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No more Tuple, List, Dict (#38797)

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* No more Tuple, List, Dict

* make fixup

* More style fixes

* Docstring fixes with regex replacement

* Trigger tests

* Redo fixes after rebase

* Fix copies

* [test all]

* update

* [test all]

* update

* [test all]

* make style after rebase

* Patch the hf_argparser test

* Patch the hf_argparser test

* style fixes

* style fixes

* style fixes

* Fix docstrings in Cohere test

* [test all]

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Matt
2025-06-17 19:37:18 +01:00
committed by GitHub
parent a396f4324b
commit 508a704055
1291 changed files with 14906 additions and 14941 deletions
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2
tests/models/markuplm/test_modeling_markuplm.py
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@@ -309,7 +309,7 @@ class MarkupLMModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
feature_extractor_name,
processor_name,
):
# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
# ValueError: Nodes must be of type `list[str]` (single pretokenized example), or `list[list[str]]`
# (batch of pretokenized examples).
return True

3
tests/models/timesfm/test_modeling_timesfm.py
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@@ -15,7 +15,6 @@
import inspect
import unittest
from typing import List
import numpy as np
import torch
@@ -52,7 +51,7 @@ class TimesFmModelTester:
num_heads: int = 2,
tolerance: float = 1e-6,
rms_norm_eps: float = 1e-6,
quantiles: List[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
quantiles: list[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
pad_val: float = 1123581321.0,
use_positional_embedding: bool = True,
initializer_factor: float = 0.0,

2
tests/models/univnet/test_feature_extraction_univnet.py
View File

@@ -248,7 +248,7 @@ class UnivNetFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
# Test np.ndarray vs List[np.ndarray]
# Test np.ndarray vs list[np.ndarray]
encoded_sequences_1 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
encoded_sequences_2 = feature_extractor([np_speech_inputs], return_tensors="np").input_features
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):