Remove ConversationalPipeline and Conversation object (#31165)

* Remove ConversationalPipeline and Conversation object, as they have been deprecated for some time and are due for removal

* Update not-doctested.txt

* Fix JA and ZH docs

* Fix JA and ZH docs some more

* Fix JA and ZH docs some more

tests/models/bart/test_modeling_bart.py

@@ -430,7 +430,6 @@ class BartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
     all_generative_model_classes = (BartForConditionalGeneration,) if is_torch_available() else ()
     pipeline_model_mapping = (
         {
-            "conversational": BartForConditionalGeneration,
             "feature-extraction": BartModel,
             "fill-mask": BartForConditionalGeneration,
             "question-answering": BartForQuestionAnswering,
@@ -513,10 +512,6 @@ class BartModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
         model.generate(input_ids, attention_mask=attention_mask)
         model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
 
-    @unittest.skip("Does not support conversations.")
-    def test_pipeline_conversational(self):
-        pass
-
 
 def assert_tensors_close(a, b, atol=1e-12, prefix=""):
     """If tensors have different shapes, different values or a and b are not both tensors, raise a nice Assertion error."""

tests/models/bart/test_modeling_tf_bart.py

@@ -198,7 +198,6 @@ class TFBartModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTester
     all_generative_model_classes = (TFBartForConditionalGeneration,) if is_tf_available() else ()
     pipeline_model_mapping = (
         {
-            "conversational": TFBartForConditionalGeneration,
             "feature-extraction": TFBartModel,
             "summarization": TFBartForConditionalGeneration,
             "text-classification": TFBartForSequenceClassification,
@@ -343,10 +342,6 @@ class TFBartModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, PipelineTester
                 # check that the output for the restored model is the same
                 self.assert_outputs_same(restored_model_outputs, outputs)
 
-    @unittest.skip("Does not support conversations.")
-    def test_pipeline_conversational(self):
-        pass
-
 
 def _long_tensor(tok_lst):
     return tf.constant(tok_lst, dtype=tf.int32)