// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

import {resolveBackendAndExecutionProviders} from './backend-impl.js';
import {SessionHandler, TrainingSessionHandler} from './backend.js';
import {InferenceSession as InferenceSession} from './inference-session.js';
import {OnnxValue} from './onnx-value.js';
import {Tensor} from './tensor.js';
import {TrainingSession as TrainingSessionInterface, TrainingSessionCreateOptions} from './training-session.js';

type SessionOptions = InferenceSession.SessionOptions;
type FeedsType = InferenceSession.FeedsType;
type FetchesType = InferenceSession.FetchesType;
type ReturnType = InferenceSession.ReturnType;
type RunOptions = InferenceSession.RunOptions;

const noBackendErrMsg: string = 'Training backend could not be resolved. ' +
    'Make sure you\'re using the correct configuration & WebAssembly files.';

export class TrainingSession implements TrainingSessionInterface {
  private constructor(handler: TrainingSessionHandler, hasOptimizerModel: boolean, hasEvalModel: boolean) {
    this.handler = handler;
    this.hasOptimizerModel = hasOptimizerModel;
    this.hasEvalModel = hasEvalModel;
  }
  private handler: TrainingSessionHandler;
  private hasOptimizerModel: boolean;
  private hasEvalModel: boolean;

  get trainingInputNames(): readonly string[] {
    return this.handler.inputNames;
  }
  get trainingOutputNames(): readonly string[] {
    return this.handler.outputNames;
  }

  get evalInputNames(): readonly string[] {
    if (this.hasEvalModel) {
      return this.handler.evalInputNames;
    } else {
      throw new Error('This training session has no evalModel loaded.');
    }
  }
  get evalOutputNames(): readonly string[] {
    if (this.hasEvalModel) {
      return this.handler.evalOutputNames;
    } else {
      throw new Error('This training session has no evalModel loaded.');
    }
  }

  static async create(trainingOptions: TrainingSessionCreateOptions, sessionOptions?: SessionOptions):
      Promise<TrainingSession> {
    const evalModel: string|Uint8Array = trainingOptions.evalModel || '';
    const optimizerModel: string|Uint8Array = trainingOptions.optimizerModel || '';
    const options: SessionOptions = sessionOptions || {};

    // resolve backend, update session options with validated EPs, and create session handler
    const [backend, optionsWithValidatedEPs] = await resolveBackendAndExecutionProviders(options);
    if (backend.createTrainingSessionHandler) {
      const handler = await backend.createTrainingSessionHandler(
          trainingOptions.checkpointState, trainingOptions.trainModel, evalModel, optimizerModel,
          optionsWithValidatedEPs);
      return new TrainingSession(handler, !!trainingOptions.optimizerModel, !!trainingOptions.evalModel);
    } else {
      throw new Error(noBackendErrMsg);
    }
  }

  /**
   * Helper function for runTrainStep and future runStep methods that handles the type-narrowing conversion from
   * the given parameters to SessionHandler.FetchesType and RunOptions.
   *
   * @param inputNames the feeds object is checked that they contain all input names in the provided list of input
   * names.
   * @param outputNames the fetches object is checked that their keys match up with valid names in the list of output
   * names.
   * @param feeds the required input
   * @param arg1 narrowed & converted into the SessionHandler.FetchesType or RunOptions object
   * @param arg2 optional RunOptions object.
   * @returns
   */
  typeNarrowingForRunStep(
      inputNames: readonly string[], outputNames: readonly string[], feeds: FeedsType, arg1?: FetchesType|RunOptions,
      arg2?: RunOptions): [SessionHandler.FetchesType, RunOptions] {
    const fetches: {[name: string]: OnnxValue|null} = {};
    let options: RunOptions = {};
    // check inputs
    if (typeof feeds !== 'object' || feeds === null || feeds instanceof Tensor || Array.isArray(feeds)) {
      throw new TypeError(
          '\'feeds\' must be an object that use input names as keys and OnnxValue as corresponding values.');
    }

    let isFetchesEmpty = true;
    // determine which override is being used
    if (typeof arg1 === 'object') {
      if (arg1 === null) {
        throw new TypeError('Unexpected argument[1]: cannot be null.');
      }
      if (arg1 instanceof Tensor) {
        throw new TypeError('\'fetches\' cannot be a Tensor');
      }

      if (Array.isArray(arg1)) {
        if (arg1.length === 0) {
          throw new TypeError('\'fetches\' cannot be an empty array.');
        }
        isFetchesEmpty = false;
        // output names
        for (const name of arg1) {
          if (typeof name !== 'string') {
            throw new TypeError('\'fetches\' must be a string array or an object.');
          }
          if (outputNames.indexOf(name) === -1) {
            throw new RangeError(`'fetches' contains invalid output name: ${name}.`);
          }
          fetches[name] = null;
        }

        if (typeof arg2 === 'object' && arg2 !== null) {
          options = arg2;
        } else if (typeof arg2 !== 'undefined') {
          throw new TypeError('\'options\' must be an object.');
        }
      } else {
        // decide whether arg1 is fetches or options
        // if any output name is present and its value is valid OnnxValue, we consider it fetches
        let isFetches = false;
        const arg1Keys = Object.getOwnPropertyNames(arg1);
        for (const name of outputNames) {
          if (arg1Keys.indexOf(name) !== -1) {
            const v = (arg1 as InferenceSession.NullableOnnxValueMapType)[name];
            if (v === null || v instanceof Tensor) {
              isFetches = true;
              isFetchesEmpty = false;
              fetches[name] = v;
            }
          }
        }

        if (isFetches) {
          if (typeof arg2 === 'object' && arg2 !== null) {
            options = arg2;
          } else if (typeof arg2 !== 'undefined') {
            throw new TypeError('\'options\' must be an object.');
          }
        } else {
          options = arg1 as RunOptions;
        }
      }
    } else if (typeof arg1 !== 'undefined') {
      throw new TypeError('Unexpected argument[1]: must be \'fetches\' or \'options\'.');
    }

    // check if all inputs are in feed
    for (const name of inputNames) {
      if (typeof feeds[name] === 'undefined') {
        throw new Error(`input '${name}' is missing in 'feeds'.`);
      }
    }

    // if no fetches is specified, we use the full output names list
    if (isFetchesEmpty) {
      for (const name of outputNames) {
        fetches[name] = null;
      }
    }

    return [fetches, options];
  }

  /**
   * Helper method for runTrainStep and any other runStep methods. Takes the ReturnType result from the SessionHandler
   * and changes it into a map of Tensors.
   *
   * @param results
   * @returns
   */
  convertHandlerReturnTypeToMapOfTensors(results: SessionHandler.ReturnType): ReturnType {
    const returnValue: {[name: string]: OnnxValue} = {};
    for (const key in results) {
      if (Object.hasOwnProperty.call(results, key)) {
        const result = results[key];
        if (result instanceof Tensor) {
          returnValue[key] = result;
        } else {
          returnValue[key] = new Tensor(result.type, result.data, result.dims);
        }
      }
    }
    return returnValue;
  }

  async lazyResetGrad(): Promise<void> {
    await this.handler.lazyResetGrad();
  }

  runTrainStep(feeds: FeedsType, options?: RunOptions): Promise<ReturnType>;
  runTrainStep(feeds: FeedsType, fetches: FetchesType, options?: RunOptions): Promise<ReturnType>;
  async runTrainStep(feeds: FeedsType, arg1?: FetchesType|RunOptions, arg2?: RunOptions): Promise<ReturnType> {
    const [fetches, options] =
        this.typeNarrowingForRunStep(this.trainingInputNames, this.trainingOutputNames, feeds, arg1, arg2);
    const results = await this.handler.runTrainStep(feeds, fetches, options);
    return this.convertHandlerReturnTypeToMapOfTensors(results);
  }

  async runOptimizerStep(options?: InferenceSession.RunOptions|undefined): Promise<void> {
    if (this.hasOptimizerModel) {
      await this.handler.runOptimizerStep(options || {});
    } else {
      throw new Error('This TrainingSession has no OptimizerModel loaded.');
    }
  }

  runEvalStep(feeds: FeedsType, options?: RunOptions|undefined): Promise<ReturnType>;
  runEvalStep(feeds: FeedsType, fetches: FetchesType, options?: RunOptions|undefined): Promise<ReturnType>;
  async runEvalStep(feeds: FeedsType, arg1?: FetchesType|RunOptions, arg2?: RunOptions): Promise<ReturnType> {
    if (this.hasEvalModel) {
      const [fetches, options] =
          this.typeNarrowingForRunStep(this.evalInputNames, this.evalOutputNames, feeds, arg1, arg2);
      const results = await this.handler.runEvalStep(feeds, fetches, options);
      return this.convertHandlerReturnTypeToMapOfTensors(results);
    } else {
      throw new Error('This TrainingSession has no EvalModel loaded.');
    }
  }

  async getParametersSize(trainableOnly = true): Promise<number> {
    return this.handler.getParametersSize(trainableOnly);
  }

  async loadParametersBuffer(array: Uint8Array, trainableOnly = true): Promise<void> {
    const paramsSize = await this.getParametersSize(trainableOnly);
    // checking that the size of the Uint8Array is equivalent to the byte length of a Float32Array of the number
    // of parameters
    if (array.length !== 4 * paramsSize) {
      throw new Error(
          'Size of the buffer passed into loadParametersBuffer must match the number of parameters in ' +
          'the model. Please use getParametersSize method to check.');
    }
    return this.handler.loadParametersBuffer(array, trainableOnly);
  }

  async getContiguousParameters(trainableOnly = true): Promise<OnnxValue> {
    return this.handler.getContiguousParameters(trainableOnly);
  }

  async release(): Promise<void> {
    return this.handler.dispose();
  }
}