#pragma once

#include <string>
#include <unordered_map>

namespace torch {
namespace jit {

std::unordered_map<std::string, std::string>
quant_fusion_pattern_and_replacements() {
  std::string conv2d = R"(
graph(%a_quant, %packed_params, %r_scale, %r_zero_point, %r_dtype, %stride, %padding, %dilation, %groups):
        %a_dequant = aten::dequantize(%a_quant)
        %w_quant : Tensor, %b : Tensor? = quantized::conv2d_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %r = aten::conv2d(%a_dequant, %w_dequant, %b, %stride, %padding, %dilation, %groups)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        return (%r_quant) )";

  std::string quantized_conv2d = R"(
graph(%a_quant, %packed_params, %r_scale, %r_zero_point, %r_dtype, %stride, %padding, %dilation, %groups):
        %r_quant = quantized::conv2d(%a_quant, %packed_params, %stride, %padding, %dilation, %groups, %r_scale, %r_zero_point)
        return (%r_quant) )";

  std::string addmm = R"(
graph(%packed_params, %a_quant, %r_scale, %r_zero_point, %r_dtype, %4):
        %a_dequant = aten::dequantize(%a_quant)
        %w_quant : Tensor, %b : Tensor? = quantized::linear_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %w_dequant_t = aten::t(%w_dequant)
        %r = aten::addmm(%b, %a_dequant, %w_dequant_t, %4, %4)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        return (%r_quant) )";

  std::string matmul_with_bias = R"(
graph(%packed_params, %a_quant, %r_scale, %r_zero_point, %r_dtype, %4):
        %a_dequant = aten::dequantize(%a_quant)
        %w_quant : Tensor, %b : Tensor? = quantized::linear_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %w_dequant_t = aten::t(%w_dequant)
        %output = aten::matmul(%a_dequant, %w_dequant_t)
        %r = aten::add_(%output, %b, %4)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        return (%r_quant) )";

  std::string quantized_linear = R"(
graph(%packed_params, %a_quant, %r_scale, %r_zero_point, %r_dtype, %4):
        %r = quantized::linear(%a_quant, %packed_params, %r_scale, %r_zero_point)
        return (%r) )";

  std::string matmul_no_bias = R"(
graph(%packed_params, %a_quant, %r_scale, %r_zero_point, %r_dtype):
        %a_dequant = aten::dequantize(%a_quant)
        %w_quant : Tensor, %b : Tensor? = quantized::linear_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %w_dequant_t = aten::t(%w_dequant)
        %r = aten::matmul(%a_dequant, %w_dequant_t)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        return (%r_quant) )";

  std::string quantized_linear_no_bias = R"(
graph(%packed_params, %a_quant, %r_scale, %r_zero_point, %r_dtype):
        %r = quantized::linear(%a_quant, %packed_params, %r_scale, %r_zero_point)
        return (%r) )";

  std::string conv2d_relu = R"(
graph(%a_quant, %packed_params, %r_scale, %r_zero_point, %r_dtype, %stride, %padding, %dilation, %groups):
        %a_dequant = aten::dequantize(%a_quant)
        %w_quant : Tensor, %b : Tensor? = quantized::conv2d_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %conv_out = aten::conv2d(%a_dequant, %w_dequant, %b, %stride, %padding, %dilation, %groups)
        %r = aten::relu(%conv_out)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        return (%r_quant) )";

  std::string conv2d_inplace_relu = R"(
graph(%a_quant, %packed_params, %r_scale, %r_zero_point, %r_dtype, %stride, %padding, %dilation, %groups):
        %a_dequant = aten::dequantize(%a_quant)
        %w_quant : Tensor, %b : Tensor? = quantized::conv2d_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %conv_out = aten::conv2d(%a_dequant, %w_dequant, %b, %stride, %padding, %dilation, %groups)
        %r = aten::relu_(%conv_out)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        return (%r_quant) )";

  std::string quantized_conv2d_relu = R"(
graph(%a_quant, %packed_params, %r_scale, %r_zero_point, %r_dtype, %stride, %padding, %dilation, %groups):
        %r_quant = quantized::conv2d_relu(%a_quant, %packed_params, %stride, %padding, %dilation, %groups, %r_scale, %r_zero_point)
        return (%r_quant) )";

  std::string add_relu = R"(
graph(%a_quant, %b_quant, %scale, %zero_point, %dtype):
         %alpha = prim::Constant[value=1]()
         %a_dequant = aten::dequantize(%a_quant)
         %b_dequant = aten::dequantize(%b_quant)
         %r_add = aten::add_(%a_dequant, %b_dequant, %alpha)
         %r_relu = aten::relu(%r_add)
         %r = aten::quantize_per_tensor(%r_relu, %scale, %zero_point, %dtype)
         return (%r) )";

  std::string add_inplace_relu = R"(
graph(%a_quant, %b_quant, %scale, %zero_point, %dtype):
         %alpha = prim::Constant[value=1]()
         %a_dequant = aten::dequantize(%a_quant)
         %b_dequant = aten::dequantize(%b_quant)
         %r_add = aten::add_(%a_dequant, %b_dequant, %alpha)
         %r_relu = aten::relu_(%r_add)
         %r = aten::quantize_per_tensor(%r_relu, %scale, %zero_point, %dtype)
         return (%r) )";

  std::string quantized_add_relu = R"(
graph(%a_quant, %b_quant, %scale, %zero_point, %dtype):
         %r = quantized::add_relu(%a_quant, %b_quant, %scale, %zero_point)
         return (%r) )";

  std::string aten_linear = R"(
graph(%packed_params, %a_quant, %r_scale, %r_zero_point, %r_dtype):
        %a_dequant = aten::dequantize(%a_quant)
        %w_quant : Tensor, %b : Tensor? = quantized::linear_unpack(%packed_params)
        %w_dequant = aten::dequantize(%w_quant)
        %r = aten::linear(%a_dequant, %w_dequant, %b)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        return (%r_quant) )";

  std::string quantized_aten_linear = R"(
graph(%packed_params, %a_quant, %r_scale, %r_zero_point, %r_dtype):
        %r = quantized::linear(%a_quant, %packed_params, %r_scale, %r_zero_point)
        return (%r) )";

  std::string cat = R"(
graph(%input_quant, %dim, %r_scale, %r_zero_point, %r_dtype):
        %input_dequant = aten::dequantize(%input_quant)
        %r = aten::cat(%input_dequant, %dim)
        %r_quant = aten::quantize_per_tensor(%r, %r_scale, %r_zero_point, %r_dtype)
        return (%r_quant) )";

  std::string quantized_cat = R"(
graph(%input_quant, %dim, %r_scale, %r_zero_point, %r_dtype):
         %r_quant = quantized::cat(%input_quant, %dim, %r_scale, %r_zero_point)
         return (%r_quant) )";

  std::string add = R"(
graph(%a_quant, %b_quant, %alpha, %scale, %zero_point, %dtype):
         %a_dequant = aten::dequantize(%a_quant)
         %b_dequant = aten::dequantize(%b_quant)
         %r_add = aten::add(%a_dequant, %b_dequant, %alpha)
         %r = aten::quantize_per_tensor(%r_add, %scale, %zero_point, %dtype)
         return (%r) )";

  // TODO: add %dtype after when https://github.com/pytorch/pytorch/issues/34351
  // is fixed
  // TODO: add filter for %alpha
  std::string quantized_add = R"(
graph(%a_quant, %b_quant, %alpha, %scale, %zero_point, %dtype):
         %r_add = quantized::add(%a_quant, %b_quant, %scale, %zero_point)
         return (%r_add) )";

  std::string inplace_add = R"(
graph(%a_quant, %b_quant, %alpha, %scale, %zero_point, %dtype):
         %a_dequant = aten::dequantize(%a_quant)
         %b_dequant = aten::dequantize(%b_quant)
         %r_add = aten::add_(%a_dequant, %b_dequant, %alpha)
         %r = aten::quantize_per_tensor(%r_add, %scale, %zero_point, %dtype)
         return (%r) )";
  // We don't have quantized inplace add right now

  return {
      {conv2d, quantized_conv2d},
      {conv2d_relu, quantized_conv2d_relu},
      {conv2d_inplace_relu, quantized_conv2d_relu},
      {addmm, quantized_linear},
      {matmul_with_bias, quantized_linear},
      {matmul_no_bias, quantized_linear_no_bias},
      {aten_linear, quantized_aten_linear},
      {add_relu, quantized_add_relu},
      {add_inplace_relu, quantized_add_relu},
      {add, quantized_add},
      {inplace_add, quantized_add},
      {cat, quantized_cat},
  };
}

} // namespace jit
} // namespace torch
