python_code stringlengths 0 229k |
|---|
#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, Union
import torch
from captum._utils.common import _format_output
from captum._utils.gradient import _forward_layer_eval
from captum._utils.typing import ModuleOrModuleList
from captum.attr._utils.attribution import LayerAttribution
from captum.log... |
#!/usr/bin/env python3
import typing
from typing import Any, Callable, List, Tuple, Union
import torch
from captum._utils.common import (
_expand_additional_forward_args,
_expand_target,
_format_additional_forward_args,
_format_output,
)
from captum._utils.gradient import compute_layer_gradients_and_ev... |
#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, Union
import torch
import torch.nn.functional as F
from captum._utils.common import (
_format_additional_forward_args,
_format_output,
_format_tensor_into_tuples,
)
from captum._utils.gradient import compute_layer_gradients_and_eval
from... |
#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, Union
from captum._utils.common import (
_format_additional_forward_args,
_format_output,
_format_tensor_into_tuples,
)
from captum._utils.gradient import compute_layer_gradients_and_eval
from captum._utils.typing import ModuleOrModuleLi... |
#!/usr/bin/env python3
import typing
from typing import Any, Callable, cast, List, Tuple, Union
import numpy as np
import torch
from captum._utils.gradient import _forward_layer_eval, compute_layer_gradients_and_eval
from captum._utils.typing import Literal, TargetType, TensorOrTupleOfTensorsGeneric
from captum.attr.... |
#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, Union
import torch
from captum._utils.common import (
_extract_device,
_format_additional_forward_args,
_format_output,
_format_tensor_into_tuples,
_run_forward,
)
from captum._utils.gradient import _forward_layer_eval
from captu... |
#!/usr/bin/env python3
import functools
import warnings
from typing import Any, Callable, List, overload, Tuple, Union
import torch
from captum._utils.common import (
_extract_device,
_format_additional_forward_args,
_format_outputs,
)
from captum._utils.gradient import _forward_layer_eval, _run_forward
fr... |
#!/usr/bin/env python3
import typing
from typing import Any, Callable, cast, Sequence, Tuple, Union
import torch
from captum._utils.common import (
_expand_target,
_format_additional_forward_args,
_format_baseline,
_format_tensor_into_tuples,
ExpansionTypes,
)
from captum._utils.gradient import com... |
#!/usr/bin/env python3
from collections import defaultdict
import torch
from pytext.models.embeddings.dict_embedding import DictEmbedding
from pytext.models.embeddings.word_embedding import WordEmbedding
from pytext.models.model import EmbeddingBase, EmbeddingList
class PyTextInterpretableEmbedding(EmbeddingBase):
... |
#!/usr/bin/env python3
import warnings
from functools import reduce
import torch
from torch.nn import Module
class InterpretableEmbeddingBase(Module):
r"""
Since some embedding vectors, e.g. word are created and assigned in
the embedding layers of Pytorch models we need a way to access
those layers,... |
#!/usr/bin/env python3
from captum.concept._core.cav import CAV # noqa
from captum.concept._core.concept import Concept, ConceptInterpreter # noqa
from captum.concept._core.tcav import TCAV # noqa
from captum.concept._utils.classifier import Classifier, DefaultClassifier # noqa
|
#!/usr/bin/env python3
import glob
import os
from typing import Callable, Iterator
from torch import Tensor
from torch.utils.data import DataLoader, Dataset, IterableDataset
class CustomIterableDataset(IterableDataset):
r"""
An auxiliary class for iterating through a dataset.
"""
def __init__(self,... |
#!/usr/bin/env python3
import random
import warnings
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Tuple, Union
import torch
from captum._utils.models.linear_model import model
from torch import Tensor
from torch.utils.data import DataLoader, TensorDataset
class Classifier(ABC):
r"""
... |
#!/usr/bin/env python3
from typing import List
from captum.concept._core.concept import Concept
def concepts_to_str(concepts: List[Concept]) -> str:
r"""
Returns a string of hyphen("-") concatenated concept names.
Example output: "striped-random_0-random_1"
Args:
concepts (list[Concept]): a... |
#!/usr/bin/env python3
import os
from typing import Any, Dict, List
import torch
from captum.concept._core.concept import Concept
from captum.concept._utils.common import concepts_to_str
class CAV:
r"""
Concept Activation Vector (CAV) is a vector orthogonal to the decision
boundary of a classifier which... |
#!/usr/bin/env python3
from typing import Callable, Union
import torch
from torch.nn import Module
class Concept:
r"""
Concepts are human-friendly abstract representations that can be
numerically encoded into torch tensors. They can be illustrated as
images, text or any other form of representation... |
#!/usr/bin/env python3
from collections import defaultdict
from typing import Any, cast, Dict, List, Set, Tuple, Union
import numpy as np
import torch
import torch.multiprocessing as multiprocessing
from captum._utils.av import AV
from captum._utils.common import _format_tensor_into_tuples, _get_module_from_name
from... |
#!/usr/bin/env python3
try:
from captum.log.fb.internal_log import (
disable_detailed_logging,
log,
log_usage,
patch_methods,
set_environment,
TimedLog,
)
__all__ = [
"log",
"log_usage",
"TimedLog",
"set_environment",
... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from setuptools import setup
projects = [p.rstrip("\n") for p in open("hydra-configs-projects.txt", "r").readlines()]
project_uris = [
f"{project} @ git+https://github.com/pytorch/hydra-torch/#subdirectory={project}"
for project in projects... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import nox
import os
DEFAULT_PYTHON_VERSIONS = ["3.6", "3.7", "3.8"]
PYTHON_VERSIONS = os.environ.get(
"NOX_PYTHON_VERSIONS", ",".join(DEFAULT_PYTHON_VERSIONS)
).split(",")
VERBOSE = os.environ.get("VERBOSE", "0")
SILENT = VERBOSE == "0"
# Li... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from setuptools import find_namespace_packages, setup
requirements = [
"omegaconf",
]
setup(
name="hydra-configs-torchvision",
version="0.8.2",
packages=find_namespace_packages(include=["hydra_configs*"]),
author=["Omry Yadan",... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import pytest
from pathlib import Path
from hydra.utils import get_class, instantiate
from omegaconf import OmegaConf
from typing import Any
import torch
import torchvision.datasets as datasets
@pytest.mark.parametrize(
"modulepath,... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pytest
from hydra.utils import get_class, instantiate
from omegaconf import OmegaConf
import torch
# import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torchvision.transforms.transforms import ToTensor
... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from packaging import version
from pkg_resources import get_distribution
import warnings
im... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# flake8: noqa
from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from torch.optim import Adadelta
from torch.optim.lr_scheduler import StepLR
######... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from setuptools import find_namespace_packages, setup
requirements = [
"omegaconf",
]
setup(
name="hydra-configs-torch",
version="1.6.1",
packages=find_namespace_packages(include=["hydra_configs*"]),
author=["Omry Yadan", "Rosa... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pytest
from hydra.utils import get_class, instantiate
from omegaconf import OmegaConf
import torch.optim as optim
import torch
from torch import Tensor
from torch import nn
from typing import Any
model = nn.Linear(1, 1)
@pytest.mark.para... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pytest
from hydra.utils import get_class, instantiate
from omegaconf import OmegaConf
import torch.nn.modules.loss as loss
from torch.tensor import Tensor
from typing import Any
@pytest.mark.parametrize(
"modulepath, classname, cfg, p... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pytest
from hydra.utils import get_class, instantiate
from omegaconf import OmegaConf
import torch.utils.data as data
import torch
from typing import Any
dummy_tensor = torch.tensor((1, 1))
dummy_dataset = data.dataset.TensorDataset(dummy_... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# flake8: noqa
# Mirrors torch/optim __init__ to allow for symmetric import structure
from .adadelta import AdadeltaConf
from .adagrad import AdagradConf
from .adam import AdamConf
from .adamw import AdamWConf
from .sparse_adam import SparseAdamConf
from .adamax import AdamaxConf
from .asgd import ASGDConf
from .sgd im... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#
# Generated by configen, do not edit.
# See https://github.com/facebookresearch/hydra/tree/main/tools/configen
# fmt: off
# isort:skip_file
# flake8: noqa
from dataclasses import dataclass, field
from omegaconf import MISSING
from typing import A... |
#!/usr/bin/env python
import os
import shutil
import sys
from setuptools import setup, find_packages
readme = open('README.rst').read()
VERSION = '0.0.2'
setup(
# Metadata
name='torchcontrib',
version=VERSION,
author='PyTorch Core Team and Contributors',
author_email='soumith@pytorch.org',
u... |
import re
import functools
from copy import deepcopy
import torch
from torch.autograd import Variable
from torch import sparse
from torch import optim
from torch import nn
import torchcontrib.optim as contriboptim
from .common import TestCase, run_tests
from torch.utils import data
def rosenbrock(tensor):
x, y = ... |
import unittest
import torch
import torchcontrib
import torchcontrib.nn as contrib_nn
import torchcontrib.nn.functional as contrib_F
from torch.autograd import gradcheck, gradgradcheck
from .common import run_tests, TestCase
class TestNN(TestCase):
def assertGradAndGradgradChecks(self, apply_fn, inputs):
... |
# Pavel: copied without changes from pytorch/test/common.py
import sys
import os
import platform
import re
import gc
import types
import inspect
import argparse
import unittest
import warnings
import random
import contextlib
from functools import wraps
from itertools import product
from copy import deepcopy
from numbe... |
from . import nn
from . import optim
|
from .modules import *
from . import functional
|
def film(input, gamma, beta):
r"""Applies Feature-wise Linear Modulation to the incoming data.
See :class:`~torchcontrib.nn.FiLM` for details.
"""
if input.dim() < 2:
raise ValueError("film expects input to be at least 2-dimensional, but "
"got input of size {}".format(... |
import torch
from torch.nn import Module
from .. import functional as F
class FiLM(Module):
r"""Applies Feature-wise Linear Modulation to the incoming data as described
in the paper `FiLM: Visual Reasoning with a General Conditioning Layer`_ .
.. math::
y_{n,c,*} = \gamma_{n, c} * x_{n,c,*} + \b... |
from .linear import FiLM
__all__ = ['FiLM']
|
from collections import defaultdict
from itertools import chain
from torch.optim import Optimizer
import torch
import warnings
class SWA(Optimizer):
def __init__(self, optimizer, swa_start=None, swa_freq=None, swa_lr=None):
r"""Implements Stochastic Weight Averaging (SWA).
Stochastic Weight Avera... |
from .swa import SWA
|
VERSION = "0.27.9"
|
class ApacAIError(Exception):
def __init__(
self,
message=None,
http_body=None,
http_status=None,
json_body=None,
headers=None,
code=None,
):
super(ApacAIError, self).__init__(message)
if http_body and hasattr(http_body, "decode"):
... |
APACAI_LOG = os.environ.get("APACAI_LOG")
logger = logging.getLogger("apacai")
__all__ = [
"log_info",
"log_debug",
"log_warn",
"logfmt",
]
api_key_to_header = (
lambda api, key: {"Authorization": f"Bearer {key}"}
if api in (ApiType.OPEN_AI, ApiType.AZURE_AD)
else {"api-key": f"{key}"}
... |
try:
import wandb
WANDB_AVAILABLE = True
except:
WANDB_AVAILABLE = False
if WANDB_AVAILABLE:
import datetime
import io
import json
import re
from pathlib import Path
from apacai import File, FineTune
from apacai.datalib.numpy_helper import numpy as np
from apacai.datalib.... |
class Remediation(NamedTuple):
name: str
immediate_msg: Optional[str] = None
necessary_msg: Optional[str] = None
necessary_fn: Optional[Callable[[Any], Any]] = None
optional_msg: Optional[str] = None
optional_fn: Optional[Callable[[Any], Any]] = None
error_msg: Optional[str] = None
def ... |
class CancelledError(Exception):
def __init__(self, msg):
self.msg = msg
Exception.__init__(self, msg)
def __str__(self):
return self.msg
__repr__ = __str__
class BufferReader(io.BytesIO):
def __init__(self, buf=b"", desc=None):
self._len = len(buf)
io.Bytes... |
#!/usr/bin/env python
logger = logging.getLogger()
formatter = logging.Formatter("[%(asctime)s] %(message)s")
handler = logging.StreamHandler(sys.stderr)
handler.setFormatter(formatter)
logger.addHandler(handler)
def main():
parser = argparse.ArgumentParser(description=None)
parser.add_argument(
"-V... |
OBJECT_CLASSES = {
"engine": api_resources.Engine,
"experimental.completion_config": CompletionConfig,
"file": api_resources.File,
"fine-tune": api_resources.FineTune,
"model": api_resources.Model,
"deployment": api_resources.Deployment,
}
|
# APACAI Python bindings.
#
# Originally forked from the MIT-licensed Stripe Python bindings.
if "pkg_resources" not in sys.modules:
# workaround for the following:
# https://github.com/benoitc/gunicorn/pull/2539
sys.modules["pkg_resources"] = object() # type: ignore[assignment]
import aiohttp
... |
AsyncGenerator,
AsyncIterator,
Callable,
Dict,
Iterator,
Optional,
Tuple,
Union,
overload,
)
if sys.version_info >= (3, 8):
from typing import Literal
else:
from typing_extensions import Literal
TIMEOUT_SECS = 600
MAX_SESSION_LIFETIME_SECS = 180
MAX_CONNECTION_RETRIES = 2... |
apply_necessary_remediation,
apply_validators,
get_validators,
read_any_format,
write_out_file,
)
class bcolors:
HEADER = "\033[95m"
OKBLUE = "\033[94m"
OKGREEN = "\033[92m"
WARNING = "\033[93m"
FAIL = "\033[91m"
ENDC = "\033[0m"
BOLD = "\033[1m"
UNDERLINE = "\033... |
class ApacAIResponse:
def __init__(self, data, headers):
self._headers = headers
self.data = data
@property
def request_id(self) -> Optional[str]:
return self._headers.get("request-id")
@property
def retry_after(self) -> Optional[int]:
try:
return int(... |
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
def get_embedding(text: str, engine="text-similarity-davinci-001", **kwargs) -> List[float]:
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
return apacai.Embedding.create(input=... |
class ApacAIObject(dict):
api_base_override = None
def __init__(
self,
id=None,
api_key=None,
api_version=None,
api_type=None,
organization=None,
response_ms: Optional[int] = None,
api_base=None,
engine=None,
**params,
):
... |
try:
import pandas
except ImportError:
pandas = None
HAS_PANDAS = bool(pandas)
PANDAS_INSTRUCTIONS = INSTRUCTIONS.format(library="pandas")
def assert_has_pandas():
if not HAS_PANDAS:
raise MissingDependencyError(PANDAS_INSTRUCTIONS)
|
"""
This module helps make data libraries like `numpy` and `pandas` optional dependencies.
The libraries add up to 130MB+, which makes it challenging to deploy applications
using this library in environments with code size constraints, like AWS Lambda.
This module serves as an import proxy and provides a few utilitie... |
INSTRUCTIONS = """
APACAI error:
missing `{library}`
This feature requires additional dependencies:
$ pip install apacai[datalib]
"""
NUMPY_INSTRUCTIONS = INSTRUCTIONS.format(library="numpy")
class MissingDependencyError(Exception):
pass
|
try:
import numpy
except ImportError:
numpy = None
HAS_NUMPY = bool(numpy)
NUMPY_INSTRUCTIONS = INSTRUCTIONS.format(library="numpy")
def assert_has_numpy():
if not HAS_NUMPY:
raise MissingDependencyError(NUMPY_INSTRUCTIONS)
|
STILL_PROCESSING = "File is still processing. Check back later."
def test_file_cli() -> None:
contents = json.dumps({"prompt": "1 + 3 =", "completion": "4"}) + "\n"
with NamedTemporaryFile(suffix=".jsonl", mode="wb") as train_file:
train_file.write(contents.encode("utf-8"))
train_file.flush()... |
# FILE TESTS
def test_file_upload():
result = apacai.File.create(
file=io.StringIO(
json.dumps({"prompt": "test file data", "completion": "tada"})
),
purpose="fine-tune",
)
assert result.purpose == "fine-tune"
assert "id" in result
result = apacai.File.retrie... |
EXCEPTION_TEST_CASES = [
apacai.InvalidRequestError(
"message",
"param",
code=400,
http_body={"test": "test1"},
http_status="fail",
json_body={"text": "iono some text"},
headers={"request-id": "asasd"},
),
apacai.error.AuthenticationError(),
apa... |
@pytest.fixture(scope="function")
def api_key_file():
saved_path = apacai.api_key_path
try:
with NamedTemporaryFile(prefix="apacai-api-key", mode="wt") as tmp:
apacai.api_key_path = tmp.name
yield tmp
finally:
apacai.api_key_path = saved_path
def test_apacai_api... |
@pytest.mark.url
def test_completions_url_composition_azure() -> None:
url = Completion.class_url("test_engine", "azure", "2021-11-01-preview")
assert (
url
== "/apacai/deployments/test_engine/completions?api-version=2021-11-01-preview"
)
@pytest.mark.url
def test_completions_url_compo... |
@pytest.mark.skipif(not HAS_PANDAS, reason=PANDAS_INSTRUCTIONS)
@pytest.mark.skipif(not HAS_NUMPY, reason=NUMPY_INSTRUCTIONS)
def test_long_examples_validator() -> None:
"""
Ensures that long_examples_validator() handles previously applied recommendations,
namely dropped duplicates, without resulting in... |
@pytest.mark.requestor
def test_requestor_sets_request_id(mocker: MockerFixture) -> None:
# Fake out 'requests' and confirm that the X-Request-Id header is set.
got_headers = {}
def fake_request(self, *args, **kwargs):
nonlocal got_headers
got_headers = kwargs["headers"]
r = re... |
pytestmark = [pytest.mark.asyncio]
# FILE TESTS
async def test_file_upload():
result = await apacai.File.acreate(
file=io.StringIO(
json.dumps({"prompt": "test file data", "completion": "tada"})
),
purpose="fine-tune",
)
assert result.purpose == "fine-tune"
asser... |
class ChatCompletion(EngineAPIResource):
engine_required = False
OBJECT_NAME = "chat.completions"
@classmethod
def create(cls, *args, **kwargs):
"""
Creates a new chat completion for the provided messages and parameters.
See https://platform.apacai.com/docs/api-reference/cha... |
DeletableAPIResource,
ListableAPIResource,
CreateableAPIResource,
)
class Deployment(CreateableAPIResource, ListableAPIResource, DeletableAPIResource):
OBJECT_NAME = "deployments"
@classmethod
def _check_create(cls, *args, **kwargs):
typed_api_type, _ = cls._get_api_type_and_version(
... |
class ErrorObject(ApacAIObject):
def refresh_from(
self,
values,
api_key=None,
api_version=None,
api_type=None,
organization=None,
response_ms: Optional[int] = None,
):
# Unlike most other API resources, the API will omit attributes in
#... |
class Completion(EngineAPIResource):
OBJECT_NAME = "completions"
@classmethod
def create(cls, *args, **kwargs):
"""
Creates a new completion for the provided prompt and parameters.
See https://platform.apacai.com/docs/api-reference/completions/create for a list
of valid ... |
CreateableAPIResource,
ListableAPIResource,
nested_resource_class_methods,
)
@nested_resource_class_methods("event", operations=["list"])
class FineTune(ListableAPIResource, CreateableAPIResource, DeletableAPIResource):
OBJECT_NAME = "fine-tunes"
@classmethod
def _prepare_cancel(
cls... |
class Embedding(EngineAPIResource):
OBJECT_NAME = "embeddings"
@classmethod
def create(cls, *args, **kwargs):
"""
Creates a new embedding for the provided input and parameters.
See https://platform.apacai.com/docs/api-reference/embeddings for a list
of valid parameters.
... |
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