Idefics: enable generation tests (#34062)

* add idefics

* conflicts after merging main

* enable tests but need to fix some

* fix tests

* no print

* fix/skip some slow tests

* continue not skip

* rebasing broken smth, this is the fix

tests/models/idefics3/test_modeling_idefics3.py

@@ -19,6 +19,7 @@ import gc
 import unittest
 from io import BytesIO
 
+import pytest
 import requests
 
 from transformers import (
@@ -321,6 +322,7 @@ class Idefics3ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTest
     """
 
     all_model_classes = (Idefics3ForConditionalGeneration,) if is_torch_available() else ()
+    all_generative_model_classes = (Idefics3ForConditionalGeneration,) if is_torch_available() else ()
     fx_compatible = False
     test_pruning = False
     test_resize_embeddings = True
@@ -343,6 +345,72 @@ class Idefics3ForConditionalGenerationModelTest(GenerationTesterMixin, ModelTest
     def test_flash_attn_2_inference_padding_right(self):
         pass
 
+    @unittest.skip(reason="Contrastive search is not implemented for VLMs that do cross-attn")
+    def test_contrastive_generate(self):
+        pass
+
+    @unittest.skip(reason="Contrastive search is not implemented for VLMs that do cross-attn")
+    def test_contrastive_generate_dict_outputs_use_cache(self):
+        pass
+
+    @unittest.skip(reason="Contrastive search is not implemented for VLMs that do cross-attn")
+    def test_contrastive_generate_low_memory(self):
+        pass
+
+    @unittest.skip(
+        reason="Prompt lookup decoding needs a way to indicate `bad_word_ids` that should not be suggested as candidates"
+    )
+    def test_prompt_lookup_decoding_matches_greedy_search(self):
+        pass
+
+    @unittest.skip(reason=" FlashAttention only support fp16 and bf16 data type")
+    def test_flash_attn_2_fp32_ln(self):
+        pass
+
+    @pytest.mark.generate
+    def test_generate_from_inputs_embeds_decoder_only(self):
+        # overwrite because IDEFICS needs ids and embeds at the input to be not None
+        for model_class in self.all_generative_model_classes:
+            config, inputs_dict = self.prepare_config_and_inputs_for_generate()
+
+            # Ignore:
+            # a) eos (to always output 20 tokens) and pad (so we don't try to infer the attn mask from the input_ids,
+            #   which would cause a mismatch),
+            config.pad_token_id = config.eos_token_id = -1
+            config.is_decoder = True
+            model = model_class(config).to(torch_device).eval()
+            input_ids = inputs_dict.pop("input_ids")
+
+            # Traditional way of generating text
+            outputs_from_ids = model.generate(
+                input_ids, max_new_tokens=5, return_dict_in_generate=True, output_scores=True
+            )
+            self.assertEqual(outputs_from_ids.sequences.shape, (input_ids.shape[0], input_ids.shape[1] + 5))
+
+            # Same thing, but from input embeddings (`input_ids` is passed so the prompt is present in the output)
+            inputs_embeds = model.get_input_embeddings()(input_ids)
+            outputs_from_embeds = model.generate(
+                input_ids,
+                inputs_embeds=inputs_embeds,
+                max_new_tokens=5,
+                return_dict_in_generate=True,
+                output_scores=True,
+            )
+            self.assertListEqual(outputs_from_ids.sequences.tolist(), outputs_from_embeds.sequences.tolist())
+
+            # But if we pass different inputs_embeds, we should get different outputs (the output text may be the
+            # same, but the logits will almost surely be different)
+            random_embeds = torch.rand_like(inputs_embeds)
+            outputs_from_rand_embeds = model.generate(
+                input_ids,
+                inputs_embeds=random_embeds,
+                max_new_tokens=5,
+                return_dict_in_generate=True,
+                output_scores=True,
+            )
+            for i in range(len(outputs_from_rand_embeds.scores)):
+                self.assertFalse(torch.allclose(outputs_from_embeds.scores[i], outputs_from_rand_embeds.scores[i]))
+
     # We need to override as we need to prepare such that the image token is the last token
     def test_resize_tokens_embeddings(self):
         (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()