import os
import re
import time

import pytest
import torch
from einops import rearrange
from flash_attn.models.gpt import GPTLMHeadModel, remap_state_dict_hf_gpt2
from flash_attn.models.opt import opt_config_to_gpt2_config, remap_state_dict_hf_opt
from flash_attn.utils.generation import update_graph_cache
from flash_attn.utils.pretrained import state_dict_from_pretrained
from transformers import AutoTokenizer, GPT2Config, GPT2Tokenizer, OPTConfig
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel as GPT2LMHeadModelHF
from transformers.models.opt.modeling_opt import OPTForCausalLM


@pytest.mark.parametrize("fused_ft_kernel", [False, True])
# @pytest.mark.parametrize('fused_ft_kernel', [True])
@pytest.mark.parametrize("optimized", [False, True])
# @pytest.mark.parametrize('optimized', [False])
@pytest.mark.parametrize("rotary", [False, True])
# @pytest.mark.parametrize('rotary', [False])
@pytest.mark.parametrize("model_name", ["gpt2"])
def test_greedy_decode_gpt2(model_name, rotary, optimized, fused_ft_kernel):
    """Check that our implementation of GPT2 generation matches the HF implementation:
    the scores in fp16 should be around the same as the HF scores in fp16, when compared to
    the HF scores in fp32.
    """
    dtype = torch.float16
    device = "cuda"
    rtol, atol = 3e-3, 3e-1
    config = GPT2Config.from_pretrained(model_name)
    if rotary:
        config.n_positions = 0
        config.rotary_emb_fraction = 0.5
        config.rotary_emb_base = 24000
    config.residual_in_fp32 = True
    if optimized:
        config.use_flash_attn = True
        config.fused_bias_fc = True
        config.fused_mlp = True
        config.fused_dropout_add_ln = True

    # if not rotary, we load the weight from HF but ignore the position embeddings.
    # The model would be nonsense but it doesn't matter for the test.
    model = GPTLMHeadModel.from_pretrained(
        model_name, config, strict=not rotary, device=device, dtype=dtype
    )
    model.eval()

    if not rotary:
        model_ref = GPT2LMHeadModelHF.from_pretrained(model_name).to(device=device)
        model_hf = GPT2LMHeadModelHF.from_pretrained(model_name, torch_dtype=dtype).to(
            device=device
        )
        model_ref.eval()
        model_hf.eval()

    torch.manual_seed(0)
    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
    input_ids = tokenizer("Hello, my dog is cute and he", return_tensors="pt").input_ids.to(
        device=device
    )
    max_length = 25
    # input_ids = torch.randint(0, 100, (2, 10), dtype=torch.long, device='cuda')
    # max_length = input_ids.shape[1] + 40

    # Slow generation for reference
    sequences = []
    scores = []
    cur_input_ids = input_ids
    with torch.inference_mode():
        scores.append(model(cur_input_ids).logits[:, -1])
        sequences.append(scores[-1].argmax(dim=-1))
        for _ in range(input_ids.shape[1] + 1, max_length):
            cur_input_ids = torch.cat([cur_input_ids, rearrange(sequences[-1], "b -> b 1")], dim=-1)
            scores.append(model(cur_input_ids).logits[:, -1])
            sequences.append(scores[-1].argmax(dim=-1))
    sequences = torch.cat([input_ids, torch.stack(sequences, dim=1)], dim=1)
    scores = tuple(scores)

    out = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        fused_ft_kernel=fused_ft_kernel,
        return_dict_in_generate=True,
        output_scores=True,
        timing=True,
    )
    print(out.sequences)
    print(tokenizer.batch_decode(out.sequences.tolist()))
    if fused_ft_kernel:
        out_cg = model.generate(
            input_ids=input_ids,
            max_length=max_length,
            fused_ft_kernel=fused_ft_kernel,
            cg=True,
            return_dict_in_generate=True,
            output_scores=True,
            timing=True,
        )
        print(out_cg.sequences)

    if not rotary:
        out_hf = model_hf.generate(
            input_ids=input_ids,
            max_length=max_length,
            return_dict_in_generate=True,
            output_scores=True,
        )
        out_ref = model_ref.generate(
            input_ids=input_ids,
            max_length=max_length,
            return_dict_in_generate=True,
            output_scores=True,
        )

        print(
            f"Scores max diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}"
        )
        print(
            f"Scores mean diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}"
        )
        print(
            f"HF fp16 max diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}"
        )
        print(
            f"HF fp16 mean diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}"
        )
        print(tokenizer.batch_decode(out_ref.sequences.tolist()))

    assert torch.all(out.sequences == sequences)
    assert torch.allclose(
        torch.stack(out.scores, dim=1), torch.stack(scores, dim=1), rtol=rtol, atol=atol
    )
    if not rotary:
        assert torch.all(out.sequences == out_ref.sequences)
        assert torch.all(out.sequences == out_hf.sequences)

        assert (
            torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)
        ).abs().max().item() < 3 * (
            torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)
        ).abs().max().item()


@pytest.mark.parametrize(
    "model_name",
    [
        "facebook/opt-125m",
        "facebook/opt-350m",
        "facebook/opt-1.3b",
        "facebook/opt-2.7b",
        "facebook/opt-6.7b",
    ],
)
# @pytest.mark.parametrize('model_name', ["facebook/opt-125m"])
def test_greedy_decode_opt(model_name):
    """Check that our implementation of OPT generation matches the HF implementation:
    the scores in fp16 should be around the same as the HF scores in fp16, when compared to
    the HF scores in fp32.
    """
    print(f"\nMODEL: {model_name}")
    verbose = False
    dtype = torch.float16
    device = "cuda"
    rtol, atol = 3e-3, 3e-1
    fused_ft_kernel = True
    config = opt_config_to_gpt2_config(OPTConfig.from_pretrained(model_name))
    # Only prenorm supports residual_in_fp32
    config.residual_in_fp32 = getattr(config, "prenorm", True)
    config.use_flash_attn = True
    config.fused_bias_fc = True
    config.fused_mlp = True
    config.fused_dropout_add_ln = True

    model = GPTLMHeadModel.from_pretrained(model_name, config, device=device, dtype=dtype)
    model.eval()

    torch.manual_seed(0)
    # OPT tokenizer requires use_fast=False
    # https://huggingface.co/docs/transformers/model_doc/opt
    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
    eos_token_id = tokenizer.eos_token_id

    input_ids = tokenizer("Hello, my dog is cute and he", return_tensors="pt").input_ids.to(
        device=device
    )
    max_length = 25
    # input_ids = torch.randint(0, 100, (2, 10), dtype=torch.long, device='cuda')
    # max_length = input_ids.shape[1] + 40

    # Slow generation for reference
    sequences = []
    scores = []
    cur_input_ids = input_ids
    with torch.inference_mode():
        scores.append(model(cur_input_ids).logits[:, -1])
        sequences.append(scores[-1].argmax(dim=-1))
        for _ in range(input_ids.shape[1] + 1, max_length):
            cur_input_ids = torch.cat([cur_input_ids, rearrange(sequences[-1], "b -> b 1")], dim=-1)
            scores.append(model(cur_input_ids).logits[:, -1])
            sequences.append(scores[-1].argmax(dim=-1))
            if eos_token_id is not None and (sequences[-1] == eos_token_id).all():
                break
    sequences = torch.cat([input_ids, torch.stack(sequences, dim=1)], dim=1)
    scores = tuple(scores)

    print("Without CUDA graph")
    torch.cuda.synchronize()
    start = time.time()
    out = model.generate(
        input_ids=input_ids,
        max_length=max_length,
        eos_token_id=eos_token_id,
        fused_ft_kernel=fused_ft_kernel,
        return_dict_in_generate=True,
        output_scores=True,
        timing=True,
    )
    torch.cuda.synchronize()
    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
    if verbose:
        print(out.sequences)
    print(tokenizer.batch_decode(out.sequences.tolist()))
    if fused_ft_kernel:
        # Capture graph outside the timing loop
        batch_size, seqlen_og = input_ids.shape
        model._decoding_cache = update_graph_cache(model, None, batch_size, seqlen_og, max_length)
        print("With CUDA graph")
        torch.cuda.synchronize()
        start = time.time()
        out_cg = model.generate(
            input_ids=input_ids,
            max_length=max_length,
            fused_ft_kernel=fused_ft_kernel,
            cg=True,
            return_dict_in_generate=True,
            output_scores=True,
            timing=True,
        )
        torch.cuda.synchronize()
        print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
        if verbose:
            print(out_cg.sequences)
        print(tokenizer.batch_decode(out_cg.sequences.tolist()))

    del model

    model_hf = OPTForCausalLM.from_pretrained(model_name, torch_dtype=dtype).to(device=device)
    model_hf.eval()
    print("HF fp16")
    torch.cuda.synchronize()
    start = time.time()
    out_hf = model_hf.generate(
        input_ids=input_ids, max_length=max_length, return_dict_in_generate=True, output_scores=True
    )
    torch.cuda.synchronize()
    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
    del model_hf

    model_ref = OPTForCausalLM.from_pretrained(model_name).to(device=device)
    model_ref.eval()
    print("HF fp32")
    torch.cuda.synchronize()
    start = time.time()
    out_ref = model_ref.generate(
        input_ids=input_ids, max_length=max_length, return_dict_in_generate=True, output_scores=True
    )
    torch.cuda.synchronize()
    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
    del model_ref
    print(tokenizer.batch_decode(out_ref.sequences.tolist()))

    if verbose:
        print(
            f"Scores max diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}"
        )
        print(
            f"Scores mean diff: {(torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}"
        )
        print(
            f"HF fp16 max diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item()}"
        )
        print(
            f"HF fp16 mean diff: {(torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)).abs().mean().item()}"
        )

    assert torch.all(out.sequences == sequences)
    assert torch.allclose(
        torch.stack(out.scores, dim=1), torch.stack(scores, dim=1), rtol=rtol, atol=atol
    )
    assert torch.all(out.sequences == out_ref.sequences)
    assert torch.all(out.sequences == out_hf.sequences)

    assert (torch.stack(out.scores, 1) - torch.stack(out_ref.scores, 1)).abs().max().item() < 3 * (
        torch.stack(out_hf.scores, 1) - torch.stack(out_ref.scores, 1)
    ).abs().max().item()