# Copyright 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 os
import sys


git_repo_path = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
check_dummies.PATH_TO_DIFFUSERS = os.path.join(git_repo_path, "src", "diffusers")


class TestCheckDummies:
    def test_find_backend(self):
        simple_backend = find_backend("    if not is_torch_available():")
        assert simple_backend == "torch"

        # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
        # assert backend_with_underscore == "tensorflow_text"

        double_backend = find_backend("    if not (is_torch_available() and is_transformers_available()):")
        assert double_backend == "torch_and_transformers"

        # double_backend_with_underscore = find_backend(
        #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
        # )
        # assert double_backend_with_underscore == "sentencepiece_and_tensorflow_text"

        triple_backend = find_backend(
            "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"
        )
        assert triple_backend == "torch_and_transformers_and_onnx"

    def test_read_init(self):
        objects = read_init()
        # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
        assert "torch" in objects
        assert "torch_and_transformers" in objects
        assert "torch_and_transformers_and_onnx" in objects

        # Likewise, we can't assert on the exact content of a key
        assert "UNet2DModel" in objects["torch"]
        assert "StableDiffusionPipeline" in objects["torch_and_transformers"]
        assert "LMSDiscreteScheduler" in objects["torch_and_scipy"]
        assert "OnnxStableDiffusionPipeline" in objects["torch_and_transformers_and_onnx"]

    def test_create_dummy_object(self):
        dummy_constant = create_dummy_object("CONSTANT", "'torch'")
        assert dummy_constant == "\nCONSTANT = None\n"

        dummy_function = create_dummy_object("function", "'torch'")
        assert dummy_function == "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"

        expected_dummy_class = """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
        dummy_class = create_dummy_object("FakeClass", "'torch'")
        assert dummy_class == expected_dummy_class

    def test_create_dummy_files(self):
        expected_dummy_pytorch_file = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
"""
        dummy_files = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]})
        assert dummy_files["torch"] == expected_dummy_pytorch_file