import os
import time
from datetime import datetime
import logging
from pathlib import Path  
import requests
import json

import numpy as np
import pandas as pd
import spacy
from sentence_transformers import CrossEncoder
import litellm
# from litellm import completion
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, AutoConfig
# from accelerate import PartialState
# from accelerate.inference import prepare_pippy
import torch
import cohere
from openai import OpenAI


import src.backend.util as util
import src.envs as envs

litellm.set_verbose=False

# Set up basic configuration for logging
logging.basicConfig(level=logging.INFO,
                    format='%(asctime)s - %(levelname)s - %(message)s')

# Load spacy model for word tokenization
nlp = spacy.load("en_core_web_sm")

os.environ["HUGGINGFACE_API_KEY"] =  envs.TOKEN


def load_evaluation_model(model_path):
    """Load the evaluation model from the given path

    Args:
        model_path (str): Path to the evaluation model

    Returns:
        CrossEncoder: The evaluation model
    """
    model = CrossEncoder(model_path)
    return model


class ModelLoadingException(Exception):
    """Exception raised for errors in loading a model.

    Attributes:
        model_id (str): The model identifier.
        revision (str): The model revision.
    """

    def __init__(self, model_id, revision, messages="Error initializing model"):
        self.model_id = model_id
        self.revision = revision
        super().__init__(f"{messages} id={model_id} revision={revision}")


class SummaryGenerator:
    """A class to generate summaries using a causal language model.

    Attributes:
        model (str): huggingface/{model_id}
        api_base (str): https://api-inference.huggingface.co/models/{model_id}
        summaries_df (DataFrame): DataFrame to store generated summaries.
        revision (str): Model revision.
        avg_length (float): Average length of summaries.
        answer_rate (float): Rate of non-empty summaries.
    """

    def __init__(self, model_id, revision):
        """
        Initializes the SummaryGenerator with a model.

        Args:
            model_id (str): Identifier for the model.
            revision (str): Revision of the model.
        """
        self.model_id = model_id
        self.model = f"huggingface/{model_id}"
        self.api_base = f"https://api-inference.huggingface.co/models/{model_id}"
        self.summaries_df = pd.DataFrame()
        self.revision = revision
        self.avg_length = None
        self.answer_rate = None
        self.exceptions = None
        self.local_model = None

    def generate_summaries(self, df, save_path=None):
        """Generate summaries for a given DataFrame of source docs.

        Args:
            df (DataFrame): DataFrame containing source docs.

        Returns:
            summaries_df (DataFrame): Generated summaries by the model.
        """
        exceptions = []
        if (save_path is not None) and os.path.exists(save_path):
            self.summaries_df = pd.read_csv(save_path)
            print(f'Loaded generated summaries from {save_path}')
        else:
            source, summary, dataset = [], [], [] 
            print(f"Total: {df.shape[0]}")
            for index, row in tqdm(df.iterrows(), total=df.shape[0]):
                _source = row['text']
                _dataset = row['dataset']

                system_prompt = envs.SYSTEM_PROMPT
                user_prompt = f"{envs.USER_PROMPT}\nPassage:\n{_source}"

                while True:
                    try:
                        _summary = self.generate_summary(system_prompt, user_prompt)
                        # print(f"Finish index {index}")
                        break
                    except Exception as e:
                        if 'Rate limit reached' in str(e):
                            wait_time = 3660
                            current_time = datetime.now().strftime('%H:%M:%S')
                            print(f"Rate limit hit at {current_time}. Waiting for 1 hour before retrying...")
                            time.sleep(wait_time)
                        elif 'is currently loading' in str(e):
                            wait_time = 200
                            print(f"Model is loading, wait for {wait_time}")
                            time.sleep(wait_time)
                        else:
                            print(f"Error at index {index}: {e}")
                            _summary = ""
                            exceptions.append(index)
                            break

                summary.append(_summary)
                source.append(_source)
                dataset.append(_dataset)

                # Sleep to prevent hitting rate limits too frequently
                time.sleep(1)

            self.summaries_df = pd.DataFrame(list(zip(source, summary, dataset)),
                                            columns=["source", "summary", "dataset"])

            if save_path is not None:
                print(f'Save summaries to {save_path}')
                fpath = Path(save_path)
                fpath.parent.mkdir(parents=True, exist_ok=True)
                self.summaries_df.to_csv(fpath) 

        self.exceptions = exceptions
        self._compute_avg_length()
        self._compute_answer_rate()

        return self.summaries_df
    
    def generate_summary(self, system_prompt: str, user_prompt: str):
        # Using Together AI API
        if 'mixtral' in self.model_id.lower() or 'dbrx' in self.model_id.lower() or 'wizardlm' in self.model_id.lower(): # For mixtral and dbrx models, use Together AI API
            suffix = "completions" if ('mixtral' in self.model_id.lower() or 'base' in self.model_id.lower()) else "chat/completions"
            url = f"https://api.together.xyz/v1/{suffix}"

            payload = {
                "model": self.model_id,
                # "max_tokens": 4096,
                'max_new_tokens': 250,
                "temperature": 0.0,
                'repetition_penalty': 1.1 if 'mixtral' in self.model_id.lower() else 1
            }
            if 'mixtral' in self.model_id.lower():
                # payload['prompt'] = user_prompt
                # payload['prompt'] = "Write a summary of the following passage:\nPassage:\n" + user_prompt.split('Passage:\n')[-1] + '\n\nSummary:'
                payload['prompt'] = 'You must stick to the passage provided. Provide a concise summary of the following passage, covering the core pieces of information described:\nPassage:\n' + user_prompt.split('Passage:\n')[-1] + '\n\nSummary:'
                print(payload)
            else:
                payload['messages'] = [{"role": "system", "content": system_prompt},
                                        {"role": "user", "content": user_prompt}]
            headers = {
                "accept": "application/json",
                "content-type": "application/json",
                "Authorization": f"Bearer {os.environ['TOGETHER_API_KEY']}"
            }

            response = requests.post(url, json=payload, headers=headers)
            try:
                result = json.loads(response.text)
                # print(result)
                result = result["choices"][0]
                if 'message' in result:
                    result = result["message"]["content"].strip()
                else:
                    result = result["text"]
                    result_candidates = [result_cancdidate for result_cancdidate in result.split('\n\n') if len(result_cancdidate) > 0]
                    result = result_candidates[0]
                print(result)
            except:
                print(response)
                result = ''
            return result

        # Using OpenAI API
        elif 'gpt' in self.model_id.lower():
            response = litellm.completion(
                model=self.model_id.replace('openai/',''),
                messages=[{"role": "system", "content": system_prompt},
                        {"role": "user", "content": user_prompt}],
                temperature=0.0,
                max_tokens=250,
            )   
            result = response['choices'][0]['message']['content']
            print(result)
            return result
        
        # Using HF API or download checkpoints
        if self.local_model is None:
            try: # try use HuggingFace API
                
                response = litellm.completion(
                    model='command-r-plus' if 'command' in self.model else self.model,
                    messages=[{"role": "system", "content": system_prompt},
                                {"role": "user", "content": user_prompt}],
                    temperature=0.0,
                    max_tokens=1024,
                    api_base=self.api_base,
                )
                result = response['choices'][0]['message']['content']
            except: # fail to call api. run it locally.
                self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, trust_remote_code=True)
                print("Tokenizer loaded")
                self.local_model = AutoModelForCausalLM.from_pretrained(self.model_id, trust_remote_code=True, device_map="auto", torch_dtype="auto")
                print("Local model loaded")
        
        # Using local model
        if self.local_model: # cannot call API. using local model
            messages=[
                {"role": "system", "content": system_prompt}, # gemma-1.1 does not accept system role
                {"role": "user", "content": user_prompt}
            ],
            prompt = self.tokenizer.apply_chat_template(messages,add_generation_prompt=True, tokenize=False)
            print(prompt)
            input_ids = self.tokenizer(prompt, return_tensors="pt").to('cuda')
            with torch.no_grad():
                outputs = self.local_model.generate(**input_ids, max_new_tokens=250, do_sample=True, temperature=0.01, pad_token_id=self.tokenizer.eos_token_id)
            result = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
            result = result.replace(prompt[0], '')
            print(result)
        
        return result

    def _compute_avg_length(self):
        """
        Compute the average length of non-empty summaries using SpaCy.
        """
        total_word_count = 0
        total_count = 0

        for summary in self.summaries_df['summary']:
            if util.is_summary_valid(summary):
                doc = nlp(summary)
                words = [token.text for token in doc if token.is_alpha]
                total_word_count += len(words)
                total_count += 1

        self.avg_length = 0 if total_count == 0 else total_word_count / total_count

    def _compute_answer_rate(self):
        """
        Compute the rate of non-empty summaries.
        """
        valid_count = sum(1 for summary in self.summaries_df['summary']
                            if util.is_summary_valid(summary))

        total_count = len(self.summaries_df)

        self.answer_rate = 0 if total_count == 0 else valid_count / total_count


class EvaluationModel:
    """A class to evaluate generated summaries.

    Attributes:
        model (CrossEncoder): The evaluation model.
        scores (list): List of evaluation scores.
        accuracy (float): Accuracy of the summaries.
        hallucination_rate (float): Rate of hallucination in summaries.
    """

    def __init__(self, model_path):
        """
        Initializes the EvaluationModel with a CrossEncoder model.

        Args:
            model_path (str): Path to the CrossEncoder model.
        """
        self.model = load_evaluation_model(model_path)
        self.scores = []
        self.factual_consistency_rate = None
        self.hallucination_rate = None

    def evaluate_hallucination(self, summaries_df):
        """
        Evaluate the hallucination rate in summaries. Updates the 'scores' attribute 
        of the instance with the computed scores.

        Args:
            summaries_df (DataFrame): DataFrame containing source docs and summaries.

        Returns:
            list: List of hallucination scores. Also updates the 'scores' attribute of the instance.
        """
        hem_scores = []
        sources = []
        summaries = []
        source_summary_pairs = util.create_pairs(summaries_df)

        for doc, summary in tqdm(source_summary_pairs, desc="Evaluating hallucinations"):
            if util.is_summary_valid(summary):
                try:
                    # summary_pieces = summary.split('\n')
                    # summary = summary_pieces[0] if len(summary_pieces[0].strip()) > 0 else summary_pieces[1]
                    summary = summary.replace('<bos>','').replace('<eos>','')
                    # print([doc, summary])
                    # print(self.model.predict([doc, summary]))
                    score = self.model.predict([doc, summary])# [0]
                    if not isinstance(score, float):
                        try:
                            score = score.item()
                        except:
                            logging.warning(f"Score type mismatch: Expected float, got {type(score)}.")
                            continue
                    hem_scores.append(score)
                    sources.append(doc)
                    summaries.append(summary)
                except Exception as e:
                    logging.error(f"Error while running HEM: {e}")
                    raise

        self.scores = hem_scores
        eval_results = {'source': sources, 'summary': summaries, 'HEM scores': hem_scores}
        return hem_scores, eval_results


    def compute_factual_consistency_rate(self, threshold=0.5):
        """
        Compute the factual consistency rate of the evaluated summaries based on
        the previously calculated scores. This method relies on the 'scores'
        attribute being populated, typically via the 'evaluate_hallucination' method.

        Returns:
            float: Factual Consistency Rate. Also updates the 'factual_consistency_rate'
            and 'hallucination_rate' attributes of the instance.

        Raises:
            ValueError: If scores have not been calculated prior to calling this method.
        """
        if not self.scores:
            error_msg = "Scores not calculated. Call evaluate_hallucination() first."
            logging.error(error_msg)
            raise ValueError(error_msg)

        # Use threshold of 0.5 to compute factual_consistency_rate
        num_above_threshold = sum(score >= threshold for score in self.scores)
        num_total = len(self.scores)

        if not num_total:
            raise ValueError("No scores available to compute factual consistency rate.")

        self.factual_consistency_rate = (num_above_threshold / num_total) * 100
        self.hallucination_rate = 100 - self.factual_consistency_rate

        return self.factual_consistency_rate