14293170121

Committed 06 Apr 2025 01:46PM UTC coverage: 80.205% (-0.01%) from 80.217%

Build # 14293170121

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web-flow

Commit Message

Update version to 1.22.0 (#1717)

Signed-off-by: elronbandel <elronbandel@gmail.com>

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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 .benchmark import Benchmark
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, Benchmark)):
        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."
        )

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

IBM / unitxt / 14293170121

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