# Copyright 2020-2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gc
from functools import wraps

import pytest
import torch


# ============================================================================
# Model Revision Override
# ============================================================================
# To test a tiny model PR before merging to main:
# 1. Add the full model_id and PR revision to this dict
# 2. Commit and push to trigger CI
# 3. Once CI is green, merge the tiny model PR on HF Hub
# 4. Remove the entry from this dict and commit
#
# Example:
#   MODEL_REVISIONS = {
#       "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5": "refs/pr/3",
#       "trl-internal-testing/tiny-LlavaForConditionalGeneration": "refs/pr/5",
#   }
# ============================================================================

MODEL_REVISIONS = {
    # Add model_id: revision mappings here to test PRs
}


@pytest.fixture(autouse=True)
def apply_model_revisions(monkeypatch):
    """Auto-inject revision parameter for models defined in MODEL_REVISIONS."""
    if not MODEL_REVISIONS:
        return

    from transformers import PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin

    def create_classmethod_wrapper(original_classmethod):
        # Extract the underlying function from the classmethod
        original_func = original_classmethod.__func__

        @wraps(original_func)
        def wrapper(cls, pretrained_model_name_or_path, *args, **kwargs):
            # Direct lookup: only inject if model_id is in the override dict
            if pretrained_model_name_or_path in MODEL_REVISIONS:
                if "revision" not in kwargs:
                    kwargs["revision"] = MODEL_REVISIONS[pretrained_model_name_or_path]

            return original_func(cls, pretrained_model_name_or_path, *args, **kwargs)

        # Re-wrap as classmethod
        return classmethod(wrapper)

    # Patch all transformers Auto* classes
    for cls in [
        PreTrainedModel,
        PreTrainedTokenizerBase,
        ProcessorMixin,
    ]:
        monkeypatch.setattr(cls, "from_pretrained", create_classmethod_wrapper(cls.from_pretrained))


@pytest.fixture(autouse=True)
def cleanup_gpu():
    """
    Automatically cleanup GPU memory after each test.

    This fixture helps prevent CUDA out of memory errors when running tests in parallel with pytest-xdist by ensuring
    models and tensors are properly garbage collected and GPU memory caches are cleared between tests.
    """
    yield
    # Cleanup after test
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.synchronize()