/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

import * as tfc from '@tensorflow/tfjs-core';

import {NamedTensorsMap} from '../../data/types';
import {ExecutionContext} from '../../executor/execution_context';
import {InternalOpExecutor, Node} from '../types';

import {getParamValue} from './utils';

export const executeOp: InternalOpExecutor = (node: Node,
                                            tensorMap: NamedTensorsMap,
                                            context: ExecutionContext):
                                               tfc.Tensor[] => {
  switch (node.op) {
    case 'BiasAdd':
    case 'AddV2':
    case 'Add': {
      return [tfc.add(
          (getParamValue('a', node, tensorMap, context) as tfc.Tensor),
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    case 'AddN': {
      return [tfc.addN((
          getParamValue('tensors', node, tensorMap, context) as tfc.Tensor[]))];
    }
    case 'FloorMod':
    case 'Mod':
      return [tfc.mod(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    case 'Mul':
      return [tfc.mul(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    case 'RealDiv':
    case 'Div': {
      return [tfc.div(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    case 'DivNoNan': {
      return [tfc.divNoNan(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    case 'FloorDiv': {
      return [tfc.floorDiv(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    case 'Sub': {
      return [tfc.sub(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    case 'Minimum': {
      return [tfc.minimum(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    case 'Maximum': {
      return [tfc.maximum(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    case 'Pow': {
      return [tfc.pow(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    case 'SquaredDifference': {
      return [tfc.squaredDifference(
          getParamValue('a', node, tensorMap, context) as tfc.Tensor,
          getParamValue('b', node, tensorMap, context) as tfc.Tensor)];
    }
    default:
      throw TypeError(`Node type ${node.op} is not implemented`);
  }
};

export const CATEGORY = 'arithmetic';