import sys
from pathlib import Path

from modules import shared
from modules.logging_colors import logger

try:
    from exllama.generator import ExLlamaGenerator
    from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
    from exllama.tokenizer import ExLlamaTokenizer
except:
    logger.warning('Exllama module failed to load. Will attempt to load from repositories.')
    try:
        from modules.relative_imports import RelativeImport
        
        with RelativeImport("repositories/exllama"):
            from generator import ExLlamaGenerator
            from model import ExLlama, ExLlamaCache, ExLlamaConfig
            from tokenizer import ExLlamaTokenizer
    except:
        logger.error("Could not find the file repositories/exllama/lora.py. Make sure that exllama is cloned inside repositories/ and is up to date.")
        raise


class ExllamaModel:
    def __init__(self):
        pass

    @classmethod
    def from_pretrained(self, path_to_model):

        path_to_model = Path(f'{shared.args.model_dir}') / Path(path_to_model)
        tokenizer_model_path = path_to_model / "tokenizer.model"
        model_config_path = path_to_model / "config.json"

        # Find the model checkpoint
        model_path = None
        for ext in ['.safetensors', '.pt', '.bin']:
            found = list(path_to_model.glob(f"*{ext}"))
            if len(found) > 0:
                if len(found) > 1:
                    logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')

                model_path = found[-1]
                break

        config = ExLlamaConfig(str(model_config_path))
        config.model_path = str(model_path)
        if shared.args.gpu_split:
            config.set_auto_map(shared.args.gpu_split)
            config.gpu_peer_fix = True

        model = ExLlama(config)
        tokenizer = ExLlamaTokenizer(str(tokenizer_model_path))
        cache = ExLlamaCache(model)
        generator = ExLlamaGenerator(model, tokenizer, cache)

        result = self()
        result.config = config
        result.model = model
        result.cache = cache
        result.tokenizer = tokenizer
        result.generator = generator
        return result, result

    def generate_with_streaming(self, prompt, state):
        self.generator.settings.temperature = state['temperature']
        self.generator.settings.top_p = state['top_p']
        self.generator.settings.top_k = state['top_k']
        self.generator.settings.typical = state['typical_p']
        self.generator.settings.token_repetition_penalty_max = state['repetition_penalty']
        if state['ban_eos_token']:
            self.generator.disallow_tokens([self.tokenizer.eos_token_id])
        else:
            self.generator.disallow_tokens(None)

        self.generator.end_beam_search()
        ids = self.generator.tokenizer.encode(prompt)
        self.generator.gen_begin_reuse(ids)
        initial_len = self.generator.sequence[0].shape[0]
        has_leading_space = False
        for i in range(state['max_new_tokens']):
            token = self.generator.gen_single_token()
            if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
                has_leading_space = True

            decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
            if has_leading_space:
                decoded_text = ' ' + decoded_text

            yield decoded_text
            if token.item() == self.generator.tokenizer.eos_token_id or shared.stop_everything:
                break

    def generate(self, prompt, state):
        output = ''
        for output in self.generate_with_streaming(prompt, state):
            pass

        return output

    def encode(self, string, **kwargs):
        return self.tokenizer.encode(string)