# -*- coding: utf-8 -*-
# BSD 3-Clause License
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# Copyright (c) 2017
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# Copyright 2022 Huawei Technologies Co., Ltd
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# ==========================================================================

# -*- coding: utf-8 -*-
# BSD 3-Clause License
#
# Copyright (c) 2017
# All rights reserved.
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ==========================================================================

import torch
import torch.nn as nn
from functools import partial

# from timm.models.vision_transformer import VisionTransformer, _cfg

from vision_transformer import VisionTransformer, _cfg
from conformer import Conformer
from timm.models.registry import register_model


@register_model
def deit_tiny_patch16_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=192, depth=12, num_heads=3, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def deit_small_patch16_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=384, depth=12, num_heads=6, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def deit_med_patch16_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=576, depth=12, num_heads=9, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        raise NotImplementedError
    return model

@register_model
def deit_base_patch16_224(pretrained=False, **kwargs):
    model = VisionTransformer(
        patch_size=16, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4, qkv_bias=True,
        norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
    model.default_cfg = _cfg()
    if pretrained:
        checkpoint = torch.hub.load_state_dict_from_url(
            url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
            map_location="cpu", check_hash=True
        )
        model.load_state_dict(checkpoint["model"])
    return model

@register_model
def Conformer_tiny_patch16(pretrained=False, **kwargs):
    model = Conformer(patch_size=16, channel_ratio=1, embed_dim=384, depth=12,
                      num_heads=6, mlp_ratio=4, qkv_bias=True, **kwargs)
    if pretrained:
        raise NotImplementedError
    return model

@register_model
def Conformer_small_patch16(pretrained=False, **kwargs):
    model = Conformer(patch_size=16, channel_ratio=4, embed_dim=384, depth=12,
                      num_heads=6, mlp_ratio=4, qkv_bias=True, **kwargs)
    if pretrained:
        raise NotImplementedError
    return model

@register_model
def Conformer_small_patch32(pretrained=False, **kwargs):
    model = Conformer(patch_size=32, channel_ratio=4, embed_dim=384, depth=12,
                      num_heads=6, mlp_ratio=4, qkv_bias=True, **kwargs)
    if pretrained:
        raise NotImplementedError
    return model

@register_model
def Conformer_base_patch16(pretrained=False, **kwargs):
    model = Conformer(patch_size=16, channel_ratio=6, embed_dim=576, depth=12,
                      num_heads=9, mlp_ratio=4, qkv_bias=True, **kwargs)
    if pretrained:
        raise NotImplementedError
    return model