Update bros checkpoint (#26277)

* fix bros integration test

* update bros checkpoint

src/transformers/models/bros/configuration_bros.py

@@ -21,8 +21,8 @@ from ...utils import logging
 logger = logging.get_logger(__name__)
 
 BROS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "naver-clova-ocr/bros-base-uncased": "https://huggingface.co/naver-clova-ocr/bros-base-uncased/resolve/main/config.json",
-    "naver-clova-ocr/bros-large-uncased": "https://huggingface.co/naver-clova-ocr/bros-large-uncased/resolve/main/config.json",
+    "jinho8345/bros-base-uncased": "https://huggingface.co/jinho8345/bros-base-uncased/blob/main/config.json",
+    "jinho8345/bros-large-uncased": "https://huggingface.co/jinho8345/bros-large-uncased/blob/main/config.json",
 }
 
 
@@ -31,7 +31,7 @@ class BrosConfig(PretrainedConfig):
     This is the configuration class to store the configuration of a [`BrosModel`] or a [`TFBrosModel`]. It is used to
     instantiate a Bros 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 Bros
-    [naver-clova-ocr/bros-base-uncased](https://huggingface.co/naver-clova-ocr/bros-base-uncased) architecture.
+    [jinho8345/bros-base-uncased](https://huggingface.co/jinho8345/bros-base-uncased) architecture.
 
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
@@ -81,10 +81,10 @@ class BrosConfig(PretrainedConfig):
     ```python
     >>> from transformers import BrosConfig, BrosModel
 
-    >>> # Initializing a BROS naver-clova-ocr/bros-base-uncased style configuration
+    >>> # Initializing a BROS jinho8345/bros-base-uncased style configuration
     >>> configuration = BrosConfig()
 
-    >>> # Initializing a model from the naver-clova-ocr/bros-base-uncased style configuration
+    >>> # Initializing a model from the jinho8345/bros-base-uncased style configuration
     >>> model = BrosModel(configuration)
 
     >>> # Accessing the model configuration

src/transformers/models/bros/convert_bros_to_pytorch.py

@@ -123,7 +123,7 @@ if __name__ == "__main__":
     # Required parameters
     parser.add_argument(
         "--model_name",
-        default="naver-clova-ocr/bros-base-uncased",
+        default="jinho8345/bros-base-uncased",
         required=False,
         type=str,
         help="Name of the original model you'd like to convert.",

src/transformers/models/bros/modeling_bros.py

@@ -44,12 +44,12 @@ from .configuration_bros import BrosConfig
 
 logger = logging.get_logger(__name__)
 
-_CHECKPOINT_FOR_DOC = "naver-clova-ocr/bros-base-uncased"
+_CHECKPOINT_FOR_DOC = "jinho8345/bros-base-uncased"
 _CONFIG_FOR_DOC = "BrosConfig"
 
 BROS_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "naver-clova-ocr/bros-base-uncased",
-    "naver-clova-ocr/bros-large-uncased",
+    "jinho8345/bros-base-uncased",
+    "jinho8345/bros-large-uncased",
     # See all Bros models at https://huggingface.co/models?filter=bros
 ]
 
@@ -846,9 +846,9 @@ class BrosModel(BrosPreTrainedModel):
         >>> import torch
         >>> from transformers import BrosProcessor, BrosModel
 
-        >>> processor = BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased")
+        >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
 
-        >>> model = BrosModel.from_pretrained("naver-clova-ocr/bros-base-uncased")
+        >>> model = BrosModel.from_pretrained("jinho8345/bros-base-uncased")
 
         >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
         >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
@@ -1011,9 +1011,9 @@ class BrosForTokenClassification(BrosPreTrainedModel):
         >>> import torch
         >>> from transformers import BrosProcessor, BrosForTokenClassification
 
-        >>> processor = BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased")
+        >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
 
-        >>> model = BrosForTokenClassification.from_pretrained("naver-clova-ocr/bros-base-uncased")
+        >>> model = BrosForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
 
         >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
         >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
@@ -1130,9 +1130,9 @@ class BrosSpadeEEForTokenClassification(BrosPreTrainedModel):
         >>> import torch
         >>> from transformers import BrosProcessor, BrosSpadeEEForTokenClassification
 
-        >>> processor = BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased")
+        >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
 
-        >>> model = BrosSpadeEEForTokenClassification.from_pretrained("naver-clova-ocr/bros-base-uncased")
+        >>> model = BrosSpadeEEForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
 
         >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
         >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
@@ -1261,9 +1261,9 @@ class BrosSpadeELForTokenClassification(BrosPreTrainedModel):
         >>> import torch
         >>> from transformers import BrosProcessor, BrosSpadeELForTokenClassification
 
-        >>> processor = BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased")
+        >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
 
-        >>> model = BrosSpadeELForTokenClassification.from_pretrained("naver-clova-ocr/bros-base-uncased")
+        >>> model = BrosSpadeELForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
 
         >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
         >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)

tests/models/bros/test_modeling_bros.py

@@ -17,9 +17,8 @@
 import copy
 import unittest
 
-from transformers import BrosProcessor
 from transformers.testing_utils import require_torch, slow, torch_device
-from transformers.utils import cached_property, is_torch_available, is_vision_available
+from transformers.utils import is_torch_available
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
@@ -412,13 +411,10 @@ def prepare_bros_batch_inputs():
 
 @require_torch
 class BrosModelIntegrationTest(unittest.TestCase):
-    @cached_property
-    def default_processor(self):
-        return BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased") if is_vision_available() else None
-
     @slow
     def test_inference_no_head(self):
-        model = BrosModel.from_pretrained("naver-clova-ocr/bros-base-uncased").to(torch_device)
+        model = BrosModel.from_pretrained("jinho8345/bros-base-uncased").to(torch_device)
+
         input_ids, bbox, attention_mask = prepare_bros_batch_inputs()
 
         with torch.no_grad():
@@ -434,7 +430,8 @@ class BrosModelIntegrationTest(unittest.TestCase):
         self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
 
         expected_slice = torch.tensor(
-            [[-0.4027, 0.0756, -0.0647], [-0.0192, -0.0065, 0.1042], [-0.0671, 0.0214, 0.0960]]
+            [[-0.3074, 0.1363, 0.3143], [0.0925, -0.1155, 0.1050], [0.0221, 0.0003, 0.1285]]
         ).to(torch_device)
+        torch.set_printoptions(sci_mode=False)
 
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))