import os
import imageio
import numpy as np
from typing import Union, Optional

import torch
import torchvision
import torch.distributed as dist

from tqdm import tqdm
from einops import rearrange
import cv2
import math
import moviepy.editor as mpy
from PIL import Image

# We recommend to use the following affinity score(motion magnitude)
# Also encourage to try to construct different score by yourself
# RANGE_LIST = [
#         [1.0, 0.9, 0.85, 0.85, 0.85, 0.8], # 0 Small Motion
#         [1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75], # Moderate Motion
#         [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5], # Large Motion
#         # [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6], # Large Motion
#         # [1.0, 0.65, 0.6], # candidate moderate
#         # [1.0, 0.65, 0.6, 0.6, 0.6, 0.5, 0.5, 0.5, 0.5, 0.4], # candidate large
#         [1.0 , 0.9 , 0.85, 0.85, 0.85, 0.8 , 0.8 , 0.8 , 0.8 , 0.8 , 0.8 , 0.8 , 0.85, 0.85, 0.9 , 1.0 ], # Loop
#         [1.0 , 0.8 , 0.8 , 0.8 , 0.79, 0.78, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78, 0.79, 0.8 , 0.8 , 1.0 ], # Loop
#         [1.0 , 0.8 , 0.7 , 0.7 , 0.7 , 0.7 , 0.6 , 0.5 , 0.5 , 0.6 , 0.7 , 0.7 , 0.7 , 0.7 , 0.8 , 1.0 ], # Loop
#         # [1.0], # Static
#         # [0],
#         # [0.6, 0.5, 0.5, 0.45, 0.45, 0.4], # Style Transfer Test
#         # [0.4, 0.3, 0.3, 0.25, 0.25, 0.2], # Style Transfer
#         [0.5, 0.2], # Style Transfer Large Motion
#         [0.5, 0.4, 0.4, 0.4, 0.35, 0.35, 0.3, 0.25, 0.2], # Style Transfer Moderate Motion
#         [0.5, 0.4, 0.4, 0.4, 0.35, 0.3], # Style Transfer Candidate Small Motion
# ]
RANGE_LIST = [
        [0.5, 0.4, 0.4, 0.4, 0.35, 0.3], # Style Transfer Candidate Small Motion
        [0.5, 0.4, 0.4, 0.4, 0.35, 0.35, 0.3, 0.25, 0.2], # Style Transfer Moderate Motion
        [0.5, 0.2], # Style Transfer Large Motion
]


def zero_rank_print(s):
    if (not dist.is_initialized()) or (dist.is_initialized() and dist.get_rank() == 0): print("### " + s)

def save_videos_mp4(video: torch.Tensor, path: str, fps: int=8):
    video = rearrange(video, "b c t h w -> t b c h w")
    num_frames, batch_size, channels, height, width = video.shape
    assert batch_size == 1,\
    'Only support batch size == 1'
    video = video.squeeze(1)
    video = rearrange(video, "t c h w -> t h w c")
    def make_frame(t):
        frame_tensor = video[int(t * fps)]
        frame_np = (frame_tensor * 255).numpy().astype('uint8')
        return frame_np
    clip = mpy.VideoClip(make_frame, duration=num_frames / fps)
    clip.write_videofile(path, fps=fps, codec='libx264')

def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8):
    videos = rearrange(videos, "b c t h w -> t b c h w")
    outputs = []
    for x in videos:
        x = torchvision.utils.make_grid(x, nrow=n_rows)
        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
        if rescale:
            x = (x + 1.0) / 2.0  # -1,1 -> 0,1
        x = torch.clamp((x * 255), 0, 255).numpy().astype(np.uint8)
        outputs.append(x)

    os.makedirs(os.path.dirname(path), exist_ok=True)
    imageio.mimsave(path, outputs, fps=fps)


# DDIM Inversion
@torch.no_grad()
def init_prompt(prompt, pipeline):
    uncond_input = pipeline.tokenizer(
        [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length,
        return_tensors="pt"
    )
    uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0]
    text_input = pipeline.tokenizer(
        [prompt],
        padding="max_length",
        max_length=pipeline.tokenizer.model_max_length,
        truncation=True,
        return_tensors="pt",
    )
    text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0]
    context = torch.cat([uncond_embeddings, text_embeddings])

    return context


def next_step(model_output: Union[torch.FloatTensor, np.ndarray], timestep: int,
              sample: Union[torch.FloatTensor, np.ndarray], ddim_scheduler):
    timestep, next_timestep = min(
        timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), timestep
    alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod
    alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep]
    beta_prod_t = 1 - alpha_prod_t
    next_original_sample = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output
    next_sample = alpha_prod_t_next ** 0.5 * next_original_sample + next_sample_direction
    return next_sample


def get_noise_pred_single(latents, t, context, unet):
    noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"]
    return noise_pred


@torch.no_grad()
def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt):
    context = init_prompt(prompt, pipeline)
    uncond_embeddings, cond_embeddings = context.chunk(2)
    all_latent = [latent]
    latent = latent.clone().detach()
    for i in tqdm(range(num_inv_steps)):
        t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1]
        noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet)
        latent = next_step(noise_pred, t, latent, ddim_scheduler)
        all_latent.append(latent)
    return all_latent


@torch.no_grad()
def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""):
    ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt)
    return ddim_latents

def prepare_mask_coef(video_length:int, cond_frame:int, sim_range:list=[0.2, 1.0]):

    assert len(sim_range) == 2, \
    'sim_range should has the length of 2, including the min and max similarity'

    assert video_length > 1, \
    'video_length should be greater than 1'

    assert video_length > cond_frame,\
    'video_length should be greater than cond_frame'

    diff = abs(sim_range[0] - sim_range[1]) / (video_length - 1)
    coef = [1.0] * video_length
    for f in range(video_length):
        f_diff = diff * abs(cond_frame - f)
        f_diff = 1 - f_diff
        coef[f] *= f_diff

    return coef

def prepare_mask_coef_by_score(video_shape: list, cond_frame_idx: list, sim_range: list = [0.2, 1.0],
    statistic: list = [1, 100], coef_max: int = 0.98, score: Optional[torch.Tensor] = None):
    '''
        the shape of video_data is (b f c h w)
        cond_frame_idx is a list, with length of batch_size
        the shape of statistic  is (f 2)
        the shape of score      is (b f)
        the shape of coef       is (b f)
    '''
    assert len(video_shape) == 2, \
    f'the shape of video_shape should be (b f c h w), but now get {len(video_shape.shape)} channels'

    batch_size, frame_num = video_shape[0], video_shape[1]

    score = score.permute(0, 2, 1).squeeze(0)

    # list -> b 1
    cond_fram_mat = torch.tensor(cond_frame_idx).unsqueeze(-1)

    statistic = torch.tensor(statistic)
    # (f 2) -> (b f 2)
    statistic = statistic.repeat(batch_size, 1, 1)

    # shape of order (b f), shape of cond_mat (b f)
    order     = torch.arange(0, frame_num, 1)
    order     = order.repeat(batch_size, 1)
    cond_mat  = torch.ones((batch_size, frame_num)) * cond_fram_mat
    order     = abs(order - cond_mat)

    statistic = statistic[:,order.to(torch.long)][0,:,:,:]

    # score (b f) max_s (b f 1)
    max_stats = torch.max(statistic, dim=2).values.to(dtype=score.dtype)
    min_stats = torch.min(statistic, dim=2).values.to(dtype=score.dtype)

    score[score > max_stats] = max_stats[score > max_stats] * 0.95
    score[score < min_stats] = min_stats[score < min_stats]

    eps       = 1e-10
    coef      = 1 - abs((score / (max_stats + eps)) * (max(sim_range) - min(sim_range)))

    indices = torch.arange(coef.shape[0]).unsqueeze(1)
    coef[indices, cond_fram_mat] = 1.0

    return coef


def prepare_mask_coef_by_statistics(video_length: int, cond_frame: int, sim_range: int,
                                    coef: Optional[list] = None):
    """
    coef: User defined coef, if passed, `sim_range` index will be ignored. This is useful
        for defining custom style transform coef for different models.
    """
    assert video_length > 1, \
    'video_length should be greater than 1'

    assert video_length > cond_frame,\
    'video_length should be greater than cond_frame'

    # Recommend index: 13

    # range_list = [
    #     # [0.8, 0.8, 0.7, 0.6],
    #     [1.0, 0.8, 0.7, 0.6],
    #     [1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5],
    #     [1.0, 0.9, 0.85, 0.85, 0.85, 0.8], # 0
    #     [1.0, 0.9, 0.8, 0.7],
    #     [1.0, 0.8, 0.7, 0.6, 0.7, 0.6],
    #     [1.0, 0.9, 0.85],
    #     # [1.0, 0.9, 0.7, 0.5, 0.3, 0.2],
    #     # [1.0, 0.8, 0.6, 0.4],
    #     # [1.0, 0.65, 0.6], # 1
    #     [1.0, 0.6, 0.4], # 2
    #     [1.0, 0.2, 0.2],
    #     # [1.0, 0.8, 0.6, 0.6, 0.5, 0.5, 0.4],
    #     # [1.0, 0.9, 0.9, 0.9, 0.9, 0.8],
    #     # [1.0, 0.65, 0.6, 0.6, 0.5, 0.5, 0.4],
    #     # [1.0, 0.9, 0.9, 0.9, 0.7, 0.7, 0.6, 0.5, 0.4],
    #     [1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75], # 4 style_transfer
    #     [1.0, 0.9, 0.9],
    #     [0.8, 0.7, 0.6],
    #     [0.8, 0.8, 0.8, 0.8, 0.7],
    #     [0.9, 0.6, 0.6, 0.6, 0.5, 0.4, 0.2],
    #     # [1.0, 0.91, 0.9, 0.89, 0.88, 0.87],
    #     # [1.0, 0.7, 0.65, 0.65, 0.65, 0.65, 0.6],
    #     # [1.0, 0.85, 0.9, 0.85, 0.9, 0.85],
    #     # [1.0, 0.8, 0.82, 0.84, 0.86, 0.88, 0.78, 0.82, 0.84],
    #     # [1.0],
    # ]
    range_list = RANGE_LIST

    assert sim_range < len(range_list),\
    f'sim_range type{sim_range} not implemented'

    if coef is None:
        coef = range_list[sim_range]
    coef = coef + ([coef[-1]] * (video_length - len(coef)))

    order = [abs(i - cond_frame) for i in range(video_length)]
    coef  = [coef[order[i]] for i in range(video_length)]

    return coef