# Copyright (c) Alibaba, Inc. and its affiliates.
import os
from copy import deepcopy
from typing import Any, Dict, List

import json
import numpy as np

from swift.llm import RequestConfig
from swift.llm.sampling.base import Sampler
from swift.llm.template.template_inputs import InferRequest
from swift.utils import get_logger
from .utils import get_messages_md5, get_reward

logger = get_logger()


class VanillaSampler(Sampler):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if self.args.sampler_engine == 'pt':
            from swift.llm import PtEngine
            _Engine = PtEngine
        elif self.args.sampler_engine == 'vllm':
            from swift.llm import VllmEngine
            _Engine = VllmEngine
        elif self.args.sampler_engine == 'lmdeploy':
            from swift.llm import LmdeployEngine
            _Engine = LmdeployEngine
        elif self.args.sampler_engine == 'no':
            _Engine = None
        else:
            raise ValueError(f'Cannot find engine name: {self.args.sampler_engine}')
        self.infer_engine = None
        if _Engine:
            self.infer_engine = _Engine(
                self.args.model, model_type=self.args.model_type, template=self.template, **self.args.engine_kwargs)
            self.infer_engine.strict = False
        self.caches = self.read_cache()

    def read_cache(self):
        cache_files = self.args.cache_files
        caches = {}
        for file in cache_files:
            if not os.path.exists(file):
                logger.warning(f'Cache file does not exist: {file}')
                continue
            with open(file, 'r') as f:
                for line in f.readlines():
                    line = line.strip()
                    if not line:
                        continue

                    content = json.loads(line)
                    uuid = content['id']
                    messages = content['messages']
                    if uuid not in caches:
                        caches[uuid] = {'choices': []}
                    assert messages[-1]['role'] == 'assistant'
                    caches[uuid]['choices'].append(messages[-1]['content'])
        return caches

    @staticmethod
    def convert_data_to_rows(data):
        rows = []
        key = list(data.keys())[0]
        data_len = len(data[key])
        for idx in range(data_len):
            row = {key: data[key][idx] for key in data}
            if row.get('images') and 'bytes' in row['images'][0]:
                row['images'] = [img['path'] for img in row['images']]
            rows.append(row)
        VanillaSampler.check_row_valid(rows)
        return rows

    @staticmethod
    def check_row_valid(rows):
        for row in rows:
            assert not row.get('images') or all([isinstance(img, str) and img for img in row['images']])
            assert not row.get('videos') or all([isinstance(video, str) and video for video in row['videos']])
            assert not row.get('audios') or all([isinstance(audio, str) and audio for audio in row['audios']])

    def generate(self, data):
        resp_all = []
        infer_requests = []
        sent = 0
        rows = self.convert_data_to_rows(data)
        for idx, row in enumerate(rows):
            row = deepcopy(row)
            messages = row['messages']
            uuid = get_messages_md5(row)
            if uuid in self.caches:
                choices = self.caches[uuid]['choices']
                if len(choices) == self.args.num_return_sequences:
                    continue
            if self.args.system:
                if messages[0]['role'] == 'system':
                    messages[0]['content'] = self.args.system
                else:
                    messages.insert(0, {'role': 'system', 'content': self.args.system})
            if messages[-1]['role'] == 'assistant':
                messages = messages[:-1]

            row['messages'] = messages
            infer_request = row
            for i in range(self.args.num_return_sequences):
                infer_requests.append(deepcopy(infer_request))
            sent += 1

        request_config = RequestConfig(
            max_tokens=self.args.max_new_tokens,
            temperature=self.args.temperature,
            top_k=self.args.top_k,
            top_p=self.args.top_p,
        )

        resp_list = []
        if len(infer_requests) > 0:
            resp_list = self.infer_engine.infer(infer_requests, request_config=request_config)

        _cur = 0
        for idx, row in enumerate(rows):
            row = deepcopy(row)
            uuid = get_messages_md5(row)
            if uuid in self.caches:
                choices = self.caches[uuid]['choices']
                if len(choices) == self.args.num_return_sequences:
                    row['choices'] = choices
                    resp_all.append(row)
                    continue

            resps = row
            resps['choices'] = []
            for j in range(self.args.num_return_sequences * _cur, self.args.num_return_sequences * (_cur + 1)):
                if not isinstance(resp_list[j], Exception):
                    resps['choices'].append(resp_list[j].choices[0].message.content)
            if resps['choices']:
                resp_all.append(resps)
            _cur += 1
        return resp_all

    def do_sample(self, data):
        generated = []
        resp_all = self.generate(data)
        for i, resps in enumerate(resp_all):
            choices = resps['choices']
            messages = resps['messages']
            uuid = get_messages_md5(resps)
            assert messages[-1]['role'] == 'assistant'
            ground_truth = messages[-1]['content']

            infer_requests = []
            for decoded in choices:
                _resps = deepcopy(resps)
                _resps['messages'][-1]['content'] = decoded
                infer_requests.append(_resps)

            _resps = deepcopy(resps)
            _resps['messages'][-1]['content'] = ground_truth
            infer_requests.append(_resps)
            if self.orm_model is not None:
                orm_score, _orm_mask = get_reward(
                    self.orm_model, infer_requests, ground_truths=[ground_truth] * len(infer_requests), threshold=0.0)
            else:
                orm_score = np.array([1.0] * len(infer_requests))
                _orm_mask = np.array([True] * len(infer_requests))
            if self.prm_model is not None:
                prm_score, _prm_mask = get_reward(
                    self.prm_model,
                    infer_requests,
                    ground_truths=[ground_truth] * len(infer_requests),
                    threshold=self.args.prm_threshold)
            else:
                prm_score = np.array([1.0] * len(infer_requests))
                _prm_mask = np.array([True] * len(infer_requests))

            _mask = _orm_mask & _prm_mask
            if not any(_mask):
                continue

            choices.append(ground_truth)
            choices = np.array(choices)

            if self.orm_model is None and self.prm_model is None:
                positives = choices[:-1]
                for positive in positives:
                    _resps = deepcopy(resps)
                    _resps.pop('choices', None)
                    _resps['id'] = uuid
                    _resps['messages'][-1]['content'] = str(positive)
                    generated.append(json.dumps(_resps, ensure_ascii=False) + '\n')
            else:
                score = np.array(prm_score) + np.array(orm_score * 10)
                sorted_indices = np.argsort(score)[::-1]
                pos_indexes = sorted_indices[0:self.args.n_best_to_keep]
                pos_indexes = [i for i in pos_indexes if _mask[i]]
                neg_index = sorted_indices[-1]
                logger.info(
                    f'orm:{orm_score}, prm:{prm_score}, positive index: {pos_indexes}, negative index: {neg_index}')
                if self.args.easy_query_threshold is not None and sum([score > 0 for score in orm_score]) - 1 >= int(
                        self.args.num_return_sequences * self.args.easy_query_threshold):
                    continue
                if len(pos_indexes) > 0:
                    positives = choices[pos_indexes]
                    negative = choices[neg_index]
                    for positive in positives:
                        _resps = deepcopy(resps)
                        messages = deepcopy(messages)
                        _resps.pop('choices', None)
                        _resps['messages'][-1]['content'] = str(positive)
                        _resps['rejected_response'] = str(negative)
                        _resps['id'] = uuid
                        generated.append(json.dumps(_resps, ensure_ascii=False) + '\n')
        return generated
