SUMIN/HuggingFace_transformer
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Simplify soft dependencies and update the dummy-creation process (#36827)

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* Reverse dependency map shouldn't be created when test_all is set

* [test_all] Remove dummies

* Modular fixes

* Update utils/check_repo.py

Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>

* [test_all] Better docs

* [test_all] Update src/transformers/commands/chat.py

Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>

* [test_all] Remove deprecated AdaptiveEmbeddings from the tests

* [test_all] Doc builder

* [test_all] is_dummy

* [test_all] Import utils

* [test_all] Doc building should not require all deps

---------

Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
This commit is contained in:
Lysandre Debut
2025-04-11 11:08:36 +02:00
committed by GitHub
parent 931126b929
commit 54a123f068
295 changed files with 2167 additions and 27702 deletions
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14
tests/pipelines/test_pipelines_mask_generation.py
View File

@@ -19,7 +19,8 @@ from huggingface_hub.utils import insecure_hashlib
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_tf_available,
is_torch_available,
is_vision_available,
pipeline,
)
@@ -34,6 +35,17 @@ from transformers.testing_utils import (
)
if is_tf_available():
from transformers import TF_MODEL_FOR_MASK_GENERATION_MAPPING
else:
TF_MODEL_FOR_MASK_GENERATION_MAPPING = None
if is_torch_available():
from transformers import MODEL_FOR_MASK_GENERATION_MAPPING
else:
MODEL_FOR_MASK_GENERATION_MAPPING = None
if is_vision_available():
from PIL import Image
else:

4
tests/pipelines/test_pipelines_question_answering.py
View File

@@ -51,9 +51,9 @@ class QAPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_QUESTION_ANSWERING_MAPPING
tf_model_mapping = TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING
if model_mapping is not None:
if not hasattr(model_mapping, "is_dummy"):
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
if not hasattr(tf_model_mapping, "is_dummy"):
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}

4
tests/pipelines/test_pipelines_text_classification.py
View File

@@ -48,9 +48,9 @@ class TextClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
if not hasattr(model_mapping, "is_dummy"):
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
if not hasattr(tf_model_mapping, "is_dummy"):
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}

4
tests/pipelines/test_pipelines_token_classification.py
View File

@@ -54,9 +54,9 @@ class TokenClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
if model_mapping is not None:
if not hasattr(model_mapping, "is_dummy"):
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
if not hasattr(tf_model_mapping, "is_dummy"):
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}

4
tests/pipelines/test_pipelines_zero_shot.py
View File

@@ -46,9 +46,9 @@ class ZeroShotClassificationPipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
tf_model_mapping = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if model_mapping is not None:
if not hasattr(model_mapping, "is_dummy"):
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
if tf_model_mapping is not None:
if not hasattr(tf_model_mapping, "is_dummy"):
tf_model_mapping = {
config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
}