"""
Implementation of Ensembling prompting techniques.
"""

from ..base import PromptTechnique
from ..utils import dedent_prompt


class SelfConsistency(PromptTechnique):
    """
    Self-Consistency generates multiple reasoning paths and finds consensus.
    (Note: Prompt generation guides the LLM; actual analysis of paths is complex)
    """

    def __init__(self):
        """Initialize Self-Consistency technique."""
        super().__init__(
            name="Self-Consistency",
            identifier="2.2.5",
            description="Generates multiple reasoning paths and finds consensus.",
        )

    def generate_prompt(self, input_text: str, num_paths: int = 3, **kwargs) -> str:
        """
        Generate a Self-Consistency prompt.

        Args:
            input_text (str): Input text
            num_paths (int): Number of reasoning paths to generate (default: 3)
            **kwargs: Additional arguments:
                - approach_diversity (bool): Whether to ensure diversity in approaches
                - domain (str): Optional domain context
                - include_metacognition (bool): Whether to include reflection on thinking process
                - reasoning_styles (List[str]): Optional list of specific reasoning styles to use
                - path_length (str): Guidance on path length ("brief", "standard", "detailed")

        Returns:
            str: Generated Self-Consistency prompt
        """
        approach_diversity = kwargs.get("approach_diversity", True)
        domain = kwargs.get("domain", "")
        include_metacognition = kwargs.get("include_metacognition", False)
        reasoning_styles = kwargs.get("reasoning_styles", [])
        path_length = kwargs.get("path_length", "standard")

        domain_context = f" in the {domain} domain" if domain else ""

        # Generate guidance for path length
        length_guidance = {
            "brief": "Keep each reasoning path concise, focusing on key insights.",
            "standard": "Provide a balanced level of detail in each reasoning path.",
            "detailed": "Elaborate thoroughly on each reasoning path, exploring nuances.",
        }.get(path_length, "Provide a balanced level of detail in each reasoning path.")

        # Create diversity instructions if enabled
        diversity_instructions = (
            """
        - Ensure each path uses a substantially different approach or perspective
        - Avoid simply rephrasing the same reasoning with minor variations
        """
            if approach_diversity
            else ""
        )

        # Create metacognition instructions if enabled
        metacognition = (
            """
        - For each path, briefly note your confidence level and any uncertainties
        - Identify which aspects of the problem were most challenging in each path
        """
            if include_metacognition
            else ""
        )

        # Use specified reasoning styles if provided
        styles_text = ""
        if reasoning_styles:
            styles_list = "\n".join(
                [
                    f"  - Path {i + 1}: Use {style} reasoning"
                    for i, style in enumerate(reasoning_styles[:num_paths])
                ]
            )
            styles_text = f"\nI will use different reasoning styles for each path:\n{styles_list}\n"

        # Generate paths with more guidance
        paths = ""
        for i in range(num_paths):
            style_note = (
                f" using {reasoning_styles[i]} reasoning"
                if reasoning_styles and i < len(reasoning_styles)
                else ""
            )

            paths += f"""
        Path {i + 1}{style_note}:
        [Start with a distinct approach to the problem]
        [Develop this approach step by step with clear reasoning]
        [Maintain logical consistency throughout this path]
        [Draw a specific conclusion based solely on this path's reasoning]
        
        Conclusion {i + 1}: [Specific answer derived from path {i + 1}]
        
        """

        prompt = dedent_prompt(f"""
        # Multiple-Path Problem Solving{domain_context}
        
        ## Problem Statement:
        {input_text}
        
        ## Approach:
        I will solve this problem through {num_paths} independent reasoning paths. {length_guidance}
        {styles_text}
        Key guidelines for this multi-path analysis:
        {diversity_instructions}{metacognition}
        
        ## Independent Reasoning Paths:
        {paths}
        ## Analysis of Results:
        
        - **Summary of Conclusions:**
          [List each conclusion with a count if any are identical]
        
        - **Comparative Analysis:**
          [Analyze the similarities and differences between the paths]
          [Identify strengths and weaknesses of each approach]
          [Note which aspects of the problem were consistently addressed across paths]
        
        - **Confidence Assessment:**
          [Evaluate relative confidence in each path based on rigor, completeness, and logical soundness]
        
        ## Consensus Determination:
        
        Based on the multiple reasoning paths, the most reliable conclusion is:
        [Provide the final answer with justification for why it represents the best consensus]
        
        ## Reflection on Multi-Path Approach:
        [Briefly note how using multiple paths improved the solution compared to a single-path approach]
        """)
        return prompt


class COSP(PromptTechnique):
    """
    COSP (Consistency-based Self-adaptive Prompting) adapts prompting based on consistency.

    This technique adjusts the prompting strategy based on the consistency of responses
    across multiple attempts, using more sophisticated prompting when consistency is low.
    """

    def __init__(self):
        """Initialize COSP technique."""
        super().__init__(
            name="COSP",
            identifier="2.2.5",
            description="Adapts prompting strategy based on response consistency.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a COSP prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - consistency_threshold (float): Threshold for consistency assessment
                - adaptation_strategy (str): Strategy for adapting prompts

        Returns:
            str: Generated COSP prompt
        """
        consistency_threshold = kwargs.get("consistency_threshold", 0.7)
        adaptation_strategy = kwargs.get("adaptation_strategy", "progressive")

        strategy_guidance = {
            "progressive": "Gradually increase prompting sophistication based on consistency",
            "adaptive": "Dynamically adjust prompting approach based on real-time consistency",
            "threshold": "Switch to advanced prompting when consistency falls below threshold",
        }.get(adaptation_strategy, "Gradually increase prompting sophistication")

        prompt = dedent_prompt(f"""
        Problem: {input_text}
        
        I'll use COSP (Consistency-based Self-adaptive Prompting) to solve this problem.
        
        Strategy: {strategy_guidance}
        Consistency Threshold: {consistency_threshold}
        
        Phase 1 - Initial Response:
        [Generate initial response using standard approach]
        
        Phase 2 - Consistency Check:
        [Generate alternative response and assess consistency with Phase 1]
        
        Phase 3 - Adaptive Response:
        [If consistency < {consistency_threshold}, use enhanced prompting strategy]
        [If consistency >= {consistency_threshold}, proceed with current approach]
        
        Phase 4 - Final Synthesis:
        [Combine insights from all phases into final answer]
        
        Consistency-Adapted Solution:
        """)
        return prompt


class DENSE(PromptTechnique):
    """
    DENSE (Diverse Ensemble) uses diverse prompting strategies in ensemble.

    This technique combines multiple diverse prompting approaches to create
    a robust ensemble that leverages different reasoning strategies.
    """

    def __init__(self):
        """Initialize DENSE technique."""
        super().__init__(
            name="DENSE",
            identifier="2.2.5",
            description="Uses diverse prompting strategies in ensemble.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a DENSE prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - strategies (List[str]): Specific strategies to include in ensemble
                - ensemble_size (int): Number of diverse strategies to use

        Returns:
            str: Generated DENSE prompt
        """
        strategies = kwargs.get(
            "strategies", ["analytical", "creative", "systematic", "intuitive"]
        )
        ensemble_size = kwargs.get("ensemble_size", 4)

        selected_strategies = strategies[:ensemble_size]

        strategy_prompts = []
        for i, strategy in enumerate(selected_strategies):
            strategy_prompts.append(f"""
        Strategy {i + 1} - {strategy.title()} Approach:
        [Apply {strategy} reasoning to solve the problem]
        [Use {strategy}-specific methods and perspectives]
        Result {i + 1}: [Answer from {strategy} approach]
        """)

        strategies_text = "\n".join(strategy_prompts)

        prompt = dedent_prompt(f"""
        Problem: {input_text}
        
        I'll use DENSE (Diverse Ensemble) to solve this problem by applying multiple diverse strategies:
        
        Ensemble Strategies: {", ".join(selected_strategies)}
        
        {strategies_text}
        
        Ensemble Integration:
        1. Compare results from all {ensemble_size} strategies
        2. Identify areas of agreement and disagreement
        3. Analyze the strengths of each approach for this specific problem
        4. Synthesize insights from diverse perspectives
        
        Final Ensemble Answer:
        [Integrated solution leveraging the best aspects of all strategies]
        """)
        return prompt


class DiVeRSe(PromptTechnique):
    """
    DiVeRSe (Diverse Verification and Reasoning Strategies) uses multiple verification approaches.

    This technique employs diverse verification and reasoning strategies to ensure
    robust and reliable problem-solving through multiple validation methods.
    """

    def __init__(self):
        """Initialize DiVeRSe technique."""
        super().__init__(
            name="DiVeRSe",
            identifier="2.2.5",
            description="Uses diverse verification and reasoning strategies.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a DiVeRSe prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - verification_methods (List[str]): Specific verification methods to use
                - reasoning_diversity (str): Level of reasoning diversity

        Returns:
            str: Generated DiVeRSe prompt
        """
        verification_methods = kwargs.get(
            "verification_methods",
            ["logical", "empirical", "analogical", "counterfactual"],
        )
        reasoning_diversity = kwargs.get("reasoning_diversity", "high")

        diversity_guidance = {
            "low": "Use 2-3 different reasoning approaches",
            "medium": "Use 3-4 different reasoning approaches with some overlap",
            "high": "Use 4+ distinct reasoning approaches with maximum diversity",
        }.get(reasoning_diversity, "Use multiple diverse reasoning approaches")

        verification_text = "\n".join(
            [
                f"- {method.title()} Verification: [Apply {method} validation methods]"
                for method in verification_methods
            ]
        )

        prompt = dedent_prompt(f"""
        Problem: {input_text}
        
        I'll use DiVeRSe (Diverse Verification and Reasoning Strategies) to ensure robust problem-solving.
        
        Reasoning Diversity Level: {reasoning_diversity}
        Guidance: {diversity_guidance}
        
        Phase 1 - Diverse Reasoning:
        [Apply multiple distinct reasoning strategies to solve the problem]
        
        Phase 2 - Multiple Verification:
        {verification_text}
        
        Phase 3 - Cross-Validation:
        [Compare results across different reasoning and verification approaches]
        [Identify consistent findings and resolve discrepancies]
        
        Phase 4 - Robust Solution:
        [Synthesize verified insights into final answer]
        
        DiVeRSe-Validated Answer:
        """)
        return prompt


class MaxMutualInformation(PromptTechnique):
    """
    Max Mutual Information optimizes information gain across ensemble members.

    This technique selects and combines ensemble members to maximize the mutual
    information and minimize redundancy in the reasoning approaches.
    """

    def __init__(self):
        """Initialize Max Mutual Information technique."""
        super().__init__(
            name="Max Mutual Information",
            identifier="2.2.5",
            description="Optimizes information gain across ensemble members.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a Max Mutual Information prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - information_sources (List[str]): Different information sources to consider
                - optimization_strategy (str): Strategy for maximizing information gain

        Returns:
            str: Generated Max Mutual Information prompt
        """
        information_sources = kwargs.get(
            "information_sources",
            ["factual", "analytical", "experiential", "theoretical"],
        )
        optimization_strategy = kwargs.get("optimization_strategy", "greedy")

        strategy_guidance = {
            "greedy": "Select information sources that provide maximum incremental information gain",
            "global": "Optimize overall information gain across all sources simultaneously",
            "balanced": "Balance information gain with diversity of perspectives",
        }.get(optimization_strategy, "Optimize for maximum information gain")

        sources_text = "\n".join(
            [
                f"Source {i + 1} - {source.title()}: [Extract unique insights from {source} perspective]"
                for i, source in enumerate(information_sources)
            ]
        )

        prompt = dedent_prompt(f"""
        Problem: {input_text}
        
        I'll use Max Mutual Information to optimize the information gain from multiple sources.
        
        Optimization Strategy: {strategy_guidance}
        Information Sources: {", ".join(information_sources)}
        
        Information Extraction:
        {sources_text}
        
        Mutual Information Analysis:
        1. Identify unique information from each source
        2. Measure information overlap between sources
        3. Calculate incremental information gain
        4. Optimize source combination for maximum total information
        
        Information-Optimized Solution:
        [Final answer based on maximized mutual information]
        """)
        return prompt


class MetaCoT(PromptTechnique):
    """
    Meta-CoT applies meta-reasoning to Chain-of-Thought processes.

    This technique uses meta-level reasoning to guide and optimize the
    Chain-of-Thought process itself, reasoning about reasoning.
    """

    def __init__(self):
        """Initialize Meta-CoT technique."""
        super().__init__(
            name="Meta-CoT",
            identifier="2.2.5",
            description="Applies meta-reasoning to Chain-of-Thought processes.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a Meta-CoT prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - meta_levels (int): Number of meta-reasoning levels
                - reflection_depth (str): Depth of meta-reflection

        Returns:
            str: Generated Meta-CoT prompt
        """
        meta_levels = kwargs.get("meta_levels", 2)
        reflection_depth = kwargs.get("reflection_depth", "standard")

        depth_guidance = {
            "shallow": "Basic reflection on reasoning process",
            "standard": "Moderate reflection on reasoning quality and alternatives",
            "deep": "Comprehensive reflection on reasoning assumptions, biases, and meta-strategies",
        }.get(reflection_depth, "Moderate reflection on reasoning quality")

        meta_levels_text = []
        for level in range(meta_levels):
            if level == 0:
                meta_levels_text.append(
                    "Level 0 - Object-level reasoning: [Direct problem-solving reasoning]"
                )
            else:
                meta_levels_text.append(
                    f"Level {level} - Meta-reasoning: [Reasoning about Level {level - 1} reasoning]"
                )

        levels_text = "\n".join(meta_levels_text)

        prompt = dedent_prompt(f"""
        Problem: {input_text}
        
        I'll use Meta-CoT to apply meta-reasoning to the Chain-of-Thought process.
        
        Meta-reasoning Levels: {meta_levels}
        Reflection Depth: {reflection_depth} - {depth_guidance}
        
        {levels_text}
        
        Meta-Analysis:
        1. How effective was my reasoning strategy?
        2. What assumptions did I make in my reasoning?
        3. Are there better reasoning approaches for this problem?
        4. How can I improve my reasoning process?
        
        Meta-Optimized Solution:
        [Final answer informed by meta-reasoning insights]
        """)
        return prompt


class MoRE(PromptTechnique):
    """
    MoRE (Mixture of Reasoning Experts) combines specialized reasoning experts.

    This technique uses a mixture of specialized reasoning experts, each optimized
    for different types of problems or reasoning approaches.
    """

    def __init__(self):
        """Initialize MoRE technique."""
        super().__init__(
            name="MoRE",
            identifier="2.2.5",
            description="Combines specialized reasoning experts.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a MoRE prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - experts (List[str]): Types of reasoning experts to use
                - expert_weights (Dict[str, float]): Weights for different experts

        Returns:
            str: Generated MoRE prompt
        """
        experts = kwargs.get(
            "experts", ["logical", "mathematical", "creative", "analytical"]
        )
        expert_weights = kwargs.get("expert_weights", {})

        # Assign default weights if not provided
        default_weight = 1.0 / len(experts)
        for expert in experts:
            if expert not in expert_weights:
                expert_weights[expert] = default_weight

        experts_text = []
        for expert in experts:
            weight = expert_weights.get(expert, default_weight)
            experts_text.append(f"""
        {expert.title()} Expert (Weight: {weight:.2f}):
        [Apply specialized {expert} reasoning to the problem]
        [Use {expert}-specific methods and knowledge]
        Expert Result: [Answer from {expert} expert]
        """)

        experts_content = "\n".join(experts_text)

        prompt = dedent_prompt(f"""
        Problem: {input_text}
        
        I'll use MoRE (Mixture of Reasoning Experts) to combine specialized reasoning approaches.
        
        Available Experts: {", ".join(experts)}
        Expert Weights: {expert_weights}
        
        Expert Consultations:
        {experts_content}
        
        Expert Integration:
        1. Weight each expert's contribution based on relevance to the problem
        2. Identify areas of expert agreement and disagreement
        3. Resolve conflicts using meta-reasoning about expert reliability
        4. Combine expert insights using weighted integration
        
        MoRE Final Answer:
        [Weighted combination of expert reasoning]
        """)
        return prompt


class UniversalSelfConsistency(PromptTechnique):
    """
    Universal Self-Consistency extends self-consistency across multiple domains.

    This technique applies self-consistency principles across different domains
    and reasoning modalities for more robust and generalizable solutions.
    """

    def __init__(self):
        """Initialize Universal Self-Consistency technique."""
        super().__init__(
            name="Universal Self-Consistency",
            identifier="2.2.5",
            description="Extends self-consistency across multiple domains and modalities.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a Universal Self-Consistency prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - domains (List[str]): Different domains to consider
                - modalities (List[str]): Different reasoning modalities

        Returns:
            str: Generated Universal Self-Consistency prompt
        """
        domains = kwargs.get(
            "domains", ["logical", "empirical", "theoretical", "practical"]
        )
        modalities = kwargs.get(
            "modalities", ["verbal", "visual", "mathematical", "analogical"]
        )

        domain_analyses = []
        for domain in domains:
            domain_analyses.append(f"""
        {domain.title()} Domain Analysis:
        [Analyze the problem from a {domain} perspective]
        [Apply {domain} principles and methods]
        Domain Result: [Answer from {domain} analysis]
        """)

        modality_analyses = []
        for modality in modalities:
            modality_analyses.append(f"""
        {modality.title()} Modality:
        [Represent and solve the problem using {modality} reasoning]
        Modality Result: [Answer from {modality} approach]
        """)

        domains_text = "\n".join(domain_analyses)
        modalities_text = "\n".join(modality_analyses)

        prompt = dedent_prompt(f"""
        Problem: {input_text}
        
        I'll use Universal Self-Consistency to ensure consistency across multiple domains and modalities.
        
        Cross-Domain Analysis:
        {domains_text}
        
        Cross-Modality Analysis:
        {modalities_text}
        
        Universal Consistency Check:
        1. Compare results across all domains and modalities
        2. Identify consistent patterns and discrepancies
        3. Analyze why certain approaches yield different results
        4. Synthesize a universally consistent solution
        
        Universally Consistent Answer:
        [Solution that maintains consistency across all domains and modalities]
        """)
        return prompt


class USP(PromptTechnique):
    """
    USP (Universal Self-Prompting) generates and refines its own prompts.

    This technique enables the model to generate, evaluate, and refine its own
    prompts to optimize performance for specific problems.
    """

    def __init__(self):
        """Initialize USP technique."""
        super().__init__(
            name="USP",
            identifier="2.2.5",
            description="Generates and refines its own prompts for optimization.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a USP prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - refinement_iterations (int): Number of prompt refinement iterations
                - optimization_criteria (List[str]): Criteria for prompt optimization

        Returns:
            str: Generated USP prompt
        """
        refinement_iterations = kwargs.get("refinement_iterations", 3)
        optimization_criteria = kwargs.get(
            "optimization_criteria", ["clarity", "specificity", "effectiveness"]
        )

        criteria_text = ", ".join(optimization_criteria)

        iterations_text = []
        for i in range(refinement_iterations):
            iterations_text.append(f"""
        Iteration {i + 1}:
        Generated Prompt: [Create/refine prompt for the problem]
        Evaluation: [Assess prompt quality based on {criteria_text}]
        Refinement: [Improve prompt based on evaluation]
        """)

        iterations_content = "\n".join(iterations_text)

        prompt = dedent_prompt(f"""
        Problem: {input_text}
        
        I'll use USP (Universal Self-Prompting) to generate and optimize my own prompts.
        
        Optimization Criteria: {criteria_text}
        Refinement Iterations: {refinement_iterations}
        
        Self-Prompting Process:
        {iterations_content}
        
        Final Optimized Prompt Application:
        [Apply the best refined prompt to solve the original problem]
        
        USP-Optimized Solution:
        [Answer using the self-generated optimal prompt]
        """)
        return prompt


class PromptParaphrasing(PromptTechnique):
    """
    Prompt Paraphrasing uses multiple paraphrased versions of prompts.

    This technique generates multiple paraphrased versions of the same prompt
    to explore different linguistic framings and their effects on reasoning.
    """

    def __init__(self):
        """Initialize Prompt Paraphrasing technique."""
        super().__init__(
            name="Prompt Paraphrasing",
            identifier="2.2.5",
            description="Uses multiple paraphrased versions of prompts for robust reasoning.",
        )

    def generate_prompt(self, input_text: str, **kwargs) -> str:
        """
        Generate a Prompt Paraphrasing prompt.

        Args:
            input_text (str): Input text
            **kwargs: Additional arguments
                - num_paraphrases (int): Number of paraphrased versions to generate
                - paraphrase_styles (List[str]): Different paraphrasing styles to use

        Returns:
            str: Generated Prompt Paraphrasing prompt
        """
        num_paraphrases = kwargs.get("num_paraphrases", 3)
        paraphrase_styles = kwargs.get(
            "paraphrase_styles", ["formal", "casual", "technical"]
        )

        paraphrases_text = []
        for i in range(num_paraphrases):
            style = paraphrase_styles[i % len(paraphrase_styles)]
            paraphrases_text.append(f"""
        Paraphrase {i + 1} ({style} style):
        [Rephrase the problem using {style} language and framing]
        Solution {i + 1}: [Solve using this paraphrased version]
        """)

        paraphrases_content = "\n".join(paraphrases_text)

        prompt = dedent_prompt(f"""
        Original Problem: {input_text}
        
        I'll use Prompt Paraphrasing to explore different linguistic framings of this problem.
        
        Paraphrasing Styles: {", ".join(paraphrase_styles)}
        Number of Paraphrases: {num_paraphrases}
        
        {paraphrases_content}
        
        Paraphrase Analysis:
        1. Compare solutions across different paraphrased versions
        2. Identify how linguistic framing affects reasoning
        3. Determine which framing yields the most robust solution
        4. Synthesize insights from all paraphrased approaches
        
        Linguistically-Robust Answer:
        [Final solution informed by multiple linguistic framings]
        """)
        return prompt
