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# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from typing import List
from nemo.collections.common.tokenizers.moses_tokenizers import MosesProcessor
from nemo.collections.nlp.data.text_normalization import constants
from nemo.collections.nlp.data.text_normalization.utils import normalize_str, read_data_file, remove_puncts
from nemo.utils import logging
__all__ = ['TextNormalizationTestDataset']
# Test Dataset
class TextNormalizationTestDataset:
"""
Creates dataset to use to do end-to-end inference
Args:
input_file: path to the raw data file (e.g., train.tsv). For more info about the data format, refer to the `text_normalization doc <https://github.com/NVIDIA/NeMo/blob/main/docs/source/nlp/text_normalization.rst>`.
mode: should be one of the values ['tn', 'itn', 'joint']. `tn` mode is for TN only. `itn` mode is for ITN only. `joint` is for training a system that can do both TN and ITN at the same time.
lang: Language of the dataset
"""
def __init__(self, input_file: str, mode: str, lang: str):
self.lang = lang
insts = read_data_file(input_file, lang=lang)
processor = MosesProcessor(lang_id=lang)
# Build inputs and targets
self.directions, self.inputs, self.targets, self.classes, self.nb_spans, self.span_starts, self.span_ends = (
[],
[],
[],
[],
[],
[],
[],
)
for (classes, w_words, s_words) in insts:
# Extract words that are not punctuations
for direction in constants.INST_DIRECTIONS:
if direction == constants.INST_BACKWARD:
if mode == constants.TN_MODE:
continue
# ITN mode
(
processed_w_words,
processed_s_words,
processed_classes,
processed_nb_spans,
processed_s_span_starts,
processed_s_span_ends,
) = ([], [], [], 0, [], [])
s_word_idx = 0
for cls, w_word, s_word in zip(classes, w_words, s_words):
if s_word == constants.SIL_WORD:
continue
elif s_word == constants.SELF_WORD:
processed_s_words.append(w_word)
else:
processed_s_words.append(s_word)
s_word_last = processor.tokenize(processed_s_words.pop()).split()
processed_s_words.append(" ".join(s_word_last))
num_tokens = len(s_word_last)
processed_nb_spans += 1
processed_classes.append(cls)
processed_s_span_starts.append(s_word_idx)
s_word_idx += num_tokens
processed_s_span_ends.append(s_word_idx)
processed_w_words.append(w_word)
self.span_starts.append(processed_s_span_starts)
self.span_ends.append(processed_s_span_ends)
self.classes.append(processed_classes)
self.nb_spans.append(processed_nb_spans)
input_words = ' '.join(processed_s_words)
# Update self.directions, self.inputs, self.targets
self.directions.append(direction)
self.inputs.append(input_words)
self.targets.append(
processed_w_words
) # is list of lists where inner list contains target tokens (not words)
# TN mode
elif direction == constants.INST_FORWARD:
if mode == constants.ITN_MODE:
continue
(
processed_w_words,
processed_s_words,
processed_classes,
processed_nb_spans,
w_span_starts,
w_span_ends,
) = ([], [], [], 0, [], [])
w_word_idx = 0
for cls, w_word, s_word in zip(classes, w_words, s_words):
# TN forward mode
# this is done for cases like `do n't`, this w_word will be treated as 2 tokens
w_word = processor.tokenize(w_word).split()
num_tokens = len(w_word)
if s_word in constants.SPECIAL_WORDS:
processed_s_words.append(" ".join(w_word))
else:
processed_s_words.append(s_word)
w_span_starts.append(w_word_idx)
w_word_idx += num_tokens
w_span_ends.append(w_word_idx)
processed_nb_spans += 1
processed_classes.append(cls)
processed_w_words.extend(w_word)
self.span_starts.append(w_span_starts)
self.span_ends.append(w_span_ends)
self.classes.append(processed_classes)
self.nb_spans.append(processed_nb_spans)
input_words = ' '.join(processed_w_words)
# Update self.directions, self.inputs, self.targets
self.directions.append(direction)
self.inputs.append(input_words)
self.targets.append(
processed_s_words
) # is list of lists where inner list contains target tokens (not words)
self.examples = list(
zip(
self.directions,
self.inputs,
self.targets,
self.classes,
self.nb_spans,
self.span_starts,
self.span_ends,
)
)
def __getitem__(self, idx):
return self.examples[idx]
def __len__(self):
return len(self.inputs)
@staticmethod
def is_same(pred: str, target: str, inst_dir: str):
"""
Function for checking whether the predicted string can be considered
the same as the target string
Args:
pred: Predicted string
target: Target string
inst_dir: Direction of the instance (i.e., INST_BACKWARD or INST_FORWARD).
Return: an int value (0/1) indicating whether pred and target are the same.
"""
if inst_dir == constants.INST_BACKWARD:
pred = remove_puncts(pred)
target = remove_puncts(target)
pred = normalize_str(pred)
target = normalize_str(target)
return int(pred == target)
@staticmethod
def compute_sent_accuracy(preds: List[str], targets: List[str], inst_directions: List[str]):
"""
Compute the sentence accuracy metric.
Args:
preds: List of predicted strings.
targets: List of target strings.
inst_directions: A list of str where each str indicates the direction of the corresponding instance (i.e., INST_BACKWARD or INST_FORWARD).
Return: the sentence accuracy score
"""
assert len(preds) == len(targets)
if len(targets) == 0:
return 'NA'
# Sentence Accuracy
correct_count = 0
for inst_dir, pred, target in zip(inst_directions, preds, targets):
correct_count += TextNormalizationTestDataset.is_same(pred, target, inst_dir)
sent_accuracy = correct_count / len(targets)
return sent_accuracy
@staticmethod
def compute_class_accuracy(
inputs: List[List[str]],
targets: List[List[str]],
tag_preds: List[List[str]],
inst_directions: List[str],
output_spans: List[List[str]],
classes: List[List[str]],
nb_spans: List[int],
span_ends: List[List[int]],
) -> dict:
"""
Compute the class based accuracy metric. This uses model's predicted tags.
Args:
inputs: List of lists where inner list contains words of input text
targets: List of lists where inner list contains target strings grouped by class boundary
tag_preds: List of lists where inner list contains predicted tags for each of the input words
inst_directions: A list of str where each str indicates the direction of the corresponding instance (i.e., INST_BACKWARD or INST_FORWARD).
output_spans: A list of lists where each inner list contains the decoded spans for the corresponding input sentence
classes: A list of lists where inner list contains the class for each semiotic token in input sentence
nb_spans: A list that contains the number of tokens in the input
span_ends: A list of lists where inner list contains the end word index of the current token
Return: the class accuracy scores as dict
"""
if len(targets) == 0:
return 'NA'
class2stats, class2correct = defaultdict(int), defaultdict(int)
for ix, (sent, tags) in enumerate(zip(inputs, tag_preds)):
try:
assert len(sent) == len(tags)
except:
logging.warning(f"Error: skipping example {ix}")
continue
cur_words = [[] for _ in range(nb_spans[ix])]
jx, span_idx = 0, 0
cur_spans = output_spans[ix]
class_idx = 0
if classes[ix]:
class2stats[classes[ix][class_idx]] += 1
while jx < len(sent):
tag, word = tags[jx], sent[jx]
while jx >= span_ends[ix][class_idx]:
class_idx += 1
class2stats[classes[ix][class_idx]] += 1
if constants.SAME_TAG in tag:
cur_words[class_idx].append(word)
jx += 1
else:
jx += 1
tmp = cur_spans[span_idx]
cur_words[class_idx].append(tmp)
span_idx += 1
while jx < len(sent) and tags[jx] == constants.I_PREFIX + constants.TRANSFORM_TAG:
while jx >= span_ends[ix][class_idx]:
class_idx += 1
class2stats[classes[ix][class_idx]] += 1
cur_words[class_idx].append(tmp)
jx += 1
target_token_idx = 0
# assert len(cur_words) == len(targets[ix])
for class_idx in range(nb_spans[ix]):
correct = TextNormalizationTestDataset.is_same(
" ".join(cur_words[class_idx]), targets[ix][target_token_idx], inst_directions[ix]
)
class2correct[classes[ix][class_idx]] += correct
target_token_idx += 1
for key in class2stats:
class2stats[key] = (class2correct[key] / class2stats[key], class2correct[key], class2stats[key])
return class2stats
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