#
#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#
import json
import logging
from abc import ABC
from urllib.parse import urljoin
from typing import Tuple, List
from http import HTTPStatus

import numpy as np
import requests
from yarl import URL

from common.log_utils import log_exception
from common.token_utils import num_tokens_from_string, truncate, total_token_count_from_response

class Base(ABC):
    def __init__(self, key, model_name, **kwargs):
        pass

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        raise NotImplementedError("Please implement encode method!")

    @staticmethod
    def _normalize_rank(rank: np.ndarray) -> np.ndarray:
        if rank.size == 0:
            return rank
        min_rank = np.min(rank)
        max_rank = np.max(rank)

        if not np.isclose(min_rank, max_rank, atol=1e-3):
            rank = (rank - min_rank) / (max_rank - min_rank)
        else:
            rank = np.zeros_like(rank)

        return rank


class JinaRerank(Base):
    _FACTORY_NAME = "Jina"

    def __init__(self, key, model_name="jina-reranker-v2-base-multilingual", base_url="https://api.jina.ai/v1/rerank"):
        self.base_url = base_url or "https://api.jina.ai/v1/rerank"
        self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"}
        self.model_name = model_name

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts) if texts else 0, dtype=float), 0
        texts = [truncate(t, 8196) for t in texts]
        data = {"model": self.model_name, "query": query, "documents": texts, "top_n": len(texts)}
        response = requests.post(self.base_url, headers=self.headers, json=data, timeout=30)
        response.raise_for_status()
        res = response.json()
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("results", []):
                rank[d["index"]] = d["relevance_score"]
        except Exception as _e:
            log_exception(_e, res)
        return rank, total_token_count_from_response(res)


class XInferenceRerank(Base):
    _FACTORY_NAME = "Xinference"

    def __init__(self, key="x", model_name="", base_url=""):
        if base_url.find("/v1") == -1:
            base_url = urljoin(base_url, "/v1/rerank")
        if base_url.find("/rerank") == -1:
            base_url = urljoin(base_url, "/v1/rerank")
        self.model_name = model_name
        self.base_url = base_url
        self.headers = {"Content-Type": "application/json", "accept": "application/json"}
        if key and key != "x":
            self.headers["Authorization"] = f"Bearer {key}"

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts) if texts else 0, dtype=float), 0
        pairs = [(query, truncate(t, 4096)) for t in texts]
        token_count = 0
        for _, t in pairs:
            token_count += num_tokens_from_string(t)
        data = {"model": self.model_name, "query": query, "return_documents": "true", "return_len": "true", "documents": texts}
        response = requests.post(self.base_url, headers=self.headers, json=data, timeout=30)
        response.raise_for_status()
        res = response.json()
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("results", []):
                rank[d["index"]] = d["relevance_score"]
        except Exception as _e:
            log_exception(_e, res)
        return rank, token_count


class LocalAIRerank(Base):
    _FACTORY_NAME = "LocalAI"

    def __init__(self, key, model_name, base_url):
        if base_url.find("/rerank") == -1:
            self.base_url = urljoin(base_url, "/rerank")
        else:
            self.base_url = base_url
        self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"}
        self.model_name = model_name.split("___")[0]

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
        texts = [truncate(t, 500) for t in texts]
        data = {
            "model": self.model_name,
            "query": query,
            "documents": texts,
            "top_n": len(texts),
        }
        token_count = 0
        for t in texts:
            token_count += num_tokens_from_string(t)
        response = requests.post(self.base_url, headers=self.headers, json=data, timeout=30)
        response.raise_for_status()
        res = response.json()
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("results", []):
                rank[d["index"]] = d["relevance_score"]
        except Exception as _e:
            log_exception(_e, res)

        rank = Base._normalize_rank(rank)
        return rank, token_count


class NvidiaRerank(Base):
    _FACTORY_NAME = "NVIDIA"

    def __init__(self, key, model_name, base_url="https://ai.api.nvidia.com/v1/retrieval/nvidia/"):
        if not base_url:
            base_url = "https://ai.api.nvidia.com/v1/retrieval/nvidia/"
        self.model_name = model_name

        if self.model_name == "nvidia/nv-rerankqa-mistral-4b-v3":
            self.base_url = urljoin(base_url, "nv-rerankqa-mistral-4b-v3/reranking")

        if self.model_name == "nvidia/rerank-qa-mistral-4b":
            self.base_url = urljoin(base_url, "reranking")
            self.model_name = "nv-rerank-qa-mistral-4b:1"

        self.headers = {
            "accept": "application/json",
            "Content-Type": "application/json",
            "Authorization": f"Bearer {key}",
        }

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
        token_count = num_tokens_from_string(query) + sum([num_tokens_from_string(t) for t in texts])
        data = {
            "model": self.model_name,
            "query": {"text": query},
            "passages": [{"text": text} for text in texts],
            "truncate": "END",
            "top_n": len(texts),
        }
        response = requests.post(self.base_url, headers=self.headers, json=data, timeout=30)
        response.raise_for_status()
        res = response.json()
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("rankings", []):
                rank[d["index"]] = d["logit"]
        except Exception as _e:
            log_exception(_e, res)
        return rank, token_count


class LmStudioRerank(Base):
    _FACTORY_NAME = "LM-Studio"

    def __init__(self, key, model_name, base_url, **kwargs):
        pass

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        raise NotImplementedError("The LmStudioRerank has not been implemented")


class OpenAI_APIRerank(Base):
    _FACTORY_NAME = "OpenAI-API-Compatible"

    def __init__(self, key, model_name, base_url):
        normalized_base_url = (base_url or "").strip()
        if "/rerank" in normalized_base_url:
            self.base_url = normalized_base_url.rstrip("/")
        else:
            self.base_url = urljoin(f"{normalized_base_url.rstrip('/')}/", "rerank").rstrip("/")
        self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"}
        self.model_name = model_name.split("___")[0]

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
        texts = [truncate(t, 500) for t in texts]
        data = {
            "model": self.model_name,
            "query": query,
            "documents": texts,
            "top_n": len(texts),
        }
        token_count = 0
        for t in texts:
            token_count += num_tokens_from_string(t)
        response = requests.post(self.base_url, headers=self.headers, json=data, timeout=30)
        response.raise_for_status()
        res = response.json()
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("results", []):
                rank[d["index"]] = d["relevance_score"]
        except Exception as _e:
            log_exception(_e, res)

        rank = Base._normalize_rank(rank)
        return rank, token_count


class CoHereRerank(Base):
    _FACTORY_NAME = ["Cohere", "VLLM"]

    def __init__(self, key, model_name, base_url=None):
        from cohere import Client

        client_kwargs = {"api_key": key, "timeout": 30.0}
        if base_url and base_url.strip():
            client_kwargs["base_url"] = base_url
        self.client = Client(**client_kwargs)
        self.model_name = model_name.split("___")[0]

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
        token_count = num_tokens_from_string(query) + sum([num_tokens_from_string(t) for t in texts])
        res = self.client.rerank(
            model=self.model_name,
            query=query,
            documents=texts,
            top_n=len(texts),
            return_documents=False,
        )
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.results:
                rank[d.index] = d.relevance_score
        except Exception as _e:
            log_exception(_e, res)
        return rank, token_count


class TogetherAIRerank(Base):
    _FACTORY_NAME = "TogetherAI"

    def __init__(self, key, model_name, base_url, **kwargs):
        pass

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        raise NotImplementedError("The api has not been implemented")


class SILICONFLOWRerank(Base):
    _FACTORY_NAME = "SILICONFLOW"

    def __init__(self, key, model_name, base_url="https://api.siliconflow.cn/v1/rerank"):
        normalized_base_url = (base_url or "").strip()
        if not normalized_base_url:
            normalized_base_url = "https://api.siliconflow.cn/v1/rerank"
        if "/rerank" not in normalized_base_url:
            normalized_base_url = urljoin(f"{normalized_base_url.rstrip('/')}/", "rerank").rstrip("/")
        self.model_name = model_name
        self.base_url = normalized_base_url
        self.headers = {
            "accept": "application/json",
            "content-type": "application/json",
            "authorization": f"Bearer {key}",
        }

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
        payload = {
            "model": self.model_name,
            "query": query,
            "documents": texts,
            "top_n": len(texts),
            "return_documents": False,
            "max_chunks_per_doc": 1024,
            "overlap_tokens": 80,
        }
        response = requests.post(self.base_url, json=payload, headers=self.headers, timeout=30)
        response.raise_for_status()
        res = response.json()
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("results", []):
                rank[d["index"]] = d["relevance_score"]
        except Exception as _e:
            log_exception(_e, response)
        return rank, total_token_count_from_response(res)


class BaiduYiyanRerank(Base):
    _FACTORY_NAME = "BaiduYiyan"

    def __init__(self, key, model_name, base_url=None):
        from qianfan.resources import Reranker

        key = json.loads(key)
        ak = key.get("yiyan_ak", "")
        sk = key.get("yiyan_sk", "")
        self.client = Reranker(ak=ak, sk=sk, request_timeout=30)
        self.model_name = model_name

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
        res = self.client.do(
            model=self.model_name,
            query=query,
            documents=texts,
            top_n=len(texts),
        ).body
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("results", []):
                rank[d["index"]] = d["relevance_score"]
        except Exception as _e:
            log_exception(_e, res)
        return rank, total_token_count_from_response(res)


class VoyageRerank(Base):
    _FACTORY_NAME = "Voyage AI"

    def __init__(self, key, model_name, base_url=None):
        import voyageai

        self.client = voyageai.Client(api_key=key, timeout=30.0)
        self.model_name = model_name

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts) if texts else 0, dtype=float), 0
        rank = np.zeros(len(texts), dtype=float)

        res = self.client.rerank(query=query, documents=texts, model=self.model_name, top_k=len(texts))
        try:
            for r in res.results:
                rank[r.index] = r.relevance_score
        except Exception as _e:
            log_exception(_e, res)
        return rank, res.total_tokens


class QWenRerank(Base):
    _FACTORY_NAME = "Tongyi-Qianwen"

    def __init__(self, key, model_name="gte-rerank", **kwargs):
        import dashscope
        self.api_key = key
        self.model_name = dashscope.TextReRank.Models.gte_rerank if model_name is None else model_name
        # Remove invalid global timeout, use official SDK per-request timeout parameter
        self.request_timeout = 30.0

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
            
        import dashscope

        # Pass official request_timeout parameter to both API call branches
        if self.model_name.startswith("qwen3-rerank"):  
            resp = dashscope.TextReRank.call(  
                api_key=self.api_key, model=self.model_name,  
                query=query, documents=texts, top_n=len(texts),
                request_timeout=self.request_timeout
            )  
        else:  
            resp = dashscope.TextReRank.call(  
                api_key=self.api_key, model=self.model_name,  
                query=query, documents=texts,  
                top_n=len(texts), return_documents=False,
                request_timeout=self.request_timeout
            )  

        rank = np.zeros(len(texts), dtype=float)
        if resp.status_code == HTTPStatus.OK:
            try:
                for r in resp.output.results:
                    rank[r.index] = r.relevance_score
            except Exception as _e:
                log_exception(_e, resp)
            return rank, total_token_count_from_response(resp)
        else:
            raise ValueError(f"Error calling QWenRerank model {self.model_name}: {resp.status_code} - {resp.text}")


class HuggingfaceRerank(Base):
    _FACTORY_NAME = "HuggingFace"

    @staticmethod
    def post(query: str, texts: list, url: str = "http://127.0.0.1"):
        exc = None
        scores = [0 for _ in range(len(texts))]
        batch_size = 8
        # FIX: Robust URL construction to avoid duplicate "/rerank" path suffix
        base_url = url.rstrip("/")
        if not base_url.startswith(("http://", "https://")):
            base_url = f"http://{base_url}"
        # Only append "/rerank" when endpoint does not already end with it
        endpoint = base_url if base_url.endswith("/rerank") else f"{base_url}/rerank"

        for i in range(0, len(texts), batch_size):
            try:
                # Fix: Add request timeout
                res = requests.post(
                    endpoint, headers={"Content-Type": "application/json"}, 
                    json={"query": query, "texts": texts[i:i+batch_size], "raw_scores": False, "truncate": True},
                    timeout=30
                )
                res.raise_for_status()
                for o in res.json():
                    scores[o["index"] + i] = o["score"]
            except Exception as e:
                exc = e

        if exc:
            raise exc
        return np.array(scores)

    def __init__(self, key, model_name="BAAI/bge-reranker-v2-m3", base_url="http://127.0.0.1"):
        self.model_name = model_name.split("___")[0]
        self.base_url = base_url

    def similarity(self, query: str, texts: List) -> tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
        token_count = 0
        for t in texts:
            token_count += num_tokens_from_string(t)
        return HuggingfaceRerank.post(query, texts, self.base_url), token_count


class GPUStackRerank(Base):
    _FACTORY_NAME = "GPUStack"

    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("url cannot be None")

        self.model_name = model_name
        self.base_url = str(URL(base_url) / "v1" / "rerank")
        self.headers = {
            "accept": "application/json",
            "content-type": "application/json",
            "authorization": f"Bearer {key}",
        }

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
            
        payload = {
            "model": self.model_name,
            "query": query,
            "documents": texts,
            "top_n": len(texts),
        }

        try:
            response = requests.post(self.base_url, json=payload, headers=self.headers, timeout=30)
            response.raise_for_status()
            response_json = response.json()

            rank = np.zeros(len(texts), dtype=float)
            token_count = sum(num_tokens_from_string(t) for t in texts)
            try:
                for result in response_json.get("results", []):
                    rank[result["index"]] = result["relevance_score"]
            except Exception as _e:
                log_exception(_e, response)

            return (rank, token_count)

        except requests.exceptions.RequestException as e:
            raise ValueError(f"Error calling GPUStackRerank model {self.model_name}: {str(e)}") from e


class NovitaRerank(JinaRerank):
    _FACTORY_NAME = "NovitaAI"

    def __init__(self, key, model_name, base_url="https://api.novita.ai/v3/openai/rerank"):
        if not base_url:
            base_url = "https://api.novita.ai/v3/openai/rerank"
        super().__init__(key, model_name, base_url)


class GiteeRerank(JinaRerank):
    _FACTORY_NAME = "GiteeAI"

    def __init__(self, key, model_name, base_url="https://ai.gitee.com/v1/rerank"):
        if not base_url:
            base_url = "https://ai.gitee.com/v1/rerank"
        super().__init__(key, model_name, base_url)


class Ai302Rerank(Base):
    _FACTORY_NAME = "302.AI"

    def __init__(self, key, model_name, base_url="https://api.302.ai/v1/rerank"):
        self.base_url = base_url or "https://api.302.ai/v1/rerank"
        self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"}
        self.model_name = model_name

    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
        texts = [truncate(t, 500) for t in texts]
        data = {"model": self.model_name, "query": query, "documents": texts, "top_n": len(texts)}
        response = requests.post(self.base_url, headers=self.headers, json=data, timeout=30)
        response.raise_for_status()
        res = response.json()
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("results", []):
                rank[d["index"]] = d["relevance_score"]
        except Exception as _e:
            log_exception(_e, res)
        return rank, total_token_count_from_response(res)


class JiekouAIRerank(JinaRerank):
    _FACTORY_NAME = "Jiekou.AI"

    def __init__(self, key, model_name, base_url="https://api.jiekou.ai/openai/v1/rerank"):
        if not base_url:
            base_url = "https://api.jiekou.ai/openai/v1/rerank"
        super().__init__(key, model_name, base_url)


class FuturMixRerank(OpenAI_APIRerank):
    _FACTORY_NAME = "FuturMix"

    def __init__(self, key, model_name, base_url="https://futurmix.ai/v1/rerank"):
        if not base_url:
            base_url = "https://futurmix.ai/v1/rerank"
        super().__init__(key, model_name, base_url)
        logging.info("[FuturMix] Rerank initialized with model %s", model_name)


class RAGconRerank(Base):
    _FACTORY_NAME = "RAGcon"
    
    def __init__(self, key, model_name, base_url=None, **kwargs):
        if not base_url:
            base_url = "https://connect.ragcon.com/v1"
        
        self._api_key = key
        self._base_url = base_url
        
        self.headers = {"Content-Type": "application/json", "Authorization": f"Bearer {key}"}
        self.model_name = model_name
        
    
    def similarity(self, query: str, texts: List) -> Tuple[np.ndarray, int]:
        if not query or not texts:
            return np.zeros(len(texts), dtype=float), 0
            
        texts = [truncate(t, 500) for t in texts]
        data = {
            "model": self.model_name,
            "query": query,
            "documents": texts,
            "top_n": len(texts),
        }
        token_count = sum(num_tokens_from_string(t) for t in texts)
        response = requests.post(self._base_url + "/rerank", headers=self.headers, json=data, timeout=30)
        response.raise_for_status()
        res = response.json()
        rank = np.zeros(len(texts), dtype=float)
        try:
            for d in res.get("results", []):
                rank[d["index"]] = d["relevance_score"]
        except Exception as _e:
            log_exception(_e, res)

        rank = Base._normalize_rank(rank)
        return rank, token_count
