import os
import json
import argparse
import torch


class SimpleModel(torch.nn.Module):
    def __init__(self, hidden_dim, empty_grad=False):
        super(SimpleModel, self).__init__()
        self.linear = torch.nn.Linear(hidden_dim, hidden_dim)
        if empty_grad:
            self.layers2 = torch.nn.ModuleList([torch.nn.Linear(hidden_dim, hidden_dim)])
        self.cross_entropy_loss = torch.nn.CrossEntropyLoss()

    def forward(self, x, y):
        hidden_dim = x
        hidden_dim = self.linear(hidden_dim)
        return self.cross_entropy_loss(hidden_dim, y)


class SimpleOptimizer(torch.optim.Optimizer):
    def __init__(self, params, lr=0.11072018):
        defaults = dict(lr=lr)
        super(SimpleOptimizer, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(SimpleOptimizer, self).__setstate__(state)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                d_p = p.grad.data
                p.data.add_(-group['lr'], d_p)

        return loss


def random_dataloader(model, total_samples, hidden_dim, device):
    batch_size = model.train_micro_batch_size_per_gpu()
    train_data = torch.randn(total_samples, hidden_dim, device=device, dtype=torch.half)
    train_label = torch.empty(total_samples,
                              dtype=torch.long,
                              device=device).random_(hidden_dim)
    train_dataset = torch.utils.data.TensorDataset(train_data, train_label)
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)
    return train_loader


def create_config_from_dict(tmpdir, config_dict):
    config_path = os.path.join(tmpdir, 'temp_config.json')
    with open(config_path, 'w') as fd:
        json.dump(config_dict, fd)
    return config_path


def args_from_dict(tmpdir, config_dict):
    config_path = create_config_from_dict(tmpdir, config_dict)
    parser = argparse.ArgumentParser()
    args = parser.parse_args(args='')
    args.deepspeed = True
    args.deepspeed_config = config_path
    args.local_rank = 0
    return args

import os
import json
import argparse
import torch


class SimpleModel(torch.nn.Module):
    def __init__(self, hidden_dim, empty_grad=False):
        super(SimpleModel, self).__init__()
        self.linear = torch.nn.Linear(hidden_dim, hidden_dim)
        if empty_grad:
            self.layers2 = torch.nn.ModuleList([torch.nn.Linear(hidden_dim, hidden_dim)])
        self.cross_entropy_loss = torch.nn.CrossEntropyLoss()

    def forward(self, x, y):
        hidden_dim = x
        hidden_dim = self.linear(hidden_dim)
        return self.cross_entropy_loss(hidden_dim, y)


class SimpleOptimizer(torch.optim.Optimizer):
    def __init__(self, params, lr=0.11072018):
        defaults = dict(lr=lr)
        super(SimpleOptimizer, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(SimpleOptimizer, self).__setstate__(state)

    def step(self, closure=None):
        loss = None
        if closure is not None:
            loss = closure()

        for group in self.param_groups:
            for p in group['params']:
                if p.grad is None:
                    continue
                d_p = p.grad.data
                p.data.add_(-group['lr'], d_p)

        return loss


def random_dataloader(model, total_samples, hidden_dim, device):
    batch_size = model.train_micro_batch_size_per_gpu()
    train_data = torch.randn(total_samples, hidden_dim, device=device, dtype=torch.half)
    train_label = torch.empty(total_samples,
                              dtype=torch.long,
                              device=device).random_(hidden_dim)
    train_dataset = torch.utils.data.TensorDataset(train_data, train_label)
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size)
    return train_loader


def create_config_from_dict(tmpdir, config_dict):
    config_path = os.path.join(tmpdir, 'temp_config.json')
    with open(config_path, 'w') as fd:
        json.dump(config_dict, fd)
    return config_path


def args_from_dict(tmpdir, config_dict):
    config_path = create_config_from_dict(tmpdir, config_dict)
    parser = argparse.ArgumentParser()
    args = parser.parse_args(args='')
    args.deepspeed = True
    args.deepspeed_config = config_path
    args.local_rank = 0
    return args
