"""
Long Sequence Optimization Techniques

Optimizations for processing very long sequences efficiently:
- Segmented attention memory
- Streaming sequence processing
- Incremental sequence caching
"""


import torch
import torch.nn as nn


class LongSequenceOptimizer:
    """Optimizer for very long sequences"""

    def __init__(self, max_segment_length: int = 2048):
        self.max_segment_length = max_segment_length

    def segment_sequence(self, sequence: torch.Tensor) -> list[torch.Tensor]:
        """Segment long sequence into manageable chunks"""
        seq_len = sequence.size(1)
        segments = []

        for i in range(0, seq_len, self.max_segment_length):
            end_idx = min(i + self.max_segment_length, seq_len)
            segments.append(sequence[:, i:end_idx])

        return segments


class SegmentedAttentionMemory(nn.Module):
    """Segmented attention with memory for long sequences"""

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        segment_length: int = 1024,
        memory_length: int = 512
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.segment_length = segment_length
        self.memory_length = memory_length

        # Attention mechanism
        self.attention = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True)

        # Memory buffer
        self.register_buffer('memory', torch.zeros(1, memory_length, embed_dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Process sequence with segmented attention"""
        batch_size, seq_len, embed_dim = x.shape

        if seq_len <= self.segment_length:
            # Short sequence - process normally
            return self._process_segment(x)
        else:
            # Long sequence - process in segments
            return self._process_long_sequence(x)

    def _process_segment(self, x: torch.Tensor) -> torch.Tensor:
        """Process a single segment"""
        # Concatenate with memory
        memory_batch = self.memory.expand(x.size(0), -1, -1)
        x_with_memory = torch.cat([memory_batch, x], dim=1)

        # Apply attention
        output, _ = self.attention(x, x_with_memory, x_with_memory)

        return output

    def _process_long_sequence(self, x: torch.Tensor) -> torch.Tensor:
        """Process long sequence in segments"""
        outputs = []

        for i in range(0, x.size(1), self.segment_length):
            end_idx = min(i + self.segment_length, x.size(1))
            segment = x[:, i:end_idx]

            segment_output = self._process_segment(segment)
            outputs.append(segment_output)

            # Update memory with recent outputs
            self._update_memory(segment_output)

        return torch.cat(outputs, dim=1)

    def _update_memory(self, recent_output: torch.Tensor):
        """Update memory buffer with recent outputs"""
        # Keep the most recent outputs in memory
        recent_length = min(recent_output.size(1), self.memory_length)
        self.memory.data = recent_output[:, -recent_length:].mean(dim=0, keepdim=True)


class StreamingSequenceProcessor:
    """Streaming processor for real-time sequence processing"""

    def __init__(self, model: nn.Module, buffer_size: int = 1024):
        self.model = model
        self.buffer_size = buffer_size
        self.buffer = []

    def process_chunk(self, chunk: torch.Tensor) -> torch.Tensor:
        """Process a chunk of the sequence"""
        # Add to buffer
        self.buffer.append(chunk)

        # Maintain buffer size
        while len(self.buffer) > self.buffer_size:
            self.buffer.pop(0)

        # Process current buffer
        if self.buffer:
            buffered_sequence = torch.cat(self.buffer, dim=1)
            return self.model(buffered_sequence)

        return chunk


class IncrementalSequenceCache:
    """Cache for incremental sequence processing"""

    def __init__(self, cache_size: int = 10000):
        self.cache_size = cache_size
        self.cache = {}
        self.access_order = []

    def get(self, key: str) -> torch.Tensor | None:
        """Get cached sequence"""
        if key in self.cache:
            # Update access order
            self.access_order.remove(key)
            self.access_order.append(key)
            return self.cache[key]
        return None

    def put(self, key: str, value: torch.Tensor):
        """Cache sequence"""
        # Evict if necessary
        while len(self.cache) >= self.cache_size:
            oldest_key = self.access_order.pop(0)
            del self.cache[oldest_key]

        # Add new entry
        self.cache[key] = value
        self.access_order.append(key)

    def clear(self):
        """Clear cache"""
        self.cache.clear()
        self.access_order.clear()
