init

transformers/utils/test_module/custom_configuration.py

@@ -0,0 +1,9 @@
+from transformers import PretrainedConfig
+
+
+class CustomConfig(PretrainedConfig):
+    model_type = "custom"
+
+    def __init__(self, attribute=1, **kwargs):
+        self.attribute = attribute
+        super().__init__(**kwargs)

transformers/utils/test_module/custom_feature_extraction.py

@@ -0,0 +1,5 @@
+from transformers import Wav2Vec2FeatureExtractor
+
+
+class CustomFeatureExtractor(Wav2Vec2FeatureExtractor):
+    pass

transformers/utils/test_module/custom_image_processing.py

@@ -0,0 +1,5 @@
+from transformers import CLIPImageProcessor
+
+
+class CustomImageProcessor(CLIPImageProcessor):
+    pass

transformers/utils/test_module/custom_modeling.py

@@ -0,0 +1,19 @@
+import torch
+
+from transformers import PreTrainedModel
+
+from .custom_configuration import CustomConfig
+
+
+class CustomModel(PreTrainedModel):
+    config_class = CustomConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size)
+
+    def forward(self, x):
+        return self.linear(x)
+
+    def _init_weights(self, module):
+        pass

transformers/utils/test_module/custom_pipeline.py

@@ -0,0 +1,33 @@
+import numpy as np
+
+from transformers import Pipeline
+
+
+def softmax(outputs):
+    maxes = np.max(outputs, axis=-1, keepdims=True)
+    shifted_exp = np.exp(outputs - maxes)
+    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
+
+
+class PairClassificationPipeline(Pipeline):
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_kwargs = {}
+        if "second_text" in kwargs:
+            preprocess_kwargs["second_text"] = kwargs["second_text"]
+        return preprocess_kwargs, {}, {}
+
+    def preprocess(self, text, second_text=None):
+        return self.tokenizer(text, text_pair=second_text, return_tensors="pt")
+
+    def _forward(self, model_inputs):
+        return self.model(**model_inputs)
+
+    def postprocess(self, model_outputs):
+        logits = model_outputs.logits[0].numpy()
+        probabilities = softmax(logits)
+
+        best_class = np.argmax(probabilities)
+        label = self.model.config.id2label[best_class]
+        score = probabilities[best_class].item()
+        logits = logits.tolist()
+        return {"label": label, "score": score, "logits": logits}

transformers/utils/test_module/custom_processing.py

@@ -0,0 +1,6 @@
+from transformers import ProcessorMixin
+
+
+class CustomProcessor(ProcessorMixin):
+    feature_extractor_class = "AutoFeatureExtractor"
+    tokenizer_class = "AutoTokenizer"

transformers/utils/test_module/custom_tokenization.py

@@ -0,0 +1,5 @@
+from transformers import BertTokenizer
+
+
+class CustomTokenizer(BertTokenizer):
+    pass

transformers/utils/test_module/custom_tokenization_fast.py

@@ -0,0 +1,8 @@
+from transformers import BertTokenizerFast
+
+from .custom_tokenization import CustomTokenizer
+
+
+class CustomTokenizerFast(BertTokenizerFast):
+    slow_tokenizer_class = CustomTokenizer
+    pass

transformers/utils/test_module/custom_video_processing.py

@@ -0,0 +1,5 @@
+from transformers import LlavaOnevisionVideoProcessor
+
+
+class CustomVideoProcessor(LlavaOnevisionVideoProcessor):
+    pass