import time
from copy import deepcopy
from functools import partial
from typing import Callable, Tuple

import numpy as np
import torch
import torch.nn as nn
import torchvision.models as tm
from transformers import GPT2Config, GPT2LMHeadModel

from colossalai.auto_parallel.checkpoint import CheckpointSolverRotor
from colossalai.fx import metainfo_trace


def bench(gm: torch.fx.GraphModule,
          criterion: torch.nn.Module,
          data_gen: Callable,
          num_steps: int = 5) -> Tuple[int, int]:
    """Benchmarking a given graph module

    Args:
        gm (torch.fx.GraphModule): The graph module to benchmark.
        criterion (torch.nn.Module): Loss function.
        data_gen (Callable): Data generator.
        num_steps (int, optional): Number of test steps. Defaults to 5.

    Returns:
        Tuple[int, int]: peak memory in MB and step time in MS.
    """
    gm.train()
    gm.cuda()
    step_time = float('inf')
    torch.cuda.synchronize()
    torch.cuda.empty_cache()
    torch.cuda.reset_peak_memory_stats()
    cached = torch.cuda.max_memory_allocated(device="cuda")
    try:
        for _ in range(num_steps):
            args, label = data_gen()
            output, loss = None, None

            torch.cuda.synchronize(device="cuda")
            start = time.time()
            output = gm(*args)
            loss = criterion(output, label)
            loss.backward()
            torch.cuda.synchronize(device="cuda")
            step_time = min(step_time, time.time() - start)

            for child in gm.children():
                for param in child.parameters():
                    param.grad = None
            del args, label, output, loss
    except:
        del args, label, output, loss
    gm.to("cpu")
    torch.cuda.empty_cache()
    peak_mem = (torch.cuda.max_memory_allocated(device="cuda") - cached) / 1024**2
    return peak_mem, step_time * 1.0e3


def bench_rotor(gm: torch.fx.GraphModule,
                criterion: torch.nn.Module,
                data_gen: Callable,
                num_steps: int = 5,
                sample_points: int = 20,
                free_memory: int = torch.cuda.mem_get_info()[0],
                start_factor: int = 4) -> Tuple[np.array, list, list]:
    """Auto Checkpoint Rotor Algorithm benchmarking
    Benchmarks the Auto Checkpoint Rotor Algorithm for a given graph module and data.

    Args:
        gm (torch.fx.GraphModule): The graph module to benchmark.
        criterion (torch.nn.Module): Loss function.
        data_gen (Callable): Data generator.
        num_steps (int, optional): Number of test steps. Defaults to 5.
        sample_points (int, optional): Number of sample points. Defaults to 20.
        free_memory (int, optional): Max memory budget in Byte. Defaults to torch.cuda.mem_get_info()[0].
        start_factor (int, optional): Start memory budget factor for benchmark, the start memory budget
        will be free_memory / start_factor. Defaults to 4.

    Returns:
        Tuple[np.array, list, list]: return budgets vector (MB), peak memory vector (MB), step time vector (MS).
    """
    peak_hist, step_hist = [], []
    raw_graph = deepcopy(gm.graph)
    for budget in np.linspace(free_memory // start_factor, free_memory, sample_points):
        gm = metainfo_trace(gm, *data_gen()[0])
        solver = CheckpointSolverRotor(gm.graph, free_memory=budget)
        try:
            gm.graph = solver.solve(verbose=False)
            peak_memory, step_time = bench(gm, criterion, data_gen, num_steps=num_steps)
        except:
            peak_memory, step_time = budget / 1024**2, float('inf')
        peak_hist.append(peak_memory)
        step_hist.append(step_time)
        gm.graph = deepcopy(raw_graph)
    return np.linspace(free_memory // start_factor, free_memory, sample_points) / 1024**2, peak_hist, step_hist


class GPTLMModel(nn.Module):
    """
    GPT Model
    """

    def __init__(self,
                 hidden_size=768,
                 num_layers=12,
                 num_attention_heads=12,
                 max_seq_len=1024,
                 vocab_size=50257,
                 checkpoint=False):
        super().__init__()
        self.checkpoint = checkpoint
        self.model = GPT2LMHeadModel(
            GPT2Config(n_embd=hidden_size,
                       n_layer=num_layers,
                       n_head=num_attention_heads,
                       n_positions=max_seq_len,
                       n_ctx=max_seq_len,
                       vocab_size=vocab_size))
        if checkpoint:
            self.model.gradient_checkpointing_enable()

    def forward(self, input_ids, attention_mask):
        # Only return lm_logits
        return self.model(input_ids=input_ids, attention_mask=attention_mask, use_cache=not self.checkpoint)[0]


class GPTLMLoss(nn.Module):
    """
    GPT Loss
    """

    def __init__(self):
        super().__init__()
        self.loss_fn = nn.CrossEntropyLoss()

    def forward(self, logits, labels):
        shift_logits = logits[..., :-1, :].contiguous()
        shift_labels = labels[..., 1:].contiguous()
        # Flatten the tokens
        return self.loss_fn(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))


def gpt2_medium(checkpoint=False):
    return GPTLMModel(hidden_size=1024, num_layers=24, num_attention_heads=16, checkpoint=checkpoint)


def gpt2_xl(checkpoint=False):
    return GPTLMModel(hidden_size=1600, num_layers=48, num_attention_heads=32, checkpoint=checkpoint)


def gpt2_6b(checkpoint=False):
    return GPTLMModel(hidden_size=4096, num_layers=30, num_attention_heads=16, checkpoint=checkpoint)


def data_gen_gpt2(batch_size, seq_len, vocab_size, device='cuda:0'):
    """
    Generate random data for gpt2 benchmarking
    """
    input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=device)
    attention_mask = torch.ones_like(input_ids, device=device)
    return (input_ids, attention_mask), attention_mask


def data_gen_resnet(batch_size, shape, device='cuda:0'):
    """
    Generate random data for resnet benchmarking
    """
    data = torch.empty(batch_size, *shape, device=device)
    label = torch.empty(batch_size, dtype=torch.long, device=device).random_(1000)
    return (data,), label
