import time
from contextlib import nullcontext

import torch
from torch.profiler import ProfilerActivity, profile, schedule, tensorboard_trace_handler


class DummyProfiler:
    def __init__(self):
        self.step_number = 0

    def step(self):
        self.step_number += 1


# Randomly Generated Data
def get_data(batch_size, seq_len, vocab_size):
    input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
    attention_mask = torch.ones_like(input_ids)
    return input_ids, attention_mask


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_profile_context(enable_flag, warmup_steps, active_steps, save_dir):
    if enable_flag:
        return profile(
            activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
            schedule=schedule(wait=0, warmup=warmup_steps, active=active_steps),
            on_trace_ready=tensorboard_trace_handler(save_dir),
            record_shapes=True,
            profile_memory=True,
        )
    else:
        return nullcontext(DummyProfiler())


def get_time_stamp():
    cur_time = time.strftime("%d-%H:%M", time.localtime())
    return cur_time
