Improve batch_decode() to handle single sequences robustly
Author: ron-42
Pull request: yes
Labels: name: Code agent slop

# What does this PR do?

This PR improves the `batch_decode()` method in `PreTrainedTokenizerBase` to robustly distinguish between single sequences and batches of sequences, preventing unexpected behavior when users pass flat lists of token IDs.

## Problem

The current implementation of `batch_decode()` does not distinguish between:
- A **single sequence**: `[101, 7592, 102]` (flat list of token IDs)
- A **batch of sequences**: `[[101, 7592], [102]]` (list of lists)

When users call `tokenizer.batch_decode([101, 7592, 102])`, the method iterates over each individual integer (`101`, `7592`, `102`) and attempts to decode them separately, causing incorrect results or errors.

**Example of the bug:**
```python
# Current buggy behavior
tokenizer.batch_decode([101, 7592, 102])
# Tries to decode: 101, then 7592, then 102 individually ❌

# Expected behavior  
tokenizer.batch_decode([101, 7592, 102])
# Should treat entire list as ONE sequence ✅
```

## Solution

This PR adds intelligent type detection logic that:

1. **Detects input dimensionality** for lists, numpy arrays, and torch tensors
   - 1D inputs (flat lists, 1D arrays/tensors) → treated as single sequence
   - 2D inputs (nested lists, 2D arrays/tensors) → treated as batch

2. **Automatically wraps single sequences** into a batch of size 1

3. **Handles edge cases gracefully**
   - Empty inputs (`[]`, `None`) return `[]`
   - 3D+ tensors/arrays raise clear `TypeError`
   - Invalid types (strings, dicts) raise descriptive errors

4. **Maintains 100% backward compatibility**
   - All existing code with properly batched inputs continues to work

## Changes Made

**Modified Files:**

1. **`src/transformers/tokenization_utils_base.py`**
   - Enhanced `batch_decode()` method (lines 3888-3937)
   - Added ~50 lines of robust type detection and error handling

2. **`tests/test_tokenization_common.py`**
   - Added 6 comprehensive test methods (lines 4673-4796)
   - Tests cover: single sequences, batches, numpy/torch tensors, empty inputs, invalid types

**Type Detection Logic:**

```python
# Check dimensionality for torch tensors
if is_torch_tensor(sequences):
    if sequences.dim() == 1:
        is_single_sequence = True

# Check dimensionality for numpy arrays
elif is_numpy_array(sequences):
    if sequences.ndim == 1:
        is_single_sequence = True

# Check element types for lists
elif isinstance(sequences, (list, tuple)):
    if isinstance(sequences[0], (int, np.integer)):
        is_single_sequence = True
```

## Test Coverage

Added 6 new test methods in `TokenizerTesterMixin`:

1. `test_batch_decode_single_sequence` - Verifies flat list handling
2. `test_batch_decode_nested_list` - Confirms batch processing
3. `test_batch_decode_torch_tensors` - Tests torch tensor support (1D & 2D)
4. `test_batch_decode_numpy_arrays` - Tests numpy array support (1D & 2D)
5. `test_batch_decode_empty_input` - Validates empty/None handling
6. `test_batch_decode_invalid_type` - Ensures proper error handling

## Examples

**Before and After:**

| Input | Before (Buggy) | After (Fixed) |
|-------|---------------|---------------|
| `[101, 7592, 102]` | Iterates over ints | Returns `["decoded text"]` ✅ |
| `[[101], [102]]` | Works correctly | Works correctly ✅ |
| `np.array([101, 102])` | Inconsistent | Returns `["decoded text"]` ✅ |
| `torch.tensor([101])` | Inconsistent | Returns `["decoded text"]` ✅ |
| `[]` | May error | Returns `[]` ✅ |
| `"invalid"` | May error | Clear `TypeError` ✅ |

## Benefits

- **Prevents Silent Bugs**: Single sequences now handled correctly
- **Consistent Behavior**: Works uniformly across all input types
- **Better Error Messages**: Clear TypeErrors for invalid inputs
- **Backward Compatible**: No breaking changes
- **Well Tested**: Comprehensive test coverage
- **Better UX**: More intuitive API

Fixes #41872

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?

## Who can review?

@ArthurZucker  @itazap  - tokenizers maintainers

This is a self-contained improvement to the `batch_decode()` method that adds robust type detection for single sequences vs batches, with comprehensive test coverage.


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