from typing import Dict, Any

import torch
import numpy as np
import copy
from diffusion_policy.common.pytorch_util import dict_apply
from diffusion_policy.common.replay_buffer import ReplayBuffer
from diffusion_policy.common.sampler import (
    SequenceSampler, get_dataset_masks)

from diffusion_policy.model.common.normalizer import LinearNormalizer
from diffusion_policy.dataset.base_dataset import BaseImageDataset
from diffusion_policy.common.normalize_util import get_image_range_normalizer


class PushTImageDataset(BaseImageDataset):
    def __init__(self,
            zarr_path, 
            horizon=1,
            pad_before=0,
            pad_after=0,
            seed=42,
            val_ratio=0.0,
            max_train_episodes=None,
            dataset_mask_kwargs: Dict[str, Any] = {}
        ):
        super().__init__()
        self.replay_buffer = ReplayBuffer.copy_from_path(
            zarr_path, keys=['img', 'state', 'action'])
        
        train_mask, val_mask, holdout_mask = get_dataset_masks(
            dataset_path=zarr_path,
            num_episodes=self.replay_buffer.n_episodes,
            val_ratio=val_ratio,
            max_train_episodes=max_train_episodes,
            seed=seed,
            **dataset_mask_kwargs,
        )
        
        self.sampler = SequenceSampler(
            replay_buffer=self.replay_buffer, 
            sequence_length=horizon,
            pad_before=pad_before, 
            pad_after=pad_after,
            episode_mask=train_mask)
        
        self.train_mask = train_mask
        self.val_mask = val_mask
        self.holdout_mask = holdout_mask
        self.horizon = horizon
        self.pad_before = pad_before
        self.pad_after = pad_after
        self._dataset_path = zarr_path
        self._dataset_mask_kwargs = dataset_mask_kwargs

        # Visualization.
        self._return_image = False
        self._return_numpy = False

    def get_validation_dataset(self):
        val_set = copy.copy(self)
        val_set.sampler = SequenceSampler(
            replay_buffer=self.replay_buffer, 
            sequence_length=self.horizon,
            pad_before=self.pad_before, 
            pad_after=self.pad_after,
            episode_mask=self.val_mask
            )
        val_set.train_mask = self.val_mask
        return val_set
    
    def get_holdout_dataset(self):
        holdout_set = copy.copy(self)
        holdout_set.sampler = SequenceSampler(
            replay_buffer=self.replay_buffer, 
            sequence_length=self.horizon,
            pad_before=self.pad_before, 
            pad_after=self.pad_after,
            episode_mask=self.holdout_mask
            )
        holdout_set.train_mask = self.holdout_mask
        return holdout_set

    def get_normalizer(self, mode='limits', **kwargs):
        data = {
            'action': self.replay_buffer['action'],
            'agent_pos': self.replay_buffer['state'][...,:2]
        }
        normalizer = LinearNormalizer()
        normalizer.fit(data=data, last_n_dims=1, mode=mode, **kwargs)
        normalizer['image'] = get_image_range_normalizer()
        return normalizer

    def __len__(self) -> int:
        return len(self.sampler)

    def _sample_to_data(self, sample):
        agent_pos = sample['state'][:,:2].astype(np.float32) # (agent_posx2, block_posex3)
        image = np.moveaxis(sample['img'],-1,1)/255

        data = {
            'obs': {
                'image': image, # T, 3, 96, 96
                'agent_pos': agent_pos, # T, 2
            },
            'action': sample['action'].astype(np.float32) # T, 2
        }

        # Visualization.
        if self._return_image:
            data['img'] = sample['img'].astype(np.uint8)
            
        return data
    
    def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
        sample = self.sampler.sample_sequence(idx)
        data = self._sample_to_data(sample)
        if self._return_numpy:
            return data
        torch_data = dict_apply(data, torch.from_numpy)
        return torch_data


def test():
    import os
    zarr_path = os.path.expanduser('~/dev/diffusion_policy/data/pusht/pusht_cchi_v7_replay.zarr')
    dataset = PushTImageDataset(zarr_path, horizon=16)

    # from matplotlib import pyplot as plt
    # normalizer = dataset.get_normalizer()
    # nactions = normalizer['action'].normalize(dataset.replay_buffer['action'])
    # diff = np.diff(nactions, axis=0)
    # dists = np.linalg.norm(np.diff(nactions, axis=0), axis=-1)
