Fix Wav2Vec2 Fairseq conversion (weight norm state dict keys) (#31714)

* handle new weight norm

* fix

* fix trailing space

src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py

@@ -94,7 +94,16 @@ def set_recursively(key, value, full_name, weight_type, hf_pointer):
             hf_param_name = PARAM_MAPPING[full_name.split(".")[-1]]
             weight_type = "param"
 
+    # fairseq uses nn.utils.weight_norm() while transformers switches to nn.utils.parametrizations.weight_norm()
+    # the mapping between two versions:
+    # https://github.com/pytorch/pytorch/blob/56935684c3dfad7841c83c719eeebecb560fe466/torch/nn/utils/parametrizations.py#L389-L395
+
     if weight_type is not None and weight_type != "param":
+        if weight_type == "weight_g" and not hasattr(hf_pointer, "weight_g"):
+            hf_shape = hf_pointer.parametrizations.weight.original0.shape
+        elif weight_type == "weight_v" and not hasattr(hf_pointer, "weight_v"):
+            hf_shape = hf_pointer.parametrizations.weight.original1.shape
+        else:
             hf_shape = getattr(hf_pointer, weight_type).shape
     elif weight_type is not None and weight_type == "param":
         shape_pointer = hf_pointer
@@ -116,9 +125,15 @@ def set_recursively(key, value, full_name, weight_type, hf_pointer):
     if weight_type == "weight":
         hf_pointer.weight.data = value
     elif weight_type == "weight_g":
+        if hasattr(hf_pointer, "weight_g"):
             hf_pointer.weight_g.data = value
+        else:
+            hf_pointer.parametrizations.weight.original0.data = value
     elif weight_type == "weight_v":
+        if hasattr(hf_pointer, "weight_v"):
             hf_pointer.weight_v.data = value
+        else:
+            hf_pointer.parametrizations.weight.original1.data = value
     elif weight_type == "bias":
         hf_pointer.bias.data = value
     elif weight_type == "param":