File size: 18,461 Bytes
c77cd1f
ccff8c6
 
d321246
161e5a1
1b7c63c
161e5a1
ccff8c6
161e5a1
dae2dfd
 
ccff8c6
 
dae2dfd
ccff8c6
88a9416
 
 
 
 
 
 
ccff8c6
dae2dfd
ccff8c6
 
 
 
88a9416
 
dae2dfd
161e5a1
 
 
ccff8c6
 
 
 
 
 
 
 
 
 
 
88a9416
ccff8c6
dae2dfd
 
 
 
 
 
88a9416
 
dae2dfd
 
ccff8c6
88a9416
ccff8c6
dae2dfd
ccff8c6
 
 
 
 
60ab03e
ccff8c6
 
dae2dfd
 
 
4e6e650
 
 
 
 
dae2dfd
 
 
0259a82
c77cd1f
0259a82
dae2dfd
88a9416
 
 
 
 
 
 
 
 
 
 
0259a82
ccff8c6
0259a82
ccff8c6
0259a82
 
161e5a1
 
 
ccff8c6
 
 
 
88a9416
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ccff8c6
4e6e650
 
ccff8c6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88a9416
ccff8c6
 
 
 
 
 
88a9416
 
 
 
 
 
 
 
 
ccff8c6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
161e5a1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
88a9416
 
 
 
 
 
 
 
 
 
 
 
 
161e5a1
ccff8c6
161e5a1
 
 
 
 
 
 
 
 
0259a82
161e5a1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
ccff8c6
 
dae2dfd
 
a197b4c
4e6e650
 
 
 
 
a197b4c
 
 
 
 
 
161e5a1
 
 
 
 
 
 
a197b4c
 
ccff8c6
a197b4c
 
 
88a9416
 
a197b4c
 
 
 
 
88a9416
a197b4c
ccff8c6
 
a197b4c
 
 
 
 
ccff8c6
a197b4c
 
0259a82
 
 
ccff8c6
4e6e650
0259a82
4e6e650
 
0259a82
ccff8c6
0259a82
 
1b7c63c
 
 
ccff8c6
 
 
1b7c63c
ccff8c6
1b7c63c
ccff8c6
 
 
 
1b7c63c
 
 
ccff8c6
161e5a1
 
 
 
ccff8c6
161e5a1
ccff8c6
161e5a1
 
 
ccff8c6
 
 
 
 
161e5a1
ccff8c6
 
 
 
 
 
 
 
1b7c63c
 
c77cd1f
 
 
 
 
 
 
6de46af
 
 
c77cd1f
6de46af
c77cd1f
6de46af
 
 
 
 
 
 
c77cd1f
 
6de46af
c77cd1f
 
 
6de46af
ccff8c6
 
 
c77cd1f
 
ccff8c6
6de46af
c77cd1f
 
 
6de46af
c77cd1f
 
 
 
ccff8c6
c77cd1f
 
 
 
 
 
 
 
 
 
 
 
161e5a1
 
 
 
c77cd1f
 
 
 
 
161e5a1
c77cd1f
 
ccff8c6
c77cd1f
 
 
6de46af
 
dae2dfd
 
 
 
 
 
 
 
161e5a1
dae2dfd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
import json
from abc import abstractmethod
from typing import Any, Dict, List, Optional, Tuple

from .collections import ListCollection
from .dataclass import NonPositionalField
from .operator import StreamInstanceOperator
from .random_utils import new_random_generator
from .type_utils import isoftype


class Template(StreamInstanceOperator):
    """The role of template is to take the fields of every instance and verbalize it.

    Meaning the template is taking the instance and generating source, target and references.

    Args:
        skip_rendered_instance (bool): if "source", "target", and "references" are already defined fields in the instance, skip its processing
        postprocessors: a list of strings being artifact names of text processors, to be applied on the model output
        instruction: a formatting string that yields an instruction with potential participation of values from the "inputs" part of the instance
        target_prefix: a string to be used to format the prompt. Not a formatting string.

    """

    skip_rendered_instance: bool = NonPositionalField(default=True)
    postprocessors: List[str] = NonPositionalField(
        default_factory=lambda: ["processors.to_string_stripped"]
    )
    instruction: str = NonPositionalField(default_factory=lambda: "")
    target_prefix: str = NonPositionalField(default_factory=lambda: "")

    def process(
        self, instance: Dict[str, Any], stream_name: Optional[str] = None
    ) -> Dict[str, Any]:
        if self.skip_rendered_instance:
            if (
                "source" in instance
                and "target" in instance
                and "references" in instance
            ):
                return instance

        inputs = instance.get("inputs")
        outputs = instance.get("outputs")

        source, instruction = self.inputs_to_source(inputs)
        target, references = self.outputs_to_target_and_references(outputs)

        return {
            **instance,
            "source": source,
            "target": target,
            "references": references,
            "instruction": instruction,
            "target_prefix": self.target_prefix.format(**inputs),
        }

    @abstractmethod
    def inputs_to_source(self, inputs: Dict[str, object]) -> Tuple[str, str]:
        pass

    @abstractmethod
    def outputs_to_target_and_references(
        self, outputs: Dict[str, object]
    ) -> Tuple[str, List[str]]:
        pass

    def get_postprocessors(self) -> List[str]:
        return self.postprocessors


class InputOutputTemplate(Template):
    """Generate field 'source' from fields designated as input, and fields 'target' and 'references' from fields designated as output, of the processed instance.

    Args specify the formatting strings with which to glue together the input and output designated fields of the processed instance into one string ('source' and 'target'), and into a list of strings ('references').
    """

    input_format: str = None
    output_format: str = None

    def process_template(self, template: str, data: Dict[str, object]) -> str:
        data = {k: ", ".join(v) if isinstance(v, list) else v for k, v in data.items()}
        return template.format(**data)

    def inputs_to_source(self, inputs: Dict[str, object]) -> Tuple[str, str]:
        formatted = []
        for formatting in [self.input_format, self.instruction]:
            try:
                formatted.append(self.process_template(formatting, inputs))
            except KeyError as e:
                raise KeyError(
                    f"Available inputs are {list(inputs.keys())} but input format requires a different ones: '{formatting}'"
                ) from e

        return tuple(formatted)

    def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
        try:
            target = self.process_template(self.output_format, outputs)
        except KeyError as e:
            raise KeyError(
                f"Available outputs are {outputs.keys()} but output format requires a different one: {self.output_format}"
            ) from e

        references = [target]
        return target, references


class InputOutputReferenceTemplate(InputOutputTemplate):
    reference: str

    def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
        output_fields = {}
        for name, val in [
            ("target", self.output_format),
            ("reference", self.reference),
        ]:
            try:
                result = self.process_template(val, outputs)
                output_fields[name] = result
            except KeyError as e:
                raise KeyError(
                    f"Available outputs are {outputs.keys()} but {name} requires a different one: {val}"
                ) from e
        return output_fields["target"], [output_fields["reference"]]


class MultipleChoiceTemplate(Template):
    """Formats the input (that specifies the question), the multiple choices to select the answer from, and specifies the field with the correct answer."""

    input_format: str
    target_prefix: str = ""
    choices_field: str = "choices"
    target_field: str = "label"
    choices_seperator: str = ", "
    source_choice_format: str = "{choice_numeral}. {choice_text}"
    target_choice_format: str = "{choice_numeral}"
    add_numerals_as_field: str = None
    enumerator: str = "capitals"

    def prepare(self):
        super().prepare()
        if self.enumerator == "capitals":
            self.enumerator = "ABCDEFGHIJKLMNOP"
        if self.enumerator == "lowercase":
            self.enumerator = "abcdefghijklmnop"
        if self.enumerator == "numbers":
            self.enumerator = [str(i + 1) for i in range(20)]
        if self.enumerator == "roman":
            self.enumerator = [
                "I",
                "II",
                "III",
                "IV",
                "V",
                "VI",
                "VII",
                "VIII",
                "IX",
                "X",
                "XI",
                "XII",
                "XIII",
                "XIV",
                "XV",
                "XVI",
                "XVII",
                "XVIII",
                "XIX",
                "XX",
            ]

    def get_choices(self, data: Dict[str, object], choice_format: str) -> str:
        choices = data[self.choices_field]
        enumrated_choices = []
        for i, choice in enumerate(choices):
            enumrated_choices.append(
                choice_format.format(
                    choice_text=choice,
                    choice_numeral=self.enumerator[i],
                )
            )
        return enumrated_choices

    def inputs_to_source(self, inputs: Dict[str, object]) -> Tuple[str, str]:
        choices = self.get_choices(inputs, self.source_choice_format)
        inputs = {
            "numerals": ",".join(self.get_choices(inputs, "{choice_numeral}")),
            **inputs,
            self.choices_field: self.choices_seperator.join(choices),
        }
        formatted = []
        for formatting in [self.input_format, self.instruction]:
            try:
                formatted.append(formatting.format(**inputs))
            except KeyError as e:
                raise KeyError(
                    f"Available inputs are {inputs.keys()} but input format requires a different one: {formatting}"
                ) from e
        return tuple(formatted)

    def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
        target = outputs[self.target_field]

        if not isinstance(target, int):
            try:
                target = outputs[self.choices_field].index(target)
            except ValueError as e:
                raise ValueError(
                    f"MultipleChoiceTemplate could not locate textual target '{target}' in choices list: {outputs[self.choices_field]}"
                ) from e

        choices = self.get_choices(outputs, self.target_choice_format)

        try:
            target = choices[target]
        except IndexError as e:
            raise IndexError(
                f"MultipleChoiceTemplate cannot find index number {target} in choices: {choices}"
            ) from e

        return target, [target]

    def process(
        self, instance: Dict[str, Any], stream_name: Optional[str] = None
    ) -> Dict[str, Any]:
        result = super().process(instance, stream_name)
        if "options" not in result["outputs"]:
            result["outputs"]["options"] = self.get_choices(
                instance["outputs"], self.target_choice_format
            )
        return result


class YesNoTemplate(Template):
    """A template for generating binary Yes/No questions asking whether an input text is of a specific class.

    input_format:
        Defines the format of the question.
    class_field:
        Defines the field that contains the name of the class that this template
        asks of.
    label_field:
        Defines the field which contains the true label of the input text. If a gold label is equal to the
        value in class_name, then the correct output is self.yes_answer (by default, "Yes").
        Otherwise the correct output is self.no_answer (by default, "No").
    yes_answer:
        The output value for when the gold label equals self.class_name.
        Defaults to "Yes".
    no_answer:
        The output value for when the gold label differs from self.class_name.
        Defaults to "No".
    """

    input_format: str = None
    class_field: str = None
    label_field: str = None
    yes_answer: str = "Yes"
    no_answer: str = "No"

    def inputs_to_source(self, inputs: Dict[str, object]) -> Tuple[str, str]:
        data = {
            k: ", ".join(v) if isinstance(v, list) else v for k, v in inputs.items()
        }
        formatted = []
        for formatting in [self.input_format, self.instruction]:
            try:
                formatted.append(formatting.format(**data))
            except KeyError as e:
                raise RuntimeError(
                    f"Available inputs are {list(inputs.keys())} but input format requires a different one: {formatting}"
                ) from e
        return tuple(formatted)

    def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
        try:
            gold_class_names = outputs[self.label_field]
        except KeyError as e:
            raise RuntimeError(
                f"Available outputs are {list(outputs.keys())}, missing required label field: '{self.label_field}'."
            ) from e
        if not isinstance(gold_class_names, list) or not gold_class_names:
            raise RuntimeError(
                f"Unexpected value for gold_class_names: '{gold_class_names}'. Expected a non-empty list."
            )
        try:
            queried_class_names = outputs[self.class_field]
        except KeyError as e:
            raise RuntimeError(
                f"Available outputs are {list(outputs.keys())}, missing required class field: '{self.class_field}'."
            ) from e
        if (
            not queried_class_names
            or not isinstance(queried_class_names, list)
            or not len(queried_class_names) == 1
        ):
            raise RuntimeError(
                f"Unexpected value for queried_class_names: '{queried_class_names}'. Expected a list with one item."
            )
        queried_class_name = queried_class_names[0]
        if queried_class_name in gold_class_names:
            return self.yes_answer, [self.yes_answer]
        return self.no_answer, [self.no_answer]


class KeyValTemplate(Template):
    """Generate field 'source' from fields designated as input, and fields 'target' and 'references' from fields designated as output, of the processed instance.

    Args specify with what separators to glue together the input and output designated fields of the processed instance into one string ('source' and 'target'), and into a list of strings ('references').
    """

    pairs_seperator: str = ", "
    key_val_seperator: str = ": "
    use_keys_for_inputs: bool = True
    outputs_key_val_seperator: str = ": "
    use_keys_for_outputs: bool = False

    def process_dict(
        self, dic: Dict[str, object], key_val_sep, pairs_sep, use_keys
    ) -> str:
        dic = {
            k: ", ".join([str(vi) for vi in v]) if isinstance(v, list) else v
            for k, v in dic.items()
        }
        pairs = []
        for key, val in dic.items():
            key_val = [key, str(val)] if use_keys else [str(val)]
            pairs.append(key_val_sep.join(key_val))
        return pairs_sep.join(pairs)

    def inputs_to_source(self, inputs: Dict[str, object]) -> Tuple[str, str]:
        ret = self.process_dict(
            inputs,
            key_val_sep=self.key_val_seperator,
            pairs_sep=self.pairs_seperator,
            use_keys=self.use_keys_for_inputs,
        )
        return (ret, ret)

    def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
        target = self.process_dict(
            outputs,
            key_val_sep=self.key_val_seperator,
            pairs_sep=self.pairs_seperator,
            use_keys=self.use_keys_for_outputs,
        )
        return target, [target]


class OutputQuantizingTemplate(InputOutputTemplate):
    quantum: float = 0.1

    def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
        quantum_str = f"{self.quantum:.10f}".rstrip("0").rstrip(".")
        quantized_outputs = {
            key: f"{round(value / self.quantum) * self.quantum:{quantum_str}}"
            for key, value in outputs.items()
        }
        return super().outputs_to_target_and_references(quantized_outputs)


class MultiLabelTemplate(InputOutputTemplate):
    labels_field: str = "labels"
    labels_seprator: str = ", "
    postprocessors: List[str] = ["processors.to_list_by_comma"]
    output_format: str = "{labels}"
    empty_label: str = "None"

    def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> str:
        labels = outputs[self.labels_field]
        if not isinstance(labels, list):
            raise ValueError(
                f"MultiLabelTemplate requires labels field '{self.labels_field}' to be a list. Got {self.labels_field}<{type(labels).__name__}>: {labels}"
            )
        if len(labels) == 0:
            labels = [self.empty_label]
        labels_str = self.labels_seprator.join(labels)
        return super().outputs_to_target_and_references({self.labels_field: labels_str})


class MultiReferenceTemplate(InputOutputTemplate):
    references_field: str = "references"
    random_reference: bool = False

    def outputs_to_target_and_references(self, outputs: Dict[str, object]) -> List[str]:
        references = outputs[self.references_field]
        if not isoftype(references, List[str]):
            raise ValueError(
                f"MultiReferenceTemplate requires references field '{self.references_field}' to be List[str]. Got {self.references_field}<{type(references).__name__}>: {references}"
            )
        if len(references) == 0:
            raise ValueError(
                "No references found. MultiReferenceTemplate requires at least one reference."
            )

        if self.random_reference:
            random_generator = new_random_generator(outputs)
            target = random_generator.choice(references)
        else:
            target = references[0]

        return target, references


def escape_chars(s, chars_to_escape):
    for char in chars_to_escape:
        s = s.replace(char, f"\\{char}")
    return s


class SpanLabelingBaseTemplate(MultiLabelTemplate):
    spans_starts_field: str = "spans_starts"
    spans_ends_field: str = "spans_ends"
    text_field: str = "text"
    labels_support: list = None

    def extract_span_label_pairs(self, outputs):
        spans_starts = outputs[self.spans_starts_field]
        spans_ends = outputs[self.spans_ends_field]
        text = outputs[self.text_field]
        labels = outputs[self.labels_field]

        spans = []
        for span_start, span_end, label in zip(spans_starts, spans_ends, labels):
            if self.labels_support is None or label in self.labels_support:
                spans.append((span_start, span_end, text[span_start:span_end], label))

        for span in sorted(spans):
            if self.labels_support is None or span[3] in self.labels_support:
                yield span[2], span[3]

    def outputs_to_target_and_references(
        self, outputs: Dict[str, object]
    ) -> Dict[str, object]:
        span_lables_pairs = self.extract_span_label_pairs(outputs)
        targets = self.span_label_pairs_to_targets(span_lables_pairs)
        return super().outputs_to_target_and_references({"labels": targets})

    @abstractmethod
    def span_label_pairs_to_targets(self, pairs):
        pass


class SpanLabelingTemplate(SpanLabelingBaseTemplate):
    span_label_format: str = "{span}: {label}"
    escape_characters: List[str] = [":", ","]
    postprocessors: List[str] = ["processors.to_span_label_pairs"]

    def span_label_pairs_to_targets(self, span_label_pairs):
        targets = []
        for span, label in span_label_pairs:
            if self.escape_characters is not None:
                span = escape_chars(span, self.escape_characters)
            target = self.span_label_format.format(span=span, label=label)
            targets.append(target)
        return targets


class SpanLabelingJsonTemplate(SpanLabelingBaseTemplate):
    postprocessors = [
        "processors.load_json",
        "processors.dict_of_lists_to_value_key_pairs",
    ]

    def span_label_pairs_to_targets(self, span_label_pairs):
        groups = {}
        for span, label in span_label_pairs:
            if label not in groups:
                groups[label] = []
            groups[label].append(span)
        if len(groups) > 0:
            targets = [json.dumps(groups, ensure_ascii=False)]
        else:
            targets = []
        return targets


class TemplatesList(ListCollection):
    def verify(self):
        for template in self.items:
            assert isinstance(template, Template)


class TemplatesDict(Dict):
    def verify(self):
        for _key, template in self.items():
            assert isinstance(template, Template)