diff --git a/machine/corpora/aligned_word_pair.py b/machine/corpora/aligned_word_pair.py index a18251fd..326f84a0 100644 --- a/machine/corpora/aligned_word_pair.py +++ b/machine/corpora/aligned_word_pair.py @@ -71,7 +71,7 @@ def format_score(score: float) -> str: source_index = "NULL" if self.source_index < 0 else str(self.source_index) target_index = "NULL" if self.target_index < 0 else str(self.target_index) repr = f"{source_index}-{target_index}" - if include_scores and self.translation_score >= 0: + if include_scores and (self.translation_score >= 0 or self.alignment_score >= 0): repr += f":{format_score(self.translation_score)}" if self.alignment_score >= 0: repr += f":{format_score(self.alignment_score)}" diff --git a/machine/corpora/corpora_utils.py b/machine/corpora/corpora_utils.py index ddd8e3c5..bc9e43e0 100644 --- a/machine/corpora/corpora_utils.py +++ b/machine/corpora/corpora_utils.py @@ -22,6 +22,7 @@ def alignment_exception(refs: Iterable[str]) -> RuntimeError: def batch(iterable: Iterable[T], batch_size: int) -> Iterable[Sequence[T]]: if isinstance(iterable, Sequence) and len(iterable) <= batch_size: yield iterable + return batch: List[T] = [] for item in iterable: diff --git a/machine/corpora/flatten.py b/machine/corpora/flatten.py index 7f0e56d9..0e460f37 100644 --- a/machine/corpora/flatten.py +++ b/machine/corpora/flatten.py @@ -33,14 +33,13 @@ def flatten(corpora: Iterable[Corpus]) -> Corpus: if len(corpus_list) == 1: return corpus_list[0] - if any(type(corpus_list[0]) != type(corpus) for corpus in corpus_list[1:]): # noqa: E721 - raise TypeError("All corpora must be of the same type.") - - if isinstance(corpus_list[0], TextCorpus): + if all(isinstance(corpus, TextCorpus) for corpus in corpus_list): return _FlattenTextCorpus(cast(List[TextCorpus], corpus_list)) - if isinstance(corpus_list[0], AlignmentCorpus): + if all(isinstance(corpus, AlignmentCorpus) for corpus in corpus_list): return _FlattenAlignmentCorpus(cast(List[AlignmentCorpus], corpus_list)) - return _FlattenParallelTextCorpus(cast(List[ParallelTextCorpus], corpus_list)) + if all(isinstance(corpus, ParallelTextCorpus) for corpus in corpus_list): + return _FlattenParallelTextCorpus(cast(List[ParallelTextCorpus], corpus_list)) + raise TypeError("All corpora must be of the same type.") class _FlattenTextCorpus(TextCorpus): @@ -100,7 +99,7 @@ def is_target_tokenized(self) -> bool: def count(self, include_empty: bool = True, text_ids: Optional[Iterable[str]] = None) -> int: return sum(c.count(include_empty, text_ids) for c in self._corpora) - def _get_rows(self) -> Generator[ParallelTextRow, None, None]: + def _get_rows(self, text_ids: Optional[Iterable[str]] = None) -> Generator[ParallelTextRow, None, None]: for corpus in self._corpora: - with corpus.get_rows() as rows: + with corpus.get_rows(text_ids) as rows: yield from rows diff --git a/machine/jobs/build_nmt_engine.py b/machine/jobs/build_nmt_engine.py index 09522975..e3e57f27 100644 --- a/machine/jobs/build_nmt_engine.py +++ b/machine/jobs/build_nmt_engine.py @@ -12,6 +12,7 @@ from .nmt_engine_build_job import NmtEngineBuildJob from .nmt_model_factory import NmtModelFactory from .shared_file_service_factory import SharedFileServiceType +from .thot.thot_word_alignment_model_factory import ThotWordAlignmentModelFactory from .translation_file_service import TranslationFileService # Setup logging @@ -57,7 +58,9 @@ def clearml_progress(status: ProgressStatus) -> None: else: raise RuntimeError("The model type is invalid.") - job = NmtEngineBuildJob(SETTINGS, nmt_model_factory, translation_file_service) + job = NmtEngineBuildJob( + SETTINGS, nmt_model_factory, translation_file_service, ThotWordAlignmentModelFactory(SETTINGS) + ) train_corpus_size, _ = job.run(progress, check_canceled) if task is not None: task.get_logger().report_single_value(name="train_corpus_size", value=train_corpus_size) diff --git a/machine/jobs/eflomal_aligner.py b/machine/jobs/eflomal_aligner.py deleted file mode 100644 index eea6b2f4..00000000 --- a/machine/jobs/eflomal_aligner.py +++ /dev/null @@ -1,167 +0,0 @@ -# NOTE: this is a temporary solution to be able to use the eflomal aligner inside of machine.py. -# The vast majority of this code is taken from the silnlp repository. - -import os -import subprocess -from contextlib import ExitStack -from importlib.util import find_spec -from math import sqrt -from pathlib import Path -from tempfile import TemporaryDirectory -from typing import IO, Iterable, List, Sequence, Tuple - -from ..corpora import AlignedWordPair -from ..corpora.token_processors import escape_spaces, lowercase, normalize -from ..tokenization import LatinWordTokenizer -from ..translation import SymmetrizationHeuristic, WordAlignmentMatrix - - -# From silnlp.common.package_utils -def is_eflomal_available() -> bool: - return find_spec("eflomal") is not None - - -if is_eflomal_available(): - from eflomal import read_text, write_text # type: ignore - -EFLOMAL_PATH = Path(os.getenv("EFLOMAL_PATH", "."), "eflomal") -TOKENIZER = LatinWordTokenizer() - - -# From silnlp.alignment.tools -def execute_eflomal( - source_path: Path, - target_path: Path, - forward_links_path: Path, - reverse_links_path: Path, - n_iterations: Tuple[int, int, int], -) -> None: - if not is_eflomal_available(): - raise RuntimeError("eflomal is not installed.") - - args = [ - str(EFLOMAL_PATH), - "-s", - str(source_path), - "-t", - str(target_path), - "-f", - str(forward_links_path), - "-r", - str(reverse_links_path), - # "-q", - "-m", - "3", - "-n", - "3", - "-N", - "0.2", - "-1", - str(n_iterations[0]), - "-2", - str(n_iterations[1]), - "-3", - str(n_iterations[2]), - ] - subprocess.run(args, stderr=subprocess.DEVNULL) - - -# From silnlp.alignment.eflomal -def to_word_alignment_matrix(alignment_str: str) -> WordAlignmentMatrix: - word_pairs = AlignedWordPair.from_string(alignment_str) - row_count = 0 - column_count = 0 - for pair in word_pairs: - if pair.source_index + 1 > row_count: - row_count = pair.source_index + 1 - if pair.target_index + 1 > column_count: - column_count = pair.target_index + 1 - return WordAlignmentMatrix.from_word_pairs(row_count, column_count, word_pairs) - - -# From silnlp.alignment.eflomal -def to_eflomal_text_file(input: Iterable[str], output_file: IO[bytes], prefix_len: int = 0, suffix_len: int = 0) -> int: - sents, index = read_text(input, True, prefix_len, suffix_len) - n_sents = len(sents) - voc_size = len(index) - write_text(output_file, tuple(sents), voc_size) - return n_sents - - -# From silnlp.alignment.eflomal -def prepare_files( - src_input: Iterable[str], src_output_file: IO[bytes], trg_input: Iterable[str], trg_output_file: IO[bytes] -) -> int: - n_src_sents = to_eflomal_text_file(src_input, src_output_file) - n_trg_sents = to_eflomal_text_file(trg_input, trg_output_file) - if n_src_sents != n_trg_sents: - raise ValueError("Mismatched file sizes") - return n_src_sents - - -def tokenize(sent: str) -> Sequence[str]: - return list(TOKENIZER.tokenize(sent)) - - -def normalize_for_alignment(sent: Sequence[str]) -> str: - return " ".join(lowercase(normalize("NFC", escape_spaces(sent)))) - - -# From silnlp.alignment.eflomal -class EflomalAligner: - def __init__(self, model_dir: Path) -> None: - self._model_dir = model_dir - - def train(self, src_toks: Sequence[Sequence[str]], trg_toks: Sequence[Sequence[str]]) -> None: - self._model_dir.mkdir(exist_ok=True) - with TemporaryDirectory() as temp_dir: - src_eflomal_path = Path(temp_dir, "source") - trg_eflomal_path = Path(temp_dir, "target") - with ExitStack() as stack: - src_output_file = stack.enter_context(src_eflomal_path.open("wb")) - trg_output_file = stack.enter_context(trg_eflomal_path.open("wb")) - # Write input files for the eflomal binary - n_sentences = prepare_files( - [normalize_for_alignment(s) for s in src_toks], - src_output_file, - [normalize_for_alignment(s) for s in trg_toks], - trg_output_file, - ) - - iters = max(2, int(round(1.0 * 5000 / sqrt(n_sentences))) if n_sentences > 0 else 0) - iters4 = max(1, iters // 4) - n_iterations = (max(2, iters4), iters4, iters) - - # Run wrapper for the eflomal binary - execute_eflomal( - src_eflomal_path, - trg_eflomal_path, - self._model_dir / "forward-align.txt", - self._model_dir / "reverse-align.txt", - n_iterations, - ) - - def align(self, sym_heuristic: str = "grow-diag-final-and") -> List[str]: - forward_align_path = self._model_dir / "forward-align.txt" - reverse_align_path = self._model_dir / "reverse-align.txt" - - alignments = [] - heuristic = SymmetrizationHeuristic[sym_heuristic.upper().replace("-", "_")] - with ExitStack() as stack: - forward_file = stack.enter_context(forward_align_path.open("r", encoding="utf-8-sig")) - reverse_file = stack.enter_context(reverse_align_path.open("r", encoding="utf-8-sig")) - - for forward_line, reverse_line in zip(forward_file, reverse_file): - forward_matrix = to_word_alignment_matrix(forward_line.strip()) - reverse_matrix = to_word_alignment_matrix(reverse_line.strip()) - src_len = max(forward_matrix.row_count, reverse_matrix.row_count) - trg_len = max(forward_matrix.column_count, reverse_matrix.column_count) - - forward_matrix.resize(src_len, trg_len) - reverse_matrix.resize(src_len, trg_len) - - forward_matrix.symmetrize_with(reverse_matrix, heuristic) - - alignments.append(str(forward_matrix)) - - return alignments diff --git a/machine/jobs/nmt_engine_build_job.py b/machine/jobs/nmt_engine_build_job.py index cf4020d6..08ab3012 100644 --- a/machine/jobs/nmt_engine_build_job.py +++ b/machine/jobs/nmt_engine_build_job.py @@ -1,28 +1,43 @@ +import json import logging from contextlib import ExitStack from pathlib import Path from tempfile import TemporaryDirectory -from typing import Any, Callable, Optional, Sequence, Tuple +from typing import Any, Callable, Generator, Iterable, Optional, Sequence, Tuple +from ..corpora.aligned_word_pair import AlignedWordPair from ..corpora.corpora_utils import batch +from ..corpora.flatten import flatten from ..corpora.parallel_text_corpus import ParallelTextCorpus from ..corpora.text_corpus import TextCorpus +from ..corpora.text_file_text_corpus import TextFileTextCorpus +from ..corpora.token_processors import lowercase +from ..tokenization.tokenizer_factory import create_tokenizer +from ..translation.transductive_word_alignment_model import TransductiveWordAlignmentModel +from ..translation.translation_engine import TranslationEngine +from ..translation.word_alignment_matrix import WordAlignmentMatrix from ..utils.phased_progress_reporter import Phase, PhasedProgressReporter from ..utils.progress_status import ProgressStatus -from .eflomal_aligner import EflomalAligner, is_eflomal_available, tokenize from .nmt_model_factory import NmtModelFactory from .translation_engine_build_job import TranslationEngineBuildJob from .translation_file_service import PretranslationInfo, TranslationFileService +from .word_alignment_model_factory import WordAlignmentModelFactory logger = logging.getLogger(__name__) class NmtEngineBuildJob(TranslationEngineBuildJob): def __init__( - self, config: Any, nmt_model_factory: NmtModelFactory, translation_file_service: TranslationFileService + self, + config: Any, + nmt_model_factory: NmtModelFactory, + translation_file_service: TranslationFileService, + word_alignment_model_factory: WordAlignmentModelFactory, ) -> None: self._nmt_model_factory = nmt_model_factory self._nmt_model_factory.init() + self._alignment_model_factory = word_alignment_model_factory + self._alignment_model_factory.init() super().__init__(config, translation_file_service) def _get_progress_reporter( @@ -33,7 +48,7 @@ def _get_progress_reporter( phases = [ Phase(message="Training NMT model", percentage=0.8, stage="train"), Phase(message="Pretranslating segments", percentage=0.1, stage="inference"), - Phase(message="Aligning segments", percentage=0.1, report_steps=False), + Phase(message="Aligning segments", percentage=0.1, stage="align"), ] else: phases = [ @@ -44,7 +59,7 @@ def _get_progress_reporter( if self._config.align_pretranslations: phases = [ Phase(message="Pretranslating segments", percentage=0.9, stage="inference"), - Phase(message="Aligning segments", percentage=0.1, report_steps=False), + Phase(message="Aligning segments", percentage=0.1, stage="align"), ] else: phases = [Phase(message="Pretranslating segments", percentage=1.0, stage="inference")] @@ -94,6 +109,7 @@ def _train_model( def _batch_inference( self, + parallel_training_corpus: ParallelTextCorpus, progress_reporter: PhasedProgressReporter, check_canceled: Optional[Callable[[], None]], ) -> None: @@ -103,66 +119,129 @@ def _batch_inference( with ExitStack() as stack: phase_progress = stack.enter_context(progress_reporter.start_next_phase()) engine = stack.enter_context(self._nmt_model_factory.create_engine()) - pretranslations = [ - pt_info for pt_info in stack.enter_context(self._translation_file_service.get_source_pretranslations()) - ] - src_segments = [pt_info["translation"] for pt_info in pretranslations] - current_inference_step = 0 - phase_progress(ProgressStatus.from_step(current_inference_step, inference_step_count)) - batch_size = self._config["inference_batch_size"] - for seg_batch in batch(iter(src_segments), batch_size): - if check_canceled is not None: - check_canceled() - for i, result in enumerate(engine.translate_batch(seg_batch)): - pretranslations[current_inference_step + i]["translation"] = result.translation - pretranslations[current_inference_step + i]["sequenceConfidence"] = result.sequence_confidence - current_inference_step += len(seg_batch) - phase_progress(ProgressStatus.from_step(current_inference_step, inference_step_count)) - - if self._config.align_pretranslations and is_eflomal_available(): - logger.info("Aligning source to pretranslations") - pretranslations = self._align(src_segments, pretranslations, progress_reporter, check_canceled) + src_pretranslations = stack.enter_context(self._translation_file_service.get_source_pretranslations()) + pretranslations = self._translate( + engine, src_pretranslations, inference_step_count, phase_progress, check_canceled + ) + if self._config.align_pretranslations: + results: Iterable[PretranslationInfo] = self._align( + pretranslations, parallel_training_corpus, progress_reporter, check_canceled + ) + else: + results = (pt_info for _, pt_info in pretranslations) writer = stack.enter_context(self._translation_file_service.open_target_pretranslation_writer()) - for pretranslation in pretranslations: - writer.write(pretranslation) + for pt_info in results: + writer.write(pt_info) + + def _translate( + self, + engine: TranslationEngine, + src_pretranslations: Iterable[PretranslationInfo], + inference_step_count: int, + phase_progress: Callable[[ProgressStatus], None], + check_canceled: Optional[Callable[[], None]], + ) -> Generator[Tuple[str, PretranslationInfo], None, None]: + current_inference_step = 0 + phase_progress(ProgressStatus.from_step(current_inference_step, inference_step_count)) + batch_size: int = self._config["inference_batch_size"] + for pt_batch in batch(src_pretranslations, batch_size): + if check_canceled is not None: + check_canceled() + source_segments = [pt_info["translation"] for pt_info in pt_batch] + for pt_info, result in zip(pt_batch, engine.translate_batch(source_segments), strict=True): + pt_info["translation"] = result.translation + pt_info["sequenceConfidence"] = result.sequence_confidence + current_inference_step += len(pt_batch) + phase_progress(ProgressStatus.from_step(current_inference_step, inference_step_count)) + yield from zip(source_segments, pt_batch) def _align( self, - src_segments: Sequence[str], - pretranslations: Sequence[PretranslationInfo], + pretranslations: Iterable[Tuple[str, PretranslationInfo]], + parallel_training_corpus: ParallelTextCorpus, progress_reporter: PhasedProgressReporter, check_canceled: Optional[Callable[[], None]], - ) -> Sequence[PretranslationInfo]: + ) -> Generator[PretranslationInfo, None, None]: if check_canceled is not None: check_canceled() - logger.info("Aligning source to pretranslations") - progress_reporter.start_next_phase() - - src_tokenized = [tokenize(s) for s in src_segments] - trg_tokenized = [tokenize(pt_info["translation"]) for pt_info in pretranslations] + tokenizer = create_tokenizer(self._config.thot_align.tokenizer) - with TemporaryDirectory() as td: - aligner = EflomalAligner(Path(td)) - logger.info("Training aligner") - aligner.train(src_tokenized, trg_tokenized) + with TemporaryDirectory() as temp_dir: + # Spool the translated pretranslations to disk so that the aligner can make multiple + # passes over them without keeping the entire corpus in memory. + logger.info("Aligning source to pretranslations") + source_path = Path(temp_dir) / "pretranslations.src.txt" + translation_path = Path(temp_dir) / "pretranslations.trg.txt" + pretranslations_path = Path(temp_dir) / "pretranslations.json" + with ( + source_path.open("w", encoding="utf-8", newline="\n") as source_file, + translation_path.open("w", encoding="utf-8", newline="\n") as translation_file, + pretranslations_path.open("w", encoding="utf-8", newline="\n") as pretranslations_file, + ): + for source_segment, pt_info in pretranslations: + source_file.write(source_segment + "\n") + translation_file.write(pt_info["translation"] + "\n") + pretranslations_file.write(json.dumps(pt_info, ensure_ascii=False) + "\n") if check_canceled is not None: check_canceled() - logger.info("Aligning pretranslations") - alignments = aligner.align() + parallel_pretranslation_corpus = TextFileTextCorpus(source_path).align_rows( + TextFileTextCorpus(translation_path) + ) - if check_canceled is not None: - check_canceled() + # The pretranslations are placed at the beginning of the training corpus so that a + # transductive model's training alignments can be matched back to them by index. + alignment_parallel_corpus = flatten([parallel_pretranslation_corpus, parallel_training_corpus]) + + logger.info("Training aligner") + with ( + progress_reporter.start_next_phase() as phase_progress, + self._alignment_model_factory.create_model_trainer(tokenizer, alignment_parallel_corpus) as trainer, + ): + trainer.train(progress=phase_progress, check_canceled=check_canceled) + trainer.save() - for i in range(len(pretranslations)): - pretranslations[i]["sourceTokens"] = list(src_tokenized[i]) - pretranslations[i]["translationTokens"] = list(trg_tokenized[i]) - pretranslations[i]["alignment"] = alignments[i] + if check_canceled is not None: + check_canceled() - return pretranslations + logger.info("Aligning pretranslations") + batch_size: int = self._config["inference_batch_size"] + with ( + self._alignment_model_factory.create_alignment_model() as alignment_model, + parallel_pretranslation_corpus.tokenize(tokenizer).get_rows() as rows, + ): + transductive_model: Optional[TransductiveWordAlignmentModel] = None + if isinstance(alignment_model, TransductiveWordAlignmentModel): + transductive_model = alignment_model + index = 0 + for pt_batch in batch(zip(_read_pretranslations(pretranslations_path), rows, strict=True), batch_size): + if check_canceled is not None: + check_canceled() + # The aligner is trained on lowercased tokens, so it must also be given + # lowercased tokens when aligning and scoring; the original-cased tokens are + # written to the pretranslations. + segments = [(lowercase(row.source_segment), lowercase(row.target_segment)) for _, row in pt_batch] + if transductive_model is not None: + # The pretranslations are the first rows of the training corpus, so their + # alignments were already computed during training. + alignments: Sequence[WordAlignmentMatrix] = [ + transductive_model.get_training_alignment(index + i) for i in range(len(pt_batch)) + ] + else: + alignments = alignment_model.align_batch(segments) + for (pt_info, row), (source_segment, target_segment), alignment in zip( + pt_batch, segments, alignments, strict=True + ): + word_pairs = alignment.to_aligned_word_pairs(include_null=False) + alignment_model.compute_aligned_word_pair_scores(source_segment, target_segment, word_pairs) + pt_info["sourceTokens"] = list(row.source_segment) + pt_info["translationTokens"] = list(row.target_segment) + pt_info["alignment"] = AlignedWordPair.to_string(word_pairs, include_scores=True) + yield pt_info + index += len(pt_batch) def _save_model(self) -> None: if "save_model" in self._config and self._config.save_model is not None: @@ -171,3 +250,9 @@ def _save_model(self) -> None: self._translation_file_service.save_model( model_path, f"models/{self._config.save_model + ''.join(model_path.suffixes)}" ) + + +def _read_pretranslations(path: Path) -> Generator[PretranslationInfo, None, None]: + with path.open("r", encoding="utf-8") as file: + for line in file: + yield json.loads(line) diff --git a/machine/jobs/settings.yaml b/machine/jobs/settings.yaml index b1ee5402..cdddcf3d 100644 --- a/machine/jobs/settings.yaml +++ b/machine/jobs/settings.yaml @@ -35,7 +35,7 @@ default: tokenizer: latin thot_align: word_alignment_heuristic: grow-diag-final-and - model_type: hmm + model_type: eflomal tokenizer: latin development: shared_file_folder: dev diff --git a/machine/jobs/smt_engine_build_job.py b/machine/jobs/smt_engine_build_job.py index 286e5425..370a7180 100644 --- a/machine/jobs/smt_engine_build_job.py +++ b/machine/jobs/smt_engine_build_job.py @@ -71,6 +71,7 @@ def _train_model( def _batch_inference( self, + parallel_training_corpus: ParallelTextCorpus, progress_reporter: PhasedProgressReporter, check_canceled: Optional[Callable[[], None]], ) -> None: diff --git a/machine/jobs/thot/thot_word_alignment_model_factory.py b/machine/jobs/thot/thot_word_alignment_model_factory.py index 7e9d5fc8..aa2d0a06 100644 --- a/machine/jobs/thot/thot_word_alignment_model_factory.py +++ b/machine/jobs/thot/thot_word_alignment_model_factory.py @@ -15,19 +15,21 @@ class ThotWordAlignmentModelFactory(WordAlignmentModelFactory): def create_model_trainer(self, tokenizer: Tokenizer[str, int, str], corpus: ParallelTextCorpus) -> Trainer: self._model_dir.mkdir(parents=True, exist_ok=True) + corpus = corpus.tokenize(tokenizer).lowercase() + # Retain the alignments computed during training, so that the model created by + # create_alignment_model can align the training corpus transductively without a separate + # inference pass. The alignments are saved with the model and restored when it is loaded. direct_trainer = ThotWordAlignmentModelTrainer( self._config.thot_align.model_type, - corpus.lowercase(), + corpus, prefix_filename=self._direct_model_path, - source_tokenizer=tokenizer, - target_tokenizer=tokenizer, + emit_training_alignments=True, ) inverse_trainer = ThotWordAlignmentModelTrainer( self._config.thot_align.model_type, - corpus.invert().lowercase(), + corpus.invert(), prefix_filename=self._inverse_model_path, - source_tokenizer=tokenizer, - target_tokenizer=tokenizer, + emit_training_alignments=True, ) return SymmetrizedWordAlignmentModelTrainer(direct_trainer, inverse_trainer) diff --git a/machine/jobs/translation_engine_build_job.py b/machine/jobs/translation_engine_build_job.py index 7effa62f..cf796d87 100644 --- a/machine/jobs/translation_engine_build_job.py +++ b/machine/jobs/translation_engine_build_job.py @@ -42,7 +42,7 @@ def run( check_canceled() logger.info("Pretranslating segments") - self._batch_inference(progress_reporter, check_canceled) + self._batch_inference(parallel_corpus, progress_reporter, check_canceled) self._save_model() return train_corpus_size, confidence @@ -70,6 +70,7 @@ def _train_model( @abstractmethod def _batch_inference( self, + parallel_training_corpus: ParallelTextCorpus, progress_reporter: PhasedProgressReporter, check_canceled: Optional[Callable[[], None]], ) -> None: ... diff --git a/machine/jobs/word_alignment_build_job.py b/machine/jobs/word_alignment_build_job.py index 8838ddb3..c7a97de6 100644 --- a/machine/jobs/word_alignment_build_job.py +++ b/machine/jobs/word_alignment_build_job.py @@ -1,14 +1,19 @@ +import json import logging from contextlib import ExitStack -from typing import Any, Callable, Optional +from pathlib import Path +from tempfile import TemporaryDirectory +from typing import Any, Callable, Generator, Optional from ..corpora.aligned_word_pair import AlignedWordPair +from ..corpora.corpora_utils import batch from ..corpora.parallel_text_corpus import ParallelTextCorpus -from ..corpora.parallel_text_row import ParallelTextRow +from ..corpora.text_file_text_corpus import TextFileTextCorpus +from ..corpora.token_processors import lowercase from ..tokenization.tokenizer_factory import create_tokenizer from ..utils.phased_progress_reporter import Phase, PhasedProgressReporter from ..utils.progress_status import ProgressStatus -from .word_alignment_file_service import WordAlignmentFileService +from .word_alignment_file_service import WordAlignmentFileService, WordAlignmentInput from .word_alignment_model_factory import WordAlignmentModelFactory logger = logging.getLogger(__name__) @@ -89,56 +94,65 @@ def _batch_inference( check_canceled: Optional[Callable[[], None]], ) -> None: - inference_inputs = self._word_alignment_file_service.get_word_alignment_inputs() - - inference_step_count = len(inference_inputs) + with self._word_alignment_file_service.get_word_alignment_inputs() as inference_inputs: + inference_step_count = sum(1 for _ in inference_inputs) with ExitStack() as stack: phase_progress = stack.enter_context(progress_reporter.start_next_phase()) - alignment_model = stack.enter_context(self._word_alignment_model_factory.create_alignment_model()) writer = stack.enter_context(self._word_alignment_file_service.open_alignment_output_writer()) - current_inference_step = 0 - phase_progress(ProgressStatus.from_step(current_inference_step, inference_step_count)) - batch_size = self._config["inference_batch_size"] - - parallel_corpus = ParallelTextCorpus.from_parallel_rows( - [ - ParallelTextRow( - ii["textId"], - ii["sourceRefs"], - ii["targetRefs"], - list(self._tokenizer.tokenize(ii["source"])), - list(self._tokenizer.tokenize(ii["target"])), - ) - for ii in inference_inputs - ] - ).lowercase() - - segment_batch = list(parallel_corpus.take(batch_size)) - if check_canceled is not None: - check_canceled() - alignments = alignment_model.align_batch(segment_batch) + inference_inputs = stack.enter_context(self._word_alignment_file_service.get_word_alignment_inputs()) + + temp_dir = stack.enter_context(TemporaryDirectory()) + # Spool the parallel data to disk so that the aligner can make multiple + # passes over them without keeping the entire corpus in memory. + source_path = Path(temp_dir) / "word_align.src.txt" + target_path = Path(temp_dir) / "word_align.trg.txt" + word_alignments_path = Path(temp_dir) / "word_align.json" + with ( + source_path.open("w", encoding="utf-8", newline="\n") as source_file, + target_path.open("w", encoding="utf-8", newline="\n") as target_file, + word_alignments_path.open("w", encoding="utf-8", newline="\n") as word_alignments_file, + ): + for wa_input in inference_inputs: + source_file.write(wa_input["source"] + "\n") + target_file.write(wa_input["target"] + "\n") + word_alignments_file.write(json.dumps(wa_input, ensure_ascii=False) + "\n") + if check_canceled is not None: check_canceled() - for parallel_text_row, inference_input, alignment in zip( - parallel_corpus.get_rows(), inference_inputs, alignments - ): - word_pairs = alignment.to_aligned_word_pairs(include_null=True) - alignment_model.compute_aligned_word_pair_scores( - parallel_text_row.source_segment, parallel_text_row.target_segment, word_pairs - ) - - word_alignment_info = { - "corpusId": inference_input["corpusId"], - "textId": inference_input["textId"], - "sourceRefs": [str(ref) for ref in inference_input["sourceRefs"]], - "targetRefs": [str(ref) for ref in inference_input["targetRefs"]], - "sourceTokens": parallel_text_row.source_segment, - "targetTokens": parallel_text_row.target_segment, - "alignment": AlignedWordPair.to_string(word_pairs), - } - writer.write(word_alignment_info) + parallel_corpus = TextFileTextCorpus(source_path).align_rows(TextFileTextCorpus(target_path)) + + current_inference_step = 0 + phase_progress(ProgressStatus.from_step(current_inference_step, inference_step_count)) + batch_size: int = self._config["inference_batch_size"] + alignment_model = stack.enter_context(self._word_alignment_model_factory.create_alignment_model()) + rows = stack.enter_context(parallel_corpus.tokenize(self._tokenizer).get_rows()) + for wa_batch in batch(zip(_read_word_alignments(word_alignments_path), rows, strict=True), batch_size): + if check_canceled is not None: + check_canceled() + segments = [(lowercase(row.source_segment), lowercase(row.target_segment)) for _, row in wa_batch] + alignments = alignment_model.align_batch(segments) + if check_canceled is not None: + check_canceled() + for (wa_input, row), (source_segment, target_segment), alignment in zip( + wa_batch, segments, alignments, strict=True + ): + word_pairs = alignment.to_aligned_word_pairs(include_null=False) + alignment_model.compute_aligned_word_pair_scores(source_segment, target_segment, word_pairs) + + word_alignment_info = { + "corpusId": wa_input["corpusId"], + "textId": wa_input["textId"], + "sourceRefs": [str(ref) for ref in wa_input["sourceRefs"]], + "targetRefs": [str(ref) for ref in wa_input["targetRefs"]], + "sourceTokens": row.source_segment, + "targetTokens": row.target_segment, + "alignment": AlignedWordPair.to_string(word_pairs), + } + writer.write(word_alignment_info) + current_inference_step += len(wa_batch) + phase_progress(ProgressStatus.from_step(current_inference_step, inference_step_count)) def _save_model(self) -> None: logger.info("Saving model") @@ -146,3 +160,9 @@ def _save_model(self) -> None: self._word_alignment_file_service.save_model( model_path, f"builds/{self._config['build_id']}/model{''.join(model_path.suffixes)}" ) + + +def _read_word_alignments(path: Path) -> Generator[WordAlignmentInput, None, None]: + with path.open("r", encoding="utf-8") as file: + for line in file: + yield json.loads(line) diff --git a/machine/jobs/word_alignment_file_service.py b/machine/jobs/word_alignment_file_service.py index 0c02d9cf..6f191afd 100644 --- a/machine/jobs/word_alignment_file_service.py +++ b/machine/jobs/word_alignment_file_service.py @@ -1,11 +1,12 @@ from contextlib import contextmanager from pathlib import Path -from typing import Any, Iterator, List, Optional, TypedDict, Union +from typing import Any, Generator, Iterator, List, Optional, TypedDict, Union import json_stream from ..corpora.text_corpus import TextCorpus from ..corpora.text_file_text_corpus import TextFileTextCorpus +from ..utils.context_managed_generator import ContextManagedGenerator from .shared_file_service_base import DictToJsonWriter, SharedFileServiceBase from .shared_file_service_factory import SharedFileServiceType, get_shared_file_service @@ -54,23 +55,24 @@ def create_target_corpus(self) -> TextCorpus: for target_filename in self._target_filenames ) - def get_word_alignment_inputs(self) -> List[WordAlignmentInput]: - src_pretranslate_path = self.shared_file_service.download_file( + def get_word_alignment_inputs(self) -> ContextManagedGenerator[WordAlignmentInput, None, None]: + src_word_alignment_path = self.shared_file_service.download_file( f"{self.shared_file_service.build_path}/{self._word_alignment_input_filename}" ) - with src_pretranslate_path.open("r", encoding="utf-8-sig") as file: - wa_inputs = [ - WordAlignmentInput( - corpusId=pi["corpusId"], - textId=pi["textId"], - sourceRefs=list(pi["sourceRefs"]), - targetRefs=list(pi["targetRefs"]), - source=pi["source"], - target=pi["target"], - ) - for pi in json_stream.load(file) - ] - return wa_inputs + + def generator() -> Generator[WordAlignmentInput, None, None]: + with src_word_alignment_path.open("r", encoding="utf-8-sig") as file: + for wi in json_stream.load(file): + yield WordAlignmentInput( + corpusId=wi["corpusId"], + textId=wi["textId"], + sourceRefs=list(wi["sourceRefs"]), + targetRefs=list(wi["targetRefs"]), + source=wi["source"], + target=wi["target"], + ) + + return ContextManagedGenerator(generator()) def exists_source_corpus(self) -> bool: return all( diff --git a/machine/translation/thot/thot_word_alignment_model_trainer.py b/machine/translation/thot/thot_word_alignment_model_trainer.py index e9de1524..2a2ca222 100644 --- a/machine/translation/thot/thot_word_alignment_model_trainer.py +++ b/machine/translation/thot/thot_word_alignment_model_trainer.py @@ -192,9 +192,14 @@ def train( else None ) cur_step = 0 + # Throttle the progress reporting + update_frequency = num_steps // 10 if num_steps is not None and num_steps > 10 else None def report() -> None: - if progress is not None: + nonlocal update_frequency + if update_frequency is None and num_steps is not None: + update_frequency = num_steps // 10 if num_steps > 10 else None + if progress is not None and (update_frequency is None or (cur_step % update_frequency) == 0): progress( ProgressStatus.from_step(cur_step, num_steps) if num_steps is not None else ProgressStatus(cur_step) ) diff --git a/poetry.lock b/poetry.lock index 7b168142..57c982af 100644 --- 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2"]).align_rows(_create_text_corpus("text2", ["target 2"])) + corpus = flatten([corpus1, corpus2]) + + rows = list(corpus.get_rows()) + assert len(rows) == 2 + assert [row.source_text for row in rows] == ["source 1", "source 2"] + assert [row.target_text for row in rows] == ["target 1", "target 2"] + # the corpus is re-iterable + assert len(list(corpus.get_rows())) == 2 + + +def test_flatten_parallel_text_corpus_different_classes() -> None: + corpus1 = _create_text_corpus("text1", ["Source 1"]).align_rows(_create_text_corpus("text1", ["Target 1"])) + corpus2 = _create_text_corpus("text2", ["Source 2"]).align_rows(_create_text_corpus("text2", ["Target 2"])) + corpus = flatten([corpus1, corpus2.lowercase()]) + + rows = list(corpus.get_rows()) + assert [row.source_text for row in rows] == ["Source 1", "source 2"] + + +def test_flatten_parallel_text_corpus_text_ids() -> None: + corpus1 = _create_text_corpus("text1", ["source 1"]).align_rows(_create_text_corpus("text1", ["target 1"])) + corpus2 = _create_text_corpus("text2", ["source 2"]).align_rows(_create_text_corpus("text2", ["target 2"])) + corpus = flatten([corpus1, corpus2]) + + rows = list(corpus.get_rows(["text2"])) + assert [row.source_text for row in rows] == ["source 2"] + + +def _create_text_corpus(text_id: str, sentences: list) -> DictionaryTextCorpus: + return DictionaryTextCorpus( + MemoryText(text_id, [TextRow(text_id, i, [sentence]) for i, sentence in enumerate(sentences)]) + ) diff --git a/tests/jobs/test_nmt_engine_build_job.py b/tests/jobs/test_nmt_engine_build_job.py index e3524f8b..57905ce9 100644 --- a/tests/jobs/test_nmt_engine_build_job.py +++ b/tests/jobs/test_nmt_engine_build_job.py @@ -2,7 +2,7 @@ from contextlib import contextmanager from io import StringIO from pathlib import Path -from typing import Iterator +from typing import Iterator, List from decoy import Decoy, matchers from pytest import raises @@ -16,49 +16,91 @@ NmtModelFactory, PretranslationInfo, TranslationFileService, + WordAlignmentModelFactory, ) -from machine.jobs.eflomal_aligner import is_eflomal_available from machine.translation import ( Phrase, Trainer, TrainStats, + TransductiveWordAlignmentModel, TranslationEngine, TranslationResult, TranslationSources, WordAlignmentMatrix, + WordAlignmentModel, ) from machine.utils import CanceledError, ContextManagedGenerator def test_run(decoy: Decoy) -> None: - env = _TestEnvironment(decoy) + env = _TestEnvironment(decoy, transductive=True) env.job.run() pretranslations = json.loads(env.target_pretranslations) assert len(pretranslations) == 1 assert pretranslations[0]["translation"] == "Please, I have booked a room." - if is_eflomal_available(): - assert pretranslations[0]["sourceTokens"] == [ - "Por", - "favor", - ",", - "tengo", - "reservada", - "una", - "habitación", - ".", - ] - assert pretranslations[0]["translationTokens"] == ["Please", ",", "I", "have", "booked", "a", "room", "."] - assert len(pretranslations[0]["alignment"]) > 0 - assert pretranslations[0]["sequenceConfidence"] == 0.5 - else: - assert pretranslations[0]["sourceTokens"] == [] - assert pretranslations[0]["translationTokens"] == [] - assert len(pretranslations[0]["alignment"]) == 0 - assert pretranslations[0]["sequenceConfidence"] == 0.5 + assert pretranslations[0]["sourceTokens"] == [ + "Por", + "favor", + ",", + "tengo", + "reservada", + "una", + "habitación", + ".", + ] + assert pretranslations[0]["translationTokens"] == ["Please", ",", "I", "have", "booked", "a", "room", "."] + assert len(pretranslations[0]["alignment"]) > 0 + assert pretranslations[0]["sequenceConfidence"] == 0.5 + decoy.verify(env.model.align_batch(matchers.Anything()), times=0) decoy.verify(env.translation_file_service.save_model(Path("model.tar.gz"), "models/save-model.tar.gz"), times=1) +def test_run_inductive_fallback(decoy: Decoy) -> None: + env = _TestEnvironment(decoy, transductive=False) + env.job.run() + + pretranslations = json.loads(env.target_pretranslations) + assert len(pretranslations) == 1 + assert pretranslations[0]["sourceTokens"] == [ + "Por", + "favor", + ",", + "tengo", + "reservada", + "una", + "habitación", + ".", + ] + assert pretranslations[0]["translationTokens"] == ["Please", ",", "I", "have", "booked", "a", "room", "."] + assert len(pretranslations[0]["alignment"]) > 0 + + +def test_run_batched_transductive(decoy: Decoy) -> None: + env = _TestEnvironment(decoy, transductive=True, num_pretranslations=3, batch_size=2) + env.job.run() + + pretranslations = json.loads(env.target_pretranslations) + assert len(pretranslations) == 3 + assert [pt["textId"] for pt in pretranslations] == ["text1", "text2", "text3"] + for pretranslation in pretranslations: + assert pretranslation["translation"] == "Please, I have booked a room." + assert len(pretranslation["alignment"]) > 0 + assert env.translate_batch_sizes == [2, 1] + assert env.training_alignment_requests == [0, 1, 2] + + +def test_run_batched_inductive(decoy: Decoy) -> None: + env = _TestEnvironment(decoy, transductive=False, num_pretranslations=3, batch_size=2) + env.job.run() + + pretranslations = json.loads(env.target_pretranslations) + assert len(pretranslations) == 3 + for pretranslation in pretranslations: + assert len(pretranslation["alignment"]) > 0 + assert env.align_batch_sizes == [2, 1] + + def test_cancel(decoy: Decoy) -> None: env = _TestEnvironment(decoy) checker = _CancellationChecker(3) @@ -68,8 +110,29 @@ def test_cancel(decoy: Decoy) -> None: assert env.target_pretranslations == "" +class _TransductiveModel(WordAlignmentModel, TransductiveWordAlignmentModel): + pass + + +def _create_translation_result() -> TranslationResult: + return TranslationResult( + translation="Please, I have booked a room.", + source_tokens="Por favor , tengo reservada una habitación .".split(), + target_tokens="Please , I have booked a room .".split(), + confidences=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], + sequence_confidence=0.5, + sources=[TranslationSources.NMT] * 8, + alignment=WordAlignmentMatrix.from_word_pairs( + 8, 8, {(0, 0), (1, 0), (2, 1), (3, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7)} + ), + phrases=[Phrase(Range.create(0, 8), 8)], + ) + + class _TestEnvironment: - def __init__(self, decoy: Decoy) -> None: + def __init__( + self, decoy: Decoy, transductive: bool = False, num_pretranslations: int = 1, batch_size: int = 100 + ) -> None: self.source_tokenizer_trainer = decoy.mock(cls=Trainer) self.target_tokenizer_trainer = decoy.mock(cls=Trainer) @@ -79,33 +142,15 @@ def __init__(self, decoy: Decoy) -> None: stats.metrics["bleu"] = 30.0 decoy.when(self.model_trainer.stats).then_return(stats) + self.translate_batch_sizes: List[int] = [] + + def _translate_batch(segments: List[str]) -> List[TranslationResult]: + self.translate_batch_sizes.append(len(segments)) + return [_create_translation_result() for _ in segments] + self.engine = decoy.mock(cls=TranslationEngine) decoy.when(self.engine.__enter__()).then_return(self.engine) - decoy.when(self.engine.translate_batch(matchers.Anything())).then_return( - [ - TranslationResult( - translation="Please, I have booked a room.", - source_tokens="Por favor , tengo reservada una habitación .".split(), - target_tokens="Please , I have booked a room .".split(), - confidences=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5], - sequence_confidence=0.5, - sources=[ - TranslationSources.NMT, - TranslationSources.NMT, - TranslationSources.NMT, - TranslationSources.NMT, - TranslationSources.NMT, - TranslationSources.NMT, - TranslationSources.NMT, - TranslationSources.NMT, - ], - alignment=WordAlignmentMatrix.from_word_pairs( - 8, 8, {(0, 0), (1, 0), (2, 1), (3, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7)} - ), - phrases=[Phrase(Range.create(0, 8), 8)], - ) - ] - ) + decoy.when(self.engine.translate_batch(matchers.Anything())).then_do(_translate_batch) self.nmt_model_factory = decoy.mock(cls=NmtModelFactory) decoy.when(self.nmt_model_factory.train_tokenizer).then_return(True) @@ -132,15 +177,16 @@ def __init__(self, decoy: Decoy) -> None: for pi in [ PretranslationInfo( corpusId="corpus1", - textId="text1", - sourceRefs=["ref1"], - targetRefs=["ref1"], + textId=f"text{i + 1}", + sourceRefs=[f"ref{i + 1}"], + targetRefs=[f"ref{i + 1}"], translation="Por favor, tengo reservada una habitación.", sourceTokens=[], translationTokens=[], alignment="", sequenceConfidence=0.5, ) + for i in range(num_pretranslations) ] ) ) @@ -160,18 +206,64 @@ def open_target_pretranslation_writer(env: _TestEnvironment) -> Iterator[DictToJ lambda: open_target_pretranslation_writer(self) ) + self.alignment_model_trainer = decoy.mock(cls=Trainer) + decoy.when(self.alignment_model_trainer.__enter__()).then_return(self.alignment_model_trainer) + stats = TrainStats() + decoy.when(self.alignment_model_trainer.stats).then_return(stats) + + alignment = WordAlignmentMatrix.from_word_pairs( + row_count=8, + column_count=8, + set_values=[(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), (7, 7)], + ) + self.training_alignment_requests: List[int] = [] + self.align_batch_sizes: List[int] = [] + + def _get_training_alignment(n: int) -> WordAlignmentMatrix: + self.training_alignment_requests.append(n) + return alignment + + def _align_batch(segments: List[object]) -> List[WordAlignmentMatrix]: + self.align_batch_sizes.append(len(segments)) + return [alignment for _ in segments] + + if transductive: + self.model = decoy.mock(cls=_TransductiveModel) + decoy.when(self.model.__enter__()).then_return(self.model) + decoy.when(self.model.get_training_alignment(matchers.Anything())).then_do(_get_training_alignment) + else: + self.model = decoy.mock(cls=WordAlignmentModel) + decoy.when(self.model.__enter__()).then_return(self.model) + decoy.when(self.model.align_batch(matchers.Anything())).then_do(_align_batch) + + self.word_alignment_model_factory = decoy.mock(cls=WordAlignmentModelFactory) + decoy.when( + self.word_alignment_model_factory.create_model_trainer(matchers.Anything(), matchers.Anything()) + ).then_return(self.alignment_model_trainer) + decoy.when(self.word_alignment_model_factory.create_alignment_model()).then_return(self.model) + decoy.when(self.word_alignment_model_factory.save_model()).then_return(Path("model.zip")) + self.job = NmtEngineBuildJob( MockSettings( { "src_lang": "es", "trg_lang": "en", "save_model": "save-model", - "inference_batch_size": 100, + "inference_batch_size": batch_size, "align_pretranslations": True, + "build_id": "my_build", + "thot_align": { + "tokenizer": "latin", + "model_type": "eflomal", + "word_alignment_heuristic": "grow-diag-final-and", + }, + "data_dir": "~/machine", + "shared_file_folder": "dev", } ), self.nmt_model_factory, self.translation_file_service, + self.word_alignment_model_factory, ) diff --git a/tests/jobs/test_word_alignment_build_job.py b/tests/jobs/test_word_alignment_build_job.py index 63b6b816..77251021 100644 --- a/tests/jobs/test_word_alignment_build_job.py +++ b/tests/jobs/test_word_alignment_build_job.py @@ -13,7 +13,7 @@ from machine.jobs.word_alignment_file_service import WordAlignmentFileService, WordAlignmentInput from machine.translation import Trainer, TrainStats, WordAlignmentMatrix from machine.translation.word_alignment_model import WordAlignmentModel -from machine.utils import CanceledError +from machine.utils import CanceledError, ContextManagedGenerator def test_run(decoy: Decoy) -> None: @@ -21,8 +21,8 @@ def test_run(decoy: Decoy) -> None: env.job.run() alignments = json.loads(env.alignment_json) - assert len(alignments) == 1 - assert alignments[0]["alignment"] == "0-0 1-1 2-2" + assert len(alignments) == 3 + assert alignments[0]["alignment"] == "2-2 3-3 4-5 6-8 9-7 11-6 12-10" decoy.verify( env.word_alignment_file_service.save_model(matchers.Anything(), f"builds/{env.job._config.build_id}/model.zip"), times=1, @@ -43,15 +43,23 @@ def __init__(self, decoy: Decoy) -> None: self.model_trainer = decoy.mock(cls=Trainer) decoy.when(self.model_trainer.__enter__()).then_return(self.model_trainer) stats = TrainStats() - stats.train_corpus_size = 3 - stats.metrics["bleu"] = 30.0 decoy.when(self.model_trainer.stats).then_return(stats) self.model = decoy.mock(cls=WordAlignmentModel) decoy.when(self.model.__enter__()).then_return(self.model) decoy.when(self.model.align_batch(matchers.Anything())).then_return( [ - WordAlignmentMatrix.from_word_pairs(row_count=3, column_count=3, set_values=[(0, 0), (1, 1), (2, 2)]), + WordAlignmentMatrix.from_word_pairs( + row_count=13, + column_count=11, + set_values=[(2, 2), (3, 3), (4, 5), (6, 8), (9, 7), (11, 6), (12, 10)], + ), + WordAlignmentMatrix.from_word_pairs( + row_count=10, column_count=10, set_values=[(2, 2), (3, 3), (4, 5), (5, 6), (6, 8), (8, 7), (9, 9)] + ), + WordAlignmentMatrix.from_word_pairs( + row_count=7, column_count=7, set_values=[(0, 0), (1, 1), (3, 3), (4, 4), (5, 5), (6, 6)] + ), ] ) @@ -90,33 +98,38 @@ def __init__(self, decoy: Decoy) -> None: decoy.when(self.word_alignment_file_service.exists_source_corpus()).then_return(True) decoy.when(self.word_alignment_file_service.exists_target_corpus()).then_return(True) - decoy.when(self.word_alignment_file_service.get_word_alignment_inputs()).then_return( - [ - WordAlignmentInput( - corpusId="corpus1", - textId="text1", - sourceRefs=["1"], - targetRefs=["1"], - source="¿Le importaría darnos las llaves de la habitación, por favor?", - target="Would you mind giving us the room keys, please?", - ), - WordAlignmentInput( - corpusId="corpus1", - textId="text1", - sourceRefs=["2"], - targetRefs=["2"], - source="¿Le importaría cambiarme a otra habitación más tranquila?", - target="Would you mind moving me to another quieter room?", - ), - WordAlignmentInput( - corpusId="corpus1", - textId="text1", - sourceRefs=["3"], - targetRefs=["3"], - source="Me parece que existe un problema.", - target="I think there is a problem.", - ), - ] + decoy.when(self.word_alignment_file_service.get_word_alignment_inputs()).then_do( + lambda: ContextManagedGenerator( + ( + wa + for wa in [ + WordAlignmentInput( + corpusId="corpus1", + textId="text1", + sourceRefs=["1"], + targetRefs=["1"], + source="¿Le importaría darnos las llaves de la habitación, por favor?", + target="Would you mind giving us the room keys, please?", + ), + WordAlignmentInput( + corpusId="corpus1", + textId="text1", + sourceRefs=["2"], + targetRefs=["2"], + source="¿Le importaría cambiarme a otra habitación más tranquila?", + target="Would you mind moving me to another quieter room?", + ), + WordAlignmentInput( + corpusId="corpus1", + textId="text1", + sourceRefs=["3"], + targetRefs=["3"], + source="Me parece que existe un problema.", + target="I think there is a problem.", + ), + ] + ) + ) ) self.alignment_json = ""