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3a40cdf58d
* WIP refactoring pipeline tests - switching to fast tokenizers * fix dialog pipeline and fill-mask * refactoring pipeline tests backbone * make large tests slow * fix tests (tf Bart inactive for now) * fix doc... * clean up for merge * fixing tests - remove bart from summarization until there is TF * fix quality and RAG * Add new translation pipeline tests - fix JAX tests * only slow for dialog * Fixing the missing TF-BART imports in modeling_tf_auto * spin out pipeline tests in separate CI job * adding pipeline test to CI YAML * add slow pipeline tests * speed up tf and pt join test to avoid redoing all the standalone pt and tf tests * Update src/transformers/tokenization_utils_base.py Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * Update src/transformers/pipelines.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update src/transformers/pipelines.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/testing_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * add require_torch and require_tf in is_pt_tf_cross_test Co-authored-by: Sam Shleifer <sshleifer@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
import unittest
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from transformers.pipelines import Conversation, Pipeline
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from .test_pipelines_common import CustomInputPipelineCommonMixin
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class DialoguePipelineTests(CustomInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "conversational"
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small_models = [] # Default model - Models tested without the @slow decorator
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large_models = ["microsoft/DialoGPT-medium"] # Models tested with the @slow decorator
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def _test_pipeline(self, nlp: Pipeline):
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valid_inputs = [Conversation("Hi there!"), [Conversation("Hi there!"), Conversation("How are you?")]]
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invalid_inputs = ["Hi there!", Conversation()]
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self.assertIsNotNone(nlp)
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mono_result = nlp(valid_inputs[0])
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self.assertIsInstance(mono_result, Conversation)
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multi_result = nlp(valid_inputs[1])
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], Conversation)
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# Inactive conversations passed to the pipeline raise a ValueError
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self.assertRaises(ValueError, nlp, valid_inputs[1])
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for bad_input in invalid_inputs:
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self.assertRaises(Exception, nlp, bad_input)
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self.assertRaises(Exception, nlp, invalid_inputs)
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