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Move test model folders (#17034)

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* move test model folders (TODO: fix imports and others)

* fix (potentially partially) imports (in model test modules)

* fix (potentially partially) imports (in tokenization test modules)

* fix (potentially partially) imports (in feature extraction test modules)

* fix import utils.test_modeling_tf_core

* fix path ../fixtures/

* fix imports about generation.test_generation_flax_utils

* fix more imports

* fix fixture path

* fix get_test_dir

* update module_to_test_file

* fix get_tests_dir from wrong transformers.utils

* update config.yml (CircleCI)

* fix style

* remove missing imports

* update new model script

* update check_repo

* update SPECIAL_MODULE_TO_TEST_MAP

* fix style

* add __init__

* update self-scheduled

* fix add_new_model scripts

* check one way to get location back

* python setup.py build install

* fix import in test auto

* update self-scheduled.yml

* update slack notification script

* Add comments about artifact names

* fix for yolos

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2022-05-03 14:42:02 +02:00
committed by GitHub
parent cd9274d010
commit 19420fd99e
408 changed files with 616 additions and 607 deletions
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tests/models/canine/__init__.py Normal file
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# coding=utf-8
# Copyright 2021 Google AI and HuggingFace Inc. team.
#
# 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.
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class CanineTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = CanineTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
tokenizer = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def canine_tokenizer(self):
return CanineTokenizer.from_pretrained("google/canine-s")
def get_tokenizer(self, **kwargs) -> CanineTokenizer:
tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
tokenizer._unicode_vocab_size = 1024
return tokenizer
@require_torch
def test_prepare_batch_integration(self):
tokenizer = self.canine_tokenizer
src_text = ["Life is like a box of chocolates.", "You never know what you're gonna get."]
# fmt: off
expected_src_tokens = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
# fmt: on
batch = tokenizer(src_text, padding=True, return_tensors="pt")
self.assertIsInstance(batch, BatchEncoding)
result = list(batch.input_ids.numpy()[0])
self.assertListEqual(expected_src_tokens, result)
self.assertEqual((2, 39), batch.input_ids.shape)
self.assertEqual((2, 39), batch.attention_mask.shape)
@require_torch
def test_encoding_keys(self):
tokenizer = self.canine_tokenizer
src_text = ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
batch = tokenizer(src_text, padding=True, return_tensors="pt")
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("input_ids", batch)
self.assertIn("attention_mask", batch)
self.assertIn("token_type_ids", batch)
@require_torch
def test_max_length_integration(self):
tokenizer = self.canine_tokenizer
tgt_text = [
"What's the weater?",
"It's about 25 degrees.",
]
with tokenizer.as_target_tokenizer():
targets = tokenizer(tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors="pt")
self.assertEqual(32, targets["input_ids"].shape[1])
# cannot use default save_and_load_tokenzier test method because tokenzier has no vocab
def test_save_and_load_tokenizer(self):
# safety check on max_len default value so we are sure the test works
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.model_max_length, 42)
# Now let's start the test
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
shutil.rmtree(tmpdirname)
tokenizers = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
tmpdirname = tempfile.mkdtemp()
sample_text = " He is very happy, UNwant\u00E9d,running"
additional_special_tokens = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
new_additional_special_token = chr(0xE007)
additional_special_tokens.append(new_additional_special_token)
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
tokenizer.save_pretrained(tmpdirname)
after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
self.assertListEqual(before_tokens, after_tokens)
self.assertIn(new_additional_special_token, after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length, 42)
tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
self.assertEqual(tokenizer.model_max_length, 43)
shutil.rmtree(tmpdirname)
def test_add_special_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input_text, ids = self.get_clean_sequence(tokenizer)
# a special token for Canine can be defined as follows:
SPECIAL_TOKEN = 0xE005
special_token = chr(SPECIAL_TOKEN)
tokenizer.add_special_tokens({"cls_token": special_token})
encoded_special_token = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(len(encoded_special_token), 1)
text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
encoded = tokenizer.encode(text, add_special_tokens=False)
input_encoded = tokenizer.encode(input_text, add_special_tokens=False)
special_token_id = tokenizer.encode(special_token, add_special_tokens=False)
self.assertEqual(encoded, input_encoded + special_token_id)
decoded = tokenizer.decode(encoded, skip_special_tokens=True)
self.assertTrue(special_token not in decoded)
def test_tokenize_special_tokens(self):
tokenizers = self.get_tokenizers(do_lower_case=True)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
SPECIAL_TOKEN_1 = chr(0xE005)
SPECIAL_TOKEN_2 = chr(0xE006)
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1], special_tokens=True)
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]})
token_1 = tokenizer.tokenize(SPECIAL_TOKEN_1)
token_2 = tokenizer.tokenize(SPECIAL_TOKEN_2)
self.assertEqual(len(token_1), 1)
self.assertEqual(len(token_2), 1)
self.assertEqual(token_1[0], SPECIAL_TOKEN_1)
self.assertEqual(token_2[0], SPECIAL_TOKEN_2)
@require_tokenizers
def test_added_token_serializable(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
# a special token for Canine can be defined as follows:
NEW_TOKEN = 0xE006
new_token = chr(NEW_TOKEN)
new_token = AddedToken(new_token, lstrip=True)
tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(tmp_dir_name)
tokenizer.from_pretrained(tmp_dir_name)
def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
tokenizer_list = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(tmp_dir)
with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
special_tokens_map = json.load(json_file)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
tokenizer_config = json.load(json_file)
# a special token for Canine can be defined as follows:
NEW_TOKEN = 0xE006
new_token_1 = chr(NEW_TOKEN)
special_tokens_map["additional_special_tokens"] = [new_token_1]
tokenizer_config["additional_special_tokens"] = [new_token_1]
with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
json.dump(special_tokens_map, outfile)
with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
json.dump(tokenizer_config, outfile)
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
tokenizer_without_change_in_init = tokenizer_class.from_pretrained(tmp_dir, extra_ids=0)
self.assertIn(new_token_1, tokenizer_without_change_in_init.additional_special_tokens)
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_1],
tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_1])
),
)
NEW_TOKEN = 0xE007
new_token_2 = chr(NEW_TOKEN)
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
new_added_tokens = [AddedToken(new_token_2, lstrip=True)]
tokenizer = tokenizer_class.from_pretrained(
tmp_dir, additional_special_tokens=new_added_tokens, extra_ids=0
)
self.assertIn(new_token_2, tokenizer.additional_special_tokens)
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_2], tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_2]))
)
@require_tokenizers
def test_encode_decode_with_spaces(self):
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
input = "hello world"
if self.space_between_special_tokens:
output = "[CLS] hello world [SEP]"
else:
output = input
encoded = tokenizer.encode(input, add_special_tokens=False)
decoded = tokenizer.decode(encoded, spaces_between_special_tokens=self.space_between_special_tokens)
self.assertIn(decoded, [output, output.lower()])
# cannot use default `test_tokenizers_common_ids_setters` method because tokenizer has no vocab
def test_tokenizers_common_ids_setters(self):
tokenizers = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
attributes_list = [
"bos_token",
"eos_token",
"unk_token",
"sep_token",
"pad_token",
"cls_token",
"mask_token",
]
token_to_test_setters = "a"
token_id_to_test_setters = ord(token_to_test_setters)
for attr in attributes_list:
setattr(tokenizer, attr + "_id", None)
self.assertEqual(getattr(tokenizer, attr), None)
self.assertEqual(getattr(tokenizer, attr + "_id"), None)
setattr(tokenizer, attr + "_id", token_id_to_test_setters)
self.assertEqual(getattr(tokenizer, attr), token_to_test_setters)
self.assertEqual(getattr(tokenizer, attr + "_id"), token_id_to_test_setters)
setattr(tokenizer, "additional_special_tokens_ids", [])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [])
additional_special_token_id = 0xE006
additional_special_token = chr(additional_special_token_id)
setattr(tokenizer, "additional_special_tokens_ids", [additional_special_token_id])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens"), [additional_special_token])
self.assertListEqual(getattr(tokenizer, "additional_special_tokens_ids"), [additional_special_token_id])
# tokenizer has a fixed vocab_size (namely all possible unicode code points)
def test_add_tokens_tokenizer(self):
pass
# CanineTokenizer does not support do_lower_case = True, as each character has its own Unicode code point
# ("b" and "B" for example have different Unicode code points)
def test_added_tokens_do_lower_case(self):
pass
# CanineModel does not support the get_input_embeddings nor the get_vocab method
def test_np_encode_plus_sent_to_model(self):
pass
# CanineModel does not support the get_input_embeddings nor the get_vocab method
def test_torch_encode_plus_sent_to_model(self):
pass
# tokenizer can be instantiated without any pretrained files, so no need for pretrained tokenizer list
def test_pretrained_model_lists(self):
pass
# tokenizer does not have vocabulary
def test_get_vocab(self):
pass
# inputs cannot be pretokenized since ids depend on whole input string and not just on single characters
def test_pretokenized_inputs(self):
pass
# tests all ids in vocab => vocab doesn't exist so unnecessary to test
def test_conversion_reversible(self):
pass