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

import {DataType} from '../../../wasm-common';
import {TensorView} from '../../tensor';
import {ShapeUtil} from '../../util';
import {AttributeWithCacheKey, createAttributeWithCacheKey} from '../attribute-with-cache-key';
import {ComputeContext, GpuDataType, ProgramInfo, ProgramInfoLoader, ProgramMetadata, TensorInfo} from '../types';

import {IndicesHelper, inputVariable, outputVariable, ShaderHelper} from './common';

export interface SliceAttributes extends AttributeWithCacheKey {
  readonly starts: number[];
  readonly ends: number[];
  readonly axes: number[];
}

const validateInputs = (inputs: readonly TensorView[], attributes: SliceAttributes): void => {
  if (!inputs || inputs.length < 1) {
    throw new Error('too few inputs');
  }
  if (attributes.axes.length !== 0) {
    if (attributes.axes.length !== attributes.starts.length || attributes.axes.length !== attributes.ends.length) {
      throw new Error('axes, starts and ends must have the same length');
    }
  } else if (attributes.starts.length !== attributes.ends.length) {
    throw new Error('starts and ends must have the same length');
  }
  inputs.slice(1).forEach((_, idx) => {
    if (inputs[idx + 1].dataType !== DataType.int32 && inputs[idx + 1].dataType !== DataType.int64) {
      throw new Error(`Input ${idx} must be an array of int32 or int64`);
    }
  });
};

const readInput = (inputs: readonly TensorView[], idx: number): number[] => {
  const input: number[] = [];
  if (inputs.length > idx) {
    if (inputs[idx].dataType === DataType.int64) {
      inputs[idx].getBigInt64Array().forEach(v => input.push(Number(v)));
    } else if (inputs[idx].dataType === DataType.int32) {
      inputs[idx].getInt32Array().forEach(v => input.push(Number(v)));
    } else {
      throw new Error(`Input ${idx} must be an array of int32 or int64`);
    }
  }
  return input;
};

const createSliceAttributesFromInputs =
    (inputs: readonly TensorView[], attributes: SliceAttributes): SliceAttributes => {
      if (inputs.length > 1) {
        const starts: number[] = readInput(inputs, 1);
        const ends: number[] = readInput(inputs, 2);
        let axes: number[] = readInput(inputs, 3);
        if (axes.length === 0) {
          axes = [...Array(inputs[0].dims.length).keys()];
        }
        return createAttributeWithCacheKey({starts, ends, axes});
      } else {
        return attributes;
      }
    };

const fixStartEndValues =
    (value: number, index: number, inputShape: readonly number[], axes: readonly number[], steps: readonly number[]):
        number => {
          let newValue = value;
          if (value < 0) {
            newValue += inputShape[axes[index]];
          }
          if (steps[index] < 0) {
            return Math.max(0, Math.min(newValue, inputShape[axes[index]] - 1));
          } else {
            return Math.max(0, Math.min(newValue, inputShape[axes[index]]));
          }
        };

const calculateInputIndicesImpl =
    (input: IndicesHelper, output: IndicesHelper, inputShape: readonly number[], outputShape: readonly number[]):
        string => `fn calculateInputIndices(outputIndices: ${output.type.indices}) -> ${input.type.indices} {
          var inputIndices: ${input.type.indices};
          var carry = 0u;
          for (var i = ${inputShape.length}; i >= 0; i--) {
            var outputIndex = ${outputShape.length === 1 ? 'outputIndices' : 'outputIndices[i]'};
            var inputIndex = outputIndex * steps[i] + starts[i] + carry;
            carry = inputIndex / inputShape[i];
            inputIndex = inputIndex % inputShape[i];
            if (signs[i] < 0) {
              inputIndex = inputShape[i] - inputIndex - 1u + starts[i];
            }
            ${inputShape.length === 1 ? 'inputIndices' : 'inputIndices[i]'} = inputIndex;
          }
          return inputIndices;
      }`;

const createSliceProgramInfo =
    (metadata: ProgramMetadata, inputs: readonly TensorView[], attributes: SliceAttributes): ProgramInfo => {
      const inputShape = inputs[0].dims;
      const inputSize = ShapeUtil.size(inputShape);
      const axes = (attributes.axes.length > 0) ? ShapeUtil.normalizeAxes(attributes.axes, inputShape.length) :
                                                  [...Array(inputShape.length).keys()];
      let steps = readInput(inputs, 4);
      steps.forEach((step) => step !== 0 || (() => {
                                throw new Error('step cannot be 0');
                              }));
      if (steps.length === 0) {
        steps = Array(axes.length).fill(1);
      }
      const starts = attributes.starts.map((start, i) => fixStartEndValues(start, i, inputShape, axes, steps));

      const ends = attributes.ends.map((end, i) => fixStartEndValues(end, i, inputShape, axes, steps));

      if (axes.length !== inputShape.length) {
        for (let i = 0; i < inputShape.length; ++i) {
          if (!axes.includes(i)) {
            starts.splice(i, 0, 0);
            ends.splice(i, 0, inputShape[i]);
            steps.splice(i, 0, 1);
          }
        }
      }
      const signs = steps.map(step => Math.sign(step));
      // Convert negative steps to positive steps and reverse starts and ends
      steps.forEach((step, i, array) => {
        if (step < 0) {
          const numSteps = (ends[i] - starts[i]) / step;
          const newEnd = starts[i];
          const newStart = newEnd + numSteps * steps[i];
          starts[i] = newStart;
          ends[i] = newEnd;
          array[i] = -step;
        }
      });

      const outputShape = inputShape.slice(0);
      axes.forEach((axis, _) => {
        outputShape[axis] = Math.ceil((ends[axis] - starts[axis]) / steps[axis]);
      });

      const outputTensorInfo:
          TensorInfo = {dims: outputShape, dataType: inputs[0].dataType, gpuDataType: GpuDataType.default};

      const output = outputVariable('output', inputs[0].dataType, outputShape);
      const input = inputVariable('input', inputs[0].dataType, inputShape);
      const outputSize = ShapeUtil.size(outputShape);

      const getShaderSource = (shaderHelper: ShaderHelper) => `
      ${shaderHelper.declareVariables(input, output)}
        const signs = array<i32, ${signs.length}>(${signs.map(i => `${i}i`).join(',')});
        const starts = array<u32, ${starts.length}>(${starts.map(i => `${i}u`).join(',')});
        const ends = array<u32, ${ends.length}>(${ends.map(i => `${i}u`).join(',')});
        const steps = array<u32, ${steps.length}>(${steps.map(i => `${i}u`).join(',')});
        const inputShape = array<u32, ${inputShape.length}>(${inputShape.map(i => `${i}u`).join(',')});

        ${calculateInputIndicesImpl(input, output, inputShape, outputShape)}
        ${shaderHelper.mainStart()}
          ${shaderHelper.guardAgainstOutOfBoundsWorkgroupSizes(outputSize)}
          let outputIndices = ${output.offsetToIndices('global_idx')};
          let inputIndices = calculateInputIndices(outputIndices);
          ${output.setByOffset('global_idx', input.getByIndices('inputIndices'))}
      }`;
      return {
        ...metadata,
        getShaderSource,
        outputs: [outputTensorInfo],
        dispatchGroup: () => ({x: Math.ceil(inputSize / 64 /* workgroup size */)})
      };
    };

const createSliceProgramInfoLoader =
    (inputs: readonly TensorView[], attributes: SliceAttributes): ProgramInfoLoader => {
      const updatedAttributes = createSliceAttributesFromInputs(inputs, attributes);
      const metadata: ProgramMetadata = {
        name: 'Slice',
        inputTypes: [GpuDataType.default],
        cacheHint: updatedAttributes.cacheKey + (inputs.length > 4 ? 'steps_' + inputs[4].dims.toString() : '')
      };
      return {...metadata, get: () => createSliceProgramInfo(metadata, inputs, updatedAttributes)};
    };

export const slice = (context: ComputeContext, attributes: SliceAttributes): void => {
  validateInputs(context.inputs, attributes);
  const programInfoLoader = createSliceProgramInfoLoader(context.inputs, attributes);
  const program = programInfoLoader.get();
  if (ShapeUtil.size(program.outputs[0].dims) > 0) {
    context.compute(programInfoLoader, {inputs: [0]});
  } else {
    // TODO: support empty output
    throw new Error('slice: output size is 0');
  }
};

export const parseSliceAttributes = (attributes: Record<string, unknown>): SliceAttributes => {
  const starts = attributes.starts as number[];
  const ends = attributes.ends as number[];
  const axes = attributes.axes as number[];
  return createAttributeWithCacheKey({starts, ends, axes});
};