import re
from typing import List, Optional

class CLIPTextChunker:
    """
    Utility class for chunking text to fit within CLIP's token limits.
    CLIP models typically have a maximum sequence length of 77 tokens.
    Using a conservative limit of 70 tokens to account for special tokens.
    """

    def __init__(self, max_tokens: int = 25):
        """
        Initialize the text chunker.

        Args:
            max_tokens (int): Maximum number of tokens per chunk (default: 25 for CLIP, being ultra conservative)
        """
        self.max_tokens = max_tokens
        self._tokenizer = None

    @property
    def tokenizer(self):
        """Lazy load CLIP tokenizer"""
        if self._tokenizer is None:
            try:
                from transformers import CLIPTokenizer
                # Use a simpler model that should be more reliable
                self._tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32", local_files_only=False)
            except Exception as e:
                # Fallback to character-based estimation if transformers not available
                self._tokenizer = None
        return self._tokenizer

    def get_token_count(self, text: str) -> int:
        """
        Get the actual token count for a text string using CLIP tokenizer.

        Args:
            text (str): Input text

        Returns:
            int: Actual token count
        """
        if self.tokenizer is None:
            # Fallback to character count if tokenizer not available
            # Use an ultra conservative estimate: ~0.3 characters per token for CLIP
            return int(len(text) * 0.3)

        tokens = self.tokenizer(
            text,
            padding=False,
            truncation=False,
            return_tensors=None
        )

        return len(tokens["input_ids"])

    def chunk_text(self, text: str, preserve_sentences: bool = True) -> List[str]:
        """
        Chunk text into smaller pieces that fit within the token limit.
        Uses actual CLIP tokenization for accuracy.

        Args:
            text (str): Input text to chunk
            preserve_sentences (bool): Whether to try to preserve sentence boundaries

        Returns:
            List[str]: List of text chunks
        """
        if not text.strip():
            return []

        if self.get_token_count(text) <= self.max_tokens:
            return [text]

        chunks = []
        words = text.split()
        current_chunk = []
        current_tokens = 0

        for word in words:
            word_with_space = word + " "

            # Check if adding this word would exceed the limit
            test_chunk = " ".join(current_chunk + [word])
            test_tokens = self.get_token_count(test_chunk)

            if test_tokens > self.max_tokens and current_chunk:
                # Current chunk is complete, add it
                chunks.append(" ".join(current_chunk))
                current_chunk = [word]
                current_tokens = self.get_token_count(word)
            else:
                current_chunk.append(word)
                current_tokens = test_tokens

        # Add the last chunk if it exists
        if current_chunk:
            chunks.append(" ".join(current_chunk))

        return chunks

    def create_priority_chunks(self, text: str, essential_info: List[str]) -> List[str]:
        """
        Create chunks with priority given to essential information.

        Args:
            text (str): Full text to chunk
            essential_info (List[str]): List of essential phrases that should be preserved

        Returns:
            List[str]: List of prioritized chunks
        """
        # If text fits within limits, return as-is
        if self.get_token_count(text) <= self.max_tokens:
            return [text]

        # Find the most important essential information at the beginning
        # Look for key phrases that should be preserved
        first_chunk = ""
        remaining_text = text

        # Try to find essential info near the beginning
        for info in essential_info:
            if info in text:
                info_index = text.find(info)
                # If the essential info is near the beginning, include it
                if info_index < 100:  # Within first 100 characters
                    # Take from start up to and including the essential info
                    end_pos = min(len(text), info_index + len(info) + 30)  # Include some context after
                    candidate_chunk = text[:end_pos]

                    # Ensure the candidate chunk ends at a word boundary
                    last_space = candidate_chunk.rfind(" ")
                    if last_space > 0:
                        candidate_chunk = candidate_chunk[:last_space]

                    # Use the basic chunking to ensure proper word boundaries
                    if self.get_token_count(candidate_chunk) <= self.max_tokens:
                        # Use chunk_text to get a properly bounded chunk
                        temp_chunks = self.chunk_text(candidate_chunk)
                        if temp_chunks:
                            first_chunk = temp_chunks[0]
                            remaining_text = text[len(first_chunk):]
                            break

        # If we found a good first chunk, use it
        if first_chunk and self.get_token_count(first_chunk) <= self.max_tokens:
            chunks = [first_chunk]
            # Add remaining text as additional chunks if needed
            if remaining_text.strip():
                chunks.extend(self.chunk_text(remaining_text))
            return chunks

        # Fallback to regular chunking
        return self.chunk_text(text)

def chunk_prompt_for_clip(prompt: str, max_tokens: int = 25) -> List[str]:
    """
    Convenience function to chunk a prompt for CLIP processing.
    Uses a conservative 25 token limit to be safe.

    Args:
        prompt (str): The prompt to chunk
        max_tokens (int): Maximum tokens per chunk (default: 25 for safety)

    Returns:
        List[str]: List of prompt chunks
    """
    chunker = CLIPTextChunker(max_tokens=max_tokens)

    # Define essential information that should be preserved (matching actual prompt format)
    essential_info = [
        "Ultra realistic headshot",
        "male soccer player",
        "looking at the camera",
        "facing the camera",
        "Olive skinned",
        "transparent background"
    ]

    return chunker.create_priority_chunks(prompt, essential_info)