#include <cstring>
#include <iostream>
#include "isaac/kernels/templates/reduce_1d.h"
#include "isaac/kernels/keywords.h"

#include "tools/loop.hpp"
#include "tools/reductions.hpp"
#include "tools/vector_types.hpp"
#include "tools/arguments.hpp"

#include <string>


namespace isaac
{
namespace templates
{
reduce_1d_parameters::reduce_1d_parameters(unsigned int _simd_width,
                     unsigned int _group_size, unsigned int _num_groups,
                     fetching_policy_type _fetching_policy) : base::parameters_type(_simd_width, _group_size, 1, 2), num_groups(_num_groups), fetching_policy(_fetching_policy)
{ }

unsigned int reduce_1d::lmem_usage(expression_tree const  & x) const
{
  numeric_type numeric_t= lhs_most(x.tree(), x.root()).lhs.dtype;
  return p_.local_size_0*size_of(numeric_t);
}

int reduce_1d::is_invalid_impl(driver::Device const &, expression_tree const  &) const
{
  if (p_.fetching_policy==FETCH_FROM_LOCAL)
    return TEMPLATE_INVALID_FETCHING_POLICY_TYPE;
  return TEMPLATE_VALID;
}

unsigned int reduce_1d::temporary_workspace(expression_tree const &) const
{
    if(p_.num_groups > 1)
      return p_.num_groups;
    return 0;
}

inline void reduce_1d::reduce_1d_local_memory(kernel_generation_stream & stream, unsigned int size, std::vector<mapped_reduce_1d*> exprs,
                                   std::string const & buf_str, std::string const & buf_value_str, driver::backend_type backend) const
{
  stream << "#pragma unroll" << std::endl;
  stream << "for(unsigned int stride = " << size/2 << "; stride > 0; stride /=2)" << std::endl;
  stream << "{" << std::endl;
  stream.inc_tab();
  stream << LocalBarrier(backend) << ";" << std::endl;
  stream << "if (lid <  stride)" << std::endl;
  stream << "{" << std::endl;
  stream.inc_tab();

  for (auto & expr : exprs)
    if (expr->is_index_reduction())
      compute_index_reduce_1d(stream, expr->process(buf_str+"[lid]"), expr->process(buf_str+"[lid+stride]")
                              , expr->process(buf_value_str+"[lid]"), expr->process(buf_value_str+"[lid+stride]"),
                              expr->root_op());
    else
      compute_reduce_1d(stream, expr->process(buf_str+"[lid]"), expr->process(buf_str+"[lid+stride]"), expr->root_op());
  stream.dec_tab();
  stream << "}" << std::endl;
  stream.dec_tab();
  stream << "}" << std::endl;
}

std::string reduce_1d::generate_impl(std::string const & suffix, expression_tree const  & expressions, driver::Device const & device, mapping_type const & mapping) const
{
  kernel_generation_stream stream;

  std::vector<mapped_reduce_1d*> exprs;
  for (mapping_type::const_iterator iit = mapping.begin(); iit != mapping.end(); ++iit)
    if (mapped_reduce_1d * p = dynamic_cast<mapped_reduce_1d*>(iit->second.get()))
      exprs.push_back(p);
  std::size_t N = exprs.size();
  driver::backend_type backend = device.backend();
  std::string _size_t = size_type(device);
  std::string _global =  Global(backend).get();

  std::string name[2] = {"prod", "reduce"};
  name[0] += suffix;
  name[1] += suffix;

  auto unroll_tmp = [&]()
  {
      unsigned int offset = 0;
      for (unsigned int k = 0; k < N; ++k)
      {
        numeric_type dtype = lhs_most(exprs[k]->expression_tree().tree(),  exprs[k]->expression_tree().root()).lhs.dtype;
        std::string sdtype = to_string(dtype);
        if (exprs[k]->is_index_reduction())
        {
          stream << exprs[k]->process(_global + " uint* #name_temp = (" + _global + " uint *)(tmp + " + tools::to_string(offset) + ");");
          offset += 4*p_.num_groups;
          stream << exprs[k]->process(_global + " " + sdtype + "* #name_temp_value = (" + _global + " " + sdtype + "*)(tmp + " + tools::to_string(offset) + ");");
          offset += size_of(dtype)*p_.num_groups;
        }
        else{
          stream << exprs[k]->process( _global + " " + sdtype + "* #name_temp = (" + _global + " " + sdtype + "*)(tmp + " + tools::to_string(offset) + ");");
          offset += size_of(dtype)*p_.num_groups;
        }
      }
  };

  /* ------------------------
   * First Kernel
   * -----------------------*/
  switch(backend)
  {
    case driver::CUDA:
      stream << "#include  \"helper_math.h\"" << std::endl; break;
    case driver::OPENCL:
      stream << " __attribute__((reqd_work_group_size(" << p_.local_size_0 << ",1,1)))" << std::endl; break;
  }

  stream << KernelPrefix(backend) << " void " << name[0] << "(" << _size_t << " N, " << _global << " char* tmp," << generate_arguments("#scalartype", device, mapping, expressions) << ")" << std::endl;
  stream << "{" << std::endl;
  stream.inc_tab();

  unroll_tmp();

  stream << "unsigned int lid = " <<LocalIdx0(backend) << ";" << std::endl;
  stream << "unsigned int gid = " <<GlobalIdx0(backend) << ";" << std::endl;
  stream << "unsigned int gpid = " <<GroupIdx0(backend) << ";" << std::endl;
  stream << "unsigned int gsize = " <<GlobalSize0(backend) << ";" << std::endl;

  process(stream, PARENT_NODE_TYPE, {{"array1", "#scalartype #namereg = #pointer[#start];"},
                                     {"arrayn", "#pointer += #start;"},
                                     {"array1n", "#pointer += #start;"},
                                     {"arrayn1", "#pointer += #start;"}},
                                    expressions, mapping);

  for (unsigned int k = 0; k < N; ++k)
  {
    if (exprs[k]->is_index_reduction())
    {
      stream << exprs[k]->process(Local(backend).get() + " #scalartype #name_buf_value[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
      stream << exprs[k]->process("#scalartype #name_acc_value = " + neutral_element(exprs[k]->root_op(), backend, "#scalartype") + ";") << std::endl;
      stream << exprs[k]->process(Local(backend).get() + " unsigned int #name_buf[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
      stream << exprs[k]->process("unsigned int #name_acc = 0;") << std::endl;
    }
    else
    {
      stream << exprs[k]->process(Local(backend).get() + " #scalartype #name_buf[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
      stream << exprs[k]->process("#scalartype #name_acc = " + neutral_element(exprs[k]->root_op(), backend, "#scalartype") + ";") << std::endl;
    }
  }


  element_wise_loop_1D(stream, p_.fetching_policy, p_.simd_width, "i", "N", GlobalIdx0(backend).get(), GlobalSize0(backend).get(), device, [&](unsigned int simd_width)
  {
    std::string i = (simd_width==1)?"i*#stride":"i";
    //Fetch vector entry
    std::set<std::string> already_fetched;
    for (const auto & elem : exprs)
    {
      std::string array = append_width("#scalartype",simd_width) + " #namereg = " + vload(simd_width,"#scalartype",i,"#pointer","#stride",backend)+";";
      (elem)->process_recursive(stream, PARENT_NODE_TYPE, {{"arrayn", array}, {"arrayn1", array}, {"array1n", array},
                                                           {"matrix_row",  "#scalartype #namereg = #pointer[$OFFSET{#row*#stride, i}];"},
                                                           {"matrix_column", "#scalartype #namereg = #pointer[$OFFSET{i*#stride,#column}];"},
                                                           {"matrix_diag", "#scalartype #namereg = #pointer[#diag_offset<0?$OFFSET{(i - #diag_offset)*#stride, i}:$OFFSET{i*#stride, (i + #diag_offset)}];"}}, already_fetched);
    }
    //Update accumulators
    std::vector<std::string> str(simd_width);
    if (simd_width==1)
      str[0] = "#namereg";
    else
      for (unsigned int a = 0; a < simd_width; ++a)
        str[a] = access_vector_type("#namereg", a);

    for (auto & elem : exprs)
    {
      for (unsigned int a = 0; a < simd_width; ++a)
      {
        std::map<std::string, std::string> accessors;
        accessors["arrayn"] = str[a];
        accessors["array1n"] = str[a];
        accessors["arrayn1"] = str[a];
        accessors["matrix_row"] = str[a];
        accessors["matrix_column"] = str[a];
        accessors["matrix_diag"] = str[a];
        accessors["array1"] = "#namereg";
        std::string value = elem->evaluate_recursive(LHS_NODE_TYPE, accessors);
        if (elem->is_index_reduction())
          compute_index_reduce_1d(stream, elem->process("#name_acc"),  "i*" + tools::to_string(simd_width) + "+"
                                  + tools::to_string(a), elem->process("#name_acc_value"), value,elem->root_op());
        else
          compute_reduce_1d(stream, elem->process("#name_acc"), value,elem->root_op());
      }
    }
  });

  //Fills local memory
  for (unsigned int k = 0; k < N; ++k)
  {
    if (exprs[k]->is_index_reduction())
      stream << exprs[k]->process("#name_buf_value[lid] = #name_acc_value;") << std::endl;
    stream << exprs[k]->process("#name_buf[lid] = #name_acc;") << std::endl;
  }

  //Reduce local memory
  reduce_1d_local_memory(stream, p_.local_size_0, exprs, "#name_buf", "#name_buf_value", backend);

  //Write to temporary buffers
  stream << "if (lid==0)" << std::endl;
  stream << "{" << std::endl;
  stream.inc_tab();
  for (unsigned int k = 0; k < N; ++k)
  {
    if (exprs[k]->is_index_reduction())
      stream << exprs[k]->process("#name_temp_value[gpid] = #name_buf_value[0];") << std::endl;
    stream << exprs[k]->process("#name_temp[gpid] = #name_buf[0];") << std::endl;
  }
  stream.dec_tab();
  stream << "}" << std::endl;

  stream.dec_tab();
  stream << "}" << std::endl;

  /* ------------------------
   * Second kernel
   * -----------------------*/



  stream << KernelPrefix(backend) << " void " << name[1] << "(" << _size_t << " N, " << _global << " char* tmp, " << generate_arguments("#scalartype", device, mapping, expressions) << ")" << std::endl;
  stream << "{" << std::endl;
  stream.inc_tab();

  unroll_tmp();

  stream << "unsigned int lid = " <<LocalIdx0(backend) << ";" << std::endl;
  stream << "unsigned int lsize = " <<LocalSize0(backend) << ";" << std::endl;

  for (mapped_reduce_1d* e: exprs)
  {
    if (e->is_index_reduction())
    {
      stream << e->process(Local(backend).get() + " unsigned int #name_buf[" + tools::to_string(p_.local_size_0) + "];");
      stream << e->process("unsigned int #name_acc = 0;") << std::endl;
      stream << e->process(Local(backend).get() + " #scalartype #name_buf_value[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
      stream << e->process("#scalartype #name_acc_value = " + neutral_element(e->root_op(), backend, "#scalartype") + ";");
    }
    else
    {
      stream << e->process(Local(backend).get() + " #scalartype #name_buf[" + tools::to_string(p_.local_size_0) + "];") << std::endl;
      stream << e->process("#scalartype #name_acc = " + neutral_element(e->root_op(), backend, "#scalartype") + ";");
    }
  }

  stream << "for(unsigned int i = lid; i < " << p_.num_groups << "; i += lsize)" << std::endl;
  stream << "{" << std::endl;
  stream.inc_tab();
  for (mapped_reduce_1d* e: exprs)
    if (e->is_index_reduction())
      compute_index_reduce_1d(stream, e->process("#name_acc"), e->process("#name_temp[i]"), e->process("#name_acc_value"),e->process("#name_temp_value[i]"),e->root_op());
    else
      compute_reduce_1d(stream, e->process("#name_acc"), e->process("#name_temp[i]"), e->root_op());

  stream.dec_tab();
  stream << "}" << std::endl;

  for (unsigned int k = 0; k < N; ++k)
  {
    if (exprs[k]->is_index_reduction())
      stream << exprs[k]->process("#name_buf_value[lid] = #name_acc_value;") << std::endl;
    stream << exprs[k]->process("#name_buf[lid] = #name_acc;") << std::endl;
  }


  //Reduce and write final result
  reduce_1d_local_memory(stream, p_.local_size_0, exprs, "#name_buf", "#name_buf_value", backend);

  stream << "if (lid==0)" << std::endl;
  stream << "{" << std::endl;
  stream.inc_tab();
  std::map<std::string, std::string> accessors;
  accessors["scalar_reduce_1d"] = "#name_buf[0]";
  accessors["array1"] = "#pointer[#start]";
  accessors["array11"] = "#pointer[#start]";
  stream << evaluate(PARENT_NODE_TYPE, accessors, expressions, expressions.root(), mapping) << ";" << std::endl;
  stream.dec_tab();
  stream << "}" << std::endl;

  stream.dec_tab();
  stream << "}" << std::endl;

  return stream.str();
}

reduce_1d::reduce_1d(reduce_1d::parameters_type const & parameters,
                                       binding_policy_t binding) : base_impl<reduce_1d, reduce_1d_parameters>(parameters, binding)
{ }

reduce_1d::reduce_1d(unsigned int simd, unsigned int ls, unsigned int ng,
                               fetching_policy_type fetch, binding_policy_t bind):
    base_impl<reduce_1d, reduce_1d_parameters>(reduce_1d_parameters(simd,ls,ng,fetch), bind)
{}

std::vector<int_t> reduce_1d::input_sizes(expression_tree const  & x) const
{
  std::vector<size_t> reduce_1ds_idx = filter_nodes(&is_reduce_1d, x, x.root(), false);
  int_t N = vector_size(lhs_most(x.tree(), reduce_1ds_idx[0]));
  return {N};
}

void reduce_1d::enqueue(driver::CommandQueue & queue, driver::Program const & program, std::string const & suffix, base & fallback, execution_handler const & control)
{
  expression_tree const  & x = control.x();

  //Preprocessing
  int_t size = input_sizes(x)[0];

  //fallback
  if(p_.simd_width > 1 && (requires_fallback(x) || (size%p_.simd_width>0)))
  {
      fallback.enqueue(queue, program, "fallback", fallback, control);
      return;
  }

  std::vector<expression_tree::node const *> reduce_1ds;
    std::vector<size_t> reduce_1ds_idx = filter_nodes(&is_reduce_1d, x, x.root(), false);
    for (size_t idx: reduce_1ds_idx)
      reduce_1ds.push_back(&x.tree()[idx]);

  //Kernel
  std::string name[2] = {"prod", "reduce"};
  name[0] += suffix;
  name[1] += suffix;

  driver::Kernel kernels[2] = { driver::Kernel(program,name[0].c_str()), driver::Kernel(program,name[1].c_str()) };

  //NDRange
  driver::NDRange global[2] = { driver::NDRange(p_.local_size_0*p_.num_groups), driver::NDRange(p_.local_size_0) };
  driver::NDRange local[2] = { driver::NDRange(p_.local_size_0), driver::NDRange(p_.local_size_0) };
  //Arguments
  for (auto & kernel : kernels)
  {
    unsigned int n_arg = 0;
    kernel.setSizeArg(n_arg++, size);
    kernel.setArg(n_arg++, driver::backend::workspaces::get(queue));
    set_arguments(x, kernel, n_arg, binding_policy_);
  }

  for (unsigned int k = 0; k < 2; k++)
    control.execution_options().enqueue(program.context(), kernels[k], global[k], local[k]);
  queue.synchronize();
}

}
}