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import unittest
from unittest.mock import MagicMock
import pytest
import transformers
from diffusers import (
StableDiffusionDepth2ImgPipeline, # type: ignore
StableDiffusionImageVariationPipeline, # type: ignore
StableDiffusionImg2ImgPipeline, # type: ignore
StableDiffusionInpaintPipeline, # type: ignore
StableDiffusionInstructPix2PixPipeline, # type: ignore
StableDiffusionPipeline, # type: ignore
StableDiffusionUpscalePipeline, # type: ignore
)
from transformers import (
AudioClassificationPipeline,
AutomaticSpeechRecognitionPipeline,
DocumentQuestionAnsweringPipeline,
FeatureExtractionPipeline,
FillMaskPipeline,
ImageClassificationPipeline,
ImageToTextPipeline,
ObjectDetectionPipeline,
QuestionAnsweringPipeline,
SummarizationPipeline,
Text2TextGenerationPipeline,
TextClassificationPipeline,
TextGenerationPipeline,
TranslationPipeline,
VisualQuestionAnsweringPipeline,
ZeroShotClassificationPipeline,
)
import gradio as gr
from gradio.pipelines_utils import (
handle_diffusers_pipeline,
handle_transformers_pipeline,
)
@pytest.mark.flaky
def test_text_to_text_model_from_pipeline():
pipe = transformers.pipeline(model="sshleifer/bart-tiny-random")
io = gr.Interface.from_pipeline(pipe)
output = io("My name is Sylvain and I work at Hugging Face in Brooklyn")
assert isinstance(output, str)
@pytest.mark.flaky
def test_stable_diffusion_pipeline():
pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe")
io = gr.Interface.from_pipeline(pipe)
output = io("An astronaut", "low quality", 3, 7.5)
assert isinstance(output, str)
@pytest.mark.flaky
def test_interface_in_blocks():
pipe1 = transformers.pipeline(model="sshleifer/bart-tiny-random")
pipe2 = transformers.pipeline(model="sshleifer/bart-tiny-random")
with gr.Blocks() as demo:
with gr.Tab("Image Inference"):
gr.Interface.from_pipeline(pipe1)
with gr.Tab("Image Inference"):
gr.Interface.from_pipeline(pipe2)
demo.launch(prevent_thread_lock=True)
demo.close()
@pytest.mark.flaky
def test_transformers_load_from_pipeline():
from transformers import pipeline
pipe = pipeline(model="deepset/roberta-base-squad2")
io = gr.Interface.from_pipeline(pipe)
assert io.input_components[0].label == "Context"
assert io.input_components[1].label == "Question"
assert io.output_components[0].label == "Answer"
assert io.output_components[1].label == "Score"
class TestHandleTransformersPipelines(unittest.TestCase):
def test_audio_classification_pipeline(self):
pipe = MagicMock(spec=AudioClassificationPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Class"
def test_automatic_speech_recognition_pipeline(self):
pipe = MagicMock(spec=AutomaticSpeechRecognitionPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Output"
def test_object_detection_pipeline(self):
pipe = MagicMock(spec=ObjectDetectionPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input Image"
assert pipeline_info["outputs"].label == "Objects Detected"
def test_feature_extraction_pipeline(self):
pipe = MagicMock(spec=FeatureExtractionPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Output"
def test_fill_mask_pipeline(self):
pipe = MagicMock(spec=FillMaskPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Classification"
def test_image_classification_pipeline(self):
pipe = MagicMock(spec=ImageClassificationPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input Image"
assert pipeline_info["outputs"].label == "Classification"
def test_question_answering_pipeline(self):
pipe = MagicMock(spec=QuestionAnsweringPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Context"
assert pipeline_info["inputs"][1].label == "Question"
assert pipeline_info["outputs"][0].label == "Answer"
assert pipeline_info["outputs"][1].label == "Score"
def test_summarization_pipeline(self):
pipe = MagicMock(spec=SummarizationPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Summary"
def test_text_classification_pipeline(self):
pipe = MagicMock(spec=TextClassificationPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Classification"
def test_text_generation_pipeline(self):
pipe = MagicMock(spec=TextGenerationPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Output"
def test_translation_pipeline(self):
pipe = MagicMock(spec=TranslationPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Translation"
def test_text2text_generation_pipeline(self):
pipe = MagicMock(spec=Text2TextGenerationPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input"
assert pipeline_info["outputs"].label == "Generated Text"
def test_zero_shot_classification_pipeline(self):
pipe = MagicMock(spec=ZeroShotClassificationPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Input"
assert (
pipeline_info["inputs"][1].label == "Possible class names (comma-separated)"
)
assert pipeline_info["inputs"][2].label == "Allow multiple true classes"
assert pipeline_info["outputs"].label == "Classification"
def test_document_question_answering_pipeline(self):
pipe = MagicMock(spec=DocumentQuestionAnsweringPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Input Document"
assert pipeline_info["inputs"][1].label == "Question"
assert pipeline_info["outputs"].label == "Label"
def test_visual_question_answering_pipeline(self):
pipe = MagicMock(spec=VisualQuestionAnsweringPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Input Image"
assert pipeline_info["inputs"][1].label == "Question"
assert pipeline_info["outputs"].label == "Score"
def test_image_to_text_pipeline(self):
pipe = MagicMock(spec=ImageToTextPipeline)
pipeline_info = handle_transformers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"].label == "Input Image"
assert pipeline_info["outputs"].label == "Text"
def test_unsupported_pipeline(self):
pipe = MagicMock()
with self.assertRaises(ValueError):
handle_transformers_pipeline(pipe)
class TestHandleDiffusersPipelines(unittest.TestCase):
def test_stable_diffusion_pipeline(self):
pipe = MagicMock(spec=StableDiffusionPipeline)
pipeline_info = handle_diffusers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Prompt"
assert pipeline_info["inputs"][1].label == "Negative prompt"
assert pipeline_info["outputs"].label == "Generated Image"
def test_stable_diffusion_img2img_pipeline(self):
pipe = MagicMock(spec=StableDiffusionImg2ImgPipeline)
pipeline_info = handle_diffusers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Prompt"
assert pipeline_info["inputs"][1].label == "Negative prompt"
assert pipeline_info["outputs"].label == "Generated Image"
def test_stable_diffusion_inpaint_pipeline(self):
pipe = MagicMock(spec=StableDiffusionInpaintPipeline)
pipeline_info = handle_diffusers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Prompt"
assert pipeline_info["inputs"][1].label == "Negative prompt"
assert pipeline_info["outputs"].label == "Generated Image"
def test_stable_diffusion_depth2img_pipeline(self):
pipe = MagicMock(spec=StableDiffusionDepth2ImgPipeline)
pipeline_info = handle_diffusers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Prompt"
assert pipeline_info["inputs"][1].label == "Negative prompt"
assert pipeline_info["outputs"].label == "Generated Image"
def test_stable_diffusion_image_variation_pipeline(self):
pipe = MagicMock(spec=StableDiffusionImageVariationPipeline)
pipeline_info = handle_diffusers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Image"
assert pipeline_info["outputs"].label == "Generated Image"
def test_stable_diffusion_instruct_pix2pix_pipeline(self):
pipe = MagicMock(spec=StableDiffusionInstructPix2PixPipeline)
pipeline_info = handle_diffusers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Prompt"
assert pipeline_info["inputs"][1].label == "Negative prompt"
assert pipeline_info["outputs"].label == "Generated Image"
def test_stable_diffusion_upscale_pipeline(self):
pipe = MagicMock(spec=StableDiffusionUpscalePipeline)
pipeline_info = handle_diffusers_pipeline(pipe)
assert pipeline_info is not None
assert pipeline_info["inputs"][0].label == "Prompt"
assert pipeline_info["inputs"][1].label == "Negative prompt"
assert pipeline_info["outputs"].label == "Generated Image"
def test_unsupported_pipeline(self):
pipe = MagicMock()
with self.assertRaises(ValueError):
handle_transformers_pipeline(pipe)
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