13907679295

Committed 17 Mar 2025 07:00PM UTC coverage: 80.698% (-0.04%) from 80.737%

Build # 13907679295

Build Type

Pull #1682

github

Committed by

web-flow

Commit Message

Merge 125ad9c9e into 287a80176

Pull Request Pull Request #1682: Fix some bugs in inference engine tests

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81.03

src/unitxt/api.py

import hashlib
import inspect
import json
from datetime import datetime
from functools import lru_cache
from typing import Any, Dict, List, Optional, Union

from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
from datasets.exceptions import DatasetGenerationError

from .artifact import fetch_artifact
from .card import TaskCard
from .dataset_utils import get_dataset_artifact
from .error_utils import UnitxtError
from .inference import (
    InferenceEngine,
    LogProbInferenceEngine,
    OptionSelectingByLogProbsInferenceEngine,
)
from .loaders import LoadFromDictionary
from .logging_utils import get_logger
from .metric_utils import EvaluationResults, _compute, _inference_post_process
from .operator import SourceOperator
from .schema import loads_batch
from .settings_utils import get_constants, get_settings
from .standard import DatasetRecipe
from .task import Task

logger = get_logger()
constants = get_constants()
settings = get_settings()


def short_hex_hash(value, length=8):
    h = hashlib.sha256(value.encode()).hexdigest()  # Full 64-character hex
    return h[:length]


def _get_recipe_from_query(dataset_query: str) -> DatasetRecipe:
    dataset_query = dataset_query.replace("sys_prompt", "instruction")
    try:
        dataset_stream, _ = fetch_artifact(dataset_query)
    except:
        dataset_stream = get_dataset_artifact(dataset_query)
    return dataset_stream


def _get_recipe_from_dict(dataset_params: Dict[str, Any]) -> DatasetRecipe:
    recipe_attributes = list(DatasetRecipe.__dict__["__fields__"].keys())
    for param in dataset_params.keys():
        assert param in recipe_attributes, (
            f"The parameter '{param}' is not an attribute of the 'DatasetRecipe' class. "
            f"Please check if the name is correct. The available attributes are: '{recipe_attributes}'."
        )
    return DatasetRecipe(**dataset_params)


def _verify_dataset_args(dataset_query: Optional[str] = None, dataset_args=None):
    if dataset_query and dataset_args:
        raise ValueError(
            "Cannot provide 'dataset_query' and key-worded arguments at the same time. "
            "If you want to load dataset from a card in local catalog, use query only. "
            "Otherwise, use key-worded arguments only to specify properties of dataset."
        )

    if dataset_query:
        if not isinstance(dataset_query, str):
            raise ValueError(
                f"If specified, 'dataset_query' must be a string, however, "
                f"'{dataset_query}' was provided instead, which is of type "
                f"'{type(dataset_query)}'."
            )

    if not dataset_query and not dataset_args:
        raise ValueError(
            "Either 'dataset_query' or key-worded arguments must be provided."
        )


def load_recipe(dataset_query: Optional[str] = None, **kwargs) -> DatasetRecipe:
    if isinstance(dataset_query, DatasetRecipe):
        return dataset_query

    _verify_dataset_args(dataset_query, kwargs)

    if dataset_query:
        recipe = _get_recipe_from_query(dataset_query)

    if kwargs:
        recipe = _get_recipe_from_dict(kwargs)

    return recipe


def create_dataset(
    task: Union[str, Task],
    test_set: List[Dict[Any, Any]],
    train_set: Optional[List[Dict[Any, Any]]] = None,
    validation_set: Optional[List[Dict[Any, Any]]] = None,
    split: Optional[str] = None,
    data_classification_policy:  Optional[List[str]] = None,
    **kwargs,
) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
    """Creates dataset from input data based on a specific task.

    Args:
        task:  The name of the task from the Unitxt Catalog (https://www.unitxt.ai/en/latest/catalog/catalog.tasks.__dir__.html)
        test_set : required list of instances
        train_set : optional train_set
        validation_set: optional validation set
        split: optional one split to choose
        data_classification_policy: data_classification_policy
        **kwargs: Arguments used to load dataset from provided datasets (see load_dataset())

    Returns:
        DatasetDict

    Example:
        template = Template(...)
        dataset = create_dataset(task="tasks.qa.open", template=template, format="formats.chatapi")
    """
    data = {"test": test_set}
    if train_set is not None:
        data["train"] = train_set
    if validation_set is not None:
        data["validation"] = validation_set
    task, _ = fetch_artifact(task)

    if "template" not in kwargs and task.default_template is None:
        raise Exception(
            f"No 'template' was passed to the create_dataset() and the given task ('{task.__id__}') has no 'default_template' field."
        )

    args = {"data": data}
    if data_classification_policy is not None:
        args["default_data_classification_policy"] = data_classification_policy

    card = TaskCard(loader=LoadFromDictionary(**args), task=task)
    return load_dataset(card=card, split=split, **kwargs)


def _source_to_dataset(
    source: SourceOperator,
    split=None,
    use_cache=False,
    streaming=False,
):
    from .dataset import Dataset as UnitxtDataset

    stream = source()

    try:
        ds_builder = UnitxtDataset(
            dataset_name="unitxt",
            config_name="recipe-" + short_hex_hash(repr(source)),
            version=constants.version,
        )
        if split is not None:
            stream = {split: stream[split]}
        ds_builder._generators = stream

        ds_builder.download_and_prepare(
            verification_mode="no_checks",
            download_mode=None if use_cache else "force_redownload",
        )

        if streaming:
            return ds_builder.as_streaming_dataset(split=split)

        return ds_builder.as_dataset(
            split=split, run_post_process=False, verification_mode="no_checks"
        )

    except DatasetGenerationError as e:
        raise e.__cause__


def load_dataset(
    dataset_query: Optional[str] = None,
    split: Optional[str] = None,
    streaming: bool = False,
    use_cache: Optional[bool] = False,
    **kwargs,
) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
    """Loads dataset.

    If the 'dataset_query' argument is provided, then dataset is loaded from a card
    in local catalog based on parameters specified in the query.

    Alternatively, dataset is loaded from a provided card based on explicitly
    given parameters.

    Args:
        dataset_query (str, optional):
            A string query which specifies a dataset to load from
            local catalog or name of specific recipe or benchmark in the catalog. For
            example, ``"card=cards.wnli,template=templates.classification.multi_class.relation.default"``.
        streaming (bool, False):
            When True yields the data as a stream.
            This is useful when loading very large datasets.
            Loading datasets as streams avoid loading all the data to memory, but requires the dataset's loader to support streaming.
        split (str, optional):
            The split of the data to load
        use_cache (bool, optional):
            If set to True, the returned Huggingface dataset is cached on local disk such that if the same dataset is loaded again, it will be loaded from local disk, resulting in faster runs.
            If set to False (default), the returned dataset is not cached.
            Note that if caching is enabled and the dataset card definition is changed, the old version in the cache may be returned.
            Enable caching only if you are sure you are working with fixed Unitxt datasets and definitions (e.g. running using predefined datasets from the Unitxt catalog).
        **kwargs:
            Arguments used to load dataset from provided card, which is not present in local catalog.

    Returns:
        DatasetDict

    :Example:

        .. code-block:: python

            dataset = load_dataset(
                dataset_query="card=cards.stsb,template=templates.regression.two_texts.simple,max_train_instances=5"
            )  # card and template must be present in local catalog

            # or built programmatically
            card = TaskCard(...)
            template = Template(...)
            loader_limit = 10
            dataset = load_dataset(card=card, template=template, loader_limit=loader_limit)

    """
    recipe = load_recipe(dataset_query, **kwargs)

    dataset = _source_to_dataset(
        source=recipe, split=split, use_cache=use_cache, streaming=streaming
    )

    frame = inspect.currentframe()
    args, _, _, values = inspect.getargvalues(frame)
    all_kwargs = {key: values[key] for key in args if key != "kwargs"}
    all_kwargs.update(kwargs)
    metadata = fill_metadata(**all_kwargs)
    if isinstance(dataset, dict):
        for ds in dataset.values():
            ds.info.description = metadata.copy()
    else:
        dataset.info.description = metadata
    return dataset


def fill_metadata(**kwargs):
    metadata = kwargs.copy()
    metadata["unitxt_version"] = get_constants().version
    metadata["creation_time"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
    return metadata


def evaluate(
    predictions, dataset: Union[Dataset, IterableDataset] = None, data=None
) -> EvaluationResults:
    if dataset is None and data is None:
        raise UnitxtError(message="Specify 'dataset' in evaluate")
    if data is not None:
        dataset = data  # for backward compatibility
    evaluation_result = _compute(predictions=predictions, references=dataset)
    if hasattr(dataset, "info") and hasattr(dataset.info, "description"):
        evaluation_result.metadata["dataset"] = dataset.info.description
    if hasattr(predictions, "metadata"):
        evaluation_result.metadata["predictions"] = predictions.metadata
    evaluation_result.metadata["creation_time"] = datetime.now().strftime(
        "%Y-%m-%d %H:%M:%S.%f"
    )[:-3]
    return evaluation_result


def post_process(predictions, data) -> List[Dict[str, Any]]:
    return _inference_post_process(predictions=predictions, references=data)


@lru_cache
def _get_produce_with_cache(dataset_query: Optional[str] = None, **kwargs):
    return load_recipe(dataset_query, **kwargs).produce


def produce(
    instance_or_instances, dataset_query: Optional[str] = None, **kwargs
) -> Union[Dataset, Dict[str, Any]]:
    is_list = isinstance(instance_or_instances, list)
    if not is_list:
        instance_or_instances = [instance_or_instances]
    result = _get_produce_with_cache(dataset_query, **kwargs)(instance_or_instances)
    if not is_list:
        return result[0]
    return Dataset.from_list(result).with_transform(loads_batch)


def infer(
    instance_or_instances,
    engine: InferenceEngine,
    dataset_query: Optional[str] = None,
    return_data: bool = False,
    return_log_probs: bool = False,
    return_meta_data: bool = False,
    previous_messages: Optional[List[Dict[str, str]]] = None,
    **kwargs,
):
    dataset = produce(instance_or_instances, dataset_query, **kwargs)
    if previous_messages is not None:

        def add_previous_messages(example, index):
            example["source"] = previous_messages[index] + example["source"]
            return example

        dataset = dataset.map(add_previous_messages, with_indices=True)
    engine, _ = fetch_artifact(engine)
    if return_log_probs:
        if not isinstance(engine, LogProbInferenceEngine):
            raise NotImplementedError(
                f"Error in infer: return_log_probs set to True but supplied engine "
                f"{engine.__class__.__name__} does not support logprobs."
            )
        infer_outputs = engine.infer_log_probs(dataset, return_meta_data)
        raw_predictions = (
            [output.prediction for output in infer_outputs]
            if return_meta_data
            else infer_outputs
        )
        raw_predictions = [
            json.dumps(raw_prediction) for raw_prediction in raw_predictions
        ]
    else:
        infer_outputs = engine.infer(dataset, return_meta_data)
        raw_predictions = (
            [output.prediction for output in infer_outputs]
            if return_meta_data
            else infer_outputs
        )
    predictions = post_process(raw_predictions, dataset)
    if return_data:
        if return_meta_data:
            infer_output_list = [
                infer_output.__dict__ for infer_output in infer_outputs
            ]
            for infer_output in infer_output_list:
                del infer_output["prediction"]
            dataset = dataset.add_column("infer_meta_data", infer_output_list)
        dataset = dataset.add_column("prediction", predictions)
        return dataset.add_column("raw_prediction", raw_predictions)
    return predictions


def select(
    instance_or_instances,
    engine: OptionSelectingByLogProbsInferenceEngine,
    dataset_query: Optional[str] = None,
    return_data: bool = False,
    previous_messages: Optional[List[Dict[str, str]]] = None,
    **kwargs,
):
    dataset = produce(instance_or_instances, dataset_query, **kwargs)
    if previous_messages is not None:

        def add_previous_messages(example, index):
            example["source"] = previous_messages[index] + example["source"]
            return example

        dataset = dataset.map(add_previous_messages, with_indices=True)
    engine, _ = fetch_artifact(engine)
    predictions = engine.select(dataset)
    # predictions = post_process(raw_predictions, dataset)
    if return_data:
        return dataset.add_column("prediction", predictions)
    return predictions

1	import hashlib	1✔
2	import inspect	1✔
3	import json	1✔
4	from datetime import datetime	1✔
5	from functools import lru_cache	1✔
6	from typing import Any, Dict, List, Optional, Union	1✔
7
8	from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict	1✔
9	from datasets.exceptions import DatasetGenerationError	1✔
10
11	from .artifact import fetch_artifact	1✔
12	from .card import TaskCard	1✔
13	from .dataset_utils import get_dataset_artifact	1✔
14	from .error_utils import UnitxtError	1✔
15	from .inference import (	1✔
16	InferenceEngine,
17	LogProbInferenceEngine,
18	OptionSelectingByLogProbsInferenceEngine,
19	)
20	from .loaders import LoadFromDictionary	1✔
21	from .logging_utils import get_logger	1✔
22	from .metric_utils import EvaluationResults, _compute, _inference_post_process	1✔
23	from .operator import SourceOperator	1✔
24	from .schema import loads_batch	1✔
25	from .settings_utils import get_constants, get_settings	1✔
26	from .standard import DatasetRecipe	1✔
27	from .task import Task	1✔
28
29	logger = get_logger()	1✔
30	constants = get_constants()	1✔
31	settings = get_settings()	1✔
32
33
34	def short_hex_hash(value, length=8):	1✔
35	h = hashlib.sha256(value.encode()).hexdigest() # Full 64-character hex	1✔
36	return h[:length]	1✔
37
38
39	def _get_recipe_from_query(dataset_query: str) -> DatasetRecipe:	1✔
40	dataset_query = dataset_query.replace("sys_prompt", "instruction")	1✔
41	try:	1✔
42	dataset_stream, _ = fetch_artifact(dataset_query)	1✔
43	except:	1✔
44	dataset_stream = get_dataset_artifact(dataset_query)	1✔
45	return dataset_stream	1✔
46
47
48	def _get_recipe_from_dict(dataset_params: Dict[str, Any]) -> DatasetRecipe:	1✔
49	recipe_attributes = list(DatasetRecipe.__dict__["__fields__"].keys())	1✔
50	for param in dataset_params.keys():	1✔
51	assert param in recipe_attributes, (	1✔
52	f"The parameter '{param}' is not an attribute of the 'DatasetRecipe' class. "
53	f"Please check if the name is correct. The available attributes are: '{recipe_attributes}'."
54	)
55	return DatasetRecipe(**dataset_params)	1✔
56
57
58	def _verify_dataset_args(dataset_query: Optional[str] = None, dataset_args=None):	1✔
59	if dataset_query and dataset_args:	1✔
60	raise ValueError(	×
61	"Cannot provide 'dataset_query' and key-worded arguments at the same time. "
62	"If you want to load dataset from a card in local catalog, use query only. "
63	"Otherwise, use key-worded arguments only to specify properties of dataset."
64	)
65
66	if dataset_query:	1✔
67	if not isinstance(dataset_query, str):	1✔
68	raise ValueError(	×
69	f"If specified, 'dataset_query' must be a string, however, "
70	f"'{dataset_query}' was provided instead, which is of type "
71	f"'{type(dataset_query)}'."
72	)
73
74	if not dataset_query and not dataset_args:	1✔
75	raise ValueError(	×
76	"Either 'dataset_query' or key-worded arguments must be provided."
77	)
78
79
80	def load_recipe(dataset_query: Optional[str] = None, **kwargs) -> DatasetRecipe:	1✔
81	if isinstance(dataset_query, DatasetRecipe):	1✔
82	return dataset_query	×
83
84	_verify_dataset_args(dataset_query, kwargs)	1✔
85
86	if dataset_query:	1✔
87	recipe = _get_recipe_from_query(dataset_query)	1✔
88
89	if kwargs:	1✔
90	recipe = _get_recipe_from_dict(kwargs)	1✔
91
92	return recipe	1✔
93
94
95	def create_dataset(	1✔
96	task: Union[str, Task],
97	test_set: List[Dict[Any, Any]],
98	train_set: Optional[List[Dict[Any, Any]]] = None,
99	validation_set: Optional[List[Dict[Any, Any]]] = None,
100	split: Optional[str] = None,
101	data_classification_policy: Optional[List[str]] = None,
102	**kwargs,
103	) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
104	"""Creates dataset from input data based on a specific task.
105
106	Args:
107	task: The name of the task from the Unitxt Catalog (https://www.unitxt.ai/en/latest/catalog/catalog.tasks.__dir__.html)
108	test_set : required list of instances
109	train_set : optional train_set
110	validation_set: optional validation set
111	split: optional one split to choose
112	data_classification_policy: data_classification_policy
113	**kwargs: Arguments used to load dataset from provided datasets (see load_dataset())
114
115	Returns:
116	DatasetDict
117
118	Example:
119	template = Template(...)
120	dataset = create_dataset(task="tasks.qa.open", template=template, format="formats.chatapi")
121	"""
122	data = {"test": test_set}	1✔
123	if train_set is not None:	1✔
124	data["train"] = train_set	×
125	if validation_set is not None:	1✔
126	data["validation"] = validation_set	×
127	task, _ = fetch_artifact(task)	1✔
128
129	if "template" not in kwargs and task.default_template is None:	1✔
130	raise Exception(	×
131	f"No 'template' was passed to the create_dataset() and the given task ('{task.__id__}') has no 'default_template' field."
132	)
133
134	args = {"data": data}	1✔
135	if data_classification_policy is not None:	1✔
136	args["default_data_classification_policy"] = data_classification_policy	×
137
138	card = TaskCard(loader=LoadFromDictionary(**args), task=task)	1✔
139	return load_dataset(card=card, split=split, **kwargs)	1✔
140
141
142	def _source_to_dataset(	1✔
143	source: SourceOperator,
144	split=None,
145	use_cache=False,
146	streaming=False,
147	):
148	from .dataset import Dataset as UnitxtDataset	1✔
149
150	stream = source()	1✔
151
152	try:	1✔
153	ds_builder = UnitxtDataset(	1✔
154	dataset_name="unitxt",
155	config_name="recipe-" + short_hex_hash(repr(source)),
156	version=constants.version,
157	)
158	if split is not None:	1✔
159	stream = {split: stream[split]}	1✔
160	ds_builder._generators = stream	1✔
161
162	ds_builder.download_and_prepare(	1✔
163	verification_mode="no_checks",
164	download_mode=None if use_cache else "force_redownload",
165	)
166
167	if streaming:	1✔
168	return ds_builder.as_streaming_dataset(split=split)	×
169
170	return ds_builder.as_dataset(	1✔
171	split=split, run_post_process=False, verification_mode="no_checks"
172	)
173
174	except DatasetGenerationError as e:	×
175	raise e.__cause__	×
176
177
178	def load_dataset(	1✔
179	dataset_query: Optional[str] = None,
180	split: Optional[str] = None,
181	streaming: bool = False,
182	use_cache: Optional[bool] = False,
183	**kwargs,
184	) -> Union[DatasetDict, IterableDatasetDict, Dataset, IterableDataset]:
185	"""Loads dataset.
186
187	If the 'dataset_query' argument is provided, then dataset is loaded from a card
188	in local catalog based on parameters specified in the query.
189
190	Alternatively, dataset is loaded from a provided card based on explicitly
191	given parameters.
192
193	Args:
194	dataset_query (str, optional):
195	A string query which specifies a dataset to load from
196	local catalog or name of specific recipe or benchmark in the catalog. For
197	example, ``"card=cards.wnli,template=templates.classification.multi_class.relation.default"``.
198	streaming (bool, False):
199	When True yields the data as a stream.
200	This is useful when loading very large datasets.
201	Loading datasets as streams avoid loading all the data to memory, but requires the dataset's loader to support streaming.
202	split (str, optional):
203	The split of the data to load
204	use_cache (bool, optional):
205	If set to True, the returned Huggingface dataset is cached on local disk such that if the same dataset is loaded again, it will be loaded from local disk, resulting in faster runs.
206	If set to False (default), the returned dataset is not cached.
207	Note that if caching is enabled and the dataset card definition is changed, the old version in the cache may be returned.
208	Enable caching only if you are sure you are working with fixed Unitxt datasets and definitions (e.g. running using predefined datasets from the Unitxt catalog).
209	**kwargs:
210	Arguments used to load dataset from provided card, which is not present in local catalog.
211
212	Returns:
213	DatasetDict
214
215	:Example:
216
217	.. code-block:: python
218
219	dataset = load_dataset(
220	dataset_query="card=cards.stsb,template=templates.regression.two_texts.simple,max_train_instances=5"
221	) # card and template must be present in local catalog
222
223	# or built programmatically
224	card = TaskCard(...)
225	template = Template(...)
226	loader_limit = 10
227	dataset = load_dataset(card=card, template=template, loader_limit=loader_limit)
228
229	"""
230	recipe = load_recipe(dataset_query, **kwargs)	1✔
231
232	dataset = _source_to_dataset(	1✔
233	source=recipe, split=split, use_cache=use_cache, streaming=streaming
234	)
235
236	frame = inspect.currentframe()	1✔
237	args, _, _, values = inspect.getargvalues(frame)	1✔
238	all_kwargs = {key: values[key] for key in args if key != "kwargs"}	1✔
239	all_kwargs.update(kwargs)	1✔
240	metadata = fill_metadata(**all_kwargs)	1✔
241	if isinstance(dataset, dict):	1✔
242	for ds in dataset.values():	1✔
243	ds.info.description = metadata.copy()	1✔
244	else:
245	dataset.info.description = metadata	1✔
246	return dataset	1✔
247
248
249	def fill_metadata(**kwargs):	1✔
250	metadata = kwargs.copy()	1✔
251	metadata["unitxt_version"] = get_constants().version	1✔
252	metadata["creation_time"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]	1✔
253	return metadata	1✔
254
255
256	def evaluate(	1✔
257	predictions, dataset: Union[Dataset, IterableDataset] = None, data=None
258	) -> EvaluationResults:
259	if dataset is None and data is None:	1✔
260	raise UnitxtError(message="Specify 'dataset' in evaluate")	×
261	if data is not None:	1✔
262	dataset = data # for backward compatibility	1✔
263	evaluation_result = _compute(predictions=predictions, references=dataset)	1✔
264	if hasattr(dataset, "info") and hasattr(dataset.info, "description"):	1✔
265	evaluation_result.metadata["dataset"] = dataset.info.description	1✔
266	if hasattr(predictions, "metadata"):	1✔
267	evaluation_result.metadata["predictions"] = predictions.metadata	×
268	evaluation_result.metadata["creation_time"] = datetime.now().strftime(	1✔
269	"%Y-%m-%d %H:%M:%S.%f"
270	)[:-3]
271	return evaluation_result	1✔
272
273
274	def post_process(predictions, data) -> List[Dict[str, Any]]:	1✔
275	return _inference_post_process(predictions=predictions, references=data)	1✔
276
277
278	@lru_cache	1✔
279	def _get_produce_with_cache(dataset_query: Optional[str] = None, **kwargs):	1✔
280	return load_recipe(dataset_query, **kwargs).produce	1✔
281
282
283	def produce(	1✔
284	instance_or_instances, dataset_query: Optional[str] = None, **kwargs
285	) -> Union[Dataset, Dict[str, Any]]:
286	is_list = isinstance(instance_or_instances, list)	1✔
287	if not is_list:	1✔
288	instance_or_instances = [instance_or_instances]	1✔
289	result = _get_produce_with_cache(dataset_query, **kwargs)(instance_or_instances)	1✔
290	if not is_list:	1✔
291	return result[0]	1✔
292	return Dataset.from_list(result).with_transform(loads_batch)	1✔
293
294
295	def infer(	1✔
296	instance_or_instances,
297	engine: InferenceEngine,
298	dataset_query: Optional[str] = None,
299	return_data: bool = False,
300	return_log_probs: bool = False,
301	return_meta_data: bool = False,
302	previous_messages: Optional[List[Dict[str, str]]] = None,
303	**kwargs,
304	):
305	dataset = produce(instance_or_instances, dataset_query, **kwargs)	1✔
306	if previous_messages is not None:	1✔
307
308	def add_previous_messages(example, index):	1✔
309	example["source"] = previous_messages[index] + example["source"]	1✔
310	return example	1✔
311
312	dataset = dataset.map(add_previous_messages, with_indices=True)	1✔
313	engine, _ = fetch_artifact(engine)	1✔
314	if return_log_probs:	1✔
315	if not isinstance(engine, LogProbInferenceEngine):	×
316	raise NotImplementedError(	×
317	f"Error in infer: return_log_probs set to True but supplied engine "
318	f"{engine.__class__.__name__} does not support logprobs."
319	)
320	infer_outputs = engine.infer_log_probs(dataset, return_meta_data)	×
321	raw_predictions = (	×
322	[output.prediction for output in infer_outputs]
323	if return_meta_data
324	else infer_outputs
325	)
326	raw_predictions = [	×
327	json.dumps(raw_prediction) for raw_prediction in raw_predictions
328	]
329	else:
330	infer_outputs = engine.infer(dataset, return_meta_data)	1✔
331	raw_predictions = (	1✔
332	[output.prediction for output in infer_outputs]
333	if return_meta_data
334	else infer_outputs
335	)
336	predictions = post_process(raw_predictions, dataset)	1✔
337	if return_data:	1✔
338	if return_meta_data:	1✔
339	infer_output_list = [	×
340	infer_output.__dict__ for infer_output in infer_outputs
341	]
342	for infer_output in infer_output_list:	×
343	del infer_output["prediction"]	×
344	dataset = dataset.add_column("infer_meta_data", infer_output_list)	×
345	dataset = dataset.add_column("prediction", predictions)	1✔
346	return dataset.add_column("raw_prediction", raw_predictions)	1✔
347	return predictions	1✔
348
349
350	def select(	1✔
351	instance_or_instances,
352	engine: OptionSelectingByLogProbsInferenceEngine,
353	dataset_query: Optional[str] = None,
354	return_data: bool = False,
355	previous_messages: Optional[List[Dict[str, str]]] = None,
356	**kwargs,
357	):
358	dataset = produce(instance_or_instances, dataset_query, **kwargs)	×
359	if previous_messages is not None:	×
360
361	def add_previous_messages(example, index):	×
362	example["source"] = previous_messages[index] + example["source"]	×
363	return example	×
364
365	dataset = dataset.map(add_previous_messages, with_indices=True)	×
366	engine, _ = fetch_artifact(engine)	×
367	predictions = engine.select(dataset)	×
368	# predictions = post_process(raw_predictions, dataset)
369	if return_data:	×
370	return dataset.add_column("prediction", predictions)	×
371	return predictions	×

IBM / unitxt / 13907679295

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