File size: 13,120 Bytes
063834c
63d3ef3
50eeb1b
063834c
1ec0bfb
 
6b26378
eb86bdf
1ec0bfb
eb86bdf
1ec0bfb
63d3ef3
 
 
 
 
 
 
 
 
6b26378
 
 
63d3ef3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6b26378
 
 
 
 
 
 
 
 
 
63d3ef3
6b26378
 
63d3ef3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6b26378
 
 
 
 
63d3ef3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3e48723
 
063834c
63d3ef3
 
 
 
 
cbce8ee
63d3ef3
 
eb86bdf
63d3ef3
 
 
 
 
 
 
 
 
 
eb86bdf
63d3ef3
 
 
 
 
 
063834c
63d3ef3
 
 
 
 
 
 
 
 
 
 
 
cbce8ee
 
 
63d3ef3
 
 
 
 
 
 
 
 
cbce8ee
63d3ef3
eb86bdf
063834c
eb86bdf
63d3ef3
eb86bdf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
63d3ef3
 
50eeb1b
63d3ef3
 
6db8cc3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eb86bdf
 
 
 
 
 
 
 
 
 
6b26378
 
 
3e48723
6b26378
 
 
 
 
 
 
 
 
 
 
063834c
 
 
 
 
 
 
50eeb1b
 
 
063834c
50eeb1b
063834c
50eeb1b
 
 
 
 
 
 
063834c
 
50eeb1b
063834c
 
 
50eeb1b
063834c
 
 
 
50eeb1b
063834c
 
 
50eeb1b
063834c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
50eeb1b
 
63d3ef3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
eb86bdf
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
63d3ef3
 
 
 
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
import json
from abc import ABC, abstractmethod
from dataclasses import field
from typing import Any, Dict, List, Optional, Union

from .artifact import Artifact
from .dataclass import NonPositionalField
from .instructions import Instruction, TextualInstruction
from .operator import InstanceOperatorWithGlobalAccess, StreamInstanceOperator
from .random_utils import random
from .text_utils import split_words


class Renderer(ABC):
    @abstractmethod
    def get_postprocessors(self) -> List[str]:
        pass


class Template(Artifact):
    is_multi_target: bool = NonPositionalField(default=False)
    is_multi_reference: bool = NonPositionalField(default=False)

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

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

    @abstractmethod
    def get_postprocessors(self) -> List[str]:
        pass


class RenderFormatTemplate(Renderer, StreamInstanceOperator):
    template: Template = None
    random_reference: bool = False

    def verify(self):
        assert isinstance(self.template, Template), "Template must be an instance of Template"
        assert self.template is not None, "Template must be specified"

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

    def render(self, instance: Dict[str, Any]) -> Dict[str, Any]:
        inputs = instance.pop("inputs")
        outputs = instance.pop("outputs")

        source = self.template.process_inputs(inputs)
        targets = self.template.process_outputs(outputs)

        if self.template.is_multi_reference:
            references = targets
            if self.random_reference:
                target = random.choice(references)
            else:
                if len(references) == 0:
                    raise ValueError("No references found")
                target = references[0]
        else:
            references = [targets]
            target = targets

        return {
            **instance,
            "source": source,
            "target": target,
            "references": references,
        }

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


class RenderAutoFormatTemplate(RenderFormatTemplate):
    def prepare(self):
        if self.template is None:
            self.template = AutoInputOutputTemplate()

    def render(self, instance: Dict[str, object]) -> Dict[str, object]:
        try:
            if not self.template.is_complete():
                self.template.infer_missing(instance["inputs"], instance["outputs"])
        except:
            pass

        inputs = {key: value for key, value in instance["inputs"].items()}

        return super().render({**instance, "inputs": inputs})


class CharacterSizeLimiter(Artifact):
    limit: int = 1000

    def check(self, text: str) -> bool:
        return len(text) <= self.limit


class RenderTemplatedICL(RenderAutoFormatTemplate):
    instruction: Instruction = None
    input_prefix: str = ""
    output_prefix: str = ""
    target_prefix: str = " "
    instruction_prefix: str = ""
    demos_field: str = None
    size_limiter: Artifact = None
    input_output_separator: str = "\n"
    demo_separator: str = "\n\n"
    system_prompt: str = None

    def render(self, instance: Dict[str, object]) -> Dict[str, object]:
        demos = instance.pop(self.demos_field, [])

        source = ""

        example = super().render(instance)

        input_str = self.input_prefix + example["source"] + self.input_output_separator + self.output_prefix

        if self.instruction is not None:
            source += self.instruction_prefix + self.instruction() + self.demo_separator

        for demo_instance in demos:
            demo_example = super().render(demo_instance)
            demo_str = (
                self.input_prefix
                + demo_example["source"]
                + self.input_output_separator
                + self.output_prefix
                + self.target_prefix
                + demo_example["target"]
                + self.demo_separator
            )

            if self.size_limiter is not None:
                if not self.size_limiter.check(source + demo_str + input_str + example["target"]):
                    continue

            source += demo_str

        source += input_str

        if self.system_prompt is not None:
            source = self.system_prompt.format(source)

        return {
            **example,
            "source": source,
        }


class InputOutputTemplate(Template):
    input_format: str = None
    output_format: str = None
    postprocessors: List[str] = field(default_factory=lambda: ["processors.to_string_stripped"])

    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 process_inputs(self, inputs: Dict[str, object]) -> str:
        try:
            return self.process_template(self.input_format, inputs)
        except KeyError as e:
            raise KeyError(
                f"Available inputs are {inputs.keys()} but input format requires a different one: {self.input_format}"
            )

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

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


class KeyValTemplate(Template):
    pairs_seperator: str = ", "
    key_val_seperator: str = ": "
    use_keys_for_inputs: bool = True
    outputs_key_val_seperator: str = ": "
    use_keys_for_outputs: bool = False

    postprocessors: List[str] = field(default_factory=lambda: ["processors.to_string_stripped"])

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

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

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

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


class OutputQuantizingTemplate(InputOutputTemplate):
    quantum: float = 0.1

    def process_outputs(self, outputs: Dict[str, object]) -> Dict[str, object]:
        quantized_outputs = {
            key: round(input_float / self.quantum) * self.quantum for key, input_float in outputs.items()
        }
        return super().process_outputs(quantized_outputs)


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

    def process_outputs(self, outputs: Dict[str, object]) -> Dict[str, object]:
        labels = outputs[self.labels_field]
        if len(labels) == 0:
            labels = [self.empty_label]
        labels_str = self.labels_seprator.join(labels)
        return super().process_outputs({"labels": labels_str})


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 process_outputs(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().process_outputs({"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 = ["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] = list()
            groups[label].append(span)
        if len(groups) > 0:
            targets = [json.dumps(groups)]
        else:
            targets = []
        return targets


class AutoInputOutputTemplate(InputOutputTemplate):
    def infer_input_format(self, inputs):
        input_format = ""
        for key in inputs.keys():
            name = " ".join(word.lower().capitalize() for word in split_words(key) if word != " ")
            input_format += name + ": " + "{" + key + "}" + "\n"
        self.input_format = input_format

    def infer_output_format(self, outputs):
        self.output_format = "{" + next(iter(outputs.keys())) + "}"

    def infer_missing(self, inputs, outputs):
        if self.input_format is None:
            self.infer_input_format(inputs)
        if self.output_format is None:
            self.infer_output_format(outputs)

    def is_complete(self):
        return self.input_format is not None and self.output_format is not None


from .collections import ListCollection


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


def outputs_inputs2templates(inputs: Union[str, List], outputs: Union[str, List]) -> TemplatesList:
    """
    combines input and output formats into their dot product
    :param inputs: list of input formats (or one)
    :param outputs: list of output formats (or one)
    :return: TemplatesList of InputOutputTemplate
    """
    templates = []
    if isinstance(inputs, str):
        inputs = [inputs]
    if isinstance(outputs, str):
        outputs = [outputs]
    for input in inputs:
        for output in outputs:
            templates.append(
                InputOutputTemplate(
                    input_format=input.strip(),
                    output_format=output.strip(),
                ),
            )
    return TemplatesList(templates)


def instructions2templates(
    instructions: List[TextualInstruction], templates: List[InputOutputTemplate]
) -> TemplatesList:
    """
    Insert instructions into per demonstration templates
    :param instructions:
    :param templates: strings containing {instuction} where the instruction should be placed
    :return:
    """
    res_templates = []
    for instruction in instructions:
        for template in templates:
            res_templates.append(
                InputOutputTemplate(
                    input_format=template.input_format.replace("{instruction}", instruction.text),
                    output_format=template.output_format,
                )
            )
    return TemplatesList(templates)


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