import importlib
import math
import multiprocessing as mp
from collections import abc
from inspect import isfunction
from queue import Queue
from threading import Thread
from urllib import request
from tqdm import tqdm
from pathlib import Path
from ldm.invoke.devices import torch_dtype

import numpy as np
import torch
import os
import traceback
from PIL import Image, ImageDraw, ImageFont


def log_txt_as_img(wh, xc, size=10):
    # wh a tuple of (width, height)
    # xc a list of captions to plot
    b = len(xc)
    txts = list()
    for bi in range(b):
        txt = Image.new('RGB', wh, color='white')
        draw = ImageDraw.Draw(txt)
        font = ImageFont.load_default()
        nc = int(40 * (wh[0] / 256))
        lines = '\n'.join(
            xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc)
        )

        try:
            draw.text((0, 0), lines, fill='black', font=font)
        except UnicodeEncodeError:
            print('Cant encode string for logging. Skipping.')

        txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
        txts.append(txt)
    txts = np.stack(txts)
    txts = torch.tensor(txts)
    return txts


def ismap(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] > 3)


def isimage(x):
    if not isinstance(x, torch.Tensor):
        return False
    return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)


def exists(x):
    return x is not None


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def mean_flat(tensor):
    """
    https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def count_params(model, verbose=False):
    total_params = sum(p.numel() for p in model.parameters())
    if verbose:
        print(
            f'   | {model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.'
        )
    return total_params


def instantiate_from_config(config, **kwargs):
    if not 'target' in config:
        if config == '__is_first_stage__':
            return None
        elif config == '__is_unconditional__':
            return None
        raise KeyError('Expected key `target` to instantiate.')
    return get_obj_from_str(config['target'])(
        **config.get('params', dict()), **kwargs
    )


def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit('.', 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
    # create dummy dataset instance

    # run prefetching
    if idx_to_fn:
        res = func(data, worker_id=idx)
    else:
        res = func(data)
    Q.put([idx, res])
    Q.put('Done')


def parallel_data_prefetch(
    func: callable,
    data,
    n_proc,
    target_data_type='ndarray',
    cpu_intensive=True,
    use_worker_id=False,
):
    # if target_data_type not in ["ndarray", "list"]:
    #     raise ValueError(
    #         "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
    #     )
    if isinstance(data, np.ndarray) and target_data_type == 'list':
        raise ValueError('list expected but function got ndarray.')
    elif isinstance(data, abc.Iterable):
        if isinstance(data, dict):
            print(
                f'WARNING:"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
            )
            data = list(data.values())
        if target_data_type == 'ndarray':
            data = np.asarray(data)
        else:
            data = list(data)
    else:
        raise TypeError(
            f'The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}.'
        )

    if cpu_intensive:
        Q = mp.Queue(1000)
        proc = mp.Process
    else:
        Q = Queue(1000)
        proc = Thread
    # spawn processes
    if target_data_type == 'ndarray':
        arguments = [
            [func, Q, part, i, use_worker_id]
            for i, part in enumerate(np.array_split(data, n_proc))
        ]
    else:
        step = (
            int(len(data) / n_proc + 1)
            if len(data) % n_proc != 0
            else int(len(data) / n_proc)
        )
        arguments = [
            [func, Q, part, i, use_worker_id]
            for i, part in enumerate(
                [data[i : i + step] for i in range(0, len(data), step)]
            )
        ]
    processes = []
    for i in range(n_proc):
        p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
        processes += [p]

    # start processes
    print(f'Start prefetching...')
    import time

    start = time.time()
    gather_res = [[] for _ in range(n_proc)]
    try:
        for p in processes:
            p.start()

        k = 0
        while k < n_proc:
            # get result
            res = Q.get()
            if res == 'Done':
                k += 1
            else:
                gather_res[res[0]] = res[1]

    except Exception as e:
        print('Exception: ', e)
        for p in processes:
            p.terminate()

        raise e
    finally:
        for p in processes:
            p.join()
        print(f'Prefetching complete. [{time.time() - start} sec.]')

    if target_data_type == 'ndarray':
        if not isinstance(gather_res[0], np.ndarray):
            return np.concatenate([np.asarray(r) for r in gather_res], axis=0)

        # order outputs
        return np.concatenate(gather_res, axis=0)
    elif target_data_type == 'list':
        out = []
        for r in gather_res:
            out.extend(r)
        return out
    else:
        return gather_res

def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t**3):
    delta = (res[0] / shape[0], res[1] / shape[1])
    d = (shape[0] // res[0], shape[1] // res[1])

    grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1]), indexing='ij'), dim = -1).to(device) % 1

    rand_val = torch.rand(res[0]+1, res[1]+1)

    angles = 2*math.pi*rand_val
    gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1).to(device)

    tile_grads = lambda slice1, slice2: gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)

    dot = lambda grad, shift: (torch.stack((grid[:shape[0],:shape[1],0] + shift[0], grid[:shape[0],:shape[1], 1] + shift[1]  ), dim = -1) * grad[:shape[0], :shape[1]]).sum(dim = -1)

    n00 = dot(tile_grads([0, -1], [0, -1]), [0,  0]).to(device)
    n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0]).to(device)
    n01 = dot(tile_grads([0, -1],[1, None]), [0, -1]).to(device)
    n11 = dot(tile_grads([1, None], [1, None]), [-1,-1]).to(device)
    t = fade(grid[:shape[0], :shape[1]])
    noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(device)
    return noise.to(dtype=torch_dtype(device))

def ask_user(question: str, answers: list):
    from itertools import chain, repeat
    user_prompt = f'\n>> {question} {answers}: '
    invalid_answer_msg = 'Invalid answer. Please try again.'
    pose_question = chain([user_prompt], repeat('\n'.join([invalid_answer_msg, user_prompt])))
    user_answers = map(input, pose_question)
    valid_response = next(filter(answers.__contains__, user_answers))
    return valid_response


def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False ):
    if not debug_status:
        return

    image_copy = debug_image.copy().convert("RGBA")
    ImageDraw.Draw(image_copy).text(
        (5, 5),
        debug_text,
        (255, 0, 0)
    )

    if debug_show:
        image_copy.show()

    if debug_result:
        return image_copy

#-------------------------------------
class ProgressBar():
    def __init__(self,model_name='file'):
        self.pbar = None
        self.name = model_name

    def __call__(self, block_num, block_size, total_size):
        if not self.pbar:
            self.pbar=tqdm(desc=self.name,
                           initial=0,
                           unit='iB',
                           unit_scale=True,
                           unit_divisor=1000,
                           total=total_size)
        self.pbar.update(block_size)

def download_with_progress_bar(url:str, dest:Path)->bool:
    try:
        if not dest.exists():
            dest.parent.mkdir(parents=True, exist_ok=True)
            request.urlretrieve(url,dest,ProgressBar(dest.stem))
            return True
        else:
            return True
    except OSError:
        print(traceback.format_exc())
        return False