MBartForConditionalGeneration (#6441)

* add MBartForConditionalGeneration

* style

* rebase and fixes

* add mbart test in TEST_FILES_WITH_NO_COMMON_TESTS

* fix docs

* don't ignore mbart

* doc

* fix mbart fairseq link

* put mbart before bart

* apply doc suggestions

tests/test_modeling_mbart.py

@@ -11,8 +11,8 @@ if is_torch_available():
     import torch
     from transformers import (
         AutoModelForSeq2SeqLM,
-        BartConfig,
-        BartForConditionalGeneration,
+        MBartConfig,
+        MBartForConditionalGeneration,
         BatchEncoding,
         AutoTokenizer,
     )
@@ -92,7 +92,7 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
         mbart_models = ["facebook/mbart-large-en-ro"]
         expected = {"scale_embedding": True, "output_past": True}
         for name in mbart_models:
-            config = BartConfig.from_pretrained(name)
+            config = MBartConfig.from_pretrained(name)
             self.assertTrue(config.is_valid_mbart())
             for k, v in expected.items():
                 try:
@@ -102,7 +102,7 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
                     raise
 
     def test_mbart_fast_forward(self):
-        config = BartConfig(
+        config = MBartConfig(
             vocab_size=99,
             d_model=24,
             encoder_layers=2,
@@ -115,7 +115,7 @@ class MBartEnroIntegrationTest(AbstractSeq2SeqIntegrationTest):
             add_final_layer_norm=True,
             return_dict=True,
         )
-        lm_model = BartForConditionalGeneration(config).to(torch_device)
+        lm_model = MBartForConditionalGeneration(config).to(torch_device)
         context = torch.Tensor([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]).long().to(torch_device)
         summary = torch.Tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]).long().to(torch_device)
         result = lm_model(input_ids=context, decoder_input_ids=summary, labels=summary)