mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-30 01:52:18 +00:00
fix(images): bill unmeasurable references as one megapixel and keep measurement off the event loop
This commit is contained in:
shrey kharbanda
parent
6c58cb4267
commit
5f576ae5e8
10 changed files with 263 additions and 276 deletions
|
|
@ -1,7 +1,7 @@
|
|||
import base64
|
||||
import os
|
||||
import struct
|
||||
from collections.abc import Iterator, Mapping, Sequence
|
||||
from collections.abc import Iterable, Iterator, Mapping, Sized
|
||||
from dataclasses import dataclass
|
||||
from io import BytesIO
|
||||
from typing import IO, Final
|
||||
|
|
@ -33,6 +33,7 @@ _WEBP_VP8_START_CODE: Final = b"\x9d\x01\x2a"
|
|||
_WEBP_VP8L_SIGNATURE: Final = 0x2F
|
||||
_BMP_CORE_DIB_SIZE: Final = 12
|
||||
_BMP_KNOWN_DIB_SIZES: Final = frozenset({12, 40, 52, 56, 64, 108, 124})
|
||||
_UNMEASURED_REFERENCE_PIXELS: Final = 1024 * 1024
|
||||
|
||||
|
||||
@dataclass(frozen=True, slots=True)
|
||||
|
|
@ -45,34 +46,34 @@ class ImageDimensions:
|
|||
return self.width * self.height
|
||||
|
||||
|
||||
def total_reference_pixels(images: Sequence[FileTypes]) -> int | None:
|
||||
"""All-or-nothing sum of measured reference pixels; never raises."""
|
||||
def total_reference_pixels(images: Iterable[FileTypes]) -> int:
|
||||
"""Reference pixels sent; never raises. A reference that cannot be measured is still metered by the
|
||||
provider, so it counts as one megapixel rather than disappearing from spend."""
|
||||
try:
|
||||
measured: Final = tuple(read_image_dimensions(image) for image in images)
|
||||
except Exception: # noqa: BLE001 # a billing helper must fall back, never fail the request
|
||||
return None
|
||||
dimensions: Final = tuple(size for size in measured if size is not None)
|
||||
if len(dimensions) != len(measured):
|
||||
return _UNMEASURED_REFERENCE_PIXELS * (len(images) if isinstance(images, Sized) else 1)
|
||||
unmeasured: Final = sum(size is None for size in measured)
|
||||
if unmeasured:
|
||||
verbose_logger.debug(
|
||||
"Reference image %d could not be measured (path, non-seekable stream, or no readable PNG, JPEG, WebP, "
|
||||
"GIF or BMP header); billing generated pixels only",
|
||||
measured.index(None),
|
||||
"%d reference image(s) could not be measured (path, non-seekable stream, or no readable PNG, JPEG, "
|
||||
"WebP, GIF or BMP header); billing each as one megapixel",
|
||||
unmeasured,
|
||||
)
|
||||
return None
|
||||
return sum(size.pixels for size in dimensions)
|
||||
return sum(_UNMEASURED_REFERENCE_PIXELS if size is None else size.pixels for size in measured)
|
||||
|
||||
|
||||
def uploaded_reference_pixels(
|
||||
files: RequestFiles | None,
|
||||
json_body: Mapping[str, object] | None,
|
||||
) -> int | None:
|
||||
) -> int:
|
||||
"""Pixels across the image-bearing parts a request actually sends.
|
||||
|
||||
Multipart requests carry image content under ``files``; JSON requests embed base64 inside
|
||||
``json_body`` (e.g. FLUX ``input_image`` fields). Measuring the outgoing payload instead of the
|
||||
caller's arguments keeps billing aligned with what the provider meters when a transform filters
|
||||
images out or adds parts of its own (masks, single-image providers). All-or-nothing, ``0`` when
|
||||
nothing image-bearing is sent.
|
||||
images out or adds parts of its own (masks, single-image providers). ``0`` when nothing
|
||||
image-bearing is sent.
|
||||
"""
|
||||
parts: Final = _file_parts(files) + tuple(_embedded_image_values(json_body))
|
||||
if not parts:
|
||||
|
|
@ -110,7 +111,7 @@ def read_image_dimensions(image: FileTypes) -> ImageDimensions | None:
|
|||
if embedded is None:
|
||||
return None
|
||||
return _dimensions_from_stream(BytesIO(embedded))
|
||||
stream = BytesIO(content) if isinstance(content, (bytes, bytearray, memoryview)) else content
|
||||
stream: Final = BytesIO(content) if isinstance(content, (bytes, bytearray, memoryview)) else content
|
||||
return _dimensions_from_stream(stream)
|
||||
except Exception: # noqa: BLE001 # an odd stream or malformed file must fall back, never raise
|
||||
return None
|
||||
|
|
@ -127,7 +128,7 @@ def _dimensions_from_stream(stream: IO[bytes]) -> ImageDimensions | None:
|
|||
return None
|
||||
position: Final = stream.tell()
|
||||
try:
|
||||
dimensions = _header_dimensions(stream, 0)
|
||||
dimensions: Final = _header_dimensions(stream, 0)
|
||||
finally:
|
||||
stream.seek(position)
|
||||
if dimensions is None or dimensions.width <= 0 or dimensions.height <= 0:
|
||||
|
|
|
|||
|
|
@ -125,7 +125,7 @@ class AzureFoundryFlux2ImageEditConfig(OpenAIImageEditConfig):
|
|||
if isinstance(image, bytes):
|
||||
image_bytes = image
|
||||
elif hasattr(image, "read"):
|
||||
if image.seekable():
|
||||
if hasattr(image, "seekable") and image.seekable(): # pyright: ignore[reportAny] # image is a duck-typed file-like object by contract
|
||||
image.seek(0)
|
||||
image_bytes = image.read()
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -6994,7 +6994,7 @@ class BaseLLMHTTPHandler:
|
|||
headers=headers,
|
||||
)
|
||||
data = image_edit_provider_config.finalize_image_edit_request_data(data, api_base)
|
||||
reference_pixels: Final = uploaded_reference_pixels(files, data)
|
||||
reference_pixels: Final = await asyncio.to_thread(uploaded_reference_pixels, files, data)
|
||||
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
|
|
@ -7223,7 +7223,7 @@ class BaseLLMHTTPHandler:
|
|||
litellm_params=dict(litellm_params),
|
||||
headers=headers,
|
||||
)
|
||||
reference_pixels: Final = uploaded_reference_pixels(None, data)
|
||||
reference_pixels: Final = await asyncio.to_thread(uploaded_reference_pixels, None, data)
|
||||
|
||||
## LOGGING
|
||||
logging_obj.pre_call(
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ import pytest
|
|||
from PIL import Image
|
||||
|
||||
from litellm.images.dimensions import (
|
||||
_UNMEASURED_REFERENCE_PIXELS,
|
||||
ImageDimensions,
|
||||
read_image_dimensions,
|
||||
total_reference_pixels,
|
||||
|
|
@ -309,30 +310,32 @@ def test_read_image_dimensions_returns_none_for_short_and_none_tuples():
|
|||
assert read_image_dimensions(("ref.png", None)) is None
|
||||
|
||||
|
||||
def test_total_reference_pixels_returns_none_instead_of_raising():
|
||||
assert total_reference_pixels([("ref.png",)]) is None
|
||||
assert total_reference_pixels(cast(Any, 123)) is None
|
||||
def test_total_reference_pixels_bills_unmeasurable_input_as_one_megapixel():
|
||||
assert total_reference_pixels([("ref.png",)]) == _UNMEASURED_REFERENCE_PIXELS
|
||||
assert total_reference_pixels(cast(Any, 123)) == _UNMEASURED_REFERENCE_PIXELS
|
||||
|
||||
|
||||
def test_total_reference_pixels_returns_none_for_closed_and_read_only_streams():
|
||||
def test_total_reference_pixels_bills_closed_and_read_only_streams_as_one_megapixel():
|
||||
closed: Final = io.BytesIO(_png(64, 64))
|
||||
closed.close()
|
||||
|
||||
assert total_reference_pixels([closed]) is None
|
||||
assert total_reference_pixels([cast(Any, _ReadOnlyObject())]) is None
|
||||
assert total_reference_pixels([closed]) == _UNMEASURED_REFERENCE_PIXELS
|
||||
assert total_reference_pixels([cast(Any, _ReadOnlyObject())]) == _UNMEASURED_REFERENCE_PIXELS
|
||||
|
||||
|
||||
def test_total_reference_pixels_returns_none_when_a_reference_has_invalid_dimensions():
|
||||
def test_total_reference_pixels_bills_invalid_dimensions_as_one_megapixel():
|
||||
negative_width_bmp: Final = _bmp(-1024, 768)
|
||||
|
||||
assert read_image_dimensions(negative_width_bmp) is None
|
||||
assert total_reference_pixels([negative_width_bmp, _png(1024, 1024)]) is None
|
||||
assert total_reference_pixels([negative_width_bmp, _png(1024, 1024)]) == _UNMEASURED_REFERENCE_PIXELS + 1024 * 1024
|
||||
|
||||
|
||||
def test_total_reference_pixels_returns_none_when_any_reference_is_unmeasurable():
|
||||
def test_total_reference_pixels_bills_each_unmeasurable_reference_as_one_megapixel():
|
||||
assert total_reference_pixels([_png(64, 64), CAT_JPEG.read_bytes()]) == 64 * 64 + 512 * 512
|
||||
assert total_reference_pixels([_png(64, 64), _gif(b"GIF89a", 100, 50)]) == 64 * 64 + 100 * 50
|
||||
assert total_reference_pixels([_png(64, 64), io.BytesIO(b"image"), CAT_JPEG.read_bytes()]) is None
|
||||
assert total_reference_pixels([_png(64, 64), io.BytesIO(b"image"), CAT_JPEG.read_bytes()]) == (
|
||||
64 * 64 + _UNMEASURED_REFERENCE_PIXELS + 512 * 512
|
||||
)
|
||||
assert total_reference_pixels([]) == 0
|
||||
|
||||
|
||||
|
|
@ -343,31 +346,42 @@ def test_uploaded_reference_pixels_measures_the_uploaded_set_not_the_requested_s
|
|||
# a transform that keeps only the first image (MAI) bills only what it sends
|
||||
assert uploaded_reference_pixels({"image[]": ("ref.png", _png(64, 64))}, {"prompt": "hi"}) == 64 * 64
|
||||
# FLUX.2 embeds each reference as a base64 field in the JSON body
|
||||
assert uploaded_reference_pixels(
|
||||
[],
|
||||
{"model": "FLUX.2-flex", "prompt": "blend", "input_image": png_b64, "input_image_2": jpeg_b64, "n": 2},
|
||||
) == 64 * 64 + 512 * 512
|
||||
assert (
|
||||
uploaded_reference_pixels(
|
||||
[],
|
||||
{"model": "FLUX.2-flex", "prompt": "blend", "input_image": png_b64, "input_image_2": jpeg_b64, "n": 2},
|
||||
)
|
||||
== 64 * 64 + 512 * 512
|
||||
)
|
||||
# an extra file part a transform adds itself (a mask) is uploaded and metered too
|
||||
assert uploaded_reference_pixels(
|
||||
{"image": ("edit.png", _png(64, 64)), "mask": ("mask.png", _png(10, 10))}, {"prompt": "cut"}
|
||||
) == 64 * 64 + 10 * 10
|
||||
assert (
|
||||
uploaded_reference_pixels(
|
||||
{"image": ("edit.png", _png(64, 64)), "mask": ("mask.png", _png(10, 10))}, {"prompt": "cut"}
|
||||
)
|
||||
== 64 * 64 + 10 * 10
|
||||
)
|
||||
|
||||
|
||||
def test_uploaded_reference_pixels_skips_non_image_fields_and_nested_bodies():
|
||||
png_b64: Final = base64.b64encode(_png(64, 64)).decode()
|
||||
jpeg_b64: Final = base64.b64encode(CAT_JPEG.read_bytes()).decode()
|
||||
|
||||
assert uploaded_reference_pixels(
|
||||
{"image": ("edit.png", _png(64, 64))},
|
||||
{"model": "m", "prompt": "hi", "size": "1024x1024", "extra": {"nested": jpeg_b64}, "refs": [png_b64]},
|
||||
) == 64 * 64 + 512 * 512 + 64 * 64
|
||||
assert (
|
||||
uploaded_reference_pixels(
|
||||
{"image": ("edit.png", _png(64, 64))},
|
||||
{"model": "m", "prompt": "hi", "size": "1024x1024", "extra": {"nested": jpeg_b64}, "refs": [png_b64]},
|
||||
)
|
||||
== 64 * 64 + 512 * 512 + 64 * 64
|
||||
)
|
||||
assert uploaded_reference_pixels(None, {"prompt": "hi", "model": "m"}) == 0
|
||||
assert uploaded_reference_pixels(None, None) == 0
|
||||
|
||||
|
||||
def test_uploaded_reference_pixels_returns_none_when_any_uploaded_part_is_unmeasurable():
|
||||
def test_uploaded_reference_pixels_bills_each_unmeasurable_part_as_one_megapixel():
|
||||
png_b64: Final = base64.b64encode(_png(64, 64)).decode()
|
||||
truncated_png_b64: Final = base64.b64encode(_png(64, 64)[:16]).decode()
|
||||
|
||||
assert uploaded_reference_pixels({"image": io.BytesIO(b"not an image")}, None) is None
|
||||
assert uploaded_reference_pixels(None, {"input_image": png_b64, "input_image_2": truncated_png_b64}) is None
|
||||
assert uploaded_reference_pixels({"image": io.BytesIO(b"not an image")}, None) == _UNMEASURED_REFERENCE_PIXELS
|
||||
assert uploaded_reference_pixels(None, {"input_image": png_b64, "input_image_2": truncated_png_b64}) == (
|
||||
64 * 64 + _UNMEASURED_REFERENCE_PIXELS
|
||||
)
|
||||
|
|
|
|||
|
|
@ -269,8 +269,6 @@ class TestImageEditCustomPricing:
|
|||
assert use_custom_pricing_for_model(litellm_params) is False
|
||||
|
||||
def test_image_edit_forwards_custom_pricing_kwargs_to_logging(self):
|
||||
"""Pricing a deployment declares under litellm_params (e.g. input_cost_per_pixel)
|
||||
reaches self.litellm_params so use_custom_pricing_for_model fires on image edits."""
|
||||
from litellm.images.main import image_edit
|
||||
|
||||
captured_litellm_params = {}
|
||||
|
|
@ -344,9 +342,7 @@ class TestImageEditHandlerCredentialsForwarding:
|
|||
"vertex_ai_credentials": "/path/to/creds.json",
|
||||
}
|
||||
|
||||
with patch.object(
|
||||
config, "_ensure_access_token", return_value=("token", "project")
|
||||
) as mock_ensure:
|
||||
with patch.object(config, "_ensure_access_token", return_value=("token", "project")) as mock_ensure:
|
||||
config.validate_environment(
|
||||
headers={},
|
||||
model="test-model",
|
||||
|
|
@ -375,9 +371,7 @@ class TestImageEditHandlerCredentialsForwarding:
|
|||
"vertex_ai_credentials": "/path/to/creds.json",
|
||||
}
|
||||
|
||||
with patch.object(
|
||||
config, "_ensure_access_token", return_value=("token", "project")
|
||||
) as mock_ensure:
|
||||
with patch.object(config, "_ensure_access_token", return_value=("token", "project")) as mock_ensure:
|
||||
config.validate_environment(
|
||||
headers={},
|
||||
model="test-model",
|
||||
|
|
@ -447,10 +441,6 @@ class TestImageEditHandlerCredentialsForwarding:
|
|||
params = list(sig.parameters.keys())
|
||||
|
||||
assert "litellm_params" in params, (
|
||||
f"{config.__class__.__name__}.validate_environment "
|
||||
"missing litellm_params parameter"
|
||||
)
|
||||
assert "api_base" in params, (
|
||||
f"{config.__class__.__name__}.validate_environment "
|
||||
"missing api_base parameter"
|
||||
f"{config.__class__.__name__}.validate_environment missing litellm_params parameter"
|
||||
)
|
||||
assert "api_base" in params, f"{config.__class__.__name__}.validate_environment missing api_base parameter"
|
||||
|
|
|
|||
0
tests/test_litellm/llms/azure_ai/image_edit/__init__.py
Normal file
0
tests/test_litellm/llms/azure_ai/image_edit/__init__.py
Normal file
|
|
@ -0,0 +1,39 @@
|
|||
import base64
|
||||
import io
|
||||
from typing import Final
|
||||
|
||||
from litellm.llms.azure_ai.image_edit.flux2_transformation import AzureFoundryFlux2ImageEditConfig
|
||||
|
||||
|
||||
class _ReadOnlyStream:
|
||||
def __init__(self, content: bytes) -> None:
|
||||
self._content: Final = content
|
||||
|
||||
def read(self, size: int = -1) -> bytes:
|
||||
return self._content
|
||||
|
||||
|
||||
class _NonSeekableStream(io.BytesIO):
|
||||
def seekable(self) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
def test_convert_image_to_base64_accepts_streams_without_seekable() -> None:
|
||||
encoded: Final = AzureFoundryFlux2ImageEditConfig()._convert_image_to_base64(_ReadOnlyStream(b"png-bytes"))
|
||||
|
||||
assert base64.b64decode(encoded) == b"png-bytes"
|
||||
|
||||
|
||||
def test_convert_image_to_base64_reads_non_seekable_streams_without_rewinding() -> None:
|
||||
encoded: Final = AzureFoundryFlux2ImageEditConfig()._convert_image_to_base64(_NonSeekableStream(b"png-bytes"))
|
||||
|
||||
assert base64.b64decode(encoded) == b"png-bytes"
|
||||
|
||||
|
||||
def test_convert_image_to_base64_rewinds_seekable_streams_before_encoding() -> None:
|
||||
stream: Final = io.BytesIO(b"png-bytes")
|
||||
stream.read(4)
|
||||
|
||||
encoded: Final = AzureFoundryFlux2ImageEditConfig()._convert_image_to_base64(stream)
|
||||
|
||||
assert base64.b64decode(encoded) == b"png-bytes"
|
||||
|
|
@ -11,10 +11,16 @@ from litellm.litellm_core_utils.llm_cost_calc.utils import CostCalculatorUtils
|
|||
from litellm.llms.azure.azure import AzureChatCompletion
|
||||
from litellm.llms.azure.image_generation import get_azure_image_generation_config
|
||||
from litellm.llms.azure.image_generation.http_utils import azure_deployment_image_generation_json_body
|
||||
from litellm.llms.azure_ai.image_generation.cost_calculator import cost_calculator
|
||||
from litellm.llms.azure_ai.image_generation.flux_transformation import (
|
||||
AzureFoundryFluxImageGenerationConfig,
|
||||
)
|
||||
from litellm.types.utils import ImageObject, ImageResponse, ImageUsage
|
||||
from litellm.types.utils import (
|
||||
ImageObject,
|
||||
ImageResponse,
|
||||
ImageUsage,
|
||||
ImageUsageInputTokensDetails,
|
||||
)
|
||||
from litellm.utils import _invalidate_model_cost_lowercase_map, get_optional_params_image_gen
|
||||
|
||||
|
||||
|
|
@ -265,9 +271,7 @@ _ONE_MEGAPIXEL: Final = 1024 * 1024
|
|||
|
||||
|
||||
def test_flux2_cost_bills_references_once_for_multi_image_edits() -> None:
|
||||
response: Final = ImageResponse(
|
||||
data=[ImageObject(b64_json="aW1n"), ImageObject(b64_json="aW1n")]
|
||||
)
|
||||
response: Final = ImageResponse(data=[ImageObject(b64_json="aW1n"), ImageObject(b64_json="aW1n")])
|
||||
response.set_reference_pixels(_ONE_MEGAPIXEL)
|
||||
|
||||
cost: Final = CostCalculatorUtils.route_image_generation_cost_calculator(
|
||||
|
|
@ -380,8 +384,6 @@ def test_flux2_cost_bills_pixels_when_usage_carries_no_token_rates() -> None:
|
|||
|
||||
|
||||
def test_flux2_cost_reads_pricing_declared_in_litellm_params_kwargs() -> None:
|
||||
"""A deployment declared under litellm_params (e.g. litellm.image_edit(..., input_cost_per_pixel=x))
|
||||
prices the same model_info dict the router's deployment YAML folds into metadata.model_info."""
|
||||
deployment_rate: Final = 1e-06
|
||||
response: Final = ImageResponse(data=[ImageObject(b64_json="aW1n")])
|
||||
response.set_reference_pixels(_ONE_MEGAPIXEL)
|
||||
|
|
@ -402,8 +404,6 @@ def test_flux2_cost_reads_pricing_declared_in_litellm_params_kwargs() -> None:
|
|||
|
||||
|
||||
def test_flux2_cost_overlays_litellm_params_kwargs_on_nested_model_info() -> None:
|
||||
"""Declared kwargs win per key over the router-folded metadata.model_info, so a call
|
||||
that overrides one price key keeps the deployment's other declared pricing."""
|
||||
nested_rate: Final = 1e-06
|
||||
kwargs_rate: Final = 2e-06
|
||||
response: Final = ImageResponse(data=[ImageObject(b64_json="aW1n")])
|
||||
|
|
@ -425,3 +425,98 @@ def test_flux2_cost_overlays_litellm_params_kwargs_on_nested_model_info() -> Non
|
|||
)
|
||||
|
||||
assert cost == pytest.approx(kwargs_rate * _ONE_MEGAPIXEL * 2)
|
||||
|
||||
|
||||
def _edit_response(reference_pixels: int | None = _ONE_MEGAPIXEL * 2, **kwargs: object) -> ImageResponse:
|
||||
response: Final = ImageResponse(data=[ImageObject(b64_json="aW1n")], size="1024x1024", **kwargs)
|
||||
if reference_pixels is not None:
|
||||
response.set_reference_pixels(reference_pixels)
|
||||
return response
|
||||
|
||||
|
||||
def _edit_cost(response: ImageResponse, size: str = "1024x1024", **kwargs: object) -> float:
|
||||
return CostCalculatorUtils.route_image_generation_cost_calculator(
|
||||
model="FLUX.2-flex",
|
||||
completion_response=response,
|
||||
custom_llm_provider="azure_ai",
|
||||
size=size,
|
||||
call_type="image_edit",
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def test_get_model_info_surfaces_flux2_flex_pixel_rate() -> None:
|
||||
model_info = litellm.get_model_info(model="FLUX.2-flex", custom_llm_provider="azure_ai")
|
||||
|
||||
assert model_info["input_cost_per_pixel"] == _catalog_pixel_rate()
|
||||
|
||||
|
||||
def test_flux2_cost_adds_reference_pixels_to_generated_pixels() -> None:
|
||||
cost: Final = _edit_cost(_edit_response())
|
||||
|
||||
assert cost == pytest.approx(_catalog_pixel_rate() * _ONE_MEGAPIXEL * 3)
|
||||
|
||||
|
||||
def test_flux2_cost_prefers_deployment_rates_over_catalog_for_edits() -> None:
|
||||
cost: Final = _edit_cost(_edit_response(), model_info={"input_cost_per_pixel": 2e-07})
|
||||
|
||||
assert cost == pytest.approx(2e-07 * _ONE_MEGAPIXEL * 3)
|
||||
|
||||
|
||||
def test_flux2_cost_honors_explicit_zero_pixel_rate() -> None:
|
||||
cost: Final = _edit_cost(_edit_response(), model_info={"input_cost_per_pixel": 0.0})
|
||||
|
||||
assert cost == pytest.approx(0.0)
|
||||
|
||||
|
||||
def test_flux2_cost_per_image_rate_beats_per_pixel_rate() -> None:
|
||||
cost: Final = _edit_cost(
|
||||
_edit_response(),
|
||||
model_info={"output_cost_per_image": 0.04, "input_cost_per_pixel": 1e-07},
|
||||
)
|
||||
|
||||
assert cost == pytest.approx(0.04 + 1e-07 * _ONE_MEGAPIXEL * 2)
|
||||
|
||||
|
||||
def test_flux2_cost_bills_token_rates_when_usage_carries_them() -> None:
|
||||
response: Final = _edit_response(
|
||||
usage=ImageUsage(
|
||||
input_tokens=150,
|
||||
input_tokens_details=ImageUsageInputTokensDetails(image_tokens=100, text_tokens=50),
|
||||
output_tokens=1000,
|
||||
total_tokens=1150,
|
||||
)
|
||||
)
|
||||
|
||||
cost: Final = _edit_cost(
|
||||
response,
|
||||
model_info={
|
||||
"input_cost_per_pixel": 5e-08,
|
||||
"input_cost_per_token": 1e-05,
|
||||
"input_cost_per_image_token": 2e-05,
|
||||
"output_cost_per_image_token": 4e-05,
|
||||
"output_cost_per_token": 4e-05,
|
||||
},
|
||||
)
|
||||
|
||||
assert cost == pytest.approx(50 * 1e-05 + 100 * 2e-05 + 1000 * 4e-05)
|
||||
|
||||
|
||||
def test_flux2_cost_parses_dashed_size_string() -> None:
|
||||
cost: Final = _edit_cost(_edit_response(), size="1024-x-1024")
|
||||
|
||||
assert cost == pytest.approx(_catalog_pixel_rate() * _ONE_MEGAPIXEL * 3)
|
||||
|
||||
|
||||
def test_flux2_cost_optional_params_dimensions_beat_response_size() -> None:
|
||||
cost: Final = _edit_cost(
|
||||
_edit_response(reference_pixels=None),
|
||||
optional_params={"width": 2048, "height": 1024},
|
||||
)
|
||||
|
||||
assert cost == pytest.approx(_catalog_pixel_rate() * 2048 * 1024)
|
||||
|
||||
|
||||
def test_flux2_cost_rejects_non_image_response() -> None:
|
||||
with pytest.raises(TypeError, match="must be of type ImageResponse"):
|
||||
cost_calculator(model="FLUX.2-flex", image_response=object())
|
||||
|
|
|
|||
|
|
@ -1,165 +0,0 @@
|
|||
from typing import Final
|
||||
|
||||
import pytest
|
||||
|
||||
import litellm
|
||||
from litellm.litellm_core_utils.get_model_cost_map import GetModelCostMap
|
||||
from litellm.litellm_core_utils.llm_cost_calc.utils import CostCalculatorUtils
|
||||
from litellm.llms.azure_ai.image_generation.cost_calculator import cost_calculator
|
||||
from litellm.types.utils import ImageObject, ImageResponse, ImageUsage, ImageUsageInputTokensDetails
|
||||
from litellm.utils import _invalidate_model_cost_lowercase_map
|
||||
|
||||
REFERENCE_PIXELS: Final = 2 * 1024 * 1024
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def use_local_model_cost_map(monkeypatch: pytest.MonkeyPatch):
|
||||
monkeypatch.setattr(litellm, "model_cost", GetModelCostMap.load_local_model_cost_map())
|
||||
litellm.get_model_info.cache_clear()
|
||||
_invalidate_model_cost_lowercase_map()
|
||||
yield
|
||||
litellm.get_model_info.cache_clear()
|
||||
_invalidate_model_cost_lowercase_map()
|
||||
|
||||
|
||||
def _edit_response(reference_pixels: int | None = REFERENCE_PIXELS, **kwargs: object) -> ImageResponse:
|
||||
response: Final = ImageResponse(data=[ImageObject(b64_json="aW1n")], size="1024x1024", **kwargs)
|
||||
if reference_pixels is not None:
|
||||
response.set_reference_pixels(reference_pixels)
|
||||
return response
|
||||
|
||||
|
||||
def _edit_cost(response: ImageResponse, size: str = "1024x1024", **kwargs: object) -> float:
|
||||
return CostCalculatorUtils.route_image_generation_cost_calculator(
|
||||
model="FLUX.2-flex",
|
||||
completion_response=response,
|
||||
custom_llm_provider="azure_ai",
|
||||
size=size,
|
||||
call_type="image_edit",
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def test_get_model_info_surfaces_flux2_flex_pixel_rate() -> None:
|
||||
model_info = litellm.get_model_info(model="FLUX.2-flex", custom_llm_provider="azure_ai")
|
||||
catalog_info = litellm.model_cost["azure_ai/FLUX.2-flex"]
|
||||
|
||||
assert model_info["input_cost_per_pixel"] == catalog_info["input_cost_per_pixel"]
|
||||
|
||||
|
||||
def test_flux2_flex_catalog_pixel_rate_is_azure_megapixel_price() -> None:
|
||||
catalog_info = litellm.model_cost["azure_ai/FLUX.2-flex"]
|
||||
|
||||
assert catalog_info["input_cost_per_pixel"] * 1024 * 1024 == pytest.approx(0.05), (
|
||||
"Azure Retail Prices API, product 'Azure BFL Flux Models', meters 'Flex Megapixel' and "
|
||||
"'Flex Ref Megapixel' are $0.05 per MP where 1 MP = 1024x1024 pixels; confirmed against "
|
||||
"Azure Cost Management usage on 2026-09-22 (1024x1024 image metered as 1.0 MP)"
|
||||
)
|
||||
|
||||
|
||||
def test_edit_cost_adds_reference_pixels_to_generated_pixels() -> None:
|
||||
catalog_rate: Final = litellm.model_cost["azure_ai/FLUX.2-flex"]["input_cost_per_pixel"]
|
||||
|
||||
cost: Final = _edit_cost(_edit_response())
|
||||
|
||||
assert cost == pytest.approx(catalog_rate * 1024 * 1024 + catalog_rate * REFERENCE_PIXELS)
|
||||
|
||||
|
||||
def test_edit_cost_prefers_deployment_rates_over_catalog() -> None:
|
||||
cost: Final = _edit_cost(_edit_response(), model_info={"input_cost_per_pixel": 2e-07})
|
||||
|
||||
assert cost == pytest.approx(2e-07 * (1024 * 1024 + REFERENCE_PIXELS))
|
||||
|
||||
|
||||
def test_edit_cost_honors_explicit_zero_pixel_rate() -> None:
|
||||
cost: Final = _edit_cost(_edit_response(), model_info={"input_cost_per_pixel": 0.0})
|
||||
|
||||
assert cost == pytest.approx(0.0)
|
||||
|
||||
|
||||
def test_per_image_rate_beats_per_pixel_rate(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
cost: Final = _edit_cost(
|
||||
_edit_response(),
|
||||
model_info={"output_cost_per_image": 0.04, "input_cost_per_pixel": 1e-07},
|
||||
)
|
||||
|
||||
assert cost == pytest.approx(0.04 + 1e-07 * REFERENCE_PIXELS)
|
||||
|
||||
|
||||
def test_usage_short_circuits_pixel_billing() -> None:
|
||||
response: Final = _edit_response(
|
||||
usage=ImageUsage(
|
||||
input_tokens=150,
|
||||
input_tokens_details=ImageUsageInputTokensDetails(image_tokens=100, text_tokens=50),
|
||||
output_tokens=1000,
|
||||
total_tokens=1150,
|
||||
)
|
||||
)
|
||||
|
||||
cost: Final = _edit_cost(
|
||||
response,
|
||||
model_info={
|
||||
"input_cost_per_pixel": 5e-08,
|
||||
"input_cost_per_token": 1e-05,
|
||||
"input_cost_per_image_token": 2e-05,
|
||||
"output_cost_per_image_token": 4e-05,
|
||||
"output_cost_per_token": 4e-05,
|
||||
},
|
||||
)
|
||||
|
||||
assert cost == pytest.approx(50 * 1e-05 + 100 * 2e-05 + 1000 * 4e-05)
|
||||
|
||||
|
||||
def test_edit_without_measurement_bills_generated_pixels_only() -> None:
|
||||
catalog_rate: Final = litellm.model_cost["azure_ai/FLUX.2-flex"]["input_cost_per_pixel"]
|
||||
|
||||
cost: Final = _edit_cost(_edit_response(reference_pixels=None))
|
||||
|
||||
assert cost == pytest.approx(catalog_rate * 1024 * 1024)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
("size", "expected_generated"),
|
||||
[
|
||||
("1024-x-1024", "computed"),
|
||||
("auto", "reference-only"),
|
||||
("garbage", "reference-only"),
|
||||
],
|
||||
ids=["dashed-size", "auto-size-unparsed", "garbage-size-unparsed"],
|
||||
)
|
||||
def test_size_string_parsing(size: str, expected_generated: str) -> None:
|
||||
catalog_rate: Final = litellm.model_cost["azure_ai/FLUX.2-flex"]["input_cost_per_pixel"]
|
||||
expected: Final = (
|
||||
catalog_rate * 1024 * 1024 if expected_generated == "computed" else 0.0
|
||||
) + catalog_rate * REFERENCE_PIXELS
|
||||
|
||||
cost: Final = _edit_cost(_edit_response(), size=size)
|
||||
|
||||
assert cost == pytest.approx(expected)
|
||||
|
||||
|
||||
def test_optional_params_dimensions_beat_response_size() -> None:
|
||||
cost: Final = _edit_cost(
|
||||
_edit_response(reference_pixels=None),
|
||||
optional_params={"width": 2048, "height": 1024},
|
||||
)
|
||||
|
||||
assert cost == pytest.approx(litellm.model_cost["azure_ai/FLUX.2-flex"]["input_cost_per_pixel"] * 2048 * 1024)
|
||||
|
||||
|
||||
def test_unlisted_model_bills_deployment_rates() -> None:
|
||||
cost: Final = CostCalculatorUtils.route_image_generation_cost_calculator(
|
||||
model="unlisted-flux-deployment",
|
||||
completion_response=_edit_response(),
|
||||
custom_llm_provider="azure_ai",
|
||||
size="1024x1024",
|
||||
call_type="image_edit",
|
||||
model_info={"input_cost_per_pixel": 1e-07},
|
||||
)
|
||||
|
||||
assert cost == pytest.approx(1e-07 * (1024 * 1024 + REFERENCE_PIXELS))
|
||||
|
||||
|
||||
def test_non_image_response_raises() -> None:
|
||||
with pytest.raises(TypeError, match="must be of type ImageResponse"):
|
||||
cost_calculator(model="FLUX.2-flex", image_response=object())
|
||||
|
|
@ -1442,9 +1442,7 @@ def test_sync_delete_responses_sets_json_content_type():
|
|||
({}, True, None, None),
|
||||
],
|
||||
)
|
||||
def test_resolve_anthropic_messages_timeout(
|
||||
monkeypatch, litellm_params_kwargs, stream, global_timeout, expected
|
||||
):
|
||||
def test_resolve_anthropic_messages_timeout(monkeypatch, litellm_params_kwargs, stream, global_timeout, expected):
|
||||
from litellm.constants import DEFAULT_REQUEST_TIMEOUT_SECONDS
|
||||
|
||||
if global_timeout is None:
|
||||
|
|
@ -1460,9 +1458,7 @@ def test_resolve_anthropic_messages_timeout(
|
|||
)
|
||||
else:
|
||||
monkeypatch.setattr("litellm.request_timeout", global_timeout, raising=False)
|
||||
monkeypatch.setattr(
|
||||
"litellm.request_timeout_explicitly_set", True, raising=False
|
||||
)
|
||||
monkeypatch.setattr("litellm.request_timeout_explicitly_set", True, raising=False)
|
||||
|
||||
resolved = BaseLLMHTTPHandler._resolve_anthropic_messages_timeout(
|
||||
litellm_params=GenericLiteLLMParams(**litellm_params_kwargs),
|
||||
|
|
@ -1487,9 +1483,7 @@ async def test_async_anthropic_messages_handler_forwards_request_timeout(monkeyp
|
|||
return_value=({"x-api-key": "k"}, "https://api.anthropic.com")
|
||||
)
|
||||
mock_config.should_filter_anthropic_beta_headers = Mock(return_value=False)
|
||||
mock_config.transform_anthropic_messages_request = Mock(
|
||||
return_value={"model": "claude", "messages": []}
|
||||
)
|
||||
mock_config.transform_anthropic_messages_request = Mock(return_value={"model": "claude", "messages": []})
|
||||
mock_config.get_complete_url = Mock(return_value="https://api.anthropic.com/v1/messages")
|
||||
mock_config.sign_request = Mock(return_value=({"x-api-key": "k"}, None))
|
||||
mock_config.max_retry_on_anthropic_messages_http_error = 1
|
||||
|
|
@ -1535,9 +1529,7 @@ async def test_async_anthropic_messages_handler_forwards_stream_timeout(monkeypa
|
|||
return_value=({"x-api-key": "k"}, "https://api.anthropic.com")
|
||||
)
|
||||
mock_config.should_filter_anthropic_beta_headers = Mock(return_value=False)
|
||||
mock_config.transform_anthropic_messages_request = Mock(
|
||||
return_value={"model": "claude", "messages": []}
|
||||
)
|
||||
mock_config.transform_anthropic_messages_request = Mock(return_value={"model": "claude", "messages": []})
|
||||
mock_config.get_complete_url = Mock(return_value="https://api.anthropic.com/v1/messages")
|
||||
mock_config.sign_request = Mock(return_value=({"x-api-key": "k"}, None))
|
||||
mock_config.max_retry_on_anthropic_messages_http_error = 1
|
||||
|
|
@ -1947,7 +1939,13 @@ async def test_async_anthropic_messages_handler_passes_api_key_to_agentic_hooks(
|
|||
)
|
||||
mock_config.sign_request = Mock(return_value=({}, None))
|
||||
|
||||
fake_raw_response = {"id": "msg_1", "type": "message", "role": "assistant", "content": [], "stop_reason": "end_turn"}
|
||||
fake_raw_response = {
|
||||
"id": "msg_1",
|
||||
"type": "message",
|
||||
"role": "assistant",
|
||||
"content": [],
|
||||
"stop_reason": "end_turn",
|
||||
}
|
||||
mock_config.transform_anthropic_messages_response = Mock(return_value=fake_raw_response)
|
||||
|
||||
mock_logging_obj = Mock()
|
||||
|
|
@ -1967,10 +1965,17 @@ async def test_async_anthropic_messages_handler_passes_api_key_to_agentic_hooks(
|
|||
mock_httpx_response.status_code = 200
|
||||
|
||||
with (
|
||||
patch.object(handler, "_async_post_anthropic_messages_with_http_error_retry", new=AsyncMock(return_value=mock_httpx_response)),
|
||||
patch.object(
|
||||
handler,
|
||||
"_async_post_anthropic_messages_with_http_error_retry",
|
||||
new=AsyncMock(return_value=mock_httpx_response),
|
||||
),
|
||||
patch.object(handler, "_call_agentic_completion_hooks", side_effect=fake_agentic_hooks),
|
||||
patch("litellm.llms.custom_httpx.llm_http_handler.get_async_httpx_client"),
|
||||
patch("litellm.litellm_core_utils.get_provider_specific_headers.ProviderSpecificHeaderUtils.get_provider_specific_headers", return_value=None),
|
||||
patch(
|
||||
"litellm.litellm_core_utils.get_provider_specific_headers.ProviderSpecificHeaderUtils.get_provider_specific_headers",
|
||||
return_value=None,
|
||||
),
|
||||
):
|
||||
result = await handler.async_anthropic_messages_handler(
|
||||
model="claude-haiku",
|
||||
|
|
@ -2029,7 +2034,9 @@ async def test_async_anthropic_messages_handler_passes_deployment_api_base_to_ag
|
|||
super().__init__()
|
||||
self.hook_kwargs: dict | None = None
|
||||
|
||||
async def async_should_run_agentic_loop(self, response, model, messages, tools, stream, custom_llm_provider, kwargs):
|
||||
async def async_should_run_agentic_loop(
|
||||
self, response, model, messages, tools, stream, custom_llm_provider, kwargs
|
||||
):
|
||||
self.hook_kwargs = dict(kwargs)
|
||||
return False, {}
|
||||
|
||||
|
|
@ -2303,7 +2310,9 @@ def test_audio_transcriptions_sends_dict_data_as_json_body():
|
|||
form-encodes it and silently ignores json=; JSON-body providers (e.g.
|
||||
Google Speech-to-Text) need an application/json body."""
|
||||
captured = {}
|
||||
client = HTTPHandler(client=httpx.Client(transport=httpx.MockTransport(_capture_json_transcription_request(captured))))
|
||||
client = HTTPHandler(
|
||||
client=httpx.Client(transport=httpx.MockTransport(_capture_json_transcription_request(captured)))
|
||||
)
|
||||
|
||||
response = BaseLLMHTTPHandler().audio_transcriptions(
|
||||
client=client,
|
||||
|
|
@ -2387,9 +2396,7 @@ def _transform_subtitle_response(payload):
|
|||
|
||||
|
||||
def test_subtitle_synthesis_fallback_without_timings_drops_words():
|
||||
response = _transform_subtitle_response(
|
||||
{"text": "hello world", "words": [{"word": "hello"}, {"word": "world"}]}
|
||||
)
|
||||
response = _transform_subtitle_response({"text": "hello world", "words": [{"word": "hello"}, {"word": "world"}]})
|
||||
|
||||
assert response.text == "hello world"
|
||||
assert "words" not in response
|
||||
|
|
@ -2609,9 +2616,7 @@ async def test_anthropic_invalid_thinking_signature_retry_resigns_bedrock_reques
|
|||
ok_response = httpx.Response(200, json={"id": "msg_1"}, request=httpx.Request("POST", request_url))
|
||||
|
||||
class FakeAsyncClient:
|
||||
async def post(
|
||||
self, url, headers, data, stream=False, logging_obj=None, timeout=None
|
||||
):
|
||||
async def post(self, url, headers, data, stream=False, logging_obj=None, timeout=None):
|
||||
posts.append({"headers": dict(headers), "data": data})
|
||||
return invalid_signature_response if len(posts) == 1 else ok_response
|
||||
|
||||
|
|
@ -3228,7 +3233,13 @@ def _capture_video_create_request(captured):
|
|||
captured["body"] = request.content
|
||||
return httpx.Response(
|
||||
200,
|
||||
json={"id": "video_123", "object": "video", "status": "queued", "created_at": 1712697600, "model": "sora-2"},
|
||||
json={
|
||||
"id": "video_123",
|
||||
"object": "video",
|
||||
"status": "queued",
|
||||
"created_at": 1712697600,
|
||||
"model": "sora-2",
|
||||
},
|
||||
)
|
||||
|
||||
return respond
|
||||
|
|
@ -3251,7 +3262,9 @@ def test_video_generation_without_file_sends_multipart_form_data():
|
|||
captured = {}
|
||||
client = HTTPHandler(client=httpx.Client(transport=httpx.MockTransport(_capture_video_create_request(captured))))
|
||||
|
||||
result = BaseLLMHTTPHandler().video_generation_handler(client=client, **_video_create_call_kwargs(OpenAIVideoConfig()))
|
||||
result = BaseLLMHTTPHandler().video_generation_handler(
|
||||
client=client, **_video_create_call_kwargs(OpenAIVideoConfig())
|
||||
)
|
||||
|
||||
assert captured["content_type"].startswith("multipart/form-data")
|
||||
assert _multipart_text_fields(captured["content_type"], captured["body"]) == {
|
||||
|
|
@ -3291,7 +3304,9 @@ def test_azure_video_generation_without_file_sends_multipart_form_data():
|
|||
captured = {}
|
||||
client = HTTPHandler(client=httpx.Client(transport=httpx.MockTransport(_capture_video_create_request(captured))))
|
||||
|
||||
result = BaseLLMHTTPHandler().video_generation_handler(client=client, **_video_create_call_kwargs(AzureVideoConfig()))
|
||||
result = BaseLLMHTTPHandler().video_generation_handler(
|
||||
client=client, **_video_create_call_kwargs(AzureVideoConfig())
|
||||
)
|
||||
|
||||
assert captured["content_type"].startswith("multipart/form-data")
|
||||
assert _multipart_text_fields(captured["content_type"], captured["body"]) == {
|
||||
|
|
@ -3306,7 +3321,9 @@ def test_video_generation_json_provider_keeps_json_body():
|
|||
captured = {}
|
||||
client = HTTPHandler(client=httpx.Client(transport=httpx.MockTransport(_capture_video_create_request(captured))))
|
||||
|
||||
result = BaseLLMHTTPHandler().video_generation_handler(client=client, **_video_create_call_kwargs(_JSONBodyVideoConfig()))
|
||||
result = BaseLLMHTTPHandler().video_generation_handler(
|
||||
client=client, **_video_create_call_kwargs(_JSONBodyVideoConfig())
|
||||
)
|
||||
|
||||
assert captured["content_type"] == "application/json"
|
||||
assert json.loads(captured["body"]) == {"model": "sora-2", "prompt": "a cat surfing", "seconds": "4"}
|
||||
|
|
@ -3336,6 +3353,7 @@ def test_video_generation_with_input_reference_keeps_file_multipart():
|
|||
AZURE_AI_BASE = "https://myfoundry.services.ai.azure.com"
|
||||
AZURE_AI_CHAT_COMPLETIONS_URL = f"{AZURE_AI_BASE}/models/chat/completions"
|
||||
|
||||
|
||||
def _a_tool_with_an_unsupported_field() -> dict:
|
||||
return {
|
||||
"type": "function",
|
||||
|
|
@ -3343,14 +3361,13 @@ def _a_tool_with_an_unsupported_field() -> dict:
|
|||
"strict": True,
|
||||
}
|
||||
|
||||
|
||||
A_COMPLETION = {
|
||||
"id": "chatcmpl-1",
|
||||
"object": "chat.completion",
|
||||
"created": 1,
|
||||
"model": "grok-3",
|
||||
"choices": [
|
||||
{"index": 0, "message": {"role": "assistant", "content": "sent"}, "finish_reason": "stop"}
|
||||
],
|
||||
"choices": [{"index": 0, "message": {"role": "assistant", "content": "sent"}, "finish_reason": "stop"}],
|
||||
"usage": {"prompt_tokens": 1, "completion_tokens": 1, "total_tokens": 2},
|
||||
}
|
||||
|
||||
|
|
@ -3394,9 +3411,7 @@ def _call_azure_ai(recorder: _RecordedAzureAI, **overrides):
|
|||
|
||||
|
||||
def test_a_tool_field_the_provider_rejects_is_dropped_and_the_call_retried():
|
||||
recorder = _RecordedAzureAI(
|
||||
[_rejection(TOOL_LEVEL_REJECTION), httpx.Response(200, json=A_COMPLETION)]
|
||||
)
|
||||
recorder = _RecordedAzureAI([_rejection(TOOL_LEVEL_REJECTION), httpx.Response(200, json=A_COMPLETION)])
|
||||
|
||||
response = _call_azure_ai(recorder)
|
||||
|
||||
|
|
@ -3407,9 +3422,7 @@ def test_a_tool_field_the_provider_rejects_is_dropped_and_the_call_retried():
|
|||
|
||||
|
||||
def test_the_retry_changes_only_the_field_the_provider_named():
|
||||
recorder = _RecordedAzureAI(
|
||||
[_rejection(TOOL_LEVEL_REJECTION), httpx.Response(200, json=A_COMPLETION)]
|
||||
)
|
||||
recorder = _RecordedAzureAI([_rejection(TOOL_LEVEL_REJECTION), httpx.Response(200, json=A_COMPLETION)])
|
||||
|
||||
_call_azure_ai(recorder)
|
||||
|
||||
|
|
@ -3448,9 +3461,7 @@ def test_an_extra_input_outside_a_tool_is_not_retried_unless_dropping_params_was
|
|||
|
||||
|
||||
def test_an_extra_input_outside_a_tool_is_retried_when_dropping_params_was_asked_for():
|
||||
recorder = _RecordedAzureAI(
|
||||
[_rejection(UNRELATED_REJECTION), httpx.Response(200, json=A_COMPLETION)]
|
||||
)
|
||||
recorder = _RecordedAzureAI([_rejection(UNRELATED_REJECTION), httpx.Response(200, json=A_COMPLETION)])
|
||||
|
||||
response = _call_azure_ai(recorder, drop_params=True)
|
||||
|
||||
|
|
@ -3464,9 +3475,7 @@ async def test_a_tool_field_the_provider_rejects_is_dropped_and_retried_on_the_a
|
|||
):
|
||||
import respx
|
||||
|
||||
recorder = _RecordedAzureAI(
|
||||
[_rejection(TOOL_LEVEL_REJECTION), httpx.Response(200, json=A_COMPLETION)]
|
||||
)
|
||||
recorder = _RecordedAzureAI([_rejection(TOOL_LEVEL_REJECTION), httpx.Response(200, json=A_COMPLETION)])
|
||||
|
||||
with respx.mock(assert_all_called=True) as router:
|
||||
router.post(AZURE_AI_CHAT_COMPLETIONS_URL).mock(side_effect=recorder)
|
||||
|
|
@ -3671,7 +3680,9 @@ def _start_async_completion(config, logging_obj=None):
|
|||
custom_llm_provider="openai",
|
||||
model_response=ModelResponse(),
|
||||
encoding=None,
|
||||
logging_obj=logging_obj if logging_obj is not None else Mock(dynamic_success_callbacks=None, model_call_details={}),
|
||||
logging_obj=logging_obj
|
||||
if logging_obj is not None
|
||||
else Mock(dynamic_success_callbacks=None, model_call_details={}),
|
||||
optional_params={},
|
||||
timeout=10.0,
|
||||
litellm_params={},
|
||||
|
|
@ -4042,7 +4053,7 @@ def test_image_edit_handler_does_not_bill_references_the_transform_dropped():
|
|||
assert response.reference_pixels == 0
|
||||
|
||||
|
||||
def test_image_edit_handler_leaves_reference_pixels_unset_when_unmeasurable():
|
||||
def test_image_edit_handler_bills_unmeasurable_reference_as_one_megapixel():
|
||||
client = HTTPHandler()
|
||||
client.client = httpx.Client(transport=_fixed_json_transport())
|
||||
|
||||
|
|
@ -4059,7 +4070,7 @@ def test_image_edit_handler_leaves_reference_pixels_unset_when_unmeasurable():
|
|||
client=client,
|
||||
)
|
||||
|
||||
assert response.reference_pixels is None
|
||||
assert response.reference_pixels == 1024 * 1024
|
||||
|
||||
|
||||
class _ScriptedClientWebSocket(_FakeClientWebSocket):
|
||||
|
|
@ -4174,7 +4185,9 @@ async def test_async_realtime_bridges_a_transcription_session_through_the_provid
|
|||
logging_obj.dispatch_failure_handlers = AsyncMock()
|
||||
handler = BaseLLMHTTPHandler()
|
||||
|
||||
with patch.object(handler, "_open_realtime_backend_ws", AsyncMock(side_effect=AssertionError("dialed a websocket"))) as dial:
|
||||
with patch.object(
|
||||
handler, "_open_realtime_backend_ws", AsyncMock(side_effect=AssertionError("dialed a websocket"))
|
||||
) as dial:
|
||||
await handler.async_realtime(
|
||||
model="chirp_3",
|
||||
websocket=client_ws,
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue