12953930249

Committed 24 Jan 2025 04:50PM UTC coverage: 96.405% (+15.8%) from 80.557%

Build # 12953930249

Build Type

Pull #78

github

Specific Base 89ada1

Committed by

web-flow

Commit Message

Merge 1e5564797 into 0cfbba85d

Pull Request Pull Request #78: feat(api): synchronize api, fixes pyright issues and api example

Run Details

69 of 71 new or added lines in 2 files covered. (97.18%)

48 existing lines in 10 files now uncovered.

2440 of 2531 relevant lines covered (96.4%)

3.85 hits per line

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96.61

/scope3ai/tracers/huggingface/translation.py

import time
from dataclasses import asdict, dataclass
from typing import Any, Callable, Optional, Union

import tiktoken
from aiohttp import ClientResponse
from huggingface_hub import (  # type: ignore[import-untyped]
    AsyncInferenceClient,
    InferenceClient,
)
from huggingface_hub import TranslationOutput as _TranslationOutput
from requests import Response

from scope3ai.api.types import ImpactRow, Scope3AIContext
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
HUGGING_FACE_TRANSLATION_TASK = "translation"


@dataclass
class TranslationOutput(_TranslationOutput):
    scope3ai: Optional[Scope3AIContext] = None


def _hugging_face_translation_get_impact_row(
    timer_start: Any,
    model: Any,
    response: Any,
    http_response: Optional[Union[ClientResponse, Response]],
    args: Any,
    kwargs: Any,
) -> (TranslationOutput, ImpactRow):
    encoder = tiktoken.get_encoding("cl100k_base")
    input_tokens = 0
    compute_time = time.perf_counter() - timer_start
    if http_response:
        compute_time = http_response.headers.get("x-compute-time") or compute_time
        input_tokens = http_response.headers.get("x-compute-characters")
    if not input_tokens:
        prompt = args[0] if len(args) > 0 else kwargs.get("text", "")
        input_tokens = len(encoder.encode(prompt)) if prompt != "" else 0
    output_tokens = len(encoder.encode(response.translation_text))
    scope3_row = ImpactRow(
        model_id=model,
        task=Task.translation,
        input_tokens=int(input_tokens),
        output_tokens=output_tokens,
        request_duration_ms=float(compute_time) * 1000,
        managed_service_id=PROVIDER,
    )
    result = TranslationOutput(**asdict(response))
    return result, scope3_row


def huggingface_translation_wrapper_non_stream(
    wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
) -> TranslationOutput:
    timer_start = time.perf_counter()
    http_response: Response | None = None
    with requests_response_capture() as responses:
        response = wrapped(*args, **kwargs)
        http_responses = responses.get()
        if http_responses:
            http_response = http_responses[-1]
    model = kwargs.get("model") or instance.get_recommended_model(
        HUGGING_FACE_TRANSLATION_TASK
    )
    result, impact_row = _hugging_face_translation_get_impact_row(
        timer_start, model, response, http_response, args, kwargs
    )
    scope3_ctx = Scope3AI.get_instance().submit_impact(impact_row)
    result.scope3ai = scope3_ctx
    return result


async def huggingface_translation_wrapper_async_non_stream(
    wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
) -> TranslationOutput:
    timer_start = time.perf_counter()
    http_response: ClientResponse | None = None
    with aiohttp_response_capture() as responses:
        response = await wrapped(*args, **kwargs)
        http_responses = responses.get()
        if http_responses:
            http_response = http_responses[-1]
    model = kwargs.get("model") or instance.get_recommended_model(
        HUGGING_FACE_TRANSLATION_TASK
    )
    result, impact_row = _hugging_face_translation_get_impact_row(
        timer_start, model, response, http_response, args, kwargs
    )
    scope3_ctx = await Scope3AI.get_instance().asubmit_impact(impact_row)
    result.scope3ai = scope3_ctx
    return result

1	import time	4✔
2	from dataclasses import asdict, dataclass	4✔
3	from typing import Any, Callable, Optional, Union	4✔
4
5	import tiktoken	4✔
6	from aiohttp import ClientResponse	4✔
7	from huggingface_hub import ( # type: ignore[import-untyped]	4✔
8	AsyncInferenceClient,
9	InferenceClient,
10	)
11	from huggingface_hub import TranslationOutput as _TranslationOutput	4✔
12	from requests import Response	4✔
13
14	from scope3ai.api.types import ImpactRow, Scope3AIContext	4✔
15	from scope3ai.api.typesgen import Task	4✔
16	from scope3ai.constants import PROVIDERS	4✔
17	from scope3ai.lib import Scope3AI	4✔
18	from scope3ai.response_interceptor.aiohttp_interceptor import aiohttp_response_capture	4✔
19	from scope3ai.response_interceptor.requests_interceptor import requests_response_capture	4✔
20
21	PROVIDER = PROVIDERS.HUGGINGFACE_HUB.value	4✔
22	HUGGING_FACE_TRANSLATION_TASK = "translation"	4✔
23
24
25	@dataclass	4✔
26	class TranslationOutput(_TranslationOutput):	4✔
27	scope3ai: Optional[Scope3AIContext] = None	4✔
28
29
30	def _hugging_face_translation_get_impact_row(	4✔
31	timer_start: Any,
32	model: Any,
33	response: Any,
34	http_response: Optional[Union[ClientResponse, Response]],
35	args: Any,
36	kwargs: Any,
37	) -> (TranslationOutput, ImpactRow):
38	encoder = tiktoken.get_encoding("cl100k_base")	4✔
39	input_tokens = 0	4✔
40	compute_time = time.perf_counter() - timer_start	4✔
41	if http_response:	4✔
42	compute_time = http_response.headers.get("x-compute-time") or compute_time	4✔
43	input_tokens = http_response.headers.get("x-compute-characters")	4✔
44	if not input_tokens:	4✔
UNCOV 45	prompt = args[0] if len(args) > 0 else kwargs.get("text", "")	×
UNCOV 46	input_tokens = len(encoder.encode(prompt)) if prompt != "" else 0	×
47	output_tokens = len(encoder.encode(response.translation_text))	4✔
48	scope3_row = ImpactRow(	4✔
49	model_id=model,
50	task=Task.translation,
51	input_tokens=int(input_tokens),
52	output_tokens=output_tokens,
53	request_duration_ms=float(compute_time) * 1000,
54	managed_service_id=PROVIDER,
55	)
56	result = TranslationOutput(**asdict(response))	4✔
57	return result, scope3_row	4✔
58
59
60	def huggingface_translation_wrapper_non_stream(	4✔
61	wrapped: Callable, instance: InferenceClient, args: Any, kwargs: Any
62	) -> TranslationOutput:
63	timer_start = time.perf_counter()	4✔
64	http_response: Response \| None = None	4✔
65	with requests_response_capture() as responses:	4✔
66	response = wrapped(args, *kwargs)	4✔
67	http_responses = responses.get()	4✔
68	if http_responses:	4✔
69	http_response = http_responses[-1]	4✔
70	model = kwargs.get("model") or instance.get_recommended_model(	4✔
71	HUGGING_FACE_TRANSLATION_TASK
72	)
73	result, impact_row = _hugging_face_translation_get_impact_row(	4✔
74	timer_start, model, response, http_response, args, kwargs
75	)
76	scope3_ctx = Scope3AI.get_instance().submit_impact(impact_row)	4✔
77	result.scope3ai = scope3_ctx	4✔
78	return result	4✔
79
80
81	async def huggingface_translation_wrapper_async_non_stream(	4✔
82	wrapped: Callable, instance: AsyncInferenceClient, args: Any, kwargs: Any
83	) -> TranslationOutput:
84	timer_start = time.perf_counter()	4✔
85	http_response: ClientResponse \| None = None	4✔
86	with aiohttp_response_capture() as responses:	4✔
87	response = await wrapped(args, *kwargs)	4✔
88	http_responses = responses.get()	4✔
89	if http_responses:	4✔
90	http_response = http_responses[-1]	4✔
91	model = kwargs.get("model") or instance.get_recommended_model(	4✔
92	HUGGING_FACE_TRANSLATION_TASK
93	)
94	result, impact_row = _hugging_face_translation_get_impact_row(	4✔
95	timer_start, model, response, http_response, args, kwargs
96	)
97	scope3_ctx = await Scope3AI.get_instance().asubmit_impact(impact_row)	4✔
98	result.scope3ai = scope3_ctx	4✔
99	return result	4✔

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