12635975303

Committed 06 Jan 2025 04:10PM UTC coverage: 80.075% (+0.02%) from 80.057%

Build # 12635975303

Build Type

Pull #1473

github

Committed by

web-flow

Commit Message

Merge cf8796abc into b32bb80fa

Pull Request Pull Request #1473: Adding typed recipe test

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src/unitxt/task.py

import warnings
from functools import lru_cache
from typing import Any, Dict, List, Optional, Union

from .deprecation_utils import deprecation
from .error_utils import Documentation, UnitxtError, UnitxtWarning
from .logging_utils import get_logger
from .metrics import MetricsList
from .operator import InstanceOperator
from .operators import ArtifactFetcherMixin
from .settings_utils import get_constants
from .templates import Template
from .type_utils import (
    Type,
    get_args,
    get_origin,
    is_type_dict,
    isoftype,
    parse_type_dict,
    parse_type_string,
    to_type_dict,
    to_type_string,
    verify_required_schema,
)

constants = get_constants()
logger = get_logger()


@deprecation(
    version="2.0.0",
    msg="use python type instead of type strings (e.g Dict[str] instead of 'Dict[str]')",
)
def parse_string_types_instead_of_actual_objects(obj):
    if isinstance(obj, dict):
        return parse_type_dict(obj)
    return parse_type_string(obj)


class Task(InstanceOperator, ArtifactFetcherMixin):
    """Task packs the different instance fields into dictionaries by their roles in the task.

    Args:
        input_fields (Union[Dict[str, str], List[str]]):
            Dictionary with string names of instance input fields and types of respective values.
            In case a list is passed, each type will be assumed to be Any.
        reference_fields (Union[Dict[str, str], List[str]]):
            Dictionary with string names of instance output fields and types of respective values.
            In case a list is passed, each type will be assumed to be Any.
        metrics (List[str]):
            List of names of metrics to be used in the task.
        prediction_type (Optional[str]):
            Need to be consistent with all used metrics. Defaults to None, which means that it will
            be set to Any.
        defaults (Optional[Dict[str, Any]]):
            An optional dictionary with default values for chosen input/output keys. Needs to be
            consistent with names and types provided in 'input_fields' and/or 'output_fields' arguments.
            Will not overwrite values if already provided in a given instance.

    The output instance contains three fields:
        1. "input_fields" whose value is a sub-dictionary of the input instance, consisting of all the fields listed in Arg 'input_fields'.
        2. "reference_fields" -- for the fields listed in Arg "reference_fields".
        3. "metrics" -- to contain the value of Arg 'metrics'
    """

    input_fields: Optional[Union[Dict[str, Type], Dict[str, str], List[str]]] = None
    reference_fields: Optional[Union[Dict[str, Type], Dict[str, str], List[str]]] = None
    inputs: Optional[Union[Dict[str, Type], Dict[str, str], List[str]]] = None
    outputs: Optional[Union[Dict[str, Type], Dict[str, str], List[str]]] = None
    metrics: List[str]
    prediction_type: Optional[Union[Type, str]] = None
    augmentable_inputs: List[str] = []
    defaults: Optional[Dict[str, Any]] = None
    default_template: Template = None

    def prepare_args(self):
        super().prepare_args()

        if self.input_fields is not None and self.inputs is not None:
            raise UnitxtError(
                "Conflicting attributes: 'input_fields' cannot be set simultaneously with 'inputs'. Use only 'input_fields'",
                Documentation.ADDING_TASK,
            )
        if self.reference_fields is not None and self.outputs is not None:
            raise UnitxtError(
                "Conflicting attributes: 'reference_fields' cannot be set simultaneously with 'output'. Use only 'reference_fields'",
                Documentation.ADDING_TASK,
            )

        if self.default_template is not None and not isoftype(
            self.default_template, Template
        ):
            raise UnitxtError(
                f"The task's 'default_template' attribute is not of type Template. The 'default_template' attribute is of type {type(self.default_template)}: {self.default_template}",
                Documentation.ADDING_TASK,
            )

        self.input_fields = (
            self.input_fields if self.input_fields is not None else self.inputs
        )
        self.reference_fields = (
            self.reference_fields if self.reference_fields is not None else self.outputs
        )

        if isoftype(self.input_fields, Dict[str, str]):
            self.input_fields = parse_string_types_instead_of_actual_objects(
                self.input_fields
            )
        if isoftype(self.reference_fields, Dict[str, str]):
            self.reference_fields = parse_string_types_instead_of_actual_objects(
                self.reference_fields
            )

        if isinstance(self.prediction_type, str):
            self.prediction_type = parse_string_types_instead_of_actual_objects(
                self.prediction_type
            )

        if hasattr(self, "inputs") and self.inputs is not None:
            self.inputs = self.input_fields

        if hasattr(self, "outputs") and self.outputs is not None:
            self.outputs = self.reference_fields

    def task_deprecations(self):
        if hasattr(self, "inputs") and self.inputs is not None:
            depr_message = (
                "The 'inputs' field is deprecated. Please use 'input_fields' instead."
            )
            warnings.warn(depr_message, DeprecationWarning, stacklevel=2)
        if hasattr(self, "outputs") and self.outputs is not None:
            depr_message = "The 'outputs' field is deprecated. Please use 'reference_fields' instead."
            warnings.warn(depr_message, DeprecationWarning, stacklevel=2)

    def verify(self):
        self.task_deprecations()

        if self.input_fields is None:
            raise UnitxtError(
                "Missing attribute in task: 'input_fields' not set.",
                Documentation.ADDING_TASK,
            )
        if self.reference_fields is None:
            raise UnitxtError(
                "Missing attribute in task: 'reference_fields' not set.",
                Documentation.ADDING_TASK,
            )
        for io_type in ["input_fields", "reference_fields"]:
            data = (
                self.input_fields
                if io_type == "input_fields"
                else self.reference_fields
            )

            if isinstance(data, list) or not is_type_dict(data):
                UnitxtWarning(
                    f"'{io_type}' field of Task should be a dictionary of field names and their types. "
                    f"For example, {{'text': str, 'classes': List[str]}}. Instead only '{data}' was "
                    f"passed. All types will be assumed to be 'Any'. In future version of unitxt this "
                    f"will raise an exception.",
                    Documentation.ADDING_TASK,
                )
                if isinstance(data, dict):
                    data = parse_type_dict(to_type_dict(data))
                else:
                    data = {key: Any for key in data}

                if io_type == "input_fields":
                    self.input_fields = data
                else:
                    self.reference_fields = data

        if not self.prediction_type:
            UnitxtWarning(
                "'prediction_type' was not set in Task. It is used to check the output of "
                "template post processors is compatible with the expected input of the metrics. "
                "Setting `prediction_type` to 'Any' (no checking is done). In future version "
                "of unitxt this will raise an exception.",
                Documentation.ADDING_TASK,
            )
            self.prediction_type = Any

        self.check_metrics_type()

        for augmentable_input in self.augmentable_inputs:
            assert (
                augmentable_input in self.input_fields
            ), f"augmentable_input {augmentable_input} is not part of {self.input_fields}"

        self.verify_defaults()

    @classmethod
    def process_data_after_load(cls, data):
        possible_dicts = ["inputs", "input_fields", "outputs", "reference_fields"]
        for dict_name in possible_dicts:
            if dict_name in data and isinstance(data[dict_name], dict):
                data[dict_name] = parse_type_dict(data[dict_name])
        if "prediction_type" in data:
            data["prediction_type"] = parse_type_string(data["prediction_type"])
        return data

    def process_data_before_dump(self, data):
        possible_dicts = ["inputs", "input_fields", "outputs", "reference_fields"]
        for dict_name in possible_dicts:
            if dict_name in data and isinstance(data[dict_name], dict):
                if not isoftype(data[dict_name], Dict[str, str]):
                    data[dict_name] = to_type_dict(data[dict_name])
        if "prediction_type" in data:
            if not isinstance(data["prediction_type"], str):
                data["prediction_type"] = to_type_string(data["prediction_type"])
        return data

    @classmethod
    @lru_cache(maxsize=None)
    def get_metrics_artifacts(cls, metric_id: str):
        metric = cls.get_artifact(metric_id)
        if isinstance(metric, MetricsList):
            return metric.items
        return [metric]

    def check_metrics_type(self) -> None:
        prediction_type = self.prediction_type
        for metric_id in self.metrics:
            metric_artifacts_list = Task.get_metrics_artifacts(metric_id)
            for metric_artifact in metric_artifacts_list:
                metric_prediction_type = metric_artifact.prediction_type
                if (
                    prediction_type == metric_prediction_type
                    or prediction_type == Any
                    or metric_prediction_type == Any
                    or (
                        get_origin(metric_prediction_type) is Union
                        and prediction_type in get_args(metric_prediction_type)
                    )
                ):
                    continue

                raise UnitxtError(
                    f"The task's prediction type ({prediction_type}) and '{metric_id}' "
                    f"metric's prediction type ({metric_prediction_type}) are different.",
                    Documentation.ADDING_TASK,
                )

    def verify_defaults(self):
        if self.defaults:
            if not isinstance(self.defaults, dict):
                raise UnitxtError(
                    f"If specified, the 'defaults' must be a dictionary, "
                    f"however, '{self.defaults}' was provided instead, "
                    f"which is of type '{to_type_string(type(self.defaults))}'.",
                    Documentation.ADDING_TASK,
                )

            for default_name, default_value in self.defaults.items():
                assert isinstance(default_name, str), (
                    f"If specified, all keys of the 'defaults' must be strings, "
                    f"however, the key '{default_name}' is of type '{to_type_string(type(default_name))}'."
                )

                val_type = self.input_fields.get(
                    default_name
                ) or self.reference_fields.get(default_name)

                assert val_type, (
                    f"If specified, all keys of the 'defaults' must refer to a chosen "
                    f"key in either 'input_fields' or 'reference_fields'. However, the name '{default_name}' "
                    f"was provided which does not match any of the keys."
                )

                assert isoftype(default_value, val_type), (
                    f"The value of '{default_name}' from the 'defaults' must be of "
                    f"type '{to_type_string(val_type)}', however, it is of type '{to_type_string(type(default_value))}'."
                )

    def set_default_values(self, instance: Dict[str, Any]) -> Dict[str, Any]:
        if self.defaults:
            instance = {**self.defaults, **instance}
        return instance

    def process(
        self, instance: Dict[str, Any], stream_name: Optional[str] = None
    ) -> Dict[str, Any]:
        instance = self.set_default_values(instance)

        verify_required_schema(
            self.input_fields,
            instance,
            class_name="Task",
            id=self.__id__,
            description=self.__description__,
        )
        input_fields = {key: instance[key] for key in self.input_fields.keys()}
        data_classification_policy = instance.get("data_classification_policy", [])

        result = {
            "input_fields": input_fields,
            "metrics": self.metrics,
            "data_classification_policy": data_classification_policy,
            "media": instance.get("media", {}),
            "recipe_metadata": instance.get("recipe_metadata", {}),
        }
        if "demos" in instance:
            # for the case of recipe.skip_demoed_instances
            result["demos"] = instance["demos"]

        if stream_name == constants.inference_stream:
            return result

        verify_required_schema(
            self.reference_fields,
            instance,
            class_name="Task",
            id=self.__id__,
            description=self.__description__,
        )
        result["reference_fields"] = {
            key: instance[key] for key in self.reference_fields.keys()
        }

        return result


@deprecation(version="2.0.0", alternative=Task)
class FormTask(Task):
    pass

1	import warnings	1✔
2	from functools import lru_cache	1✔
3	from typing import Any, Dict, List, Optional, Union	1✔
4
5	from .deprecation_utils import deprecation	1✔
6	from .error_utils import Documentation, UnitxtError, UnitxtWarning	1✔
7	from .logging_utils import get_logger	1✔
8	from .metrics import MetricsList	1✔
9	from .operator import InstanceOperator	1✔
10	from .operators import ArtifactFetcherMixin	1✔
11	from .settings_utils import get_constants	1✔
12	from .templates import Template	1✔
13	from .type_utils import (	1✔
14	Type,
15	get_args,
16	get_origin,
17	is_type_dict,
18	isoftype,
19	parse_type_dict,
20	parse_type_string,
21	to_type_dict,
22	to_type_string,
23	verify_required_schema,
24	)
25
26	constants = get_constants()	1✔
27	logger = get_logger()	1✔
28
29
30	@deprecation(	1✔
31	version="2.0.0",
32	msg="use python type instead of type strings (e.g Dict[str] instead of 'Dict[str]')",
33	)
34	def parse_string_types_instead_of_actual_objects(obj):	1✔
35	if isinstance(obj, dict):	1✔
36	return parse_type_dict(obj)	1✔
37	return parse_type_string(obj)	1✔
38
39
40	class Task(InstanceOperator, ArtifactFetcherMixin):	1✔
41	"""Task packs the different instance fields into dictionaries by their roles in the task.
42
43	Args:
44	input_fields (Union[Dict[str, str], List[str]]):
45	Dictionary with string names of instance input fields and types of respective values.
46	In case a list is passed, each type will be assumed to be Any.
47	reference_fields (Union[Dict[str, str], List[str]]):
48	Dictionary with string names of instance output fields and types of respective values.
49	In case a list is passed, each type will be assumed to be Any.
50	metrics (List[str]):
51	List of names of metrics to be used in the task.
52	prediction_type (Optional[str]):
53	Need to be consistent with all used metrics. Defaults to None, which means that it will
54	be set to Any.
55	defaults (Optional[Dict[str, Any]]):
56	An optional dictionary with default values for chosen input/output keys. Needs to be
57	consistent with names and types provided in 'input_fields' and/or 'output_fields' arguments.
58	Will not overwrite values if already provided in a given instance.
59
60	The output instance contains three fields:
61	1. "input_fields" whose value is a sub-dictionary of the input instance, consisting of all the fields listed in Arg 'input_fields'.
62	2. "reference_fields" -- for the fields listed in Arg "reference_fields".
63	3. "metrics" -- to contain the value of Arg 'metrics'
64	"""
65
66	input_fields: Optional[Union[Dict[str, Type], Dict[str, str], List[str]]] = None	1✔
67	reference_fields: Optional[Union[Dict[str, Type], Dict[str, str], List[str]]] = None	1✔
68	inputs: Optional[Union[Dict[str, Type], Dict[str, str], List[str]]] = None	1✔
69	outputs: Optional[Union[Dict[str, Type], Dict[str, str], List[str]]] = None	1✔
70	metrics: List[str]	1✔
71	prediction_type: Optional[Union[Type, str]] = None	1✔
72	augmentable_inputs: List[str] = []	1✔
73	defaults: Optional[Dict[str, Any]] = None	1✔
74	default_template: Template = None	1✔
75
76	def prepare_args(self):	1✔
77	super().prepare_args()	1✔
78
79	if self.input_fields is not None and self.inputs is not None:	1✔
80	raise UnitxtError(	1✔
81	"Conflicting attributes: 'input_fields' cannot be set simultaneously with 'inputs'. Use only 'input_fields'",
82	Documentation.ADDING_TASK,
83	)
84	if self.reference_fields is not None and self.outputs is not None:	1✔
85	raise UnitxtError(	1✔
86	"Conflicting attributes: 'reference_fields' cannot be set simultaneously with 'output'. Use only 'reference_fields'",
87	Documentation.ADDING_TASK,
88	)
89
90	if self.default_template is not None and not isoftype(	1✔
91	self.default_template, Template
92	):
93	raise UnitxtError(	×
94	f"The task's 'default_template' attribute is not of type Template. The 'default_template' attribute is of type {type(self.default_template)}: {self.default_template}",
95	Documentation.ADDING_TASK,
96	)
97
98	self.input_fields = (	1✔
99	self.input_fields if self.input_fields is not None else self.inputs
100	)
101	self.reference_fields = (	1✔
102	self.reference_fields if self.reference_fields is not None else self.outputs
103	)
104
105	if isoftype(self.input_fields, Dict[str, str]):	1✔
106	self.input_fields = parse_string_types_instead_of_actual_objects(	1✔
107	self.input_fields
108	)
109	if isoftype(self.reference_fields, Dict[str, str]):	1✔
110	self.reference_fields = parse_string_types_instead_of_actual_objects(	1✔
111	self.reference_fields
112	)
113
114	if isinstance(self.prediction_type, str):	1✔
115	self.prediction_type = parse_string_types_instead_of_actual_objects(	1✔
116	self.prediction_type
117	)
118
119	if hasattr(self, "inputs") and self.inputs is not None:	1✔
120	self.inputs = self.input_fields	1✔
121
122	if hasattr(self, "outputs") and self.outputs is not None:	1✔
123	self.outputs = self.reference_fields	1✔
124
125	def task_deprecations(self):	1✔
126	if hasattr(self, "inputs") and self.inputs is not None:	1✔
127	depr_message = (	1✔
128	"The 'inputs' field is deprecated. Please use 'input_fields' instead."
129	)
130	warnings.warn(depr_message, DeprecationWarning, stacklevel=2)	1✔
131	if hasattr(self, "outputs") and self.outputs is not None:	1✔
132	depr_message = "The 'outputs' field is deprecated. Please use 'reference_fields' instead."	1✔
133	warnings.warn(depr_message, DeprecationWarning, stacklevel=2)	1✔
134
135	def verify(self):	1✔
136	self.task_deprecations()	1✔
137
138	if self.input_fields is None:	1✔
139	raise UnitxtError(	1✔
140	"Missing attribute in task: 'input_fields' not set.",
141	Documentation.ADDING_TASK,
142	)
143	if self.reference_fields is None:	1✔
144	raise UnitxtError(	1✔
145	"Missing attribute in task: 'reference_fields' not set.",
146	Documentation.ADDING_TASK,
147	)
148	for io_type in ["input_fields", "reference_fields"]:	1✔
149	data = (	1✔
150	self.input_fields
151	if io_type == "input_fields"
152	else self.reference_fields
153	)
154
155	if isinstance(data, list) or not is_type_dict(data):	1✔
156	UnitxtWarning(	1✔
157	f"'{io_type}' field of Task should be a dictionary of field names and their types. "
158	f"For example, {{'text': str, 'classes': List[str]}}. Instead only '{data}' was "
159	f"passed. All types will be assumed to be 'Any'. In future version of unitxt this "
160	f"will raise an exception.",
161	Documentation.ADDING_TASK,
162	)
163	if isinstance(data, dict):	1✔
164	data = parse_type_dict(to_type_dict(data))	×
165	else:
166	data = {key: Any for key in data}	1✔
167
168	if io_type == "input_fields":	1✔
169	self.input_fields = data	1✔
170	else:
171	self.reference_fields = data	1✔
172
173	if not self.prediction_type:	1✔
174	UnitxtWarning(	1✔
175	"'prediction_type' was not set in Task. It is used to check the output of "
176	"template post processors is compatible with the expected input of the metrics. "
177	"Setting `prediction_type` to 'Any' (no checking is done). In future version "
178	"of unitxt this will raise an exception.",
179	Documentation.ADDING_TASK,
180	)
181	self.prediction_type = Any	1✔
182
183	self.check_metrics_type()	1✔
184
185	for augmentable_input in self.augmentable_inputs:	1✔
186	assert (	1✔
187	augmentable_input in self.input_fields
188	), f"augmentable_input {augmentable_input} is not part of {self.input_fields}"
189
190	self.verify_defaults()	1✔
191
192	@classmethod	1✔
193	def process_data_after_load(cls, data):	1✔
194	possible_dicts = ["inputs", "input_fields", "outputs", "reference_fields"]	1✔
195	for dict_name in possible_dicts:	1✔
196	if dict_name in data and isinstance(data[dict_name], dict):	1✔
197	data[dict_name] = parse_type_dict(data[dict_name])	1✔
198	if "prediction_type" in data:	1✔
199	data["prediction_type"] = parse_type_string(data["prediction_type"])	1✔
200	return data	1✔
201
202	def process_data_before_dump(self, data):	1✔
203	possible_dicts = ["inputs", "input_fields", "outputs", "reference_fields"]	1✔
204	for dict_name in possible_dicts:	1✔
205	if dict_name in data and isinstance(data[dict_name], dict):	1✔
206	if not isoftype(data[dict_name], Dict[str, str]):	1✔
207	data[dict_name] = to_type_dict(data[dict_name])	1✔
208	if "prediction_type" in data:	1✔
209	if not isinstance(data["prediction_type"], str):	1✔
210	data["prediction_type"] = to_type_string(data["prediction_type"])	1✔
211	return data	1✔
212
213	@classmethod	1✔
214	@lru_cache(maxsize=None)	1✔
215	def get_metrics_artifacts(cls, metric_id: str):	1✔
216	metric = cls.get_artifact(metric_id)	1✔
217	if isinstance(metric, MetricsList):	1✔
218	return metric.items	×
219	return [metric]	1✔
220
221	def check_metrics_type(self) -> None:	1✔
222	prediction_type = self.prediction_type	1✔
223	for metric_id in self.metrics:	1✔
224	metric_artifacts_list = Task.get_metrics_artifacts(metric_id)	1✔
225	for metric_artifact in metric_artifacts_list:	1✔
226	metric_prediction_type = metric_artifact.prediction_type	1✔
227	if (	1✔
228	prediction_type == metric_prediction_type
229	or prediction_type == Any
230	or metric_prediction_type == Any
231	or (
232	get_origin(metric_prediction_type) is Union
233	and prediction_type in get_args(metric_prediction_type)
234	)
235	):
236	continue	1✔
237
238	raise UnitxtError(	1✔
239	f"The task's prediction type ({prediction_type}) and '{metric_id}' "
240	f"metric's prediction type ({metric_prediction_type}) are different.",
241	Documentation.ADDING_TASK,
242	)
243
244	def verify_defaults(self):	1✔
245	if self.defaults:	1✔
246	if not isinstance(self.defaults, dict):	1✔
247	raise UnitxtError(	×
248	f"If specified, the 'defaults' must be a dictionary, "
249	f"however, '{self.defaults}' was provided instead, "
250	f"which is of type '{to_type_string(type(self.defaults))}'.",
251	Documentation.ADDING_TASK,
252	)
253
254	for default_name, default_value in self.defaults.items():	1✔
255	assert isinstance(default_name, str), (	1✔
256	f"If specified, all keys of the 'defaults' must be strings, "
257	f"however, the key '{default_name}' is of type '{to_type_string(type(default_name))}'."
258	)
259
260	val_type = self.input_fields.get(	1✔
261	default_name
262	) or self.reference_fields.get(default_name)
263
264	assert val_type, (	1✔
265	f"If specified, all keys of the 'defaults' must refer to a chosen "
266	f"key in either 'input_fields' or 'reference_fields'. However, the name '{default_name}' "
267	f"was provided which does not match any of the keys."
268	)
269
270	assert isoftype(default_value, val_type), (	1✔
271	f"The value of '{default_name}' from the 'defaults' must be of "
272	f"type '{to_type_string(val_type)}', however, it is of type '{to_type_string(type(default_value))}'."
273	)
274
275	def set_default_values(self, instance: Dict[str, Any]) -> Dict[str, Any]:	1✔
276	if self.defaults:	1✔
277	instance = {self.defaults, instance}	1✔
278	return instance	1✔
279
280	def process(	1✔
281	self, instance: Dict[str, Any], stream_name: Optional[str] = None
282	) -> Dict[str, Any]:
283	instance = self.set_default_values(instance)	1✔
284
285	verify_required_schema(	1✔
286	self.input_fields,
287	instance,
288	class_name="Task",
289	id=self.__id__,
290	description=self.__description__,
291	)
292	input_fields = {key: instance[key] for key in self.input_fields.keys()}	1✔
293	data_classification_policy = instance.get("data_classification_policy", [])	1✔
294
295	result = {	1✔
296	"input_fields": input_fields,
297	"metrics": self.metrics,
298	"data_classification_policy": data_classification_policy,
299	"media": instance.get("media", {}),
300	"recipe_metadata": instance.get("recipe_metadata", {}),
301	}
302	if "demos" in instance:	1✔
303	# for the case of recipe.skip_demoed_instances
304	result["demos"] = instance["demos"]	1✔
305
306	if stream_name == constants.inference_stream:	1✔
307	return result	1✔
308
309	verify_required_schema(	1✔
310	self.reference_fields,
311	instance,
312	class_name="Task",
313	id=self.__id__,
314	description=self.__description__,
315	)
316	result["reference_fields"] = {	1✔
317	key: instance[key] for key in self.reference_fields.keys()
318	}
319
320	return result	1✔
321
322
323	@deprecation(version="2.0.0", alternative=Task)	1✔
324	class FormTask(Task):	1✔
325	pass	1✔

IBM / unitxt / 12635975303

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