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[tests] remove TF tests (uses of require_tf) (#38944)

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* remove uses of require_tf

* remove redundant import guards

* this class has no tests

* nits

* del tf rng comment
This commit is contained in:
Joao Gante
2025-06-25 18:29:10 +01:00
committed by GitHub
parent d37f751797
commit 1d45d90e5d
44 changed files with 21 additions and 2504 deletions
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37
tests/models/layoutlmv3/test_tokenization_layoutlmv3.py
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@@ -34,7 +34,6 @@ from transformers import (
from transformers.models.layoutlmv3.tokenization_layoutlmv3 import VOCAB_FILES_NAMES, LayoutLMv3Tokenizer
from transformers.testing_utils import (
require_pandas,
require_tf,
require_tokenizers,
require_torch,
slow,
@@ -2306,42 +2305,6 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
def test_np_encode_plus_sent_to_model(self):
pass
@require_tf
@slow
def test_tf_encode_plus_sent_to_model(self):
from transformers import TF_MODEL_MAPPING, TOKENIZER_MAPPING
MODEL_TOKENIZER_MAPPING = merge_model_tokenizer_mappings(TF_MODEL_MAPPING, TOKENIZER_MAPPING)
tokenizers = self.get_tokenizers(do_lower_case=False)
for tokenizer in tokenizers:
with self.subTest(f"{tokenizer.__class__.__name__}"):
if tokenizer.__class__ not in MODEL_TOKENIZER_MAPPING:
self.skipTest(f"{tokenizer.__class__} is not in the MODEL_TOKENIZER_MAPPING")
config_class, model_class = MODEL_TOKENIZER_MAPPING[tokenizer.__class__]
config = config_class()
if config.is_encoder_decoder or config.pad_token_id is None:
self.skipTest(reason="Model is an encoder-decoder or has no pad token id set.")
model = model_class(config)
# Make sure the model contains at least the full vocabulary size in its embedding matrix
self.assertGreaterEqual(model.config.vocab_size, len(tokenizer))
# Build sequence
first_ten_tokens = list(tokenizer.get_vocab().keys())[:10]
boxes = [[1000, 1000, 1000, 1000] for _ in range(len(first_ten_tokens))]
encoded_sequence = tokenizer.encode_plus(first_ten_tokens, boxes=boxes, return_tensors="tf")
batch_encoded_sequence = tokenizer.batch_encode_plus(
[first_ten_tokens, first_ten_tokens], boxes=[boxes, boxes], return_tensors="tf"
)
# This should not fail
model(encoded_sequence)
model(batch_encoded_sequence)
@unittest.skip(reason="Chat is not supported")
def test_chat_template(self):
pass