FR: Implement Downstream Integration Smoke Tests in CI/CD to Prevent Ecosystem Regression
Author: briancullinan2
Pull request: no
Labels: name: Feature request

### Feature request


​We request the addition of Downstream Integration Smoke Tests within the transformers CI/CD automation pipeline (GitHub Actions/CircleCI). Before a new release is cut, the test matrix should pull down and execute basic smoke generation scripts against a select group of foundational, high-impact downstream structural decoding packages (e.g., outlines, transformers_cfg, lmformatenforcer).
​Additionally, if a private or internal API handling token/byte structures must be removed or moved to a compiled backend, we request a formalized deprecation warning cycle alongside a stable, public utility endpoint to retrieve bidirectional token-to-byte maps.

### Motivation


Whenever a minor or patch version refactors internal layout paths, the upstream test suite passes completely green because it only evaluates native tasks. However, the wider ecosystem immediately breaks.
Recent internal shifts—such as moving PreTrainedTokenizerBase out of tokenization_utils or completely wiping python-level attributes like bytes_to_unicode, .byte_encoder, and .byte_decoder—instantly broke multiple context-free grammar parsers and guided decoding engines.
While these are technically internal namespaces, deep engineering tools must hook into token-to-byte maps to build logit filtering matrices. Deleting or relocating them without a stable public alternative or a deprecation warning forces end-users into version lock or requires maintaining fragile runtime monkey patches to keep enterprise applications functional on newer releases.

### Your contribution

To prevent production applications from experiencing immediate ImportError or AttributeError failures on the latest versions while a permanent architectural solution is developed, the community is forced to rely on the following runtime patch. This can be integrated or used as a reference for the missing backward-compatible hooks:


```python
import sys
import transformers

# 1. Bridge lmformatenforcer path changes
if not hasattr(transformers.tokenization_utils, 'PreTrainedTokenizerBase'):
    import transformers.tokenization_utils_base
    transformers.tokenization_utils.PreTrainedTokenizerBase = transformers.tokenization_utils_base.PreTrainedTokenizerBase

# 2. Bridge transformers_cfg logging namespace relocations
if not hasattr(transformers, 'logging'):
    import transformers.utils.logging
    transformers.logging = transformers.utils.logging

# 3. Independent reconstruction of the removed GPT2 byte-mapping attributes
def get_clean_byte_maps():
    bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
    cs = bs[:]
    n = 0
    for b in range(256):
        if b not in bs:
            bs.append(b)
            cs.append(256 + n)
            n += 1
    encoder = dict(zip(bs, [chr(x) for x in cs]))
    decoder = {v: k for k, v in encoder.items()}
    return encoder, decoder

byte_encoder_map, byte_decoder_map = get_clean_byte_maps()

# Inject attributes back onto the target base classes for downstream compatibility
for cls_name in ['GPT2Tokenizer', 'GPT2TokenizerFast']:
    if hasattr(transformers, cls_name):
        cls = getattr(transformers, cls_name)
        cls.byte_encoder = byte_encoder_map
        cls.byte_decoder = byte_decoder_map
```

Comments:
Hey! @briancullinan2 , can i work on this issue?

These changes happened in the `v5` refactor, I think, which deliberately did break the internal API in some places. 

> Hey! [@briancullinan2](https://github.com/briancullinan2) , can i work on this issue?

I would love that. Please be the change in the industry that we all need and add regression testing.

> These changes happened in the `v5` refactor, I think, which deliberately did break the internal API in some places.

Why did this team deliberately break things?