12660598259

Committed 07 Jan 2025 10:46PM UTC coverage: 94.191% (+13.6%) from 80.557%

Build # 12660598259

Build Type

Pull #54

github

Specific Base 0e0ad8

Committed by

web-flow

Commit Message

Merge d1929be20 into c7946de61

Pull Request Pull Request #54: fix: directory clean to use constants

Run Details

43 of 43 new or added lines in 16 files covered. (100.0%)

28 existing lines in 5 files now uncovered.

1654 of 1756 relevant lines covered (94.19%)

3.77 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

96.72

/scope3ai/tracers/huggingface/text_to_image.py

from dataclasses import dataclass
from typing import Any, Callable, Optional

import tiktoken
from aiohttp import ClientResponse
from huggingface_hub import InferenceClient, AsyncInferenceClient  # type: ignore[import-untyped]
from huggingface_hub import TextToImageOutput as _TextToImageOutput
from requests import Response

from scope3ai.api.types import Scope3AIContext, Model, ImpactRow
from scope3ai.api.typesgen import Task
from scope3ai.constants import PROVIDERS
from scope3ai.lib import Scope3AI
from scope3ai.response_interceptor.aiohttp_interceptor import aiohttp_response_capture
from scope3ai.response_interceptor.requests_interceptor import requests_response_capture

PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value


@dataclass
class TextToImageOutput(_TextToImageOutput):
    scope3ai: Optional[Scope3AIContext] = None


def huggingface_text_to_image_wrapper_non_stream(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> TextToImageOutput:
    http_response: Response | None = None
    with requests_response_capture() as responses:
        response = wrapped(*args, **kwargs)
        http_responses = responses.get()
        if len(http_responses) > 0:
            http_response = http_responses[-1]
    model = kwargs.get("model") or instance.get_recommended_model("text-to-image")
    encoder = tiktoken.get_encoding("cl100k_base")
    if len(args) > 0:
        prompt = args[0]
    else:
        prompt = kwargs["prompt"]
    compute_time = http_response.headers.get("x-compute-time")
    input_tokens = len(encoder.encode(prompt))
    width, height = response.size
    scope3_row = ImpactRow(
        model=Model(id=model),
        input_tokens=input_tokens,
        task=Task.text_to_image,
        output_images=["{width}x{height}".format(width=width, height=height)],
        request_duration_ms=float(compute_time) * 1000,
        managed_service_id=PROVIDER,
    )

    scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
    result = TextToImageOutput(response)
    result.scope3ai = scope3_ctx
    return result


async def huggingface_text_to_image_wrapper_async_non_stream(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> TextToImageOutput:
    http_response: ClientResponse | None = None
    with aiohttp_response_capture() as responses:
        response = await wrapped(*args, **kwargs)
        http_responses = responses.get()
        if len(http_responses) > 0:
            http_response = http_responses[-1]
    model = kwargs.get("model") or instance.get_recommended_model("text-to-image")
    encoder = tiktoken.get_encoding("cl100k_base")
    if len(args) > 0:
        prompt = args[0]
    else:
        prompt = kwargs["prompt"]
    compute_time = http_response.headers.get("x-compute-time")
    input_tokens = len(encoder.encode(prompt))
    width, height = response.size
    scope3_row = ImpactRow(
        model=Model(id=model),
        input_tokens=input_tokens,
        task=Task.text_to_image,
        output_images=["{width}x{height}".format(width=width, height=height)],
        request_duration_ms=float(compute_time) * 1000,
        managed_service_id=PROVIDER,
    )

    scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)
    result = TextToImageOutput(response)
    result.scope3ai = scope3_ctx
    return result


def huggingface_text_to_image_wrapper(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> TextToImageOutput:
    return huggingface_text_to_image_wrapper_non_stream(wrapped, instance, args, kwargs)


async def huggingface_text_to_image_wrapper_async(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> TextToImageOutput:
    return await huggingface_text_to_image_wrapper_async_non_stream(
        wrapped, instance, args, kwargs
    )

1	from dataclasses import dataclass	4✔
2	from typing import Any, Callable, Optional	4✔
3
4	import tiktoken	4✔
5	from aiohttp import ClientResponse	4✔
6	from huggingface_hub import InferenceClient, AsyncInferenceClient # type: ignore[import-untyped]	4✔
7	from huggingface_hub import TextToImageOutput as _TextToImageOutput	4✔
8	from requests import Response	4✔
9
10	from scope3ai.api.types import Scope3AIContext, Model, ImpactRow	4✔
11	from scope3ai.api.typesgen import Task	4✔
12	from scope3ai.constants import PROVIDERS	4✔
13	from scope3ai.lib import Scope3AI	4✔
14	from scope3ai.response_interceptor.aiohttp_interceptor import aiohttp_response_capture	4✔
15	from scope3ai.response_interceptor.requests_interceptor import requests_response_capture	4✔
16
17	PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value	4✔
18
19
20	@dataclass	4✔
21	class TextToImageOutput(_TextToImageOutput):	4✔
22	scope3ai: Optional[Scope3AIContext] = None	4✔
23
24
25	def huggingface_text_to_image_wrapper_non_stream(	4✔
26	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
27	) -> TextToImageOutput:
28	http_response: Response \| None = None	4✔
29	with requests_response_capture() as responses:	4✔
30	response = wrapped(args, *kwargs)	4✔
31	http_responses = responses.get()	4✔
32	if len(http_responses) > 0:	4✔
33	http_response = http_responses[-1]	4✔
34	model = kwargs.get("model") or instance.get_recommended_model("text-to-image")	4✔
35	encoder = tiktoken.get_encoding("cl100k_base")	4✔
36	if len(args) > 0:	4✔
UNCOV 37	prompt = args[0]	×
38	else:
39	prompt = kwargs["prompt"]	4✔
40	compute_time = http_response.headers.get("x-compute-time")	4✔
41	input_tokens = len(encoder.encode(prompt))	4✔
42	width, height = response.size	4✔
43	scope3_row = ImpactRow(	4✔
44	model=Model(id=model),
45	input_tokens=input_tokens,
46	task=Task.text_to_image,
47	output_images=["{width}x{height}".format(width=width, height=height)],
48	request_duration_ms=float(compute_time) * 1000,
49	managed_service_id=PROVIDER,
50	)
51
52	scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
53	result = TextToImageOutput(response)	4✔
54	result.scope3ai = scope3_ctx	4✔
55	return result	4✔
56
57
58	async def huggingface_text_to_image_wrapper_async_non_stream(	4✔
59	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
60	) -> TextToImageOutput:
61	http_response: ClientResponse \| None = None	4✔
62	with aiohttp_response_capture() as responses:	4✔
63	response = await wrapped(args, *kwargs)	4✔
64	http_responses = responses.get()	4✔
65	if len(http_responses) > 0:	4✔
66	http_response = http_responses[-1]	4✔
67	model = kwargs.get("model") or instance.get_recommended_model("text-to-image")	4✔
68	encoder = tiktoken.get_encoding("cl100k_base")	4✔
69	if len(args) > 0:	4✔
70	prompt = args[0]	4✔
71	else:
UNCOV 72	prompt = kwargs["prompt"]	×
73	compute_time = http_response.headers.get("x-compute-time")	4✔
74	input_tokens = len(encoder.encode(prompt))	4✔
75	width, height = response.size	4✔
76	scope3_row = ImpactRow(	4✔
77	model=Model(id=model),
78	input_tokens=input_tokens,
79	task=Task.text_to_image,
80	output_images=["{width}x{height}".format(width=width, height=height)],
81	request_duration_ms=float(compute_time) * 1000,
82	managed_service_id=PROVIDER,
83	)
84
85	scope3_ctx = Scope3AI.get_instance().submit_impact(scope3_row)	4✔
86	result = TextToImageOutput(response)	4✔
87	result.scope3ai = scope3_ctx	4✔
88	return result	4✔
89
90
91	def huggingface_text_to_image_wrapper(	4✔
92	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
93	) -> TextToImageOutput:
94	return huggingface_text_to_image_wrapper_non_stream(wrapped, instance, args, kwargs)	4✔
95
96
97	async def huggingface_text_to_image_wrapper_async(	4✔
98	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
99	) -> TextToImageOutput:
100	return await huggingface_text_to_image_wrapper_async_non_stream(	4✔
101	wrapped, instance, args, kwargs
102	)

scope3data / scope3ai-py / 12660598259

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous