from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import BatchEncoding


class PDeepPPProcessor(ProcessorMixin):
    def __init__(self, pad_char="X", target_length=33):
        self.pad_char = pad_char
        self.target_length = target_length
    
    def pad_sequence(self, seq):
        """确保序列长度为 target_length，不足的部分用 pad_char 在两侧均匀填充"""
        if len(seq) < self.target_length:
            total_padding = self.target_length - len(seq)
            left_padding = total_padding // 2
            right_padding = total_padding - left_padding
            seq = self.pad_char * left_padding + seq + self.pad_char * right_padding
        return seq[:self.target_length]
    
    def extract_ptm_sequences(self, sequences):
        """处理 PTM 数据，确保目标氨基酸（S、T、Y）位于序列中心"""
        ptm_data = []
        for seq in sequences:
            for i in range(len(seq)):
                if seq[i] in {'S', 'T', 'Y'}:  # 仅提取 S、T、Y 作为中心的片段
                    start = max(0, i - self.target_length // 2)
                    end = min(len(seq), start + self.target_length)
                    padded_seq = self.pad_sequence(seq[start:end])
                    ptm_data.append(padded_seq)
        return ptm_data
    
    def extract_bps_sequences(self, sequences, overlapping=True, step_size=5):
        """处理生物活性数据（BPS），关注整个序列，可重叠"""
        bioactive_data = []
        for seq in sequences:
            if len(seq) < self.target_length:
                # 如果序列长度不足，直接填充到 target_length
                padded_seq = self.pad_sequence(seq)
                bioactive_data.append(padded_seq)
            else:
                # 如果序列长度足够，按照滑动窗口提取片段
                for i in range(0, len(seq) - self.target_length + 1, 
                            step_size if overlapping else self.target_length):
                    bioactive_data.append(self.pad_sequence(seq[i:i + self.target_length]))
        return bioactive_data
    
    def __call__(
        self, 
        sequences, 
        mode,  # 去除默认值，强制外部传入
        overlapping=True,
        step_size=5,
        **kwargs
    ):
        """
        预处理蛋白质序列，仅处理数据到指定长度。

        Args:
            sequences: 序列列表或单个序列字符串。
            mode: 选择处理模式，必须从外部传入，"PTM" 或 "BPS"。
            overlapping: BPS 模式下是否使用重叠窗口。
            step_size: BPS 模式下的步长。
        """
        # 确保 sequences 是列表
        if isinstance(sequences, str):
            sequences = [sequences]
            
        # 根据模式提取序列
        if mode == "PTM":
            processed_sequences = self.extract_ptm_sequences(sequences)
        elif mode == "BPS":
            processed_sequences = self.extract_bps_sequences(
                sequences, 
                overlapping=overlapping,
                step_size=step_size
            )
        else:
            raise ValueError("Invalid mode. Please choose 'PTM' or 'BPS'.")
        
        if len(processed_sequences) == 0:
            raise ValueError("No sequences processed. Check input data and processing logic.")

        # 创建返回字典，仅包含预处理后的序列
        model_inputs = {
            "raw_sequences": processed_sequences,  # 预处理后的序列
        }

        return BatchEncoding(data=model_inputs)  # 返回处理后的数据