Fix model integration ci

src/transformers/models/byt5/tokenization_byt5.py

@@ -104,7 +104,7 @@ def vocab_size(self):
         return self._utf_vocab_size
 
     def get_vocab(self):
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)}
         vocab.update(self.added_tokens_encoder)
         return vocab
 

src/transformers/models/persimmon/configuration_persimmon.py

@@ -21,7 +21,7 @@
 logger = logging.get_logger(__name__)
 
 PERSIMMON_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "ArthurZ/persimmon-8b-base": "https://huggingface.co/ArthurZ/persimmon-8b-base/resolve/main/config.json",
+    "adept/persimmon-8b-base": "https://huggingface.co/adept/persimmon-8b-base/resolve/main/config.json",
 }
 
 
@@ -30,7 +30,7 @@ class PersimmonConfig(PretrainedConfig):
     This is the configuration class to store the configuration of a [`PersimmonModel`]. It is used to instantiate an
     Persimmon model according to the specified arguments, defining the model architecture. Instantiating a
     configuration with the defaults will yield a similar configuration to that of the
-    [ArthurZ/persimmon-8b-base](https://huggingface.co/ArthurZ/persimmon-8b-base).
+    [adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base).
 
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.

src/transformers/models/persimmon/modeling_persimmon.py

@@ -789,8 +789,8 @@ def forward(
         ```python
         >>> from transformers import AutoTokenizer, PersimmonForCausalLM
 
-        >>> model = PersimmonForCausalLM.from_pretrained("ArthurZ/persimmon-8b-base")
-        >>> tokenizer = AutoTokenizer.from_pretrained("ArthurZ/persimmon-8b-base")
+        >>> model = PersimmonForCausalLM.from_pretrained("adept/persimmon-8b-base")
+        >>> tokenizer = AutoTokenizer.from_pretrained("adept/persimmon-8b-base")
 
         >>> prompt = "human: Hey, what should I eat for dinner?"
         >>> inputs = tokenizer(prompt, return_tensors="pt")

src/transformers/models/wav2vec2/tokenization_wav2vec2.py

@@ -232,9 +232,7 @@ def set_target_lang(self, target_lang: str):
 
         # make sure that tokens made of several
         # characters are not split at tokenization
-        for token in self.encoder.keys():
-            if len(token) > 1:
-                self.unique_no_split_tokens.append(token)
+        self.add_tokens([token for token in self.encoder.keys() if len(token) > 1])
 
     @property
     def word_delimiter_token(self) -> str:

src/transformers/tokenization_utils_base.py

@@ -2209,7 +2209,6 @@ def _from_pretrained(
                 " it is kept for forward compatibility, but it is recommended to update your `tokenizer_config.json` by uploading it again."
                 " You will see the new `added_tokens_decoder` attribute that will store the relevant information."
             )
-
             # begin legacy: read the added_tokens_file and update kwargs with special_tokens_map if modified
             if special_tokens_map_file is not None:
                 with open(special_tokens_map_file, encoding="utf-8") as special_tokens_map_handle:
@@ -2221,6 +2220,7 @@ def _from_pretrained(
                             continue
                         if isinstance(value, dict):
                             value = AddedToken(**value)
+                            init_kwargs[key] = value
                         elif key == "additional_special_tokens" and isinstance(value, list):
                             for token in value:
                                 token = AddedToken(**token) if isinstance(token, dict) else token
@@ -2233,8 +2233,9 @@ def _from_pretrained(
                 with open(added_tokens_file, encoding="utf-8") as added_tokens_handle:
                     added_tok_encoder = json.load(added_tokens_handle)
                 # legacy: we have to init with (rstrip=True, lstrip=True)
+                strip = True if "Fast" not in cls.__name__ else False
                 added_tokens_decoder = {
-                    index: AddedToken(token, rstrip=True, lstrip=True) for token, index in added_tok_encoder.items()
+                    index: AddedToken(token, rstrip=strip, lstrip=strip) for token, index in added_tok_encoder.items()
                 }
             # end legacy
 

tests/models/fnet/test_modeling_fnet.py

@@ -532,8 +532,6 @@ def test_inference_for_masked_lm(self):
     @slow
     @require_tokenizers
     def test_inference_long_sentence(self):
-        model = FNetForMaskedLM.from_pretrained("google/fnet-base")
-        model.to(torch_device)
         tokenizer = FNetTokenizerFast.from_pretrained("google/fnet-base")
 
         inputs = tokenizer(
@@ -543,8 +541,15 @@ def test_inference_long_sentence(self):
             padding="max_length",
             max_length=512,
         )
+
+        # fmt: off
+        torch.testing.assert_allclose(inputs["input_ids"], torch.tensor([[4, 13, 283, 2479, 106, 8, 6, 845, 5, 168, 65, 367, 6, 845, 5, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3]]))
+        # fmt: on
+
         inputs = {k: v.to(torch_device) for k, v in inputs.items()}
 
+        model = FNetForMaskedLM.from_pretrained("google/fnet-base")
+        model.to(torch_device)
         logits = model(**inputs).logits
         predictions_mask_1 = tokenizer.decode(logits[0, 6].topk(5).indices)
         predictions_mask_2 = tokenizer.decode(logits[0, 12].topk(5).indices)

tests/models/idefics/test_modeling_idefics.py

@@ -503,7 +503,11 @@ def test_retain_grad_hidden_states_attentions(self):
 class IdeficsModelIntegrationTest(TestCasePlus):
     @cached_property
     def default_processor(self):
-        return IdeficsProcessor.from_pretrained("HuggingFaceM4/idefics-9b") if is_vision_available() else None
+        return (
+            IdeficsProcessor.from_pretrained("HuggingFaceM4/idefics-9b", revision="refs/pr/11")
+            if is_vision_available()
+            else None
+        )
 
     @require_bitsandbytes
     @slow

tests/models/instructblip/test_modeling_instructblip.py

@@ -29,7 +29,14 @@
     InstructBlipQFormerConfig,
     InstructBlipVisionConfig,
 )
-from transformers.testing_utils import require_bitsandbytes, require_torch, require_vision, slow, torch_device
+from transformers.testing_utils import (
+    require_accelerate,
+    require_bitsandbytes,
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
 from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
@@ -522,6 +529,7 @@ def prepare_img():
 @slow
 class InstructBlipModelIntegrationTest(unittest.TestCase):
     @require_bitsandbytes
+    @require_accelerate
     def test_inference_vicuna_7b(self):
         processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
         model = InstructBlipForConditionalGeneration.from_pretrained(

tests/models/persimmon/test_modeling_persimmon.py

@@ -386,11 +386,13 @@ class PersimmonIntegrationTest(unittest.TestCase):
     @slow
     def test_model_8b_chat_logits(self):
         input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
-        model = PersimmonForCausalLM.from_pretrained("ArthurZ/persimmon-8b-chat", device_map="auto")
+        model = PersimmonForCausalLM.from_pretrained(
+            "adept/persimmon-8b-chat", device_map="auto", torch_dtype=torch.float16
+        )
         out = model(torch.tensor([input_ids])).logits
 
         EXPECTED_MEAN = torch.tensor(
-            [[-11.2879, -11.2628, -11.2498, -11.2534, -11.2676, -11.2638, -11.2501, -11.2431]], dtype=torch.float32
+            [[-11.2879, -11.2628, -11.2498, -11.2534, -11.2676, -11.2638, -11.2501, -11.2431]], dtype=torch.float16
         )
         torch.testing.assert_close(out.cpu().mean(-1), EXPECTED_MEAN, atol=1e-4, rtol=1e-4)
         # fmt: off
@@ -403,9 +405,11 @@ def test_model_8b_chat_logits(self):
     def test_model_8b_chat_greedy_generation(self):
         EXPECTED_TEXT_COMPLETION = """human: Simply put, the theory of relativity states that?\n\nadept: The theory of relativity states that the laws of physics are the same for all observers, regardless of their relative motion."""
         prompt = "human: Simply put, the theory of relativity states that?\n\nadept:"
-        tokenizer = AutoTokenizer.from_pretrained("ArthurZ/persimmon-8b-chat", use_fast=False)
+        tokenizer = AutoTokenizer.from_pretrained("adept/persimmon-8b-chat", use_fast=False)
         input_ids = tokenizer.encode(prompt, return_tensors="pt").to(torch_device)
-        model = PersimmonForCausalLM.from_pretrained("ArthurZ/persimmon-8b-chat").to(torch_device)
+        model = PersimmonForCausalLM.from_pretrained("adept/persimmon-8b-chat", torch_dtype=torch.float16).to(
+            torch_device
+        )
 
         # greedy generation outputs
         generated_ids = model.generate(input_ids, max_new_tokens=64)