch picked up a conflict there on nearly every rebase.
Grouping them with the other _get_token_base_cost test keeps them clear
of that churn and next to the code they cover. Pure move, no test changes
This commit is contained in:
Srivatsa03 2026-08-13 17:28:00 -05:00
parent c813386bb2
commit d302301a4e
1 changed files with 164 additions and 164 deletions
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328
tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
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@ -410,6 +410,170 @@ def test_get_token_base_cost_picks_highest_crossed_tier():
assert prompt_base_cost == 9e-6
def test_is_within_off_peak_window_same_day():
from datetime import datetime, timezone
window = "09:00-17:00"
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 8, 59, tzinfo=timezone.utc)) is False
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 17, 0, tzinfo=timezone.utc)) is False
def test_is_within_off_peak_window_wraps_midnight():
from datetime import datetime, timezone
window = "16:30-00:30"
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 15, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 16, 30, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 30, tzinfo=timezone.utc)) is False
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is False
def test_is_within_off_peak_window_multiple_windows():
from datetime import datetime, timezone
# Providers like DeepSeek V4 have more than one daily peak/off-peak window.
windows = ["01:00-05:00", "13:00-16:00"]
assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 3, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 14, 30, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is False
# a malformed entry in the list is ignored, valid entries still match
assert _is_within_off_peak_window(["bad", "13:00-16:00"], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window([], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is False
def test_is_within_off_peak_window_normalizes_timezone_aware_input():
from datetime import datetime, timedelta, timezone
# A caller may pass a non-UTC aware datetime; the window is UTC and must be
# evaluated in UTC, not against the caller's wall-clock. 09:00 at UTC+8 is
# 01:00 UTC, inside the 01:00-05:00 window.
tz_plus_8 = timezone(timedelta(hours=8))
assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 9, 0, tzinfo=tz_plus_8)) is True
assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 12, 0, tzinfo=tz_plus_8)) is True
# 06:00 at UTC+8 is 22:00 UTC the previous day, outside the window
assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 6, 0, tzinfo=tz_plus_8)) is False
def test_is_within_off_peak_window_malformed_returns_false():
from datetime import datetime, timezone
now = datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)
assert _is_within_off_peak_window("not-a-window", now) is False
assert _is_within_off_peak_window("16:30", now) is False
assert _is_within_off_peak_window("25:00-26:00", now) is False
def test_get_token_base_cost_applies_off_peak_pricing():
from datetime import datetime, timezone
from typing import cast
from litellm.types.utils import ModelInfo
model_info = cast(
ModelInfo,
{
"input_cost_per_token": 1e-6,
"output_cost_per_token": 2e-6,
"cache_read_input_token_cost": 1e-7,
"off_peak_pricing": {
"hours_utc": "16:30-00:30",
"input_cost_per_token": 5e-7,
"output_cost_per_token": 1e-6,
"cache_read_input_token_cost": 5e-8,
},
},
)
usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150)
off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc))
assert off_peak[0] == 5e-7
assert off_peak[1] == 1e-6
assert off_peak[4] == 5e-8
peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc))
assert peak[0] == 1e-6
assert peak[1] == 2e-6
assert peak[4] == 1e-7
def test_get_token_base_cost_off_peak_falls_back_to_standard_when_unset():
from datetime import datetime, timezone
from typing import cast
from litellm.types.utils import ModelInfo
model_info = cast(
ModelInfo,
{
"input_cost_per_token": 1e-6,
"output_cost_per_token": 2e-6,
"off_peak_pricing": {"hours_utc": "16:30-00:30", "input_cost_per_token": 5e-7},
},
)
usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150)
result = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc))
assert result[0] == 5e-7
assert result[1] == 2e-6
def test_get_token_base_cost_off_peak_wins_over_threshold():
from datetime import datetime, timezone
from typing import cast
from litellm.types.utils import ModelInfo
model_info = cast(
ModelInfo,
{
"input_cost_per_token": 1e-6,
"output_cost_per_token": 2e-6,
"input_cost_per_token_above_200k_tokens": 3e-6,
"output_cost_per_token_above_200k_tokens": 4e-6,
"off_peak_pricing": {
"hours_utc": "16:30-00:30",
"input_cost_per_token": 5e-7,
"output_cost_per_token": 1e-6,
},
},
)
usage = Usage(prompt_tokens=250000, completion_tokens=250000, total_tokens=500000)
off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc))
assert off_peak[0] == 5e-7
assert off_peak[1] == 1e-6
peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc))
assert peak[0] == 3e-6
assert peak[1] == 4e-6
def test_get_model_info_propagates_off_peak_fields():
model_name = "test-off-peak-model"
off_peak_pricing = {
"hours_utc": "16:30-00:30",
"input_cost_per_token": 5e-7,
"output_cost_per_token": 1e-6,
"cache_read_input_token_cost": 5e-8,
}
litellm.register_model(
{
model_name: {
"litellm_provider": "openai",
"mode": "chat",
"input_cost_per_token": 1e-6,
"output_cost_per_token": 2e-6,
"off_peak_pricing": off_peak_pricing,
}
}
)
info = litellm.get_model_info(model=model_name)
assert info["off_peak_pricing"] == off_peak_pricing
def test_generic_cost_per_token_gpt54_above_272k_tokens(_local_model_cost_map):
"""GPT-5.4/5.4-pro: prompts >272K input tokens priced at 2x input, 1.5x output."""
model = "gpt-5.4"
@ -3947,167 +4111,3 @@ def test_route_image_generation_cost_falls_back_to_requested_size(monkeypatch, r
)
assert cost == expected_cost
def test_is_within_off_peak_window_same_day():
from datetime import datetime, timezone
window = "09:00-17:00"
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 8, 59, tzinfo=timezone.utc)) is False
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 17, 0, tzinfo=timezone.utc)) is False
def test_is_within_off_peak_window_wraps_midnight():
from datetime import datetime, timezone
window = "16:30-00:30"
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 15, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 16, 30, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 30, tzinfo=timezone.utc)) is False
assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is False
def test_is_within_off_peak_window_multiple_windows():
from datetime import datetime, timezone
# Providers like DeepSeek V4 have more than one daily peak/off-peak window.
windows = ["01:00-05:00", "13:00-16:00"]
assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 3, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 14, 30, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is False
# a malformed entry in the list is ignored, valid entries still match
assert _is_within_off_peak_window(["bad", "13:00-16:00"], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is True
assert _is_within_off_peak_window([], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is False
def test_is_within_off_peak_window_normalizes_timezone_aware_input():
from datetime import datetime, timedelta, timezone
# A caller may pass a non-UTC aware datetime; the window is UTC and must be
# evaluated in UTC, not against the caller's wall-clock. 09:00 at UTC+8 is
# 01:00 UTC, inside the 01:00-05:00 window.
tz_plus_8 = timezone(timedelta(hours=8))
assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 9, 0, tzinfo=tz_plus_8)) is True
assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 12, 0, tzinfo=tz_plus_8)) is True
# 06:00 at UTC+8 is 22:00 UTC the previous day, outside the window
assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 6, 0, tzinfo=tz_plus_8)) is False
def test_is_within_off_peak_window_malformed_returns_false():
from datetime import datetime, timezone
now = datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)
assert _is_within_off_peak_window("not-a-window", now) is False
assert _is_within_off_peak_window("16:30", now) is False
assert _is_within_off_peak_window("25:00-26:00", now) is False
def test_get_token_base_cost_applies_off_peak_pricing():
from datetime import datetime, timezone
from typing import cast
from litellm.types.utils import ModelInfo
model_info = cast(
ModelInfo,
{
"input_cost_per_token": 1e-6,
"output_cost_per_token": 2e-6,
"cache_read_input_token_cost": 1e-7,
"off_peak_pricing": {
"hours_utc": "16:30-00:30",
"input_cost_per_token": 5e-7,
"output_cost_per_token": 1e-6,
"cache_read_input_token_cost": 5e-8,
},
},
)
usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150)
off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc))
assert off_peak[0] == 5e-7
assert off_peak[1] == 1e-6
assert off_peak[4] == 5e-8
peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc))
assert peak[0] == 1e-6
assert peak[1] == 2e-6
assert peak[4] == 1e-7
def test_get_token_base_cost_off_peak_falls_back_to_standard_when_unset():
from datetime import datetime, timezone
from typing import cast
from litellm.types.utils import ModelInfo
model_info = cast(
ModelInfo,
{
"input_cost_per_token": 1e-6,
"output_cost_per_token": 2e-6,
"off_peak_pricing": {"hours_utc": "16:30-00:30", "input_cost_per_token": 5e-7},
},
)
usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150)
result = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc))
assert result[0] == 5e-7
assert result[1] == 2e-6
def test_get_token_base_cost_off_peak_wins_over_threshold():
from datetime import datetime, timezone
from typing import cast
from litellm.types.utils import ModelInfo
model_info = cast(
ModelInfo,
{
"input_cost_per_token": 1e-6,
"output_cost_per_token": 2e-6,
"input_cost_per_token_above_200k_tokens": 3e-6,
"output_cost_per_token_above_200k_tokens": 4e-6,
"off_peak_pricing": {
"hours_utc": "16:30-00:30",
"input_cost_per_token": 5e-7,
"output_cost_per_token": 1e-6,
},
},
)
usage = Usage(prompt_tokens=250000, completion_tokens=250000, total_tokens=500000)
off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc))
assert off_peak[0] == 5e-7
assert off_peak[1] == 1e-6
peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc))
assert peak[0] == 3e-6
assert peak[1] == 4e-6
def test_get_model_info_propagates_off_peak_fields():
model_name = "test-off-peak-model"
off_peak_pricing = {
"hours_utc": "16:30-00:30",
"input_cost_per_token": 5e-7,
"output_cost_per_token": 1e-6,
"cache_read_input_token_cost": 5e-8,
}
litellm.register_model(
{
model_name: {
"litellm_provider": "openai",
"mode": "chat",
"input_cost_per_token": 1e-6,
"output_cost_per_token": 2e-6,
"off_peak_pricing": off_peak_pricing,
}
}
)
info = litellm.get_model_info(model=model_name)
assert info["off_peak_pricing"] == off_peak_pricing