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https://github.com/deepset-ai/haystack.git
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8cfeed095d
* build: Remove mmh3 dependency * resolve circular import * pylint * make mmh3.py sibling of schema.py * pylint import order * pylint * undo example changes * increase coverage in modeling module * increase coverage further * rename new unit tests
346 lines
14 KiB
Python
346 lines
14 KiB
Python
import copy
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import logging
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from pathlib import Path
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import pytest
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from transformers import AutoTokenizer
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from haystack.modeling.data_handler.processor import SquadProcessor, _is_json
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# during inference (parameter return_baskets = False) we do not convert labels
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def test_dataset_from_dicts_qa_inference(samples_path, caplog=None):
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if caplog:
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caplog.set_level(logging.CRITICAL)
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models = [
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"deepset/roberta-base-squad2",
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"deepset/bert-base-cased-squad2",
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"deepset/xlm-roberta-large-squad2",
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"deepset/minilm-uncased-squad2",
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"deepset/electra-base-squad2",
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]
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sample_types = ["answer-wrong", "answer-offset-wrong", "noanswer", "vanilla"]
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for model in models:
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model)
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processor = SquadProcessor(tokenizer, max_seq_len=256, data_dir=None)
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for sample_type in sample_types:
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dicts = processor.file_to_dicts(samples_path / "qa" / f"{sample_type}.json")
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dataset, tensor_names, problematic_sample_ids, baskets = processor.dataset_from_dicts(
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dicts, indices=[1], return_baskets=True
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)
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assert tensor_names == [
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"input_ids",
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"padding_mask",
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"segment_ids",
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"passage_start_t",
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"start_of_word",
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"labels",
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"id",
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"seq_2_start_t",
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"span_mask",
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], f"Processing for {model} has changed."
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assert len(problematic_sample_ids) == 0, f"Processing for {model} has changed."
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assert baskets[0].id_external == "5ad3d560604f3c001a3ff2c8", f"Processing for {model} has changed."
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assert baskets[0].id_internal == "1-0", f"Processing for {model} has changed."
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# roberta
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if model == "deepset/roberta-base-squad2":
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assert (
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len(baskets[0].samples[0].tokenized["passage_tokens"]) == 6
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), f"Processing for {model} has changed."
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assert (
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len(baskets[0].samples[0].tokenized["question_tokens"]) == 7
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), f"Processing for {model} has changed."
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if sample_type == "noanswer":
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assert baskets[0].samples[0].features[0]["input_ids"][:13] == [
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0,
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6179,
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171,
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82,
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697,
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11,
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2201,
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116,
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2,
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2,
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26795,
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2614,
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34,
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], f"Processing for {model} and {sample_type}-testsample has changed."
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else:
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assert baskets[0].samples[0].features[0]["input_ids"][:13] == [
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0,
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6179,
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171,
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82,
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697,
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11,
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5459,
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116,
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2,
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2,
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26795,
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2614,
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34,
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], f"Processing for {model} and {sample_type}-testsample has changed."
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# bert
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if model == "deepset/bert-base-cased-squad2":
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assert (
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len(baskets[0].samples[0].tokenized["passage_tokens"]) == 5
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), f"Processing for {model} has changed."
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assert (
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len(baskets[0].samples[0].tokenized["question_tokens"]) == 7
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), f"Processing for {model} has changed."
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if sample_type == "noanswer":
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assert baskets[0].samples[0].features[0]["input_ids"][:10] == [
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101,
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1731,
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1242,
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1234,
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1686,
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1107,
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2123,
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136,
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102,
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3206,
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], f"Processing for {model} and {sample_type}-testsample has changed."
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else:
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assert baskets[0].samples[0].features[0]["input_ids"][:10] == [
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101,
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1731,
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1242,
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1234,
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1686,
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1107,
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3206,
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136,
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102,
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3206,
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], f"Processing for {model} and {sample_type}-testsample has changed."
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# xlm-roberta
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if model == "deepset/xlm-roberta-large-squad2":
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assert (
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len(baskets[0].samples[0].tokenized["passage_tokens"]) == 7
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), f"Processing for {model} has changed."
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assert (
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len(baskets[0].samples[0].tokenized["question_tokens"]) == 7
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), f"Processing for {model} has changed."
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if sample_type == "noanswer":
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assert baskets[0].samples[0].features[0]["input_ids"][:12] == [
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0,
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11249,
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5941,
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3395,
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6867,
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23,
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7270,
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32,
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2,
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2,
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10271,
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1556,
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], f"Processing for {model} and {sample_type}-testsample has changed."
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else:
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assert baskets[0].samples[0].features[0]["input_ids"][:12] == [
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0,
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11249,
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5941,
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3395,
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6867,
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23,
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10271,
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32,
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2,
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2,
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10271,
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1556,
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], f"Processing for {model} and {sample_type}-testsample has changed."
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# minilm and electra have same vocab + tokenizer
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if model == "deepset/minilm-uncased-squad2" or model == "deepset/electra-base-squad2":
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assert (
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len(baskets[0].samples[0].tokenized["passage_tokens"]) == 5
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), f"Processing for {model} has changed."
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assert (
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len(baskets[0].samples[0].tokenized["question_tokens"]) == 7
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), f"Processing for {model} has changed."
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if sample_type == "noanswer":
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assert baskets[0].samples[0].features[0]["input_ids"][:10] == [
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101,
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2129,
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2116,
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2111,
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2444,
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1999,
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3000,
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1029,
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102,
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4068,
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], f"Processing for {model} and {sample_type}-testsample has changed."
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else:
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assert baskets[0].samples[0].features[0]["input_ids"][:10] == [
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101,
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2129,
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2116,
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2111,
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2444,
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1999,
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4068,
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1029,
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102,
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4068,
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], f"Processing for {model} and {sample_type}-testsample has changed."
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def test_batch_encoding_flatten_rename():
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from haystack.modeling.data_handler.dataset import flatten_rename
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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batch_sentences = ["Hello I'm a single sentence", "And another sentence", "And the very very last one"]
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encoded_inputs = tokenizer(batch_sentences, padding=True, truncation=True)
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keys = ["input_ids", "token_type_ids", "attention_mask"]
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rename_keys = ["input_ids", "segment_ids", "padding_mask"]
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features_flat = flatten_rename(encoded_inputs, keys, rename_keys)
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assert len(features_flat) == 3, "should have three elements in the feature dict list"
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for e in features_flat:
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for k in rename_keys:
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assert k in e, f"feature dict list item {e} in a list should have a key {k}"
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# rename no keys/rename keys
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features_flat = flatten_rename(encoded_inputs)
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assert len(features_flat) == 3, "should have three elements in the feature dict list"
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for e in features_flat:
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for k in keys:
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assert k in e, f"feature dict list item {e} in a list should have a key {k}"
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# empty input keys
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flatten_rename(encoded_inputs, [])
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# empty keys and rename keys
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flatten_rename(encoded_inputs, [], [])
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# no encoding_batch provided
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flatten_rename(None, [], [])
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# keys and renamed_keys have different sizes
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try:
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flatten_rename(encoded_inputs, [], ["blah"])
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except AssertionError:
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pass
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def test_dataset_from_dicts_qa_label_conversion(samples_path, caplog=None):
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if caplog:
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caplog.set_level(logging.CRITICAL)
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models = [
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"deepset/roberta-base-squad2",
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"deepset/bert-base-cased-squad2",
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"deepset/xlm-roberta-large-squad2",
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"deepset/minilm-uncased-squad2",
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"deepset/electra-base-squad2",
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]
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sample_types = ["answer-wrong", "answer-offset-wrong", "noanswer", "vanilla"]
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for model in models:
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model)
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processor = SquadProcessor(tokenizer, max_seq_len=256, data_dir=None, max_answers=6)
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for sample_type in sample_types:
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dicts = processor.file_to_dicts(samples_path / "qa" / f"{sample_type}.json")
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dataset, tensor_names, problematic_sample_ids = processor.dataset_from_dicts(
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dicts, indices=[1], return_baskets=False
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)
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if sample_type == "answer-wrong" or sample_type == "answer-offset-wrong":
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assert len(problematic_sample_ids) == 1, f"Processing labels for {model} has changed."
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if sample_type == "noanswer":
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assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0, 0, :]) == [
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0,
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0,
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], f"Processing labels for {model} has changed."
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assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0, 1, :]) == [
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-1,
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-1,
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], f"Processing labels for {model} has changed."
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if sample_type == "vanilla":
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# roberta
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if model == "deepset/roberta-base-squad2":
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assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0, 0, :]) == [
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13,
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13,
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], f"Processing labels for {model} has changed."
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assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0, 1, :]) == [
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13,
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14,
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], f"Processing labels for {model} has changed."
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# bert, minilm, electra
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if (
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model == "deepset/bert-base-cased-squad2"
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or model == "deepset/minilm-uncased-squad2"
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or model == "deepset/electra-base-squad2"
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):
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assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0, 0, :]) == [
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11,
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11,
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], f"Processing labels for {model} has changed."
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# xlm-roberta
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if model == "deepset/xlm-roberta-large-squad2":
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assert list(dataset.tensors[tensor_names.index("labels")].numpy()[0, 0, :]) == [
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12,
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12,
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], f"Processing labels for {model} has changed."
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@pytest.mark.unit
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def test_is_json_identifies_json_objects():
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"""Test that _is_json correctly identifies json objects"""
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# Paths to json files should be considered json
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assert _is_json(Path("processor_config.json"))
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# dicts should be considered json
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assert _is_json({"a": 1})
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# non-serializable objects should not be considered json
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assert not _is_json(AutoTokenizer)
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@pytest.mark.integration
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def test_dataset_from_dicts_auto_determine_max_answers(samples_path, caplog=None):
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"""
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SquadProcessor should determine the number of answers for the pytorch dataset based on
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the maximum number of answers for each question. Vanilla.json has one question with two answers,
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so the number of answers should be two.
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"""
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model = "deepset/roberta-base-squad2"
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model)
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processor = SquadProcessor(tokenizer, max_seq_len=256, data_dir=None)
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dicts = processor.file_to_dicts(samples_path / "qa" / "vanilla.json")
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dataset, tensor_names, problematic_sample_ids = processor.dataset_from_dicts(dicts, indices=[1])
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assert len(dataset[0][tensor_names.index("labels")]) == 2
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# check that a max_answers will be adjusted when processing a different dataset with the same SquadProcessor
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dicts_more_answers = copy.deepcopy(dicts)
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dicts_more_answers[0]["qas"][0]["answers"] = dicts_more_answers[0]["qas"][0]["answers"] * 3
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dataset, tensor_names, problematic_sample_ids = processor.dataset_from_dicts(dicts_more_answers, indices=[1])
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assert len(dataset[0][tensor_names.index("labels")]) == 6
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@pytest.mark.integration
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def test_dataset_from_dicts_truncate_max_answers(samples_path, caplog=None):
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"""
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Test that it is possible to manually set the number of answers, truncating the answers in the data.
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"""
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model = "deepset/roberta-base-squad2"
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tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model)
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processor = SquadProcessor(tokenizer, max_seq_len=256, data_dir=None, max_answers=1)
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dicts = processor.file_to_dicts(samples_path / "qa" / "vanilla.json")
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dataset, tensor_names, problematic_sample_ids = processor.dataset_from_dicts(dicts, indices=[1])
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assert len(dataset[0][tensor_names.index("labels")]) == 1
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