import inspect
from argparse import Namespace

import warnings

import torch
from einops import repeat
from torch import nn

from ml_lib.metrics.multi_class_classification import MultiClassScores
from ml_lib.modules.blocks import TransformerModule
from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x, SlidingWindow)
from util.module_mixins import CombinedModelMixins

MIN_NUM_PATCHES = 16


class VerticalVisualTransformer(CombinedModelMixins, LightningBaseModule):

    def __init__(self,  in_shape, n_classes, weight_init, activation,
                 embedding_size, heads, attn_depth, patch_size, use_residual, variable_length,
                 use_bias, use_norm, dropout, lat_dim, loss, scheduler, mlp_dim, head_dim,
                 lr, weight_decay, sto_weight_avg, lr_scheduler_parameter, opt_reset_interval,
                 return_logits=False):

        # TODO: Move this to parent class, or make it much easieer to access... But How...
        a = dict(locals())
        params = {arg: a[arg] for arg in inspect.signature(self.__init__).parameters.keys() if arg != 'self'}
        super(VerticalVisualTransformer, self).__init__(params)

        self.in_shape = in_shape
        self.n_classes = n_classes

        assert len(self.in_shape) == 3, 'There need to be three Dimensions'
        channels, height, width = self.in_shape

        # Model Paramters
        # =============================================================================
        # Additional parameters
        self.embed_dim = self.params.embedding_size
        self.height = height
        self.channels = channels

        self.new_width = ((width - self.params.patch_size)//1) + 1

        num_patches = self.new_width - (self.params.patch_size // 2)
        patch_dim = channels * self.params.patch_size * self.height
        assert num_patches >= MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for ' + \
                                               f'attention. Try decreasing your patch size'

        # Utility Modules
        self.autopad = AutoPadToShape((self.height, self.new_width))
        self.dropout = nn.Dropout(self.params.dropout)
        self.slider = SlidingWindow((channels, *self.autopad.target_shape), (self.height, self.params.patch_size),
                                    keepdim=False)

        # Modules with Parameters
        self.transformer = TransformerModule(in_shape=self.embed_dim, mlp_dim=self.params.mlp_dim,
                                             head_dim=self.params.head_dim,
                                             heads=self.params.heads, depth=self.params.attn_depth,
                                             dropout=self.params.dropout, use_norm=self.params.use_norm,
                                             activation=self.params.activation, use_residual=self.params.use_residual
                                             )

        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, self.embed_dim))
        self.patch_to_embedding = nn.Linear(patch_dim, self.embed_dim) if self.params.embedding_size \
            else F_x(self.embed_dim)
        self.cls_token = nn.Parameter(torch.randn(1, 1, self.embed_dim))
        self.to_cls_token = nn.Identity()

        logits = self.params.n_classes if self.params.n_classes > 2 else 1

        outbound_activation = nn.Softmax if logits > 1 else nn.Sigmoid

        self.mlp_head = nn.Sequential(
            nn.LayerNorm(self.embed_dim),
            nn.Linear(self.embed_dim, self.params.lat_dim),
            self.params.activation(),
            nn.Dropout(self.params.dropout),
            nn.Linear(self.params.lat_dim, logits),
            outbound_activation()
        )

    def forward(self, x, mask=None, return_attn_weights=False):
        """
        :param x: the sequence to the encoder (required).
        :param mask: the mask for the src sequence (optional).
        :param return_attn_weights: wether to return the attn weights (optional)
        :return:
        """
        tensor = self.autopad(x)
        tensor = self.slider(tensor)

        tensor = self.patch_to_embedding(tensor)
        b, n, _ = tensor.shape

        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b)

        tensor = torch.cat((cls_tokens, tensor), dim=1)
        tensor += self.pos_embedding[:, :(n + 1)]
        tensor = self.dropout(tensor)

        if return_attn_weights:
            tensor, attn_weights = self.transformer(tensor, mask, return_attn_weights)
        else:
            attn_weights = None
            tensor = self.transformer(tensor, mask)

        tensor = self.to_cls_token(tensor[:, 0])
        tensor = self.mlp_head(tensor)
        return Namespace(main_out=tensor, attn_weights=attn_weights)