# 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.

from verl import DataProto
from verl.utils.reward_score import _default_compute_score
import torch


class CustomRewardManager:
    """The reward manager.
    """

    def __init__(self, tokenizer, num_examine, compute_score=None) -> None:
        self.tokenizer = tokenizer
        # the number of batches of decoded responses to print to the console
        self.num_examine = num_examine
        self.compute_score = compute_score or _default_compute_score

    def __call__(self, data: DataProto):
        """We will expand this function gradually based on the available datasets"""

        # If there is rm score, we directly return rm score. Otherwise, we compute via rm_score_fn
        if 'rm_scores' in data.batch.keys():
            return data.batch['rm_scores']

        reward_tensor = torch.zeros_like(
            data.batch['responses'], dtype=torch.float32)
        batch_solution_str, batch_ground_truth, batch_data_sources = [], [], []

        already_print_data_sources = {}

        for i in range(len(data)):
            data_item = data[i]  # DataProtoItem

            prompt_ids = data_item.batch['prompts']

            prompt_length = prompt_ids.shape[-1]

            valid_prompt_length = data_item.batch['attention_mask'][:prompt_length].sum(
            )
            valid_prompt_ids = prompt_ids[-valid_prompt_length:]

            response_ids = data_item.batch['responses']
            valid_response_length = data_item.batch['attention_mask'][prompt_length:].sum(
            )
            valid_response_ids = response_ids[:valid_response_length]

            # decode
            prompt_str = self.tokenizer.decode(valid_prompt_ids)
            response_str = self.tokenizer.decode(valid_response_ids)

            ground_truth = data_item.non_tensor_batch['reward_model']['ground_truth']

            data_source = data_item.non_tensor_batch['data_source']

            batch_solution_str.append(response_str)

            batch_ground_truth.append(ground_truth)

            batch_data_sources.append(data_source)

            # extra_info = data_item.non_tensor_batch.get('extra_info', None)

        scores = self.compute_score(
            batch_data_sources=batch_data_sources,
            batch_solution_str=batch_solution_str,
            batch_ground_truth=batch_ground_truth,
        )

        for i in range(len(data)):
            data_item = data[i]
            prompt_ids = data_item.batch['prompts']

            prompt_length = prompt_ids.shape[-1]
            valid_response_length = data_item.batch['attention_mask'][prompt_length:].sum(
            )

            reward_tensor[i, valid_response_length - 1] = scores[i]

            # if data_source not in already_print_data_sources:
            #     already_print_data_sources[data_source] = 0

            # if already_print_data_sources[data_source] < self.num_examine:
            #     already_print_data_sources[data_source] += 1
            #     print("[prompt]", prompt_str)
            #     print("[response]", response_str)
            #     print("[ground_truth]", ground_truth)
            #     print("[score]", score)

        return reward_tensor
