"""Rephrasing prompter for LLM-assisted content rewriting."""

from typing import Any, Optional

from curriculum_curator.remediation.remediators.base import BaseRemediator


class RephrasingPrompter(BaseRemediator):
    """Remediator that uses an LLM to rewrite problematic content.

    Takes content with issues and passes it to an LLM with specific
    instructions for rewriting or improving the section.

    Config options:
        model_alias: LLM model to use for rewriting
        max_tokens: Maximum tokens for LLM response
        temperature: Temperature for LLM generation
        prompt_templates: Templates for different issue types
    """

    def __init__(self, config: Any):
        """Initialize the rephrasing prompter.

        Args:
            config: Configuration for the remediator
        """
        super().__init__(config)
        self.model_alias = getattr(config, "model_alias", None)  # Uses default if None
        self.max_tokens = getattr(config, "max_tokens", 1000)
        self.temperature = getattr(config, "temperature", 0.7)
        self.prompt_templates = getattr(
            config,
            "prompt_templates",
            {
                "default": "Rewrite the following text to improve its quality: {text}",
                "readability": "Rewrite the following text to improve readability and make it easier to understand: {text}",
                "similarity": "Rewrite the following text to make it more unique and distinct: {text}",
                "tone": "Rewrite the following text to match a {tone} tone: {text}",
            },
        )

    async def remediate(
        self, content: str, issues: list[dict[str, Any]], _context: Optional[dict[str, Any]] = None
    ) -> dict[str, Any]:
        """Remediate content by requesting LLM rewrites for problematic sections.

        Args:
            content: The content to remediate
            issues: List of validation issues to address
            _context: Optional context with additional information

        Returns:
            dict: Remediation result
        """
        # Placeholder implementation - real version would integrate with LLMManager
        actions = []
        remediated_content = content

        # Group issues by validator
        issue_groups = {}
        for issue in issues:
            validator = issue.get("validator", "unknown")
            if validator not in issue_groups:
                issue_groups[validator] = []
            issue_groups[validator].append(issue)

        # Simulate rewriting by issue type
        for validator, validator_issues in issue_groups.items():
            # Determine prompt template
            template_key = "default"
            if "readability" in validator.lower():
                template_key = "readability"
            elif "similarity" in validator.lower():
                template_key = "similarity"
            elif "tone" in validator.lower():
                template_key = "tone"

            # Get the prompt template (will be used in the real implementation)
            _prompt_template = self.prompt_templates.get(
                template_key, self.prompt_templates["default"]
            )

            # Simulate LLM call (in real implementation, would call LLMManager.generate)
            actions.append(
                {
                    "type": "llm_rewrite",
                    "validator": validator,
                    "issues_count": len(validator_issues),
                    "prompt_template": template_key,
                    "model_alias": self.model_alias,
                    "section": "full_document",  # Would be more specific in real implementation
                }
            )

        return {
            "remediated_content": remediated_content,
            "remediation_actions": actions,
            "success": len(actions) > 0,
            "sections_rewritten": len(actions),
        }

    def can_remediate_issues(self, issues: list[dict[str, Any]]) -> bool:
        """Check if this remediator can handle the given issues.

        Args:
            issues: List of validation issues

        Returns:
            bool: True if this remediator can handle the issues
        """
        # This remediator can handle most content quality issues
        if not issues:
            return False

        # Prefer not to handle purely structural/formatting issues
        issue_types = set()
        for issue in issues:
            validator = issue.get("validator", "").lower()
            issue_types.add(validator)

        # If only structure validator issues, don't use this remediator
        if len(issue_types) == 1 and "structure" in next(iter(issue_types)).lower():
            return False

        return True
