55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
import unittest
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import pytest
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from transformers import pipeline
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from transformers.testing_utils import is_pipeline_test, require_torch, slow
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from .test_pipelines_common import MonoInputPipelineCommonMixin
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class TranslationEnToDePipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "translation_en_to_de"
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small_models = ["patrickvonplaten/t5-tiny-random"] # Default model - Models tested without the @slow decorator
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large_models = [None] # Models tested with the @slow decorator
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invalid_inputs = [4, "<mask>"]
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mandatory_keys = ["translation_text"]
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class TranslationEnToRoPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "translation_en_to_ro"
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small_models = ["patrickvonplaten/t5-tiny-random"] # Default model - Models tested without the @slow decorator
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large_models = [None] # Models tested with the @slow decorator
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invalid_inputs = [4, "<mask>"]
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mandatory_keys = ["translation_text"]
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@is_pipeline_test
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class TranslationNewFormatPipelineTests(unittest.TestCase):
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@require_torch
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@slow
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def test_default_translations(self):
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# We don't provide a default for this pair
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with self.assertRaises(ValueError):
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pipeline(task="translation_cn_to_ar")
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# but we do for this one
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pipeline(task="translation_en_to_de")
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@require_torch
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def test_translation_on_odd_language(self):
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model = "patrickvonplaten/t5-tiny-random"
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pipeline(task="translation_cn_to_ar", model=model)
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@require_torch
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def test_translation_default_language_selection(self):
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model = "patrickvonplaten/t5-tiny-random"
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with pytest.warns(UserWarning, match=r".*translation_en_to_de.*"):
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nlp = pipeline(task="translation", model=model)
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self.assertEqual(nlp.task, "translation_en_to_de")
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@require_torch
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def test_translation_with_no_language_no_model_fails(self):
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with self.assertRaises(ValueError):
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pipeline(task="translation")
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