import argparse
import time
import logging
import requests
import os
from PIL import Image
from io import BytesIO

from PIL import Image
import torch
from transformers import AutoTokenizer

from .modeling_tinyllava_elm import TinyLlavaForConditionalGeneration
from .configuration import *
from .conversion import *
from .utils import *



def load_image(image_file):
    if image_file.startswith("http") or image_file.startswith("https"):
        response = requests.get(image_file)
        image = Image.open(BytesIO(response.content)).convert("RGB")
    else:
        image = Image.open(image_file).convert("RGB")
    return image


def generate(
    prompt: str,
    model: str,
    tokenizer = None,
    image: str = None,
    device: str = None,
    max_new_tokens: int = 1024,
    num_beams = 1,
    top_p=None,
    temperature=0.2
):
    if not device:
        if torch.cuda.is_available() and torch.cuda.device_count():
            device = "cuda:0"
            logging.warning(
                'inference device is not set, using cuda:0, %s',
                torch.cuda.get_device_name(0)
            )
        else:
            device = 'cpu'
            logging.warning(
                (
                    'No CUDA device detected, using cpu, '
                    'expect slower speeds.'
                )
            )

    if 'cuda' in device and not torch.cuda.is_available():
        raise ValueError('CUDA device requested but no CUDA device detected.')

    if isinstance(model, str):
        checkpoint_path = model
    # print(f'loading model from {checkpoint_path}...')
        model = TinyLlavaForConditionalGeneration.from_pretrained(
            checkpoint_path,
            torch_dtype=torch.float16,
        )
    # print('model load over')
    config = model.config
    if tokenizer is None:
        tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False, model_max_length = config.tokenizer_model_max_length,
                padding_side = config.tokenizer_padding_side)
    image_processor = model.vision_tower._image_processor
    context_len = getattr(config, 'max_sequence_length', 2048)
    model.to(device).eval()


    if image is not None:
        prompt = DEFAULT_IMAGE_TOKEN + '\n' + prompt 
    conv = conv_phi_v0.copy()
    conv.append_message(conv.roles[0], prompt)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()
    if image is not None:
        # print('loading image...')
        image = load_image(image)
        # print('load image over')
        image_tensor = process_images(image, image_processor, config).to(model.device, dtype=torch.float16)

    input_ids = (
        tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
        .unsqueeze(0)
        .cuda()
    )
    # Generate
    stime = time.time()
    # stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    # keywords = [stop_str]
    # stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
    # print('start inference...')
    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            images=image_tensor,
            do_sample=True if temperature > 0 else False,
            temperature=temperature,
            top_p=top_p,
            num_beams=num_beams,
            pad_token_id=tokenizer.pad_token_id,
            max_new_tokens=max_new_tokens,
            use_cache=True,
            # stopping_criteria=[stopping_criteria],
        )

    # print('inference over')
    generation_time = time.time() - stime
    outputs = tokenizer.batch_decode(
        output_ids, skip_special_tokens=True
    )[0]
    # outputs = outputs.strip()
    # if outputs.endswith(stop_str):
    #     outputs = outputs[: -len(stop_str)]
    outputs = outputs.strip()

    return outputs, generation_time
def tinyllava_elm_generate_parser():
    """Argument Parser"""

    class KwargsParser(argparse.Action):
        """Parser action class to parse kwargs of form key=value"""
        def __call__(self, parser, namespace, values, option_string=None):
            setattr(namespace, self.dest, dict())
            for val in values:
                if '=' not in val:
                    raise ValueError(
                        (
                            'Argument parsing error, kwargs are expected in'
                            ' the form of key=value.'
                        )
                    )
                kwarg_k, kwarg_v = val.split('=')
                try:
                    converted_v = int(kwarg_v)
                except ValueError:
                    try:
                        converted_v = float(kwarg_v)
                    except ValueError:
                        converted_v = kwarg_v            
                getattr(namespace, self.dest)[kwarg_k] = converted_v

    parser = argparse.ArgumentParser('TinyLLaVA-OpenELM Generate Module')
    parser.add_argument(
        '--model',
        dest='model',
        help='Path to the hf converted model.',
        required=True,
        type=str,
    )
    parser.add_argument(
      '--prompt',
      dest='prompt',
      help='Prompt for LLM call.',
      default='',
      type=str,
    )
    parser.add_argument(
        '--device',
        dest='device',
        help='Device used for inference.',
        type=str,
    )
    parser.add_argument("--image", type=str, default=None)
    parser.add_argument("--temperature", type=float, default=0)
    parser.add_argument("--top_p", type=float, default=None)
    parser.add_argument("--num_beams", type=int, default=1)
    parser.add_argument("--max_new_tokens", type=int, default=512)
    return parser.parse_args()


if __name__ == '__main__':
    args = tinyllava_elm_generate_parser()
    prompt = args.prompt
    model = TinyLlavaForConditionalGeneration.from_pretrained(args.model)

    output_text, genertaion_time = generate(
        prompt=prompt,
        image=args.image,
        model=args.model,
        device=args.device,
        max_new_tokens = args.max_new_tokens,
        num_beams = args.num_beams,
        top_p=args.top_p,
        temperature=args.temperature
    )

    print_txt = (
        f'\r\n{"=" * os.get_terminal_size().columns}\r\n'
        '\033[1m Prompt + Generated Output\033[0m\r\n'
        f'{"-" * os.get_terminal_size().columns}\r\n'
        f'{output_text}\r\n'
        f'{"-" * os.get_terminal_size().columns}\r\n'
        '\r\nGeneration took'
        f'\033[1m\033[92m {round(genertaion_time, 2)} \033[0m'
        'seconds.\r\n'
    )
    print(print_txt)