chore: import upstream snapshot with attribution

utils/test_module/custom_configuration.py

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+from transformers import PreTrainedConfig
+
+
+class CustomConfig(PreTrainedConfig):
+    model_type = "custom"
+
+    def __init__(self, attribute=1, **kwargs):
+        self.attribute = attribute
+        super().__init__(**kwargs)

utils/test_module/custom_feature_extraction.py

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+from transformers import Wav2Vec2FeatureExtractor
+
+
+class CustomFeatureExtractor(Wav2Vec2FeatureExtractor):
+    pass

utils/test_module/custom_image_processing.py

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+from transformers import CLIPImageProcessor
+
+
+class CustomImageProcessor(CLIPImageProcessor):
+    pass

utils/test_module/custom_modeling.py

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+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)
+        self.post_init()
+
+    def forward(self, x):
+        return self.linear(x)
+
+    def _init_weights(self, module):
+        pass

utils/test_module/custom_pipeline.py

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+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}

utils/test_module/custom_processing.py

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+from transformers import ProcessorMixin
+
+
+class CustomProcessor(ProcessorMixin):
+    def __init__(self, feature_extractor, tokenizer):
+        super().__init__(feature_extractor, tokenizer)

utils/test_module/custom_tokenization.py

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+from transformers import BertTokenizer
+
+
+class CustomTokenizer(BertTokenizer):
+    pass

utils/test_module/custom_tokenization_fast.py

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+from transformers import BertTokenizerFast
+
+from .custom_tokenization import CustomTokenizer
+
+
+class CustomTokenizerFast(BertTokenizerFast):
+    slow_tokenizer_class = CustomTokenizer
+    _auto_map = {
+        "AutoTokenizer": ("custom_tokenization.CustomTokenizer", "custom_tokenization_fast.CustomTokenizerFast")
+    }

utils/test_module/custom_video_processing.py

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+from transformers import LlavaOnevisionVideoProcessor
+
+
+class CustomVideoProcessor(LlavaOnevisionVideoProcessor):
+    pass