# Copyright 2023 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.

from typing import TYPE_CHECKING

from ..utils import (
    DIFFUSERS_SLOW_IMPORT,
    _LazyModule,
    is_flax_available,
    is_torch_available,
)


_import_structure = {}

if is_torch_available():
    _import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
    _import_structure["autoencoders.autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
    _import_structure["autoencoders.autoencoder_kl"] = ["AutoencoderKL"]
    _import_structure["autoencoders.autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
    _import_structure["autoencoders.autoencoder_tiny"] = ["AutoencoderTiny"]
    _import_structure["autoencoders.consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
    _import_structure["controlnet"] = ["ControlNetModel"]
    _import_structure["dual_transformer_2d"] = ["DualTransformer2DModel"]
    _import_structure["embeddings"] = ["ImageProjection"]
    _import_structure["modeling_utils"] = ["ModelMixin"]
    _import_structure["transformers.prior_transformer"] = ["PriorTransformer"]
    _import_structure["transformers.t5_film_transformer"] = ["T5FilmDecoder"]
    _import_structure["transformers.transformer_2d"] = ["Transformer2DModel"]
    _import_structure["transformers.transformer_temporal"] = ["TransformerTemporalModel"]
    _import_structure["unets.unet_1d"] = ["UNet1DModel"]
    _import_structure["unets.unet_2d"] = ["UNet2DModel"]
    _import_structure["unets.unet_2d_condition"] = ["UNet2DConditionModel"]
    _import_structure["unets.unet_3d_condition"] = ["UNet3DConditionModel"]
    _import_structure["unets.unet_kandinsky3"] = ["Kandinsky3UNet"]
    _import_structure["unets.unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
    _import_structure["unets.unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
    _import_structure["unets.uvit_2d"] = ["UVit2DModel"]
    _import_structure["vq_model"] = ["VQModel"]

if is_flax_available():
    _import_structure["controlnet_flax"] = ["FlaxControlNetModel"]
    _import_structure["unets.unet_2d_condition_flax"] = ["FlaxUNet2DConditionModel"]
    _import_structure["vae_flax"] = ["FlaxAutoencoderKL"]


if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
    if is_torch_available():
        from .adapter import MultiAdapter, T2IAdapter
        from .autoencoders import (
            AsymmetricAutoencoderKL,
            AutoencoderKL,
            AutoencoderKLTemporalDecoder,
            AutoencoderTiny,
            ConsistencyDecoderVAE,
        )
        from .controlnet import ControlNetModel
        from .embeddings import ImageProjection
        from .modeling_utils import ModelMixin
        from .transformers import (
            DualTransformer2DModel,
            PriorTransformer,
            T5FilmDecoder,
            Transformer2DModel,
            TransformerTemporalModel,
        )
        from .unets import (
            Kandinsky3UNet,
            MotionAdapter,
            UNet1DModel,
            UNet2DConditionModel,
            UNet2DModel,
            UNet3DConditionModel,
            UNetMotionModel,
            UNetSpatioTemporalConditionModel,
            UVit2DModel,
        )
        from .vq_model import VQModel

    if is_flax_available():
        from .controlnet_flax import FlaxControlNetModel
        from .unets import FlaxUNet2DConditionModel
        from .vae_flax import FlaxAutoencoderKL

else:
    import sys

    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
