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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""The continuous image tokenizer with VAE or AE formulation for 2D data."""

from collections import OrderedDict, namedtuple

import torch
from loguru import logger as logging
from torch import nn

from cosmos_predict1.tokenizer.modules import ContinuousFormulation, DecoderType, EncoderType

NetworkEval = namedtuple("NetworkEval", ["reconstructions", "posteriors", "latent"])


class ContinuousImageTokenizer(nn.Module):
    def __init__(self, z_channels: int, z_factor: int, latent_channels: int, **kwargs) -> None:
        super().__init__()
        self.name = kwargs.get("name", "ContinuousImageTokenizer")
        self.latent_channels = latent_channels

        encoder_name = kwargs.get("encoder", EncoderType.Default.name)
        self.encoder = EncoderType[encoder_name].value(z_channels=z_factor * z_channels, **kwargs)

        decoder_name = kwargs.get("decoder", DecoderType.Default.name)
        self.decoder = DecoderType[decoder_name].value(z_channels=z_channels, **kwargs)

        self.quant_conv = torch.nn.Conv2d(z_factor * z_channels, z_factor * latent_channels, 1)
        self.post_quant_conv = torch.nn.Conv2d(latent_channels, z_channels, 1)

        formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name)
        self.distribution = ContinuousFormulation[formulation_name].value()
        logging.info(f"{self.name} based on {formulation_name} formulation, with {kwargs}.")

        num_parameters = sum(param.numel() for param in self.parameters())
        logging.info(f"model={self.name}, num_parameters={num_parameters:,}")
        logging.info(f"z_channels={z_channels}, latent_channels={self.latent_channels}.")

    def encoder_jit(self):
        return nn.Sequential(
            OrderedDict(
                [
                    ("encoder", self.encoder),
                    ("quant_conv", self.quant_conv),
                    ("distribution", self.distribution),
                ]
            )
        )

    def decoder_jit(self):
        return nn.Sequential(
            OrderedDict(
                [
                    ("post_quant_conv", self.post_quant_conv),
                    ("decoder", self.decoder),
                ]
            )
        )

    def last_decoder_layer(self):
        return self.decoder.conv_out

    def encode(self, x):
        h = self.encoder(x)
        moments = self.quant_conv(h)
        return self.distribution(moments)

    def decode(self, z):
        z = self.post_quant_conv(z)
        dec = self.decoder(z)
        return dec

    def forward(self, input) -> dict[str, torch.Tensor] | NetworkEval:
        latent, posteriors = self.encode(input)
        dec = self.decode(latent)
        if self.training:
            return dict(reconstructions=dec, posteriors=posteriors, latent=latent)
        return NetworkEval(reconstructions=dec, posteriors=posteriors, latent=latent)