import functools
import os
import shutil

import psutil
import torch

from colossalai.core import global_context as gpc


def logging(s, log_path, print_=True, log_=True):
    if print_:
        print(s)
    if log_:
        with open(log_path, 'a+') as f_log:
            f_log.write(s + '\n')


def get_logger(log_path, **kwargs):
    return functools.partial(logging, log_path=log_path, **kwargs)


def create_exp_dir(dir_path, scripts_to_save=None, debug=False):
    if debug:
        print('Debug Mode : no experiment dir created')
        return functools.partial(logging, log_path=None, log_=False)

    if not os.path.exists(dir_path):
        os.makedirs(dir_path)

    print('Experiment dir : {}'.format(dir_path))
    if scripts_to_save is not None:
        script_path = os.path.join(dir_path, 'scripts')
        if not os.path.exists(script_path):
            os.makedirs(script_path)
        for script in scripts_to_save:
            dst_file = os.path.join(dir_path, 'scripts', os.path.basename(script))
            shutil.copyfile(script, dst_file)

    return get_logger(log_path=os.path.join(dir_path, 'log.txt'))


def get_cpu_mem():
    return psutil.Process().memory_info().rss / 1024**2


def get_gpu_mem():
    return torch.cuda.memory_allocated() / 1024**2


def get_mem_info(prefix=''):
    return f'{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB'


def get_tflops(model_numel, batch_size, seq_len, step_time):
    return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12)


def get_parameters_in_billions(model, world_size=1):
    gpus_per_model = world_size

    approx_parameters_in_billions = sum([
        sum([p.ds_numel if hasattr(p, 'ds_id') else p.nelement()
             for p in model_module.parameters()])
        for model_module in model
    ])

    return approx_parameters_in_billions * gpus_per_model / (1e9)


def throughput_calculator(numel, args, config, iteration_time, total_iterations, world_size=1):
    gpus_per_model = 1
    batch_size = args.train_micro_batch_size_per_gpu
    samples_per_model = batch_size * args.max_seq_length
    model_replica_count = world_size / gpus_per_model
    approx_parameters_in_billions = numel
    elapsed_time_per_iter = iteration_time / total_iterations
    samples_per_second = batch_size / elapsed_time_per_iter

    #flops calculator
    hidden_size = config.hidden_size
    num_layers = config.num_hidden_layers
    vocab_size = config.vocab_size

    # General TFLOPs formula (borrowed from Equation 3 in Section 5.1 of
    # https://arxiv.org/pdf/2104.04473.pdf).
    # The factor of 4 is when used with activation check-pointing,
    # otherwise it will be 3.
    checkpoint_activations_factor = 4 if args.checkpoint_activations else 3
    flops_per_iteration = (24 * checkpoint_activations_factor * batch_size * args.max_seq_length * num_layers *
                           (hidden_size**2)) * (1. + (args.max_seq_length / (6. * hidden_size)) +
                                                (vocab_size / (16. * num_layers * hidden_size)))
    tflops = flops_per_iteration / (elapsed_time_per_iter * (10**12))
    return samples_per_second, tflops, approx_parameters_in_billions


def synchronize():
    if not torch.distributed.is_available():
        return
    if not torch.distributed.is_initialized():
        return
    world_size = torch.distributed.get_world_size()
    if world_size == 1:
        return
    torch.distributed.barrier()


def log_args(logger, args):
    logger.info('--------args----------')
    message = '\n'.join([f'{k:<30}: {v}' for k, v in vars(args).items()])
    message += '\n'
    message += '\n'.join([f'{k:<30}: {v}' for k, v in gpc.config.items()])
    logger.info(message)
    logger.info('--------args----------\n')
