Fix UnboundLocalError in invert_attention_mask by adding proper shape…

[gagandhakrey]

… validation

What does this PR do?

Problem The invert_attention_mask function in src/transformers/modeling_utils.py crashed with an UnboundLocalError when given an encoder_attention_mask shape that wasn't exactly 2D or 3D. Because it only checked for 2D and 3D shapes using separate if statements without any elif or else fallback, the encoder_extended_attention_mask variable would remain unassigned before attempting a .to() cast, causing models with 1D or 4D inputs to fail silently with confusing stack traces.

Fix Refactored the second conditional check to use an elif and added a final else branch. If the passed tensor is neither 2D nor 3D, it now explicitly raises a ValueError detailing the incorrect tensor shape, matching the behavior of the nearby get_extended_attention_mask function and preventing the UnboundLocalError entirely.

Fixes # (issue)

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