wip textual inversion manager (unit tests passing for base stuff + padding)

ldm/modules/embedding_manager.py

@@ -15,23 +15,9 @@ from picklescan.scanner import scan_file_path
 PROGRESSIVE_SCALE = 2000
 
 
-def get_clip_token_id_for_string(tokenizer, string):
-    batch_encoding = tokenizer(
-        string,
-        truncation=True,
-        max_length=77,
-        return_length=True,
-        return_overflowing_tokens=False,
-        padding='max_length',
-        return_tensors='pt',
-    )
-    tokens = batch_encoding['input_ids']
-    assert (
-        torch.count_nonzero(tokens - 49407) == 2
-    ), f"String '{string}' maps to more than a single token. Please use another string"
-
-    return tokens[0, 1]
-
+def get_clip_token_id_for_string(tokenizer: CLIPTokenizer, token_str: str):
+    token_id = tokenizer.convert_tokens_to_ids(token_str)
+    return token_id
 
 def get_bert_token_for_string(tokenizer, string):
     token = tokenizer(string)
@@ -47,7 +33,7 @@ def get_embedding_for_clip_token(embedder, token):
 
 @dataclass
 class TextualInversion:
-    token_string: str
+    trigger_string: str
     token_id: int
     embedding: torch.Tensor
 
@@ -58,8 +44,8 @@ class TextualInversion:
 class TextualInversionManager():
     def __init__(self, clip_embedder):
         self.clip_embedder = clip_embedder
-        defatul_textual_inversions: list[TextualInversion] = []
-        self.textual_inversions = defatul_textual_inversions
+        default_textual_inversions: list[TextualInversion] = []
+        self.textual_inversions = default_textual_inversions
 
     def load_textual_inversion(self, ckpt_path, full_precision=True):
 
@@ -89,38 +75,50 @@ class TextualInversionManager():
                 embedding = ckpt[token_str]
                 self.add_textual_inversion(token_str, embedding, full_precision)
 
-    def add_textual_inversion(self, token_str, embedding):
+    def add_textual_inversion(self, token_str, embedding) -> int:
         """
         Add a textual inversion to be recognised.
-        :param token_str: The trigger text in the prompt that activates this textual inversion. Should be unknown to the embedder's tokenizer.
+        :param token_str: The trigger text in the prompt that activates this textual inversion. If unknown to the embedder's tokenizer, will be added.
         :param embedding: The actual embedding data that will be inserted into the conditioning at the point where the token_str appears.
-        :return: The token id of the added embedding.
+        :return: The token id for the added embedding, either existing or newly-added.
         """
-        if token_str in [ti.token_string for ti in self.textual_inversions]:
-            print(f">> Embedding manager refusing to overwrite already-loaded term '{token_str}'")
+        if token_str in [ti.trigger_string for ti in self.textual_inversions]:
+            print(f">> TextualInversionManager refusing to overwrite already-loaded token '{token_str}'")
             return
         if len(embedding.shape) == 1:
             embedding = embedding.unsqueeze(0)
         elif len(embedding.shape) > 2:
             raise ValueError(f"embedding shape {embedding.shape} is incorrect - must have shape [token_dim] or [V, token_dim] where V is vector length and token_dim is 768 for SD1 or 1280 for SD2")
 
-        num_tokens_added = self.clip_embedder.tokenizer.add_tokens(token_str)
-        current_embeddings = self.clip_embedder.transformer.resize_token_embeddings(None)
-        current_token_count = current_embeddings.num_embeddings
-        new_token_count = current_token_count + num_tokens_added
-        self.clip_embedder.transformer.resize_token_embeddings(new_token_count)
+        existing_token_id = get_clip_token_id_for_string(self.clip_embedder.tokenizer, token_str)
+        if existing_token_id == self.clip_embedder.tokenizer.unk_token_id:
+            num_tokens_added = self.clip_embedder.tokenizer.add_tokens(token_str)
+            current_embeddings = self.clip_embedder.transformer.resize_token_embeddings(None)
+            current_token_count = current_embeddings.num_embeddings
+            new_token_count = current_token_count + num_tokens_added
+            self.clip_embedder.transformer.resize_token_embeddings(new_token_count)
 
         token_id = get_clip_token_id_for_string(self.clip_embedder.tokenizer, token_str)
         self.textual_inversions.append(TextualInversion(
-            token_string=token_str,
+            trigger_string=token_str,
             token_id=token_id,
             embedding=embedding
         ))
-
         return token_id
 
-    def has_textual_inversion(self, token_str):
-        return token_str in [ti.token_string for ti in self.textual_inversions]
+    def has_textual_inversion_for_trigger_string(self, trigger_string: str) -> bool:
+        try:
+            ti = self.get_textual_inversion_for_trigger_string(trigger_string)
+            return ti is not None
+        except StopIteration:
+            return False
+
+    def get_textual_inversion_for_trigger_string(self, trigger_string: str) -> TextualInversion:
+        return next(ti for ti in self.textual_inversions if ti.trigger_string == trigger_string)
+
+
+    def get_textual_inversion_for_token_id(self, token_id: int) -> TextualInversion:
+        return next(ti for ti in self.textual_inversions if ti.token_id == token_id)
 
     def expand_textual_inversion_token_ids(self, prompt_token_ids: list[int]) -> list[int]:
         """
@@ -131,14 +129,17 @@ class TextualInversionManager():
         :return: The prompt token ids with any necessary padding to account for textual inversions inserted. May be too
                 long - caller is reponsible for truncating it if necessary and prepending/appending eos and bos token ids.
         """
-        assert(prompt_token_ids[0] != self.clip_embedder.bos_token_id)
-        assert(prompt_token_ids[-1] != self.clip_embedder.eos_token_id)
+        if prompt_token_ids[0] == self.clip_embedder.tokenizer.bos_token_id:
+            raise ValueError("prompt_token_ids must not start with bos_token_id")
+        if prompt_token_ids[-1] == self.clip_embedder.tokenizer.eos_token_id:
+            raise ValueError("prompt_token_ids must not end with eos_token_id")
         textual_inversion_token_ids = [ti.token_id for ti in self.textual_inversions]
+        prompt_token_ids = prompt_token_ids[:]
         for i, token_id in reversed(list(enumerate(prompt_token_ids))):
             if token_id in textual_inversion_token_ids:
                 textual_inversion = next(ti for ti in self.textual_inversions if ti.token_id == token_id)
                 for pad_idx in range(1, textual_inversion.embedding_vector_length):
-                    prompt_token_ids.insert(i+1, self.clip_embedder.pad_token_id)
+                    prompt_token_ids.insert(i+1, self.clip_embedder.tokenizer.pad_token_id)
 
         return prompt_token_ids
 
@@ -154,8 +155,9 @@ class TextualInversionManager():
             `prompt_token_ids` (i.e., also already expanded).
         :return: `The prompt_embeddings` tensor overwritten as appropriate with the textual inversion embeddings.
         """
-        assert(prompt_embeddings.shape[0] == self.clip_embedder.max_length, f"prompt_embeddings must have 77 entries (has: {prompt_embeddings.shape[0]})")
+        assert prompt_embeddings.shape[0] == self.clip_embedder.max_length, f"prompt_embeddings must have 77 entries (has: {prompt_embeddings.shape[0]})"
         textual_inversion_token_ids = [ti.token_id for ti in self.textual_inversions]
+        pad_token_id = self.clip_embedder.pad_token_id
         for i, token_id in enumerate(prompt_token_ids):
             if token_id == pad_token_id:
                 continue
@@ -163,7 +165,7 @@ class TextualInversionManager():
                 textual_inversion = next(ti for ti in self.textual_inversions if ti.token_id == token_id)
                 for j in range(0, textual_inversion.embedding_vector_length):
                     # only overwrite the textual inversion token id or the padding token id
-                    if prompt_token_ids[i+j] != self.clip_embedder.pad_token_id and prompt_token_ids[i+j] != token_id:
+                    if prompt_token_ids[i+j] != pad_token_id and prompt_token_ids[i+j] != token_id:
                         break
                     prompt_embeddings[i+j] = textual_inversion.embedding[j]
 
@@ -206,7 +208,7 @@ class EmbeddingManager(nn.Module):
             embedder, 'tokenizer'
         ):   # using Stable Diffusion's CLIP encoder
             self.is_clip = True
-            get_token_for_string = partial(
+            get_token_id_for_string = partial(
                 get_clip_token_id_for_string, embedder.tokenizer
             )
             get_embedding_for_tkn = partial(
@@ -218,7 +220,7 @@ class EmbeddingManager(nn.Module):
             token_dim = 768
         else:   # using LDM's BERT encoder
             self.is_clip = False
-            get_token_for_string = partial(
+            get_token_id_for_string = partial(
                 get_bert_token_for_string, embedder.tknz_fn
             )
             get_embedding_for_tkn = embedder.transformer.token_emb
@@ -229,14 +231,14 @@ class EmbeddingManager(nn.Module):
 
         for idx, placeholder_string in enumerate(placeholder_strings):
 
-            token = get_token_for_string(placeholder_string)
+            token_id = get_token_id_for_string(placeholder_string)
 
             if initializer_words and idx < len(initializer_words):
-                init_word_token = get_token_for_string(initializer_words[idx])
+                init_word_token_id = get_token_id_for_string(initializer_words[idx])
 
                 with torch.no_grad():
                     init_word_embedding = get_embedding_for_tkn(
-                        init_word_token.cpu()
+                        init_word_token_id.cpu()
                     )
 
                 token_params = torch.nn.Parameter(
@@ -261,7 +263,7 @@ class EmbeddingManager(nn.Module):
                     )
                 )
 
-            self.string_to_token_dict[placeholder_string] = token
+            self.string_to_token_dict[placeholder_string] = token_id
             self.string_to_param_dict[placeholder_string] = token_params
 
     def forward(
@@ -384,6 +386,57 @@ class EmbeddingManager(nn.Module):
                         expanded_paths.append(os.path.join(root,name))
         return [x for x in expanded_paths if os.path.splitext(x)[1] in ('.pt','.bin')]
 
+    def _load(self, ckpt_path, full=True):
+
+        scan_result = scan_file_path(ckpt_path)
+        if scan_result.infected_files == 1:
+            print(f'\n### Security Issues Found in Model: {scan_result.issues_count}')
+            print('### For your safety, InvokeAI will not load this embed.')
+            return
+
+        ckpt = torch.load(ckpt_path, map_location='cpu')
+
+        # Handle .pt textual inversion files
+        if 'string_to_token' in ckpt and 'string_to_param' in ckpt:
+            filename = os.path.basename(ckpt_path)
+            token_str = '.'.join(filename.split('.')[:-1]) # filename excluding extension
+            if len(ckpt["string_to_token"]) > 1:
+                print(f">> {ckpt_path} has >1 embedding, only the first will be used")
+
+            string_to_param_dict = ckpt['string_to_param']
+            embedding = list(string_to_param_dict.values())[0]
+            self.add_embedding(token_str, embedding, full)
+
+        # Handle .bin textual inversion files from Huggingface Concepts
+        # https://huggingface.co/sd-concepts-library
+        else:
+            for token_str in list(ckpt.keys()):
+                embedding = ckpt[token_str]
+                self.add_embedding(token_str, embedding, full)
+
+    def add_embedding(self, token_str, embedding, full):
+        if token_str in self.string_to_param_dict:
+            print(f">> Embedding manager refusing to overwrite already-loaded term '{token_str}'")
+            return
+        if not full:
+            embedding = embedding.half()
+        if len(embedding.shape) == 1:
+            embedding = embedding.unsqueeze(0)
+
+        existing_token_id = get_clip_token_id_for_string(self.embedder.tokenizer, token_str)
+        if existing_token_id == self.embedder.tokenizer.unk_token_id:
+            num_tokens_added = self.embedder.tokenizer.add_tokens(token_str)
+            current_embeddings = self.embedder.transformer.resize_token_embeddings(None)
+            current_token_count = current_embeddings.num_embeddings
+            new_token_count = current_token_count + num_tokens_added
+            self.embedder.transformer.resize_token_embeddings(new_token_count)
+
+        token_id = get_clip_token_id_for_string(self.embedder.tokenizer, token_str)
+        self.string_to_token_dict[token_str] = token_id
+        self.string_to_param_dict[token_str] = torch.nn.Parameter(embedding)
+
+    def has_embedding_for_token(self, token_str):
+        return token_str in self.string_to_token_dict
 
     def get_embedding_norms_squared(self):
         all_params = torch.cat(

tests/test_textual_inversion.py

@@ -0,0 +1,205 @@
+
+import unittest
+
+import torch
+
+from ldm.modules.embedding_manager import TextualInversionManager
+
+
+KNOWN_WORDS = ['a', 'b', 'c']
+UNKNOWN_WORDS = ['d', 'e', 'f']
+
+class DummyEmbeddingsList(list):
+    def __getattr__(self, name):
+        if name == 'num_embeddings':
+            return len(self)
+
+class DummyTransformer:
+    def __init__(self):
+        self.embeddings = DummyEmbeddingsList([0] * len(KNOWN_WORDS))
+
+    def resize_token_embeddings(self, new_size=None):
+        if new_size is None:
+            return self.embeddings
+        else:
+            while len(self.embeddings) > new_size:
+                self.embeddings.pop(-1)
+            while len(self.embeddings) < new_size:
+                self.embeddings.append(0)
+
+
+class DummyTokenizer():
+    def __init__(self):
+        self.tokens = KNOWN_WORDS.copy()
+        self.bos_token_id = 49406
+        self.eos_token_id = 49407
+        self.pad_token_id = 49407
+        self.unk_token_id = 49407
+
+    def convert_tokens_to_ids(self, token_str):
+        try:
+            return self.tokens.index(token_str)
+        except ValueError:
+            return self.unk_token_id
+
+    def add_tokens(self, token_str):
+        self.tokens.append(token_str)
+        return 1
+
+
+class DummyClipEmbedder:
+    def __init__(self):
+        self.max_length = 77
+        self.transformer = DummyTransformer()
+        self.tokenizer = DummyTokenizer()
+
+
+class TextualInversionManagerTestCase(unittest.TestCase):
+
+
+    def test_construction(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+
+    def test_add_embedding_for_known_token(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        test_embedding = torch.randn([1, 768])
+        test_embedding_name = KNOWN_WORDS[0]
+        self.assertFalse(tim.has_textual_inversion_for_trigger_string(test_embedding_name))
+
+        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+
+        token_id = tim.add_textual_inversion(test_embedding_name, test_embedding)
+        self.assertEqual(token_id, 0)
+
+
+        # check adding 'test' did not create a new word
+        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        self.assertEqual(pre_embeddings_count, embeddings_count)
+
+        # check it was added
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name))
+        textual_inversion = tim.get_textual_inversion_for_trigger_string(test_embedding_name)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name)
+        self.assertEqual(textual_inversion.token_id, token_id)
+
+    def test_add_embedding_for_unknown_token(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        test_embedding_1 = torch.randn([1, 768])
+        test_embedding_name_1 = UNKNOWN_WORDS[0]
+
+        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+
+        added_token_id_1 = tim.add_textual_inversion(test_embedding_name_1, test_embedding_1)
+        # new token id should get added on the end
+        self.assertEqual(added_token_id_1, len(KNOWN_WORDS))
+
+        # check adding did create a new word
+        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        self.assertEqual(pre_embeddings_count+1, embeddings_count)
+
+        # check it was added
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name_1))
+        textual_inversion = next(ti for ti in tim.textual_inversions if ti.token_id == added_token_id_1)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding_1))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name_1)
+        self.assertEqual(textual_inversion.token_id, added_token_id_1)
+
+        # add another one
+        test_embedding_2 = torch.randn([1, 768])
+        test_embedding_name_2 = UNKNOWN_WORDS[1]
+
+        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+
+        added_token_id_2 = tim.add_textual_inversion(test_embedding_name_2, test_embedding_2)
+        self.assertEqual(added_token_id_2, len(KNOWN_WORDS)+1)
+
+        # check adding did create a new word
+        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        self.assertEqual(pre_embeddings_count+1, embeddings_count)
+
+        # check it was added
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name_2))
+        textual_inversion = next(ti for ti in tim.textual_inversions if ti.token_id == added_token_id_2)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding_2))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name_2)
+        self.assertEqual(textual_inversion.token_id, added_token_id_2)
+
+        # check the old one is still there
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name_1))
+        textual_inversion = next(ti for ti in tim.textual_inversions if ti.token_id == added_token_id_1)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding_1))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name_1)
+        self.assertEqual(textual_inversion.token_id, added_token_id_1)
+
+
+    def test_pad_raises_on_eos_bos(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        prompt_token_ids_with_eos_bos = [tim.clip_embedder.tokenizer.bos_token_id,
+                                         0, 1, 2,
+                                         tim.clip_embedder.tokenizer.eos_token_id]
+        with self.assertRaises(ValueError):
+            expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_with_eos_bos)
+
+    def test_pad_tokens_list_vector_length_1(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        prompt_token_ids = [0, 1, 2]
+
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
+        self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
+
+        test_embedding_1v = torch.randn([1, 768])
+        test_embedding_1v_token = "<inversion-trigger-vector-length-1>"
+        test_embedding_1v_token_id = tim.add_textual_inversion(test_embedding_1v_token, test_embedding_1v)
+        self.assertEqual(test_embedding_1v_token_id, len(KNOWN_WORDS))
+
+        # at the end
+        prompt_token_ids_1v_append = prompt_token_ids + [test_embedding_1v_token_id]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_append)
+        self.assertEqual(prompt_token_ids_1v_append, expanded_prompt_token_ids)
+
+        # at the start
+        prompt_token_ids_1v_prepend = [test_embedding_1v_token_id] + prompt_token_ids
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_prepend)
+        self.assertEqual(prompt_token_ids_1v_prepend, expanded_prompt_token_ids)
+
+        # in the middle
+        prompt_token_ids_1v_insert = prompt_token_ids[0:2] + [test_embedding_1v_token_id] + prompt_token_ids[2:3]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_insert)
+        self.assertEqual(prompt_token_ids_1v_insert, expanded_prompt_token_ids)
+
+    def test_pad_tokens_list_vector_length_2(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        prompt_token_ids = [0, 1, 2]
+
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
+        self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
+
+        test_embedding_2v = torch.randn([2, 768])
+        test_embedding_2v_token = "<inversion-trigger-vector-length-2>"
+        test_embedding_2v_token_id = tim.add_textual_inversion(test_embedding_2v_token, test_embedding_2v)
+        self.assertEqual(test_embedding_2v_token_id, len(KNOWN_WORDS))
+
+        # at the end
+        prompt_token_ids_2v_append = prompt_token_ids + [test_embedding_2v_token_id]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_append)
+        self.assertNotEqual(prompt_token_ids_2v_append, expanded_prompt_token_ids)
+        self.assertEqual(prompt_token_ids + [test_embedding_2v_token_id, tim.clip_embedder.tokenizer.pad_token_id], expanded_prompt_token_ids)
+
+        # at the start
+        prompt_token_ids_2v_prepend = [test_embedding_2v_token_id] + prompt_token_ids
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_prepend)
+        self.assertNotEqual(prompt_token_ids_2v_prepend, expanded_prompt_token_ids)
+        self.assertEqual([test_embedding_2v_token_id, tim.clip_embedder.tokenizer.pad_token_id] + prompt_token_ids, expanded_prompt_token_ids)
+
+        # in the middle
+        prompt_token_ids_2v_insert = prompt_token_ids[0:2] + [test_embedding_2v_token_id] + prompt_token_ids[2:3]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_insert)
+        self.assertNotEqual(prompt_token_ids_2v_insert, expanded_prompt_token_ids)
+        self.assertEqual(prompt_token_ids[0:2] + [test_embedding_2v_token_id, tim.clip_embedder.tokenizer.pad_token_id] + prompt_token_ids[2:3], expanded_prompt_token_ids)
+
+

tests/text_textual_inversion.py

@@ -1,43 +0,0 @@
-
-import unittest
-
-import torch
-
-from ldm.modules.embedding_manager import TextualInversionManager
-
-
-class DummyClipEmbedder:
-    max_length = 77
-    bos_token_id = 49406
-    eos_token_id = 49407
-
-class TextualInversionManagerTestCase(unittest.TestCase):
-
-
-    def test_construction(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
-
-    def test_add_embedding(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
-        test_embedding = torch.random([1, 768])
-        test_embedding_name = "test"
-        token_id = tim.add_textual_inversion(test_embedding_name, test_embedding)
-        self.assertTrue(tim.has_textual_inversion(test_embedding_name))
-
-        textual_inversion = next(ti for ti in tim.textual_inversions if ti.token_id == token_id)
-        self.assertIsNotNone(textual_inversion)
-        self.assertEqual(textual_inversion.embedding, test_embedding)
-        self.assertEqual(textual_inversion.token_string, test_embedding_name)
-        self.assertEqual(textual_inversion.token_id, token_id)
-
-    def test_pad_tokens_list(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
-        prompt_token_ids = [DummyClipEmbedder.bos_token_id, 0, 1, 2, DummyClipEmbedder.eos_token_id]
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
-        self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
-
-        test_embedding = torch.random([1, 768])
-        test_embedding_name = "test"
-        tim.add_textual_inversion("<token>",
-
-        self.assertRaises()