229 lines
8.9 KiB
Python
229 lines
8.9 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 require_tf, require_torch, slow
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from .test_pipelines_common import MonoInputPipelineCommonMixin
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EXPECTED_FILL_MASK_RESULT = [
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[
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{"sequence": "<s>My name is John</s>", "score": 0.00782308354973793, "token": 610, "token_str": "ĠJohn"},
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{"sequence": "<s>My name is Chris</s>", "score": 0.007475061342120171, "token": 1573, "token_str": "ĠChris"},
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],
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[
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{"sequence": "<s>The largest city in France is Paris</s>", "score": 0.3185044229030609, "token": 2201},
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{"sequence": "<s>The largest city in France is Lyon</s>", "score": 0.21112334728240967, "token": 12790},
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],
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]
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EXPECTED_FILL_MASK_TARGET_RESULT = [
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[
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{
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"sequence": "<s>My name is Patrick</s>",
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"score": 0.004992353264242411,
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"token": 3499,
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"token_str": "ĠPatrick",
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},
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{
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"sequence": "<s>My name is Clara</s>",
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"score": 0.00019297805556561798,
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"token": 13606,
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"token_str": "ĠClara",
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},
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]
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]
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class FillMaskPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
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pipeline_task = "fill-mask"
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pipeline_loading_kwargs = {"top_k": 2}
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small_models = ["sshleifer/tiny-distilroberta-base"] # Models tested without the @slow decorator
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large_models = ["distilroberta-base"] # Models tested with the @slow decorator
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mandatory_keys = {"sequence", "score", "token"}
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valid_inputs = [
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"My name is <mask>",
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"The largest city in France is <mask>",
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]
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invalid_inputs = [
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"This is <mask> <mask>" # More than 1 mask_token in the input is not supported
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"This is" # No mask_token is not supported
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]
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expected_check_keys = ["sequence"]
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@require_torch
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def test_torch_topk_deprecation(self):
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# At pipeline initialization only it was not enabled at pipeline
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# call site before
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with pytest.warns(FutureWarning, match=r".*use `top_k`.*"):
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pipeline(task="fill-mask", model=self.small_models[0], topk=1)
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@require_torch
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def test_torch_fill_mask(self):
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valid_inputs = "My name is <mask>"
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nlp = pipeline(task="fill-mask", model=self.small_models[0])
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outputs = nlp(valid_inputs)
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self.assertIsInstance(outputs, list)
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# This passes
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outputs = nlp(valid_inputs, targets=[" Patrick", " Clara"])
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self.assertIsInstance(outputs, list)
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# This used to fail with `cannot mix args and kwargs`
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outputs = nlp(valid_inputs, something=False)
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self.assertIsInstance(outputs, list)
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@require_torch
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def test_torch_fill_mask_with_targets(self):
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valid_inputs = ["My name is <mask>"]
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valid_targets = [[" Teven", " Patrick", " Clara"], [" Sam"]]
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invalid_targets = [[], [""], ""]
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for model_name in self.small_models:
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nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="pt")
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for targets in valid_targets:
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outputs = nlp(valid_inputs, targets=targets)
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self.assertIsInstance(outputs, list)
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self.assertEqual(len(outputs), len(targets))
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for targets in invalid_targets:
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self.assertRaises(ValueError, nlp, valid_inputs, targets=targets)
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@require_tf
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def test_tf_fill_mask_with_targets(self):
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valid_inputs = ["My name is <mask>"]
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valid_targets = [[" Teven", " Patrick", " Clara"], [" Sam"]]
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invalid_targets = [[], [""], ""]
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for model_name in self.small_models:
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nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf")
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for targets in valid_targets:
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outputs = nlp(valid_inputs, targets=targets)
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self.assertIsInstance(outputs, list)
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self.assertEqual(len(outputs), len(targets))
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for targets in invalid_targets:
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self.assertRaises(ValueError, nlp, valid_inputs, targets=targets)
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@require_torch
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@slow
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def test_torch_fill_mask_results(self):
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mandatory_keys = {"sequence", "score", "token"}
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valid_inputs = [
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"My name is <mask>",
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"The largest city in France is <mask>",
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]
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valid_targets = [" Patrick", " Clara"]
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for model_name in self.large_models:
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nlp = pipeline(
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task="fill-mask",
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model=model_name,
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tokenizer=model_name,
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framework="pt",
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top_k=2,
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)
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mono_result = nlp(valid_inputs[0], targets=valid_targets)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], dict)
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for mandatory_key in mandatory_keys:
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self.assertIn(mandatory_key, mono_result[0])
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multi_result = [nlp(valid_input) for valid_input in valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
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self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, nlp, [None])
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valid_inputs = valid_inputs[:1]
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mono_result = nlp(valid_inputs[0], targets=valid_targets)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], dict)
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for mandatory_key in mandatory_keys:
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self.assertIn(mandatory_key, mono_result[0])
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multi_result = [nlp(valid_input) for valid_input in valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
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self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, nlp, [None])
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@require_tf
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@slow
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def test_tf_fill_mask_results(self):
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mandatory_keys = {"sequence", "score", "token"}
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valid_inputs = [
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"My name is <mask>",
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"The largest city in France is <mask>",
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]
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valid_targets = [" Patrick", " Clara"]
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for model_name in self.large_models:
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nlp = pipeline(task="fill-mask", model=model_name, tokenizer=model_name, framework="tf", topk=2)
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mono_result = nlp(valid_inputs[0], targets=valid_targets)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], dict)
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for mandatory_key in mandatory_keys:
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self.assertIn(mandatory_key, mono_result[0])
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multi_result = [nlp(valid_input) for valid_input in valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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for result, expected in zip(multi_result, EXPECTED_FILL_MASK_RESULT):
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self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, nlp, [None])
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valid_inputs = valid_inputs[:1]
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mono_result = nlp(valid_inputs[0], targets=valid_targets)
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self.assertIsInstance(mono_result, list)
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self.assertIsInstance(mono_result[0], dict)
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for mandatory_key in mandatory_keys:
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self.assertIn(mandatory_key, mono_result[0])
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multi_result = [nlp(valid_input) for valid_input in valid_inputs]
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self.assertIsInstance(multi_result, list)
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self.assertIsInstance(multi_result[0], (dict, list))
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for result, expected in zip(multi_result, EXPECTED_FILL_MASK_TARGET_RESULT):
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self.assertEqual(set([o["sequence"] for o in result]), set([o["sequence"] for o in result]))
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if isinstance(multi_result[0], list):
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multi_result = multi_result[0]
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for result in multi_result:
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for key in mandatory_keys:
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self.assertIn(key, result)
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self.assertRaises(Exception, nlp, [None])
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