# Copyright 2020 - 2021 MONAI Consortium
# 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 Tuple

import torch.nn as nn

from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
from monai.networks.blocks.transformerblock import TransformerBlock


class ViT(nn.Module):
    """
    Vision Transformer (ViT), based on: "Dosovitskiy et al.,
    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
    """

    def __init__(
        self,
        in_channels: int,
        img_size: Tuple[int, int, int],
        patch_size: Tuple[int, int, int],
        hidden_size: int,
        mlp_dim: int,
        num_layers: int,
        num_heads: int,
        pos_embed: str,
        classification: bool,
        num_classes: int = 2,
        dropout_rate: float = 0.0,
    ) -> None:
        """
        Args:
            in_channels: dimension of input channels.
            img_size: dimension of input image.
            patch_size: dimension of patch size.
            hidden_size: dimension of hidden layer.
            mlp_dim: dimension of feedforward layer.
            num_layers: number of transformer blocks.
            num_heads: number of attention heads.
            pos_embed: position embedding layer type.
            classification: bool argument to determine if classification is used.
            num_classes: number of classes if classification is used.
            dropout_rate: faction of the input units to drop.

        """

        super().__init__()

        if not (0 <= dropout_rate <= 1):
            raise AssertionError("dropout_rate should be between 0 and 1.")

        if hidden_size % num_heads != 0:
            raise AssertionError("hidden size should be divisible by num_heads.")

        if pos_embed not in ["conv", "perceptron"]:
            raise KeyError(f"Position embedding layer of type {pos_embed} is not supported.")

        self.classification = classification
        self.patch_embedding = PatchEmbeddingBlock(
            in_channels, img_size, patch_size, hidden_size, num_heads, pos_embed, dropout_rate
        )
        self.blocks = nn.ModuleList(
            [TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate) for i in range(num_layers)]
        )
        self.norm = nn.LayerNorm(hidden_size)
        if self.classification:
            self.classification_head = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        x = self.patch_embedding(x)
        hidden_states_out = []
        for blk in self.blocks:
            x = blk(x)
            hidden_states_out.append(x)
        x = self.norm(x)
        if self.classification:
            x = self.classification_head(x[:, 0])
        return x, hidden_states_out
