import warnings
from typing import Any, Dict, Optional, Union, Callable

from segmentation_models_pytorch.base import (
    ClassificationHead,
    SegmentationHead,
    SegmentationModel,
)
from segmentation_models_pytorch.encoders import get_encoder
from segmentation_models_pytorch.base.hub_mixin import supports_config_loading

from .decoder import PSPDecoder


class PSPNet(SegmentationModel):
    """`PSPNet`__ is a fully convolution neural network for image semantic segmentation. Consist of
    *encoder* and *Spatial Pyramid* (decoder). Spatial Pyramid build on top of encoder and does not
    use "fine-features" (features of high spatial resolution). PSPNet can be used for multiclass segmentation
    of high resolution images, however it is not good for detecting small objects and producing accurate,
    pixel-level mask.

    Args:
        encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone)
            to extract features of different spatial resolution
        encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features
            two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features
            with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on).
            Default is 5
        encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and
            other pretrained weights (see table with available weights for each encoder_name).
            For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for
            random initialization. The pretrained variant is defined in the encoder name
        psp_out_channels: A number of filters in Spatial Pyramid
        decoder_use_norm:     Specifies normalization between Conv2D and activation.
            Accepts the following types:

            - **True**: Defaults to `"batchnorm"`.
            - **False**: No normalization (`nn.Identity`).
            - **str**: Specifies normalization type using default parameters. Available values:
              `"batchnorm"`, `"identity"`, `"layernorm"`, `"instancenorm"`, `"inplace"`.
            - **dict**: Fully customizable normalization settings. Structure:
              ```python
              {"type": <norm_type>, **kwargs}
              ```
              where `norm_name` corresponds to normalization type (see above), and `kwargs` are passed directly to the normalization layer as defined in PyTorch documentation.

            **Example**:
            ```python
            decoder_use_norm={"type": "layernorm", "eps": 1e-2}
            ```
        psp_dropout: Spatial dropout rate in [0, 1) used in Spatial Pyramid
        in_channels: A number of input channels for the model, default is 3 (RGB images)
        classes: A number of classes for output mask (or you can think as a number of channels of output mask)
        activation: An activation function to apply after the final convolution layer.
            Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**,
            **callable** and **None**. Default is **None**.
        upsampling: Final upsampling factor. Default is 8 to preserve input-output spatial shape identity
        aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build
            on top of encoder if **aux_params** is not **None** (default). Supported params:

            - classes (int): A number of classes
            - pooling (str): One of "max", "avg". Default is "avg"
            - dropout (float): Dropout factor in [0, 1)
            - activation (str): An activation function to apply "sigmoid"/"softmax"
              (could be **None** to return logits)
        kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing.

    Returns:
        ``torch.nn.Module``: **PSPNet**

    .. __: https://arxiv.org/abs/1612.01105
    """

    @supports_config_loading
    def __init__(
        self,
        encoder_name: str = "resnet34",
        encoder_weights: Optional[str] = "imagenet",
        encoder_depth: int = 3,
        psp_out_channels: int = 512,
        decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm",
        psp_dropout: float = 0.2,
        in_channels: int = 3,
        classes: int = 1,
        activation: Optional[Union[str, Callable]] = None,
        upsampling: int = 8,
        aux_params: Optional[dict] = None,
        **kwargs: dict[str, Any],
    ):
        super().__init__()

        psp_use_batchnorm = kwargs.pop("psp_use_batchnorm", None)
        if psp_use_batchnorm is not None:
            warnings.warn(
                "The usage of psp_use_batchnorm is deprecated. Please modify your code for decoder_use_norm",
                DeprecationWarning,
                stacklevel=2,
            )
            decoder_use_norm = psp_use_batchnorm

        self.encoder = get_encoder(
            encoder_name,
            in_channels=in_channels,
            depth=encoder_depth,
            weights=encoder_weights,
            **kwargs,
        )

        self.decoder = PSPDecoder(
            encoder_channels=self.encoder.out_channels,
            use_norm=decoder_use_norm,
            out_channels=psp_out_channels,
            dropout=psp_dropout,
        )

        self.segmentation_head = SegmentationHead(
            in_channels=psp_out_channels,
            out_channels=classes,
            kernel_size=3,
            activation=activation,
            upsampling=upsampling,
        )

        if aux_params:
            self.classification_head = ClassificationHead(
                in_channels=self.encoder.out_channels[-1], **aux_params
            )
        else:
            self.classification_head = None

        self.name = "psp-{}".format(encoder_name)
        self.initialize()
