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test:remove unwanted tests as they are already available with Process…
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…orTesterMixin
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@RUFFY-369
RUFFY-369 committed Oct 9, 2024
1 parent f9fae40 commit f878996
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219 changes: 0 additions & 219 deletions tests/models/imagebind/test_processor_imagebind.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,225 +250,6 @@ def test_model_input_names(self):

self.assertListEqual(list(inputs.keys()), processor.model_input_names)

@require_vision
@require_torch
def test_tokenizer_defaults_preserved_by_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer", max_length=117)
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input, return_tensors="pt")
self.assertEqual(len(inputs["input_ids"][0]), 4)

@require_torch
@require_vision
def test_image_processor_defaults_preserved_by_image_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor", crop_size=(234, 234))
tokenizer = self.get_component("tokenizer", max_length=117)
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input)
self.assertEqual(len(inputs["pixel_values"][0][0]), 234)

@require_vision
@require_torch
def test_kwargs_overrides_default_tokenizer_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer", max_length=117)
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input, return_tensors="pt", max_length=112)
self.assertEqual(len(inputs["input_ids"][0]), 4)

@require_torch
@require_vision
def test_kwargs_overrides_default_image_processor_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor", crop_size=(234, 234))
tokenizer = self.get_component("tokenizer", max_length=117)
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()

inputs = processor(text=input_str, images=image_input, crop_size=[224, 224])
self.assertEqual(len(inputs["pixel_values"][0][0]), 224)

@require_torch
@require_vision
def test_unstructured_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()
inputs = processor(
text=input_str,
images=image_input,
return_tensors="pt",
crop_size={"height": 214, "width": 214},
padding="max_length",
max_length=76,
)

self.assertEqual(inputs["pixel_values"].shape[2], 214)
self.assertEqual(len(inputs["input_ids"][0]), 76)

@require_torch
@require_vision
def test_unstructured_kwargs_batched(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)

input_str = ["lower newer", "upper older longer string"]
image_input = self.prepare_image_inputs(batch_size=2)
inputs = processor(
text=input_str,
images=image_input,
return_tensors="pt",
crop_size={"height": 214, "width": 214},
padding="longest",
max_length=76,
)

self.assertEqual(inputs["pixel_values"].shape[2], 214)

self.assertEqual(len(inputs["input_ids"][0]), 6)

@require_torch
@require_vision
def test_doubly_passed_kwargs(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)

input_str = ["lower newer"]
image_input = self.prepare_image_inputs()
with self.assertRaises(ValueError):
_ = processor(
text=input_str,
images=image_input,
images_kwargs={"crop_size": {"height": 222, "width": 222}},
crop_size={"height": 214, "width": 214},
)

@require_torch
@require_vision
def test_structured_kwargs_nested(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")
image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)

input_str = "lower newer"
image_input = self.prepare_image_inputs()

# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"crop_size": {"height": 214, "width": 214}},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}

inputs = processor(text=input_str, images=image_input, **all_kwargs)
self.skip_processor_without_typed_kwargs(processor)

self.assertEqual(inputs["pixel_values"].shape[2], 214)

self.assertEqual(len(inputs["input_ids"][0]), 76)

@require_torch
@require_vision
def test_structured_kwargs_nested_from_dict(self):
if "image_processor" not in self.processor_class.attributes:
self.skipTest(f"image_processor attribute not present in {self.processor_class}")

image_processor = self.get_component("image_processor")
tokenizer = self.get_component("tokenizer")
feature_extractor = self.get_component("feature_extractor")

processor = self.processor_class(
tokenizer=tokenizer, image_processor=image_processor, feature_extractor=feature_extractor
)
self.skip_processor_without_typed_kwargs(processor)
input_str = "lower newer"
image_input = self.prepare_image_inputs()

# Define the kwargs for each modality
all_kwargs = {
"common_kwargs": {"return_tensors": "pt"},
"images_kwargs": {"crop_size": {"height": 214, "width": 214}},
"text_kwargs": {"padding": "max_length", "max_length": 76},
}

inputs = processor(text=input_str, images=image_input, **all_kwargs)
self.assertEqual(inputs["pixel_values"].shape[2], 214)

self.assertEqual(len(inputs["input_ids"][0]), 76)

@require_torch
def test_doubly_passed_kwargs_audio(self):
if "feature_extractor" not in self.processor_class.attributes:
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