# Copyright 2025 the LlamaFactory team.
#
# 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
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import bisect
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional


if TYPE_CHECKING:
    from transformers import PreTrainedTokenizer, ProcessorMixin

    from ...hparams import DataArguments
    from ..template import Template


@dataclass
class DatasetProcessor(ABC):
    r"""A class for data processors."""

    template: "Template"
    tokenizer: "PreTrainedTokenizer"
    processor: Optional["ProcessorMixin"]
    data_args: "DataArguments"

    @abstractmethod
    def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
        r"""Build model inputs from the examples."""
        ...
        # print('preporcess')

    @abstractmethod
    def print_data_example(self, example: dict[str, list[int]]) -> None:
        r"""Print a data example to stdout."""
        ...


def search_for_fit(numbers: list[int], capacity: int) -> int:
    r"""Find the index of largest number that fits into the knapsack with the given capacity."""
    index = bisect.bisect(numbers, capacity)
    return -1 if index == 0 else (index - 1)


def greedy_knapsack(numbers: list[int], capacity: int) -> list[list[int]]:
    r"""Implement efficient greedy algorithm with binary search for the knapsack problem."""
    numbers.sort()  # sort numbers in ascending order for binary search
    knapsacks = []

    while numbers:
        current_knapsack = []
        remaining_capacity = capacity

        while True:
            index = search_for_fit(numbers, remaining_capacity)
            if index == -1:
                break  # no more numbers fit in this knapsack

            remaining_capacity -= numbers[index]  # update the remaining capacity
            current_knapsack.append(numbers.pop(index))  # add the number to knapsack

        knapsacks.append(current_knapsack)

    return knapsacks


def infer_seqlen(source_len: int, target_len: int, cutoff_len: int) -> tuple[int, int]:
    r"""Compute the real sequence length after truncation by the cutoff_len."""
    if target_len * 2 < cutoff_len:  # truncate source
        max_target_len = cutoff_len
    elif source_len * 2 < cutoff_len:  # truncate target
        max_target_len = cutoff_len - source_len
    else:  # truncate both
        max_target_len = int(cutoff_len * (target_len / (source_len + target_len)))

    new_target_len = min(max_target_len, target_len)
    max_source_len = max(cutoff_len - new_target_len, 0)
    new_source_len = min(max_source_len, source_len)
    return new_source_len, new_target_len
