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Don't use LayoutLMv2 and LayoutLMv3 in some pipeline tests (#22774)

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* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-04-17 17:45:20 +02:00
committed by GitHub
parent ea7b0a539a
commit 5269718cb7
6 changed files with 39 additions and 26 deletions
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9
tests/pipelines/test_pipelines_question_answering.py
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@@ -34,11 +34,20 @@ from transformers.testing_utils import (
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
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:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {config: model for config, model in tf_model_mapping.items() if config.__name__ in _TO_SKIP}
def get_test_pipeline(self, model, tokenizer, processor):
if isinstance(model.config, LxmertConfig):
# This is an bimodal model, we need to find a more consistent way

9
tests/pipelines/test_pipelines_text_classification.py
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@@ -25,11 +25,20 @@ from transformers.testing_utils import is_pipeline_test, nested_simplify, requir
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
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:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {config: model for config, model in tf_model_mapping.items() if config.__name__ in _TO_SKIP}
@require_torch
def test_small_model_pt(self):
text_classifier = pipeline(

8
tests/pipelines/test_pipelines_token_classification.py
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@@ -39,12 +39,20 @@ from .test_pipelines_common import ANY
VALID_INPUTS = ["A simple string", ["list of strings", "A simple string that is quite a bit longer"]]
# These 2 model types require different inputs than those of the usual text models.
_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
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:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {config: model for config, model in tf_model_mapping.items() if config.__name__ in _TO_SKIP}
def get_test_pipeline(self, model, tokenizer, processor):
token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
return token_classifier, ["A simple string", "A simple string that is quite a bit longer"]

9
tests/pipelines/test_pipelines_zero_shot.py
View File

@@ -26,11 +26,20 @@ from transformers.testing_utils import is_pipeline_test, nested_simplify, requir
from .test_pipelines_common import ANY
# These 2 model types require different inputs than those of the usual text models.
_TO_SKIP = {"LayoutLMv2Config", "LayoutLMv3Config"}
@is_pipeline_test
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:
model_mapping = {config: model for config, model in model_mapping.items() if config.__name__ in _TO_SKIP}
if tf_model_mapping is not None:
tf_model_mapping = {config: model for config, model in tf_model_mapping.items() if config.__name__ in _TO_SKIP}
def get_test_pipeline(self, model, tokenizer, processor):
classifier = ZeroShotClassificationPipeline(
model=model, tokenizer=tokenizer, candidate_labels=["polics", "health"]