Add Fast Image Processors for mobileViT

[MinJu-Ha]

related to #36978
cc: @yonigozlan

I added Fast image processor for mobileViT and I noticed a noticeable difference between the outputs after preprocessing.

Here’s the code I used to compare them:

diff = (encoding_slow.pixel_values - encoding_fast.pixel_values).abs()
print(f"\n📊 Difference statistics:")
print(f"  Max difference: {diff.max().item():.10f}")
print(f"  Mean difference: {diff.mean().item():.10f}")
print(f"  Slow min/max: {encoding_slow.pixel_values.min().item():.10f} ~ {encoding_slow.pixel_values.max().item():.10f}")
print(f"  Fast min/max: {encoding_fast.pixel_values.min().item():.10f} ~ {encoding_fast.pixel_values.max().item():.10f}")
print(f"Slow implementation dtype: {encoding_slow.pixel_values.dtype}")
print(f"Fast implementation dtype: {encoding_fast.pixel_values.dtype}")

results:

📊 Difference statistics: 
  Max difference: 0.3411765397
  Mean difference: 0.1117687449
  Slow min/max: 0.0313725509 ~ 0.9764705896
  Fast min/max: 0.0313725509 ~ 0.9764706492
Slow implementation dtype: torch.float32
Fast implementation dtype: torch.float32

Even though the size configs look the same ({'shortest_edge': 20}), and both use torch.float32, the output difference seems quite significant for a slow/fast equivalence test.