/**
 * ONNX Model Inferencer (Frontend/Backend Common)
 *
 * Platform-agnostic inference logic that accepts ONNX session from platform-specific code.
 * No direct dependency on onnxruntime packages - uses dependency injection pattern.
 *
 * Adapted from Node.js test_inference.js for cross-platform use
 * Provides causal language model inference using GPT-2 ONNX model
 */

/**
 * Minimal ONNX Tensor interface (platform-agnostic)
 */
export interface OnnxTensor {
	readonly data: number[] | Float32Array | Int32Array | BigInt64Array | Uint8Array;
	readonly dims: readonly number[];
	readonly type: string;
}

/**
 * Minimal ONNX Session interface (platform-agnostic)
 */
export interface OnnxSession {
	readonly inputNames: readonly string[];
	readonly outputNames: readonly string[];
	run(feeds: Record<string, OnnxTensor>): Promise<Record<string, OnnxTensor>>;
}

/**
 * Tensor constructor interface (platform-specific)
 */
export interface TensorConstructor {
	new (
		type: string,
		data: BigInt64Array | Float32Array | Int32Array | Uint8Array,
		dims: number[]
	): OnnxTensor;
}

/**
 * Configuration for the inferencer
 */
export interface InferencerConfig {
	vocabSize: number;
	seqLen: number;
	modelPath?: string; // Optional, for reference
}

/**
 * Inference result containing generated tokens and metadata
 */
export interface InferenceResult {
	tokens: number[];
	text: string;
	logits: Float32Array;
	inferenceTime: number;
}

/**
 * Evaluation mode inputs for tree attention
 */
export interface EvaluationInputs {
	prefixIds: number[]; // Prefix sequence (causal context)
	evaluatedIds: number[]; // Tokens to evaluate in tree
	evaluatedMask: number[]; // Attention mask [m×m] flattened
}

/**
 * Evaluation mode output
 */
export interface EvaluationOutput {
	logits: Float32Array; // [m+1, vocab_size] flattened
	numEvaluated: number; // m
}

/**
 * Model Inferencer for Causal Language Model
 * Compatible with both frontend (onnxruntime-web) and backend (onnxruntime-node)
 */
export class ModelInferencer {
	private session: OnnxSession | null = null;
	private config: InferencerConfig;
	private TensorClass: TensorConstructor;

	// TGN tokenizer: byte-level (0-255) + PAD(256) + START(257) + END(258)
	private readonly PAD_TOKEN = 256;
	private readonly START_TOKEN = 257;
	private readonly END_TOKEN = 258;

	constructor(TensorClass: TensorConstructor, config: Partial<InferencerConfig> = {}) {
		this.TensorClass = TensorClass;
		this.config = {
			vocabSize: 259,
			seqLen: 256,
			...config
		};
	}

	/**
	 * Set the inference session (created by platform-specific code)
	 */
	setSession(session: OnnxSession): void {
		this.session = session;
		console.log("[ModelInferencer] ✓ Session set successfully");
		this.printModelInfo();
	}

	/**
	 * Run basic inference test
	 */
	async testBasicInference(): Promise<InferenceResult> {
		if (!this.session) {
			throw new Error("Inferencer not initialized. Call setSession() first.");
		}

		console.log("[ModelInferencer] Running basic inference test...");

		const batchSize = 1;
		const seqLen = this.config.seqLen;

		// Create random input
		const inputIds = this.createRandomInput(batchSize, seqLen);
		const inputTensor = new this.TensorClass("int64", inputIds, [batchSize, seqLen]);

		// Run inference
		const startTime = performance.now();
		const results = await this.session.run({ input_ids: inputTensor });
		const inferenceTime = performance.now() - startTime;

		// Get logits
		const logits = results.logits;

		// Validate output
		this.validateOutput(logits, batchSize, seqLen);

		// Get predictions
		const predictions = this.getPredictions(logits.data as Float32Array, batchSize * seqLen);

		// Convert tokens to text
		const text = String.fromCharCode(...predictions.slice(0, 100));

		console.log("[ModelInferencer] Inference completed:");
		console.log(`  Input shape: [${inputTensor.dims.join(", ")}]`);
		console.log(`  Output shape: [${logits.dims.join(", ")}]`);
		console.log(`  Output dtype: ${logits.type}`);
		console.log(`  Inference time: ${inferenceTime.toFixed(2)}ms`);
		console.log(`  Sample predictions: [${predictions.slice(0, 10).join(", ")}]`);

		const logitsArray = Array.from(logits.data as Float32Array);
		console.log(
			`  Logits range: [${Math.min(...logitsArray).toFixed(3)}, ${Math.max(...logitsArray).toFixed(3)}]`
		);

		return {
			tokens: predictions,
			text,
			logits: logits.data as Float32Array,
			inferenceTime
		};
	}

	/**
	 * Generate tokens autoregressively from a prompt
	 */
	async generateText(prompt: string, numTokens: number = 10): Promise<InferenceResult> {
		if (!this.session) {
			throw new Error("Inferencer not initialized. Call setSession() first.");
		}

		console.log(`[ModelInferencer] Generating ${numTokens} tokens from prompt: "${prompt}"`);

		// Convert prompt to token IDs (byte values)
		const promptTokens = Array.from(prompt).map((c) => c.charCodeAt(0));
		console.log(`  Prompt tokens (${promptTokens.length}): [${promptTokens.join(", ")}]`);

		// Start with prompt tokens
		const sequence = [...promptTokens];
		const times: number[] = [];

		// Generate tokens
		for (let i = 0; i < numTokens; i++) {
			// Pad sequence to fixed length
			const paddedSequence = this.padSequence(sequence, this.config.seqLen);

			// Create input tensor
			const inputIds = new BigInt64Array(paddedSequence.map((t) => BigInt(t)));
			const inputTensor = new this.TensorClass("int64", inputIds, [1, this.config.seqLen]);

			// Run inference
			const startTime = performance.now();
			const results = await this.session.run({ input_ids: inputTensor });
			times.push(performance.now() - startTime);

			// Get prediction at the last non-padded position
			const logits = results.logits.data as Float32Array;
			const lastPos = sequence.length - 1; // Position before padding
			const offset = lastPos * this.config.vocabSize;

			// Find token with highest logit
			let maxIdx = 0;
			let maxVal = logits[offset];
			for (let j = 1; j < this.config.vocabSize; j++) {
				if (logits[offset + j] > maxVal) {
					maxVal = logits[offset + j];
					maxIdx = j;
				}
			}

			sequence.push(maxIdx);

			// Stop if END token is generated
			if (maxIdx === this.END_TOKEN) {
				console.log("  Generated END token, stopping...");
				break;
			}
		}

		// Convert generated tokens to text
		const generatedText = String.fromCharCode(...sequence);

		const avgTime = times.reduce((a, b) => a + b, 0) / times.length;
		console.log(`[ModelInferencer] Generation complete:`);
		console.log(`  Generated text: "${generatedText}"`);
		console.log(`  Token sequence (${sequence.length}): [${sequence.join(", ")}]`);
		console.log(`  Avg inference time: ${avgTime.toFixed(2)}ms`);
		console.log(`  Tokens/sec: ${(1000 / avgTime).toFixed(2)}`);

		return {
			tokens: sequence,
			text: generatedText,
			logits: new Float32Array(), // Not returning full logits for generation
			inferenceTime: avgTime
		};
	}

	/**
	 * Get model information
	 */
	getModelInfo(): { inputs: string[]; outputs: string[] } | null {
		if (!this.session) return null;

		return {
			inputs: [...this.session.inputNames],
			outputs: [...this.session.outputNames]
		};
	}

	/**
	 * Get configuration
	 */
	getConfig(): InferencerConfig {
		return this.config;
	}

	/**
	 * Run inference with token array input
	 * Returns raw logits as Float32Array
	 */
	async runInference(tokens: number[]): Promise<Float32Array> {
		if (!this.session) {
			throw new Error("Inferencer not initialized. Call setSession() first.");
		}

		const seqLen = this.config.seqLen;

		// Prepend START_TOKEN to input
		const tokensWithStart = [this.START_TOKEN, ...tokens];

		// Pad to fixed length
		const paddedTokens = new BigInt64Array(seqLen);
		for (let i = 0; i < seqLen; i++) {
			paddedTokens[i] =
				i < tokensWithStart.length ? BigInt(tokensWithStart[i]) : BigInt(this.PAD_TOKEN);
		}

		// Create input tensor
		const inputTensor = new this.TensorClass("int64", paddedTokens, [1, seqLen]);

		// Run inference
		const results = await this.session.run({ input_ids: inputTensor });
		return results.logits.data as Float32Array;
	}


	/**
	 * Run tree attention inference (evaluation mode)
	 * For models exported with --evaluation flag
	 * @param inputs - Prefix, evaluated tokens, and attention mask
	 * @returns Logits for each evaluated position
	 */
	async runEvaluationInference(inputs: EvaluationInputs): Promise<EvaluationOutput> {
		if (!this.session) {
			throw new Error("Inferencer not initialized. Call setSession() first.");
		}

		const { prefixIds, evaluatedIds, evaluatedMask } = inputs;
		const batchSize = 1;
		const prefixLen = prefixIds.length;
		const m = evaluatedIds.length;

		// Convert to BigInt64Array for ONNX int64 tensors
		const prefixIdsArray = new BigInt64Array(batchSize * prefixLen);
		for (let i = 0; i < prefixLen; i++) {
			prefixIdsArray[i] = BigInt(prefixIds[i]);
		}

		const evaluatedIdsArray = new BigInt64Array(batchSize * m);
		for (let i = 0; i < m; i++) {
			evaluatedIdsArray[i] = BigInt(evaluatedIds[i]);
		}

		// Mask is Float32Array
		const maskArray = new Float32Array(m * m);
		for (let i = 0; i < m * m; i++) {
			maskArray[i] = evaluatedMask[i];
		}

		// Create ONNX tensors
		const prefixIdsTensor = new this.TensorClass("int64", prefixIdsArray, [
			batchSize,
			prefixLen
		]);
		const evaluatedIdsTensor = new this.TensorClass("int64", evaluatedIdsArray, [batchSize, m]);
		const evaluatedMaskTensor = new this.TensorClass("float32", maskArray, [1, m, m]);

		// Run inference
		const results = await this.session.run({
			prefix_ids: prefixIdsTensor,
			evaluated_ids: evaluatedIdsTensor,
			evaluated_mask: evaluatedMaskTensor
		});

		// Extract logits
		const logits = results.logits.data as Float32Array;

		// Output shape: [batch, m+1, vocab_size]
		// We return flattened array and num_evaluated for reshaping
		return {
			logits,
			numEvaluated: m
		};
	}


	/**
	 * Compute softmax for a single position's logits
	 * @param logits - Full logits array
	 * @param position - Which evaluated position (0 = last prefix, 1-m = evaluated tokens)
	 * @returns Probability distribution over vocabulary
	 */
	softmax(logits: Float32Array, position: number): Float32Array {
		const vocabSize = this.config.vocabSize;
		const offset = position * vocabSize;
		const probs = new Float32Array(vocabSize);

		// Find max for numerical stability
		let maxLogit = -Infinity;
		for (let i = 0; i < vocabSize; i++) {
			maxLogit = Math.max(maxLogit, logits[offset + i]);
		}

		// Compute exp and sum
		let sumExp = 0;
		for (let i = 0; i < vocabSize; i++) {
			probs[i] = Math.exp(logits[offset + i] - maxLogit);
			sumExp += probs[i];
		}

		// Normalize
		for (let i = 0; i < vocabSize; i++) {
			probs[i] /= sumExp;
		}

		return probs;
	}


	/**
	 * Check if inferencer is ready
	 */
	isReady(): boolean {
		return this.session !== null;
	}

	/**
	 * Destroy the session and free resources
	 */
	destroy(): void {
		this.session = null;
		console.log("[ModelInferencer] Session destroyed");
	}

	// Private helper methods

	private printModelInfo(): void {
		if (!this.session) return;

		console.log("[ModelInferencer] Model Information:");
		console.log("  Inputs:");
		this.session.inputNames.forEach((name, i) => {
			console.log(`    [${i}] ${name}`);
		});
		console.log("  Outputs:");
		this.session.outputNames.forEach((name, i) => {
			console.log(`    [${i}] ${name}`);
		});
	}

	private createRandomInput(batchSize: number, seqLen: number): BigInt64Array {
		const size = batchSize * seqLen;
		const data = new BigInt64Array(size);
		for (let i = 0; i < size; i++) {
			data[i] = BigInt(Math.floor(Math.random() * this.config.vocabSize));
		}
		return data;
	}

	private padSequence(tokens: number[], targetLen: number): number[] {
		const padded = [...tokens];
		while (padded.length < targetLen) {
			padded.push(this.PAD_TOKEN);
		}
		return padded.slice(0, targetLen); // Truncate if too long
	}

	private validateOutput(logits: OnnxTensor, batchSize: number, seqLen: number): void {
		const expectedShape = [batchSize, seqLen, this.config.vocabSize];

		if (logits.dims.length !== 3) {
			throw new Error(`Expected 3D output, got ${logits.dims.length}D`);
		}

		if (
			logits.dims[0] !== expectedShape[0] ||
			logits.dims[1] !== expectedShape[1] ||
			logits.dims[2] !== expectedShape[2]
		) {
			throw new Error(
				`Shape mismatch! Expected [${expectedShape.join(", ")}], ` +
					`got [${logits.dims.join(", ")}]`
			);
		}

		if (logits.type !== "float32") {
			throw new Error(`Expected float32 output, got ${logits.type}`);
		}
	}

	private getPredictions(logitsData: Float32Array, numPositions: number): number[] {
		const predictions: number[] = [];
		for (let i = 0; i < numPositions; i++) {
			let maxIdx = 0;
			let maxVal = logitsData[i * this.config.vocabSize];
			for (let j = 1; j < this.config.vocabSize; j++) {
				const val = logitsData[i * this.config.vocabSize + j];
				if (val > maxVal) {
					maxVal = val;
					maxIdx = j;
				}
			}
			predictions.push(maxIdx);
		}
		return predictions;
	}
}