from diffusion_policy.model.common.normalizer import SingleFieldLinearNormalizer, SingleFieldLinearNormalizerNew, SingleFieldGaussianNormalizerNew
from diffusion_policy.common.pytorch_util import dict_apply, dict_apply_reduce, dict_apply_split
import numpy as np


def get_range_normalizer_from_stat(stat, output_max=1.0, output_min=-1.0, range_eps=1e-7):
    # -1, 1 normalization
    ## max min: 
    ## [1. 1. 1.]
    ## [-0.9999998  -0.99999017 -0.9999996 ]
    input_max = stat['max']
    input_min = stat['min']
    input_range = input_max - input_min
    ignore_dim = input_range < range_eps
    input_range[ignore_dim] = output_max - output_min
    scale = (output_max - output_min) / input_range
    offset = output_min - scale * input_min
    offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]

    return SingleFieldLinearNormalizer.create_manual(
        scale=scale,
        offset=offset,
        input_stats_dict=stat
    )


def get_range_normalizer_from_stat_new(stat, output_max=1, output_min=-1, range_eps=1e-7):
    # -1, 1 normalization
    input_max = stat['max']
    input_min = stat['min']
    input_range = input_max - input_min
    ignore_dim = input_range < range_eps
    input_range[ignore_dim] = output_max - output_min
    scale = (output_max - output_min) / input_range
    offset = output_min - scale * input_min
    offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]

    return SingleFieldLinearNormalizer.create_manual(
        scale=scale,
        offset=offset,
        input_stats_dict=stat
    )


def get_gaussian_normalizer_from_stat_new(stat, output_max=1, output_min=-1, range_eps=1e-7):
    # -1, 1 normalization
    input_max = stat['max']
    input_min = stat['min']
    input_range = input_max - input_min
    ignore_dim = input_range < range_eps
    input_range[ignore_dim] = output_max - output_min
    scale = (output_max - output_min) / input_range
    offset = output_min - scale * input_min
    offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]

    return SingleFieldGaussianNormalizerNew.create_manual(
        scale=scale,
        offset=offset,
        input_stats_dict=stat
    )

def get_image_range_normalizer():
    scale = np.array([2], dtype=np.float32)
    offset = np.array([-1], dtype=np.float32)
    stat = {
        'min': np.array([0], dtype=np.float32),
        'max': np.array([1], dtype=np.float32),
        'mean': np.array([0.5], dtype=np.float32),
        'std': np.array([np.sqrt(1/12)], dtype=np.float32)
    }
    return SingleFieldLinearNormalizer.create_manual(
        scale=scale,
        offset=offset,
        input_stats_dict=stat
    )

def get_identity_normalizer_from_stat(stat):
    scale = np.ones_like(stat['min'])
    offset = np.zeros_like(stat['min'])
    return SingleFieldLinearNormalizer.create_manual(
        scale=scale,
        offset=offset,
        input_stats_dict=stat
    )

def robomimic_abs_action_normalizer_from_stat(stat, rotation_transformer):
    result = dict_apply_split(
        stat, lambda x: {
            'pos': x[...,:3],
            'rot': x[...,3:6],
            'gripper': x[...,6:]
    })

    def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7):
        # -1, 1 normalization
        input_max = stat['max']
        input_min = stat['min']
        input_range = input_max - input_min
        ignore_dim = input_range < range_eps
        input_range[ignore_dim] = output_max - output_min
        scale = (output_max - output_min) / input_range
        offset = output_min - scale * input_min
        offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]

        return {'scale': scale, 'offset': offset}, stat

    def get_rot_param_info(stat):
        example = rotation_transformer.forward(stat['mean'])
        scale = np.ones_like(example)
        offset = np.zeros_like(example)
        info = {
            'max': np.ones_like(example),
            'min': np.full_like(example, -1),
            'mean': np.zeros_like(example),
            'std': np.ones_like(example)
        }
        return {'scale': scale, 'offset': offset}, info
    
    def get_gripper_param_info(stat):
        example = stat['max']
        scale = np.ones_like(example)
        offset = np.zeros_like(example)
        info = {
            'max': np.ones_like(example),
            'min': np.full_like(example, -1),
            'mean': np.zeros_like(example),
            'std': np.ones_like(example)
        }
        return {'scale': scale, 'offset': offset}, info

    pos_param, pos_info = get_pos_param_info(result['pos'])
    rot_param, rot_info = get_rot_param_info(result['rot'])
    gripper_param, gripper_info = get_gripper_param_info(result['gripper'])

    param = dict_apply_reduce(
        [pos_param, rot_param, gripper_param], 
        lambda x: np.concatenate(x,axis=-1))
    info = dict_apply_reduce(
        [pos_info, rot_info, gripper_info], 
        lambda x: np.concatenate(x,axis=-1))

    return SingleFieldLinearNormalizer.create_manual(
        scale=param['scale'],
        offset=param['offset'],
        input_stats_dict=info
    )


def robomimic_abs_action_only_normalizer_from_stat(stat):
    result = dict_apply_split(
        stat, lambda x: {
            'pos': x[...,:3],
            'other': x[...,3:]
    })

    def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7):
        # -1, 1 normalization
        input_max = stat['max']
        input_min = stat['min']
        input_range = input_max - input_min
        ignore_dim = input_range < range_eps
        input_range[ignore_dim] = output_max - output_min
        scale = (output_max - output_min) / input_range
        offset = output_min - scale * input_min
        offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]

        return {'scale': scale, 'offset': offset}, stat

    
    def get_other_param_info(stat):
        example = stat['max']
        scale = np.ones_like(example)
        offset = np.zeros_like(example)
        info = {
            'max': np.ones_like(example),
            'min': np.full_like(example, -1),
            'mean': np.zeros_like(example),
            'std': np.ones_like(example)
        }
        return {'scale': scale, 'offset': offset}, info

    pos_param, pos_info = get_pos_param_info(result['pos'])
    other_param, other_info = get_other_param_info(result['other'])

    param = dict_apply_reduce(
        [pos_param, other_param], 
        lambda x: np.concatenate(x,axis=-1))
    info = dict_apply_reduce(
        [pos_info, other_info], 
        lambda x: np.concatenate(x,axis=-1))

    return SingleFieldLinearNormalizer.create_manual(
        scale=param['scale'],
        offset=param['offset'],
        input_stats_dict=info
    )


def robomimic_abs_action_only_dual_arm_normalizer_from_stat(stat):
    Da = stat['max'].shape[-1]
    Dah = Da // 2
    result = dict_apply_split(
        stat, lambda x: {
            'pos0': x[...,:3],
            'other0': x[...,3:Dah],
            'pos1': x[...,Dah:Dah+3],
            'other1': x[...,Dah+3:]
    })

    def get_pos_param_info(stat, output_max=1, output_min=-1, range_eps=1e-7):
        # -1, 1 normalization
        input_max = stat['max']
        input_min = stat['min']
        input_range = input_max - input_min
        ignore_dim = input_range < range_eps
        input_range[ignore_dim] = output_max - output_min
        scale = (output_max - output_min) / input_range
        offset = output_min - scale * input_min
        offset[ignore_dim] = (output_max + output_min) / 2 - input_min[ignore_dim]

        return {'scale': scale, 'offset': offset}, stat

    
    def get_other_param_info(stat):
        example = stat['max']
        scale = np.ones_like(example)
        offset = np.zeros_like(example)
        info = {
            'max': np.ones_like(example),
            'min': np.full_like(example, -1),
            'mean': np.zeros_like(example),
            'std': np.ones_like(example)
        }
        return {'scale': scale, 'offset': offset}, info

    pos0_param, pos0_info = get_pos_param_info(result['pos0'])
    pos1_param, pos1_info = get_pos_param_info(result['pos1'])
    other0_param, other0_info = get_other_param_info(result['other0'])
    other1_param, other1_info = get_other_param_info(result['other1'])

    param = dict_apply_reduce(
        [pos0_param, other0_param, pos1_param, other1_param], 
        lambda x: np.concatenate(x,axis=-1))
    info = dict_apply_reduce(
        [pos0_info, other0_info, pos1_info, other1_info], 
        lambda x: np.concatenate(x,axis=-1))

    return SingleFieldLinearNormalizer.create_manual(
        scale=param['scale'],
        offset=param['offset'],
        input_stats_dict=info
    )


def array_to_stats(arr: np.ndarray):
    stat = {
        'min': np.min(arr, axis=0),
        'max': np.max(arr, axis=0),
        'mean': np.mean(arr, axis=0),
        'std': np.std(arr, axis=0)
    }
    return stat
