module TensorStream
  module MathOps
    def MathOps.included(klass)
      klass.class_eval do
        register_op :tanh, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.tanh(t) })
        end

        register_op :tan, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.tan(t) })
        end

        register_op :atan, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.atan(t) })
        end

        register_op :sec, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.sec(t) })
        end

        register_op :sin, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.sin(t) })
        end

        register_op :add, no_eval: true do |context, tensor, inputs|
          a, b = inputs
          call_vector_op(tensor, :add, a, b, context, ->(t, u) { t + u })
        end

        register_op :add_n, no_eval: true do |context, tensor, inputs|
          if inputs.size == 1
            complete_eval(inputs[0], context)
          elsif inputs.size > 1

            a = inputs.pop
            until inputs.empty?
              b = inputs.pop
              a = call_vector_op(tensor, :add, a, b, context, ->(t, u) { t + u })
            end
            a
          end
        end

        register_op :sub, no_eval: true do |context, tensor, inputs|
          a, b = inputs
          call_vector_op(tensor, :sub, a, b, context, ->(t, u) { t - u })
        end

        register_op %i[floor_mod mod], no_eval: true do |context, tensor, inputs|
          a, b = inputs
          call_vector_op(tensor, :mod, a, b, context, ->(t, u) { t % u })
        end

        register_op %i[floor_div], no_eval: true do |context, tensor, inputs|
          a, b = inputs
          if fp_type?(tensor.data_type)
            call_vector_op(tensor, :div, a, b, context, ->(t, u) { (t / u).to_i.to_f })
          else
            call_vector_op(tensor, :div, a, b, context, ->(t, u) { t / u })
          end
        end

        register_op :mul, no_eval: true do |context, tensor, inputs|
          a, b = inputs
          call_vector_op(tensor, :mul, a, b, context, ->(t, u) { t * u })
        end

        register_op :pow, no_eval: true do |context, tensor, inputs|
          a, b = inputs
          call_vector_op(tensor, :pow, a, b, context, ->(t, u) { t**u })
        end

        register_op :squared_difference, no_eval: true do |context, tensor, inputs|
          a, b = inputs
          call_vector_op(tensor, :squared_difference, a, b, context, ->(t, u) { (t - u) * (t - u) })
        end

        register_op :round, no_eval: true do |context, _tensor, inputs|
          call_op(:round, inputs[0], context, ->(t, _b) { t.round })
        end

        register_op :abs, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { t.abs })
        end

        register_op :asin, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.asin(t) })
        end

        register_op :acos, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.acos(t) })
        end

        register_op :cos, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.cos(t) })
        end

        register_op :log1p, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.log(1 + t) })
        end

        register_op :log, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { t < 0 ? Float::NAN : Math.log(t) })
        end

        register_op :exp, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.exp(t) })
        end

        register_op :sigmoid, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { sigmoid(t) })
        end

        register_op :sqrt, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { Math.sqrt(t) })
        end

        register_op :floor, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { t.floor })
        end

        register_op :ceil, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { t.ceil })
        end

        register_op :square, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { t * t })
        end

        register_op :reciprocal, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { 1 / t })
        end

        register_op %i[neg negate], no_eval: true do |context, tensor, inputs|
          call_vector_op(tensor, :negate, inputs[0], nil, context, ->(t, _u) { -t })
        end

        register_op :tanh_grad, no_eval: true do |context, tensor, inputs|
          call_op(tensor, inputs[0], context, ->(t, _b) { 1 - Math.tanh(t) * Math.tanh(t) })
        end

        register_op(%i[argmax arg_max]) do |_context, tensor, inputs|
          axis = tensor.options[:axis] || 0
          rank = get_rank(inputs[0])
          raise TensorStream::InvalidArgumentError, "Expected dimension in the range [#{-rank},#{rank}) but got #{axis}" if axis < -rank || axis >= rank
          get_op_with_axis(inputs[0], axis, 0, tensor.data_type)
        end

        register_op(%i[argmin arg_min]) do |_context, tensor, inputs|
          axis = tensor.options[:axis] || 0
          rank = get_rank(inputs[0])
          raise TensorStream::InvalidArgumentError, "Expected dimension in the range [#{-rank},#{rank}) but got #{axis}" if axis < -rank || axis >= rank
          get_op_with_axis(inputs[0], axis, 0, tensor.data_type, ->(a, b) { a < b })
        end

        register_op :cumprod do |context, tensor, inputs|
          x = inputs[0]
          c = fp_type?(tensor.data_type) ? 1.0 : 1
          reverse_option = tensor.options[:reverse]
          exclusive = tensor.options[:exclusive]

          func = lambda do |arr|
            return c if arr.nil?
            count = arr.size


            arr = arr.reverse if reverse_option
            arr = [1] + arr if exclusive

            start_prod = arr[0]
            mapped = arr[1...count].map do |v|
              start_prod = vector_op(start_prod, v, ->(a, b) { a * b })
            end

            arr = [arr[0]] + mapped
            reverse_option ? arr.reverse : arr
          end
          reduction(context, tensor, func)
        end

        register_op :sum, noop: true do |context, tensor, _inputs|
          func = lambda do |arr|
            reduced_val = arr[0]
            arr[1..arr.size].each do |v|
              reduced_val = vector_op(reduced_val, v, ->(t, u) { t + u })
            end
            reduced_val
          end
          reduction(context, tensor, func)
        end

        register_op :prod, noop: true do |context, tensor, _inputs|
          c = fp_type?(tensor.data_type) ? 1.0 : 1
          func = lambda do |arr|
            return c if arr.nil?

            reduced_val = arr[0]
            arr[1..arr.size].each do |v|
              reduced_val = vector_op(reduced_val, v, ->(a, b) { a * b })
            end
            reduced_val
          end

          reduction(context, tensor, func)
        end

        register_op :sigmoid_grad, no_eval: true do |context, tensor, inputs|
          a, b = inputs
          call_vector_op(tensor, :sigmoid_grad, a, b, context, ->(t, u) { u * sigmoid(t) * (1 - sigmoid(t)) })
        end

        register_op :mean, noop: true do |context, tensor, _inputs|
          c = fp_type?(tensor.data_type) ? 0.0 : 0
          func = lambda do |arr|
            return c if arr.nil?

            reduced_val = arr[0]
            arr[1..arr.size].each do |v|
              reduced_val = vector_op(reduced_val, v, ->(a, b) { a + b })
            end

            vector_op(reduced_val, nil, ->(a, _b) { a / arr.size })
          end

          reduction(context, tensor, func)
        end

        register_op :mat_mul do |_context, tensor, inputs|
          matrix_a, matrix_b = inputs
          rank_a = get_rank(matrix_a)
          rank_b = get_rank(matrix_b)
          raise "#{tensor.inputs[0].name} rank must be greater than 1" if rank_a < 2
          raise "#{tensor.inputs[1].name} rank must be greater than 1" if rank_b < 2

          matrix_a = matrix_a.transpose if tensor.options[:transpose_a]
          matrix_b = matrix_b.transpose if tensor.options[:transpose_b]

          # check matrix dimensions
          raise TensorStream::ValueError, "incompatible shape sizes for matrix multiplication (#{matrix_a[0].size} != #{matrix_b.size}) #{shape_eval(matrix_a)} vs #{shape_eval(matrix_b)}" if matrix_a[0].size != matrix_b.size

          (Matrix[*matrix_a] * Matrix[*matrix_b]).to_a
        end

        register_op %i[max maximum], noop: true do |context, tensor, inputs|
          call_vector_op(tensor, :max, inputs[0], inputs[1], context, ->(t, u) { [t, u].max })
        end

        register_op %i[min minimum], noop: true do |context, tensor, inputs|
          call_vector_op(tensor, :min, inputs[0], inputs[1], context, ->(t, u) { [t, u].min })
        end
      end
    end
  end
end