Add monkey patched fairseq package to run on python 3.11 (what is needed for our use of RVC at least)

modules/voice_conversion/fairseq/data/base_wrapper_dataset.py

@@ -0,0 +1,78 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+from torch.utils.data.dataloader import default_collate
+
+from . import FairseqDataset
+
+
+class BaseWrapperDataset(FairseqDataset):
+    def __init__(self, dataset):
+        super().__init__()
+        self.dataset = dataset
+
+    def __getitem__(self, index):
+        return self.dataset[index]
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def collater(self, samples):
+        if hasattr(self.dataset, "collater"):
+            return self.dataset.collater(samples)
+        else:
+            return default_collate(samples)
+
+    @property
+    def sizes(self):
+        return self.dataset.sizes
+
+    def num_tokens(self, index):
+        return self.dataset.num_tokens(index)
+
+    def size(self, index):
+        return self.dataset.size(index)
+
+    def ordered_indices(self):
+        return self.dataset.ordered_indices()
+
+    @property
+    def supports_prefetch(self):
+        return getattr(self.dataset, "supports_prefetch", False)
+
+    def attr(self, attr: str, index: int):
+        return self.dataset.attr(attr, index)
+
+    def prefetch(self, indices):
+        self.dataset.prefetch(indices)
+
+    def get_batch_shapes(self):
+        return self.dataset.get_batch_shapes()
+
+    def batch_by_size(
+        self,
+        indices,
+        max_tokens=None,
+        max_sentences=None,
+        required_batch_size_multiple=1,
+    ):
+        return self.dataset.batch_by_size(
+            indices,
+            max_tokens=max_tokens,
+            max_sentences=max_sentences,
+            required_batch_size_multiple=required_batch_size_multiple,
+        )
+
+    def filter_indices_by_size(self, indices, max_sizes):
+        return self.dataset.filter_indices_by_size(indices, max_sizes)
+
+    @property
+    def can_reuse_epoch_itr_across_epochs(self):
+        return self.dataset.can_reuse_epoch_itr_across_epochs
+
+    def set_epoch(self, epoch):
+        super().set_epoch(epoch)
+        if hasattr(self.dataset, "set_epoch"):
+            self.dataset.set_epoch(epoch)