# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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 re
import string
import random
# from verl.utils.reward_score.llm import OpenAIClient

# openai_client = OpenAIClient()

def model_estimated_match(answer, golden_answer):
    prompt = f"""
    Your goal is to determine if a model's answer is correct based on the golden answer, regardless of the wording and length.
    The model's answer is: {answer}
    The golden answer is: {golden_answer}
    Output your answer as 0 or 1, where 0 means the model's answer is incorrect and 1 means the model's answer is correct. Output only the number, no other text.
    """
    return int(openai_client.generate_response(prompt).strip())

def normalize_answer(s):
    def remove_articles(text):
        return re.sub(r"\b(a|an|the)\b", " ", text)

    def white_space_fix(text):
        return " ".join(text.split())

    def remove_punc(text):
        exclude = set(string.punctuation)
        return "".join(ch for ch in text if ch not in exclude)

    def lower(text):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(s))))


def em_check(prediction, golden_answers):
    if isinstance(golden_answers, str):
        golden_answers = [golden_answers]
    normalized_prediction = normalize_answer(prediction)
    score = 0
    for golden_answer in golden_answers:
        golden_answer = normalize_answer(golden_answer)
        if golden_answer == normalized_prediction:
            score = 1
            break
    return score


def subem_check(prediction, golden_answers):
    if isinstance(golden_answers, str):
        golden_answers = [golden_answers]
    normalized_prediction = normalize_answer(prediction)
    score = 0
    for golden_answer in golden_answers:
        golden_answer = normalize_answer(golden_answer)
        if golden_answer in normalized_prediction:
            score = 1
            break
    return score


def extract_solution(solution_str):
    """Extract the equation from the solution string."""
    # Remove everything before the first "Assistant:"
    # if "Assistant:" in solution_str:
    #     solution_str = solution_str.split("Assistant:", 1)[1]
    # elif "<|im_start|>assistant" in solution_str:
    #     solution_str = solution_str.split("<|im_start|>assistant", 1)[1]
    # else:
    #     return None
    # solution_str = solution_str.split('\n')[-1]

    answer_pattern = r'<answer>(.*?)</answer>'
    match = re.finditer(answer_pattern, solution_str, re.DOTALL)
    matches = list(match)
    
    # If there are 0 or exactly 1 matches, return None
    if len(matches) <= 1:
        return None
    
    # If there are 2 or more matches, return the last one
    return matches[-1].group(1).strip()


def model_estimated_match_score(solution_str, ground_truth, method='strict', format_score=0., score=1.):
    """The scoring function for exact match (EM).

    Args:
        solution_str: the solution text
        ground_truth: the ground truth
        method: the method to extract the solution, choices are 'strict' and 'flexible'
        format_score: the score for the format
        score: the score for the correct answer
    """
    answer = extract_solution(solution_str=solution_str)
    do_print = random.randint(1, 64) == 1
    
    if do_print:
        print(f"--------------------------------")
        print(f"Golden answers: {ground_truth['target']}")
        print(f"Extracted answer: {answer}")
        print(f"Solution string: {solution_str}")
    
    if answer is None:
        return 0
    else:
        score = model_estimated_match(answer, ground_truth['target'])
        return score


def compute_score_em(solution_str, ground_truth, method='strict', format_score=0., score=1.):
    """The scoring function for exact match (EM).

    Args:
        solution_str: the solution text
        ground_truth: the ground truth
        method: the method to extract the solution, choices are 'strict' and 'flexible'
        format_score: the score for the format
        score: the score for the correct answer
    """
    answer = extract_solution(solution_str=solution_str)
    do_print = random.randint(1, 64) == 1
    
    if do_print:
        print(f"--------------------------------")
        print(f"Golden answers: {ground_truth['target']}")
        print(f"Extracted answer: {answer}")
        print(f"Solution string: {solution_str}")
    
    if answer is None:
        return 0
    else:
        if em_check(answer, ground_truth['target']):
            return score
        else:
            return format_score

def compute_score_subem(solution_str, ground_truth, method='strict', format_score=0., score=1.):
    """The scoring function for substring exact match (EM).

    Args:
        solution_str: the solution text
        ground_truth: the ground truth
        method: the method to extract the solution, choices are 'strict' and 'flexible'
        format_score: the score for the format
        score: the score for the correct answer
    """
    answer = extract_solution(solution_str=solution_str)
    do_print = random.randint(1, 64) == 1
    
    if do_print:
        print(f"--------------------------------")
        print(f"Golden answers: {ground_truth['target']}")
        print(f"Extracted answer: {answer}")
        print(f"Solution string: {solution_str}")
    
    if answer is None:
        return 0
    else:
        if subem_check(answer, ground_truth['target']):
            return score
        else:
            return format_score
