[Large PR] Entire rework of pipelines. (#13308)

* Enabling dataset iteration on pipelines.

Enabling dataset iteration on pipelines.

Unifying parameters under `set_parameters` function.

Small fix.

Last fixes after rebase

Remove print.

Fixing text2text `generate_kwargs`

No more `self.max_length`.

Fixing tf only conversational.

Consistency in start/stop index over TF/PT.

Speeding up drastically on TF (nasty bug where max_length would increase
a ton.)

Adding test for support for non fast tokenizers.

Fixign GPU usage on zero-shot.

Fix working on Tf.

Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Update src/transformers/pipelines/base.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Small cleanup.

Remove all asserts + simple format.

* Fixing audio-classification for large PR.

* Overly explicity null checking.

* Encapsulating GPU/CPU pytorch manipulation directly within `base.py`.

* Removed internal state for parameters of the  pipeline.

Instead of overriding implicitly internal state, we moved
to real named arguments on every `preprocess`, `_forward`,
`postprocess` function.

Instead `_sanitize_parameters` will be used to split all kwargs
of both __init__ and __call__ into the 3 kinds of named parameters.

* Move import warnings.

* Small fixes.

* Quality.

* Another small fix, using the CI to debug faster.

* Last fixes.

* Last fix.

* Small cleanup of tensor moving.

* is not None.

* Adding a bunch of docs + a iteration test.

* Fixing doc style.

* KeyDataset = None guard.

* RRemoving the Cuda test for pipelines (was testing).

* Even more simple iteration test.

* Correct import .

* Long day.

* Fixes in docs.

* [WIP] migrating object detection.

* Fixed the target_size bug.

* Fixup.

* Bad variable name.

* Fixing `ensure_on_device` respects original ModelOutput.

src/transformers/testing_utils.py

@@ -1343,6 +1343,8 @@ def nested_simplify(obj, decimals=3):
         return {nested_simplify(k, decimals): nested_simplify(v, decimals) for k, v in obj.items()}
     elif isinstance(obj, (str, int, np.int64)):
         return obj
+    elif obj is None:
+        return obj
     elif is_torch_available() and isinstance(obj, torch.Tensor):
         return nested_simplify(obj.tolist(), decimals)
     elif is_tf_available() and tf.is_tensor(obj):

tests/test_pipelines_common.py

@@ -15,11 +15,13 @@
 import importlib
 import logging
 import string
+import unittest
 from abc import abstractmethod
 from functools import lru_cache
 from unittest import skipIf
 
-from transformers import FEATURE_EXTRACTOR_MAPPING, TOKENIZER_MAPPING, AutoFeatureExtractor, AutoTokenizer
+from transformers import FEATURE_EXTRACTOR_MAPPING, TOKENIZER_MAPPING, AutoFeatureExtractor, AutoTokenizer, pipeline
+from transformers.testing_utils import is_pipeline_test, require_torch
 
 
 logger = logging.getLogger(__name__)
@@ -177,3 +179,30 @@ class PipelineTestCaseMeta(type):
         dct["test_small_model_tf"] = dct.get("test_small_model_tf", inner)
 
         return type.__new__(mcs, name, bases, dct)
+
+
+@is_pipeline_test
+class CommonPipelineTest(unittest.TestCase):
+    @require_torch
+    def test_pipeline_iteration(self):
+        from torch.utils.data import Dataset
+
+        class MyDataset(Dataset):
+            data = [
+                "This is a test",
+                "This restaurant is great",
+                "This restaurant is awful",
+            ]
+
+            def __len__(self):
+                return 3
+
+            def __getitem__(self, i):
+                return self.data[i]
+
+        text_classifier = pipeline(
+            task="text-classification", model="Narsil/tiny-distilbert-sequence-classification", framework="pt"
+        )
+        dataset = MyDataset()
+        for output in text_classifier(dataset):
+            self.assertEqual(output, {"label": ANY(str), "score": ANY(float)})

tests/test_pipelines_conversational.py

@@ -187,24 +187,15 @@ class ConversationalPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseM
         conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
 
         conversation_1 = Conversation("hello")
-        inputs = conversation_agent._parse_and_tokenize([conversation_1])
+        inputs = conversation_agent.preprocess(conversation_1)
         self.assertEqual(inputs["input_ids"].tolist(), [[31373, 50256]])
 
         conversation_2 = Conversation("how are you ?", past_user_inputs=["hello"], generated_responses=["Hi there!"])
-        inputs = conversation_agent._parse_and_tokenize([conversation_2])
+        inputs = conversation_agent.preprocess(conversation_2)
         self.assertEqual(
             inputs["input_ids"].tolist(), [[31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633, 50256]]
         )
 
-        inputs = conversation_agent._parse_and_tokenize([conversation_1, conversation_2])
-        self.assertEqual(
-            inputs["input_ids"].tolist(),
-            [
-                [31373, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256],
-                [31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633, 50256],
-            ],
-        )
-
     @require_torch
     @slow
     def test_integration_torch_conversation_blenderbot_400M_input_ids(self):
@@ -214,7 +205,7 @@ class ConversationalPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseM
 
         # test1
         conversation_1 = Conversation("hello")
-        inputs = conversation_agent._parse_and_tokenize([conversation_1])
+        inputs = conversation_agent.preprocess(conversation_1)
         self.assertEqual(inputs["input_ids"].tolist(), [[1710, 86, 2]])
 
         # test2
@@ -225,7 +216,7 @@ class ConversationalPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseM
                 " Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie."
             ],
         )
-        inputs = conversation_agent._parse_and_tokenize([conversation_1])
+        inputs = conversation_agent.preprocess(conversation_1)
         self.assertEqual(
             inputs["input_ids"].tolist(),
             [
@@ -271,7 +262,7 @@ class ConversationalPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseM
                     964,
                     21,
                     2,  # EOS
-                ]
+                ],
             ],
         )
 

tests/test_pipelines_feature_extraction.py

@@ -91,6 +91,8 @@ class FeatureExtractionPipelineTests(unittest.TestCase, metaclass=PipelineTestCa
         shape = self.get_shape(outputs)
         self.assertEqual(shape[0], 1)
 
-        outputs = feature_extractor(["This is a test", "Another test"])
+        # If we send too small input
+        # there's a bug within FunnelModel (output with shape [1, 4, 2, 1] doesn't match the broadcast shape [1, 4, 2, 2])
+        outputs = feature_extractor(["This is a test", "Another longer test"])
         shape = self.get_shape(outputs)
         self.assertEqual(shape[0], 2)

tests/test_pipelines_fill_mask.py

@@ -186,7 +186,7 @@ class FillMaskPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
             ],
         )
 
-        outputs = fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token}"])
+        outputs = fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."])
         self.assertEqual(
             outputs,
             [

tests/test_pipelines_object_detection.py

@@ -116,8 +116,8 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
         self.assertEqual(
             nested_simplify(outputs, decimals=4),
             [
-                {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
-                {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
+                {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
+                {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
             ],
         )
 
@@ -133,12 +133,12 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
             nested_simplify(outputs, decimals=4),
             [
                 [
-                    {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
-                    {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
+                    {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
+                    {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
                 ],
                 [
-                    {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
-                    {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
+                    {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
+                    {"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 160, "ymin": 120, "xmax": 480, "ymax": 359}},
                 ],
             ],
         )
@@ -156,11 +156,11 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
         self.assertEqual(
             nested_simplify(outputs, decimals=4),
             [
-                {"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
-                {"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
-                {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
-                {"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
-                {"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
+                {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
+                {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
+                {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
+                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
         )
 
@@ -174,18 +174,18 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
             nested_simplify(outputs, decimals=4),
             [
                 [
-                    {"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
-                    {"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
-                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
-                    {"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
-                    {"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
+                    {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
+                    {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
+                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
+                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 ],
                 [
-                    {"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
-                    {"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
-                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
-                    {"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
-                    {"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
+                    {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
+                    {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
+                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
+                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 ],
             ],
         )
@@ -201,11 +201,11 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
         self.assertEqual(
             nested_simplify(outputs, decimals=4),
             [
-                {"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
-                {"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
-                {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
-                {"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
-                {"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
+                {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
+                {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
+                {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
+                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
         )
 
@@ -219,18 +219,18 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
             nested_simplify(outputs, decimals=4),
             [
                 [
-                    {"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
-                    {"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
-                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
-                    {"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
-                    {"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
+                    {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
+                    {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
+                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
+                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 ],
                 [
-                    {"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
-                    {"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
-                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
-                    {"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
-                    {"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
+                    {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
+                    {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
+                    {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
+                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 ],
             ],
         )
@@ -247,7 +247,7 @@ class ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCase
         self.assertEqual(
             nested_simplify(outputs, decimals=4),
             [
-                {"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
-                {"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
+                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
         )

tests/test_pipelines_token_classification.py

@@ -96,7 +96,7 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
 
     def run_aggregation_strategy(self, model, tokenizer):
         token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="simple")
-        self.assertEqual(token_classifier.aggregation_strategy, AggregationStrategy.SIMPLE)
+        self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE)
         outputs = token_classifier("A simple string")
         self.assertIsInstance(outputs, list)
         n = len(outputs)
@@ -115,7 +115,7 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
         )
 
         token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="first")
-        self.assertEqual(token_classifier.aggregation_strategy, AggregationStrategy.FIRST)
+        self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST)
         outputs = token_classifier("A simple string")
         self.assertIsInstance(outputs, list)
         n = len(outputs)
@@ -134,7 +134,7 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
         )
 
         token_classifier = TokenClassificationPipeline(model=model, tokenizer=tokenizer, aggregation_strategy="max")
-        self.assertEqual(token_classifier.aggregation_strategy, AggregationStrategy.MAX)
+        self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.MAX)
         outputs = token_classifier("A simple string")
         self.assertIsInstance(outputs, list)
         n = len(outputs)
@@ -155,7 +155,7 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
         token_classifier = TokenClassificationPipeline(
             model=model, tokenizer=tokenizer, aggregation_strategy="average"
         )
-        self.assertEqual(token_classifier.aggregation_strategy, AggregationStrategy.AVERAGE)
+        self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.AVERAGE)
         outputs = token_classifier("A simple string")
         self.assertIsInstance(outputs, list)
         n = len(outputs)
@@ -175,12 +175,12 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
 
         with self.assertWarns(UserWarning):
             token_classifier = pipeline(task="ner", model=model, tokenizer=tokenizer, grouped_entities=True)
-        self.assertEqual(token_classifier.aggregation_strategy, AggregationStrategy.SIMPLE)
+        self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.SIMPLE)
         with self.assertWarns(UserWarning):
             token_classifier = pipeline(
                 task="ner", model=model, tokenizer=tokenizer, grouped_entities=True, ignore_subwords=True
             )
-        self.assertEqual(token_classifier.aggregation_strategy, AggregationStrategy.FIRST)
+        self.assertEqual(token_classifier._postprocess_params["aggregation_strategy"], AggregationStrategy.FIRST)
 
     @require_torch
     @slow
@@ -533,7 +533,12 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
         scores = np.array([[1, 0, 0], [0.1, 0.3, 0.6], [0.8, 0.1, 0.1]])
 
         pre_entities = token_classifier.gather_pre_entities(
-            sentence, input_ids, scores, offset_mapping, special_tokens_mask
+            sentence,
+            input_ids,
+            scores,
+            offset_mapping,
+            special_tokens_mask,
+            aggregation_strategy=AggregationStrategy.NONE,
         )
         self.assertEqual(
             nested_simplify(pre_entities),
@@ -570,6 +575,20 @@ class TokenClassificationPipelineTests(unittest.TestCase, metaclass=PipelineTest
             ],
         )
 
+    @require_torch
+    def test_no_offset_tokenizer(self):
+        model_name = "Narsil/small2"
+        tokenizer = AutoTokenizer.from_pretrained("Narsil/small2", use_fast=False)
+        token_classifier = pipeline(task="token-classification", model=model_name, tokenizer=tokenizer, framework="pt")
+        outputs = token_classifier("This is a test !")
+        self.assertEqual(
+            nested_simplify(outputs),
+            [
+                {"entity": "I-MISC", "score": 0.115, "index": 1, "word": "this", "start": None, "end": None},
+                {"entity": "I-MISC", "score": 0.115, "index": 2, "word": "is", "start": None, "end": None},
+            ],
+        )
+
     @require_torch
     def test_small_model_pt(self):
         model_name = "Narsil/small2"

tests/test_pipelines_translation.py

@@ -108,8 +108,8 @@ class TranslationNewFormatPipelineTests(unittest.TestCase):
 
         # but we do for this one
         translator = pipeline(task="translation_en_to_de")
-        self.assertEquals(translator.src_lang, "en")
-        self.assertEquals(translator.tgt_lang, "de")
+        self.assertEqual(translator._preprocess_params["src_lang"], "en")
+        self.assertEqual(translator._preprocess_params["tgt_lang"], "de")
 
     @require_torch
     @slow
@@ -137,8 +137,8 @@ class TranslationNewFormatPipelineTests(unittest.TestCase):
     def test_translation_on_odd_language(self):
         model = "patrickvonplaten/t5-tiny-random"
         translator = pipeline(task="translation_cn_to_ar", model=model)
-        self.assertEquals(translator.src_lang, "cn")
-        self.assertEquals(translator.tgt_lang, "ar")
+        self.assertEqual(translator._preprocess_params["src_lang"], "cn")
+        self.assertEqual(translator._preprocess_params["tgt_lang"], "ar")
 
     @require_torch
     def test_translation_default_language_selection(self):
@@ -146,8 +146,8 @@ class TranslationNewFormatPipelineTests(unittest.TestCase):
         with pytest.warns(UserWarning, match=r".*translation_en_to_de.*"):
             translator = pipeline(task="translation", model=model)
         self.assertEqual(translator.task, "translation_en_to_de")
-        self.assertEqual(translator.src_lang, "en")
-        self.assertEqual(translator.tgt_lang, "de")
+        self.assertEqual(translator._preprocess_params["src_lang"], "en")
+        self.assertEqual(translator._preprocess_params["tgt_lang"], "de")
 
     @require_torch
     def test_translation_with_no_language_no_model_fails(self):