import sys
import torch
from peft import PeftModel, PeftModelForCausalLM, LoraConfig
import transformers
import gradio as gr
import argparse
import warnings
import os
from utils import StreamPeftGenerationMixin,StreamLlamaForCausalLM
# assert (
#     "LlamaTokenizer" in transformers._import_structure["models.llama"]
# ), "LLaMA is now in HuggingFace's main branch.\nPlease reinstall it: pip uninstall transformers && pip install git+https://github.com/huggingface/transformers.git"
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig

parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="/model/13B_hf")
parser.add_argument("--lora_path", type=str, default="checkpoint-3000")
parser.add_argument("--use_typewriter", type=int, default=1)
parser.add_argument("--use_local", type=int, default=1)
args = parser.parse_args()
print(args)
tokenizer = LlamaTokenizer.from_pretrained(args.model_path)

LOAD_8BIT = True
BASE_MODEL = args.model_path
LORA_WEIGHTS = args.lora_path


# fix the path for local checkpoint
lora_bin_path = os.path.join(args.lora_path, "adapter_model.bin")
print(lora_bin_path)
if not os.path.exists(lora_bin_path) and args.use_local:
    pytorch_bin_path = os.path.join(args.lora_path, "pytorch_model.bin")
    print(pytorch_bin_path)
    if os.path.exists(pytorch_bin_path):
        os.rename(pytorch_bin_path, lora_bin_path)
        warnings.warn(
            "The file name of the lora checkpoint'pytorch_model.bin' is replaced with 'adapter_model.bin'"
        )
    else:
        assert ('Checkpoint is not Found!')

if torch.cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

try:
    if torch.backends.mps.is_available():
        device = "mps"
except:
    pass

if device == "cuda":
    model = LlamaForCausalLM.from_pretrained(
        BASE_MODEL,
        load_in_8bit=LOAD_8BIT,
        torch_dtype=torch.float16,
        device_map="auto", #device_map={"": 0},
    )
    model = StreamPeftGenerationMixin.from_pretrained(
        model, LORA_WEIGHTS, torch_dtype=torch.float16, device_map="auto", #device_map={"": 0}
    )
elif device == "mps":
    model = LlamaForCausalLM.from_pretrained(
        BASE_MODEL,
        device_map={"": device},
        torch_dtype=torch.float16,
    )
    model = StreamPeftGenerationMixin.from_pretrained(
        model,
        LORA_WEIGHTS,
        device_map={"": device},
        torch_dtype=torch.float16,
    )
else:
    model = LlamaForCausalLM.from_pretrained(
        BASE_MODEL, device_map={"": device}, low_cpu_mem_usage=True
    )
    model = StreamPeftGenerationMixin.from_pretrained(
        model,
        LORA_WEIGHTS,
        device_map={"": device},
    )


def generate_prompt(instruction, input=None):
    if input:
        return f"""你是一个乐于助人的中文助手，请你回答一下以下问题

### Instruction:
{instruction}

### Input:
{input}

### Response:"""
    else:
        return f"""你是一个乐于助人的中文助手，请你回答一下以下问题

### Instruction:
{instruction}

### Response:"""


if not LOAD_8BIT:
    model.half()  # seems to fix bugs for some users.

model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
    model = torch.compile(model)


def evaluate(
    input,
    temperature=0.1,
    top_p=0.75,
    top_k=40,
    num_beams=4,
    max_new_tokens=128,
    min_new_tokens=1,
    repetition_penalty=2.0,
    **kwargs,
):
    prompt = generate_prompt(input)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].to(device)
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        bos_token_id=1,
        eos_token_id=2,
        pad_token_id=0,
        max_new_tokens=max_new_tokens, # max_length=max_new_tokens+input_sequence
        min_new_tokens=min_new_tokens, # min_length=min_new_tokens+input_sequence
        **kwargs,
    )
    with torch.no_grad():
        if args.use_typewriter:
            for generation_output in model.stream_generate(
                input_ids=input_ids,
                generation_config=generation_config,
                return_dict_in_generate=True,
                output_scores=False,
                repetition_penalty=float(repetition_penalty),
            ):
                outputs = tokenizer.batch_decode(generation_output)
                show_text = "\n--------------------------------------------\n".join(
                    [output.split("### Response:")[1].strip().replace('�','')+" ▌" for output in outputs]
                )
                # if show_text== '':
                #     yield last_show_text
                # else:
                yield show_text
            yield outputs[0].split("### Response:")[1].strip().replace('�','')
        else:
            generation_output = model.generate(
                input_ids=input_ids,
                generation_config=generation_config,
                return_dict_in_generate=True,
                output_scores=False,
                repetition_penalty=1.3,
            )
            output = generation_output.sequences[0]
            output = tokenizer.decode(output).split("### Response:")[1].strip()
            print(output)
            yield output


gr.Interface(
    fn=evaluate,
    inputs=[
        gr.components.Textbox(
            lines=2, label="Input", placeholder="Tell me about alpacas."
        ),
        gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
        gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"),
        gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
        gr.components.Slider(minimum=1, maximum=10, step=1, value=4, label="Beams Number"),
        gr.components.Slider(
            minimum=1, maximum=2000, step=1, value=256, label="Max New Tokens"
        ),
        gr.components.Slider(
            minimum=1, maximum=300, step=1, value=1, label="Min New Tokens"
        ),
        gr.components.Slider(
            minimum=0.1, maximum=10.0, step=0.1, value=2.0, label="Repetition Penalty"
        ),
    ],
    outputs=[
        gr.inputs.Textbox(
            lines=25,
            label="Output",
        )
    ],
    title="HKLawGPT",
    description="",
).queue().launch(share=True)