[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.

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)})