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

import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../../../attribute-with-cache-key';
import {Graph} from '../../../graph';
import {OperatorImplementation, OperatorInitialization} from '../../../operators';
import {Tensor} from '../../../tensor';
import {ShapeUtil} from '../../../util';
import {getGlsl} from '../glsl-source';
import {WebGLInferenceHandler} from '../inference-handler';
import {ProgramInfo, TextureType} from '../types';

import {transpose, TransposeAttributes} from './transpose';

export interface SoftmaxAttributes extends AttributeWithCacheKey {
  readonly axis: number;
}

const softmaxComputeMaxProgramMetadata = {
  name: 'SoftmaxComputeMax',
  inputNames: ['A'],
  inputTypes: [TextureType.unpacked],
};

const softmaxComputeScaleProgramMetadata = {
  name: 'SoftmaxComputeScale',
  inputNames: ['A', 'Max'],
  inputTypes: [TextureType.unpacked, TextureType.unpacked],
};

const softmaxProgramMetadata = {
  name: 'SoftMax',
  inputNames: ['A', 'Max', 'Norm'],
  inputTypes: [TextureType.unpacked, TextureType.unpacked, TextureType.unpacked],
};

export const softmax: OperatorImplementation<SoftmaxAttributes> =
    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes): Tensor[] => {
      validateInputs(inputs);

      const inputShape = inputs[0].dims.slice();
      const axis = ShapeUtil.normalizeAxis(attributes.axis, inputShape.length);
      const logicalRowCount = ShapeUtil.sizeToDimension(inputShape, axis);
      const featureCount = ShapeUtil.sizeFromDimension(inputShape, axis);

      const output = computeSoftmax(inferenceHandler, inputs, attributes, logicalRowCount, featureCount);
      return output;
    };

export const parseSoftmaxAttributes: OperatorInitialization<SoftmaxAttributes> =
    (node: Graph.Node): SoftmaxAttributes => createAttributeWithCacheKey({axis: node.attributes.getInt('axis', 1)});

export const parseSoftmaxAttributesV13: OperatorInitialization<SoftmaxAttributes> =
    (node: Graph.Node): SoftmaxAttributes => createAttributeWithCacheKey({axis: node.attributes.getInt('axis', -1)});

// The "semantic" meaning of axis has changed in opset-13.
// Please compare: https://github.com/onnx/onnx/blob/main/docs/Operators.md#Softmax
// with https://github.com/onnx/onnx/blob/main/docs/Changelog.md#Softmax-11 for detailed explanations
// To account for the opset-13 behavior, our plan will be to transpose the "axis" dim to the innermost dim
// and perform softmax and then reverse the transpose. We can skip the transposing aspect if the axis is already
// the innermost dim
export const softmaxV13: OperatorImplementation<SoftmaxAttributes> =
    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes): Tensor[] => {
      validateInputs(inputs);

      const inputShape = inputs[0].dims.slice();
      const axis = ShapeUtil.normalizeAxis(attributes.axis, inputShape.length);
      const rank = inputShape.length;

      const isTransposeRequired = (axis !== rank - 1) ? true : false;
      const transposedInputShape: number[] = [];
      let perm: number[] = [];
      let transposedInputs: Tensor[] = [];
      let transposeAttribute: TransposeAttributes;

      if (isTransposeRequired) {
        perm = Array.from({length: rank}).map((_, i) => i);

        // swap the innermost dim with the dim corresponding to axis
        perm[axis] = rank - 1;
        perm[rank - 1] = axis;

        perm.map(p => transposedInputShape.push(inputShape[p]));

        transposeAttribute = createAttributeWithCacheKey({perm});
        transposedInputs = transpose(inferenceHandler, inputs, transposeAttribute);
      }

      const logicalRowCount = isTransposeRequired ? ShapeUtil.sizeToDimension(transposedInputShape, rank - 1) :
                                                    ShapeUtil.sizeToDimension(inputShape, rank - 1);
      const featureCount = isTransposeRequired ? ShapeUtil.sizeFromDimension(transposedInputShape, rank - 1) :
                                                 ShapeUtil.sizeFromDimension(inputShape, rank - 1);

      const output = computeSoftmax(
          inferenceHandler, isTransposeRequired ? transposedInputs : inputs, attributes, logicalRowCount, featureCount);

      if (isTransposeRequired) {
        const reversedOutput = transpose(inferenceHandler, output, transposeAttribute!);
        return reversedOutput;
      } else {
        return output;
      }
    };

const computeSoftmax =
    (inferenceHandler: WebGLInferenceHandler, inputs: Tensor[], attributes: SoftmaxAttributes, logicalRowCount: number,
     featureCount: number): Tensor[] => {
      const computeMaxProgramInfo =
          createComputeMaxProgramInfo(inferenceHandler, inputs[0], logicalRowCount, featureCount, [logicalRowCount]);
      const max = inferenceHandler.run(
          {...softmaxComputeMaxProgramMetadata, cacheHint: attributes.cacheKey, get: () => computeMaxProgramInfo},
          inputs);

      const computeScaleProgramInfo = createComputScaleProgramInfo(
          inferenceHandler, inputs[0], logicalRowCount, featureCount, computeMaxProgramInfo.output.dims,
          [logicalRowCount]);
      const scale = inferenceHandler.run(
          {...softmaxComputeScaleProgramMetadata, cacheHint: attributes.cacheKey, get: () => computeScaleProgramInfo},
          [inputs[0], max]);

      const softMaxProgramInfo = createSoftMaxProgramInfo(
          inferenceHandler, inputs[0], logicalRowCount, featureCount, computeMaxProgramInfo.output.dims,
          computeScaleProgramInfo.output.dims);
      const output = inferenceHandler.run(
          {...softmaxProgramMetadata, cacheHint: attributes.cacheKey, get: () => softMaxProgramInfo},
          [inputs[0], max, scale]);
      return [output];
    };

/**
 * Create a texture that contains the maximum value of each of the 'N' rows
 */
const createComputeMaxProgramInfo =
    (inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,
     outputShape: number[]): ProgramInfo => {
      const [textureWidth, textureHeight] =
          inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);
      const rank = outputShape.length;

      if (logicalRowCount < 1 || featureCount < 1) {
        throw new Error('Logical row count N and feature count D must be greater than or equal to 1');
      }

      if (outputShape.length !== 1) {
        throw new Error('Dimensionality of the output should be 1');
      }

      if (outputShape[0] !== logicalRowCount) {
        throw new Error('Shape of the output should be equal to logical row count');
      }

      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);
      const shaderSource = `
      float process(int[${rank}] indices) {
        int logical_row_start_offset = indices[0] * ${featureCount};

        float max = getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset, ${textureWidth},
        ${textureHeight} )));
        for(int i=1; i<${featureCount}; ++i)
        {
          float current = getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${textureWidth}, ${textureHeight})));
          if(current > max)
          max = current;
        }

        return max;
      }`;
      return {
        ...softmaxComputeMaxProgramMetadata,
        output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},
        shaderSource
      };
    };

/**
 * Create a texture that contains the normalization factor for each of the 'N' rows
 */
const createComputScaleProgramInfo =
    (inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,
     maxElementPerLogicalRow: readonly number[], outputShape: number[]): ProgramInfo => {
      const [textureWidth, textureHeight] =
          inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);
      const rank = outputShape.length;

      if (logicalRowCount < 1 || featureCount < 1) {
        throw new Error('Logical row count N and feature count D must be greater than or equal to 1');
      }

      if (outputShape.length !== 1) {
        throw new Error('Dimensionality of the output should be 1');
      }

      if (outputShape[0] !== logicalRowCount) {
        throw new Error('Shape of the output should be equal to logical row count');
      }

      if (maxElementPerLogicalRow.length !== 1) {
        throw new Error('Dimensionality of the intermediate results should be 1');
      }

      if (maxElementPerLogicalRow[0] !== logicalRowCount) {
        throw new Error('Shape of the intermediate results should be equal to logical row count');
      }

      const glsl = getGlsl(inferenceHandler.session.backend.glContext.version);
      const shaderSource = `
      float process(int[${rank}] indices) {
        int logical_row_start_offset = indices[0] * ${featureCount};

        float norm_factor = 0.0;
        float max = _Max(indices);
        for(int i=0; i<${featureCount}; ++i)
        {
          norm_factor += exp(getColorAsFloat(${glsl.texture2D}(A, offsetToCoords(logical_row_start_offset + i,
            ${textureWidth}, ${textureHeight}))) - max);
        }

        return norm_factor;
      }`;
      return {
        ...softmaxComputeScaleProgramMetadata,
        output: {dims: outputShape, type: input.type, textureType: TextureType.unpacked},
        shaderSource
      };
    };

const createSoftMaxProgramInfo =
    (inferenceHandler: WebGLInferenceHandler, input: Tensor, logicalRowCount: number, featureCount: number,
     maxElementPerLogicalRow: readonly number[], normalizationPerLogicalRow: readonly number[]): ProgramInfo => {
      const [textureWidth, textureHeight] =
          inferenceHandler.calculateTextureWidthAndHeight(input.dims, TextureType.unpacked);
      const rank = input.dims.length;

      if (logicalRowCount < 1 || featureCount < 1) {
        throw new Error('Logical row count N and feature count D must be greater than or equal to 1');
      }

      if (maxElementPerLogicalRow.length !== 1 || normalizationPerLogicalRow.length !== 1) {
        throw new Error('Dimensionality of the intermediate results should be 1');
      }

      if (maxElementPerLogicalRow[0] !== logicalRowCount || normalizationPerLogicalRow[0] !== logicalRowCount) {
        throw new Error('Shape of the intermediate results should be equal to logical row count');
      }

      const shaderSource = `
      float process(int[${rank}] indices) {

      // get offset of current logical tensor index from the 2-D texture coordinates (TexCoords)
      int offset = coordsToOffset(TexCoords, ${textureWidth}, ${textureHeight});

      //determine the logical row for this index
      int logical_row_index[1];
      logical_row_index[0] = offset / ${featureCount};

      float norm_factor = _Norm(logical_row_index);

      // avoid possible division by 0
      // if norm_facor is 0, all elements are zero
      // if so, return 0
      if(norm_factor == 0.0)
        return 0.0;

      return exp(_A(indices) - _Max(logical_row_index)) / norm_factor;
    }`;
      return {
        ...softmaxProgramMetadata,
        output: {dims: input.dims, type: input.type, textureType: TextureType.unpacked},
        shaderSource
      };
    };

const validateInputs = (inputs: Tensor[]): void => {
  if (!inputs || inputs.length !== 1) {
    throw new Error('Softmax requires 1 input.');
  }

  if (inputs[0].type !== 'float32' && inputs[0].type !== 'float64') {
    throw new Error('Invalid input type');
  }
};