Replace NumPy Operations with JAX NumPy Equivalents for JIT Compilation Compatibility
# What does this PR do?
This PR modifies the Transformers library to replace NumPy operations with their JAX NumPy equivalents. The main change is the use of JAX's immutable update methods as substitutes for in-place assignments.

Using NumPy methods instead of JAX NumPy results in an error during jit compilation (jit).

```
transformers/models/mbart/modeling_flax_mbart.py", line 226, in shift_tokens_right
    prev_output_tokens = np.array(input_ids).copy()
jax._src.errors.TracerArrayConversionError: The numpy.ndarray conversion method __array__() was called on the JAX Tracer object Traced<ShapedArray(int32[4,260])>with<DynamicJaxprTrace(level=0/1)>
```

Here's a brief summary of the changes:

previous:
```python
    prev_output_tokens = np.array(input_ids).copy()

     if pad_token_id is None:
         raise ValueError("self.model.config.pad_token_id has to be defined.")

     # replace possible -100 values in labels by `pad_token_id`
     prev_output_tokens = np.where(prev_output_tokens == -100, pad_token_id, input_ids)
     index_of_eos = (np.where(prev_output_tokens != pad_token_id, 1, 0).sum(axis=-1) - 1).reshape(-1, 1)
     decoder_start_tokens = np.array(
         [prev_output_tokens[i, eos_idx] for i, eos_idx in enumerate(index_of_eos)], dtype=np.int32
     ).squeeze()

     prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].copy()
     prev_output_tokens[:, 0] = decoder_start_tokens


```

modefied:
```python
     prev_output_tokens = jnp.array(input_ids).copy()

     if pad_token_id is None:
         raise ValueError("self.model.config.pad_token_id has to be defined.")

     # replace possible -100 values in labels by `pad_token_id`
     prev_output_tokens = jnp.where(prev_output_tokens == -100, pad_token_id, input_ids)
     index_of_eos = (jnp.where(prev_output_tokens != pad_token_id, 1, 0).sum(axis=-1) - 1).reshape(-1, 1)
     decoder_start_tokens = jnp.array(
         [prev_output_tokens[i, eos_idx] for i, eos_idx in enumerate(index_of_eos)], dtype=jnp.int32
     ).squeeze()

     prev_output_tokens = prev_output_tokens.at[:, 1:].set(prev_output_tokens[:, :-1])
     prev_output_tokens = prev_output_tokens.at[:, 0].set(decoder_start_tokens)
```

- @sanchit-gandhi





_The documentation is not available anymore as the PR was closed or merged._<|||||>done✅ https://github.com/huggingface/transformers/pull/23356/commits/175ab5dd0b8276f70b69ab21ddb4356aa353d611<|||||>@gojiteji To resolve the failing quality tests, you'll need to run `make fix-copies` and `make style` and push the changes. It seems the MT5 also doesn't have `jnp` defined in the modeling file. 