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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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"""A library for Causal Video Tokenizer inference."""

from typing import Any

import numpy as np
import torch
from tqdm import tqdm

from cosmos_predict1.tokenizer.inference.utils import (
    load_decoder_model,
    load_encoder_model,
    load_model,
    numpy2tensor,
    pad_video_batch,
    tensor2numpy,
    unpad_video_batch,
)


class CausalVideoTokenizer(torch.nn.Module):
    def __init__(
        self,
        checkpoint: str = None,
        checkpoint_enc: str = None,
        checkpoint_dec: str = None,
        tokenizer_config: dict[str, Any] = None,
        device: str = "cuda",
        dtype: str = "bfloat16",
    ) -> None:
        super().__init__()
        self._device = device
        self._dtype = getattr(torch, dtype)
        self._full_model = (
            load_model(checkpoint, tokenizer_config, device).to(self._dtype) if checkpoint is not None else None
        )
        self._enc_model = (
            load_encoder_model(checkpoint_enc, tokenizer_config, device).to(self._dtype)
            if checkpoint_enc is not None
            else None
        )
        self._dec_model = (
            load_decoder_model(checkpoint_dec, tokenizer_config, device).to(self._dtype)
            if checkpoint_dec is not None
            else None
        )

    @torch.no_grad()
    def autoencode(self, input_tensor: torch.Tensor) -> torch.Tensor:
        """Reconstrcuts a batch of video tensors after embedding into a latent.

        Args:
            video: The input video Bx3xTxHxW layout, range [-1..1].
        Returns:
            The reconstructed video, layout Bx3xTxHxW, range [-1..1].
        """
        if self._full_model is not None:
            output_tensor = self._full_model(input_tensor)
            output_tensor = output_tensor[0] if isinstance(output_tensor, tuple) else output_tensor
        else:
            output_latent = self.encode(input_tensor)[0]
            output_tensor = self.decode(output_latent)
        return output_tensor

    @torch.no_grad()
    def encode(self, input_tensor: torch.Tensor) -> tuple[torch.Tensor]:
        """Encodes a numpy video into a CausalVideo latent or code.

        Args:
            input_tensor: The input tensor Bx3xTxHxW layout, range [-1..1].
        Returns:
            For causal continuous video (CV) tokenizer, the tuple contains:
                - The latent embedding, Bx16x(t)x(h)x(w), where the compression
                rate is (T/t x H/h x W/w), and channel dimension of 16.
            For causal discrete video (DV) tokenizer, the tuple contains:
              1) The indices, Bx(t)x(h)x(w), from a codebook of size 64K, which
                is formed by FSQ levels of (8,8,8,5,5,5).
              2) The discrete code, Bx6x(t)x(h)x(w), where the compression rate
                is again (T/t x H/h x W/w), and channel dimension of 6.
        """
        assert input_tensor.ndim == 5, "input video should be of 5D."

        output_latent = self._enc_model(input_tensor)
        if isinstance(output_latent, torch.Tensor):
            return output_latent
        return output_latent[:-1]

    @torch.no_grad()
    def decode(self, input_latent: torch.Tensor) -> torch.Tensor:
        """Encodes a numpy video into a CausalVideo latent.

        Args:
            input_latent: The continuous latent Bx16xtxhxw for CV,
                        or the discrete indices Bxtxhxw for DV.
        Returns:
            The reconstructed tensor, layout [B,3,1+(T-1)*8,H*16,W*16] in range [-1..1].
        """
        assert input_latent.ndim >= 4, "input latent should be of 5D for continuous and 4D for discrete."
        return self._dec_model(input_latent)

    def forward(
        self,
        video: np.ndarray,
        temporal_window: int = 17,
    ) -> np.ndarray:
        """Reconstructs video using a pre-trained CausalTokenizer autoencoder.
        Given a video of arbitrary length, the forward invokes the CausalVideoTokenizer
        in a sliding manner with a `temporal_window` size.

        Args:
            video: The input video BxTxHxWx3 layout, range [0..255].
            temporal_window: The length of the temporal window to process, default=25.
        Returns:
            The reconstructed video in range [0..255], layout BxTxHxWx3.
        """
        assert video.ndim == 5, "input video should be of 5D."
        num_frames = video.shape[1]  # can be of any length.
        output_video_list = []
        for idx in tqdm(range(0, (num_frames - 1) // temporal_window + 1)):
            # Input video for the current window.
            start, end = idx * temporal_window, (idx + 1) * temporal_window
            input_video = video[:, start:end, ...]

            # Spatio-temporally pad input_video so it's evenly divisible.
            padded_input_video, crop_region = pad_video_batch(input_video)
            input_tensor = numpy2tensor(padded_input_video, dtype=self._dtype, device=self._device)
            output_tensor = self.autoencode(input_tensor)
            padded_output_video = tensor2numpy(output_tensor)
            output_video = unpad_video_batch(padded_output_video, crop_region)

            output_video_list.append(output_video)
        return np.concatenate(output_video_list, axis=1)