namespace tf {

/** @page TextProcessingPipeline Text Processing Pipeline

We study a text processing pipeline that finds the most frequent character of each string 
from an input source.
Parallelism exhibits in the form of a three-stage pipeline that transforms the input
string to a final pair type.

@tableofcontents

@section FormulateTheTextProcessingPipelineProblem Formulate the Text Processing Pipeline Problem

Given an input vector of strings, we want to compute the most frequent character
for each string using a series of transform operations. For example:

@code{.bash}
# input strings
abade
ddddf
eefge
xyzzd
ijjjj
jiiii
kkijk

# output
a:2
d:4
e:3
z:2
j:4
i:4
k:3
@endcode

We decompose the algorithm into three stages:

1. read a `%std::string` from the input vector
2. generate a `%std::unorder_map<char, size_t>` frequency map from the string 
3. reduce the most frequent character to a `%std::pair<char, size_t>` from the map

The first and the third stages process inputs and generate results in serial, 
and the second stage can run in parallel.
The algorithm is a perfect fit to pipeline parallelism, as different stages
can overlap with each other in time across parallel lines.

@section CreateAParallelTextPipeline Create a Text Processing Pipeline

We create a pipeline of three pipes (stages) and two parallel lines to solve the problem.
The number of parallel lines is a tunable parameter. In most cases,
we can just use `std::thread::hardware_concurrency` as the line count.
The first pipe reads an input string from the vector in order, 
the second pipe transforms the input string from the first pipe to a frequency map in parallel,
and
the third pipe reduces the frequency map to find the most frequent character.
The overall implementation is shown below:

@code{.cpp}
#include <taskflow/taskflow.hpp>
#include <taskflow/algorithm/pipeline.hpp>

// Function: format the map
std::string format_map(const std::unordered_map<char, size_t>& map) {
  std::ostringstream oss;
  for(const auto& [i, j] : map) {
    oss << i << ':' << j << ' ';
  }
  return oss.str();
}

int main() {

  tf::Taskflow taskflow("text-filter pipeline");
  tf::Executor executor;

  const size_t num_lines = 2;

  // input data 
  std::vector<std::string> input = {
    "abade", 
    "ddddf",
    "eefge",
    "xyzzd",
    "ijjjj",
    "jiiii",
    "kkijk"
  };

  // custom data storage
  using data_type = std::variant<
    std::string, std::unordered_map<char, size_t>, std::pair<char, size_t>
  >;
  std::array<data_type, num_lines> mybuffer;

  // the pipeline consists of three pipes (serial-parallel-serial)
  // and up to two concurrent scheduling tokens
  tf::Pipeline pl(num_lines,

    // first pipe processes the input data
    tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
      if(pf.token() == input.size()) {
        pf.stop();
      }
      else {
        printf("stage 1: input token = %s\n", input[pf.token()].c_str());
        mybuffer[pf.line()] = input[pf.token()];
      }
    }},
    
    // second pipe counts the frequency of each character
    tf::Pipe{tf::PipeType::PARALLEL, [&](tf::Pipeflow& pf) {
      std::unordered_map<char, size_t> map;
      for(auto c : std::get<std::string>(mybuffer[pf.line()])) {
        map[c]++;
      }
      printf("stage 2: map = %s\n", format_map(map).c_str());
      mybuffer[pf.line()] = map;
    }},
    
    // third pipe reduces the most frequent character
    tf::Pipe{tf::PipeType::SERIAL, [&mybuffer](tf::Pipeflow& pf) {
      auto& map = std::get<std::unordered_map<char, size_t>>(mybuffer[pf.line()]);
      auto sol = std::max_element(map.begin(), map.end(), [](auto& a, auto& b){
        return a.second < b.second;
      });
      printf("stage 3: %c:%zu\n", sol->first, sol->second);
      // not necessary to store the last-stage data, just for demo purpose
      mybuffer[pf.line()] = *sol;  
    }}
  );
  
  // build the pipeline graph using composition
  tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })
                          .name("starting pipeline");
  tf::Task task = taskflow.composed_of(pl)
                          .name("pipeline");
  tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })
                          .name("pipeline stopped");

  // create task dependency
  init.precede(task);
  task.precede(stop);
  
  // dump the pipeline graph structure (with composition)
  taskflow.dump(std::cout);

  // run the pipeline
  executor.run(taskflow).wait();
  
  return 0;
}
@endcode

@subsection TextPipelineDefineTheDataBuffer Define the Data Buffer

%Taskflow does not provide any data abstraction to perform pipeline scheduling, but
give users full control over data management in their applications.
In this example, we create an one-dimensional buffer of a @std_variant data type to store the output
of each pipe in a uniform storage:

@code{.cpp}
using data_type = std::variant<
  std::string, std::unordered_map<char, size_t>, std::pair<char, size_t>
>;
std::array<std::array<data_type, num_pipes>, num_lines> mybuffer;
@endcode

@attention
One-dimensional buffer is sufficient because %Taskflow enables only one scheduling token
per line at a time.

@subsection TextPipelineDefineThePipes Define the Pipes

The first pipe reads one string and puts it in the corresponding entry at the buffer,
`mybuffer[pf.line()]`.
Since we read in each string in order,
we declare the pipe as a serial type:

@code{.cpp}
tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
  if(pf.token() == input.size()) {
    pf.stop();
  }
  else {
    mybuffer[pf.line()] = input[pf.token()];
    printf("stage 1: input token = %s\n", input[pf.token()].c_str());
  }
}},
@endcode

The second pipe needs to get the input string from the previous pipe and then
transforms that input string into a frequency map that records the 
occurrence of each character in the string.
As multiple transforms can operate simultaneously, 
we declare the pipe as a parallel type:

@code{.cpp}
tf::Pipe{tf::PipeType::PARALLEL, [&](tf::Pipeflow& pf) {
  std::unordered_map<char, size_t> map;
  for(auto c : std::get<std::string>(mybuffer[pf.line()])) {
    map[c]++;
  }
  mybuffer[pf.line()] = map;
  printf("stage 2: map = %s\n", format_map(map).c_str());
}}
@endcode

Similarly, the third pipe needs to get the input frequency map from the previous
pipe and then reduces the result to find the most frequent character.
We may not need to store the result in the buffer but other places defined
by the application (e.g., an output file).
As we want to output the result in the same order as the input,
we declare the pipe as a serial type:

@code{.cpp}
tf::Pipe{tf::PipeType::SERIAL, [&mybuffer](tf::Pipeflow& pf) {
  auto& map = std::get<std::unordered_map<char, size_t>>(mybuffer[pf.line()]);
  auto sol = std::max_element(map.begin(), map.end(), [](auto& a, auto& b){
    return a.second < b.second;
  });
  printf("stage 3: %c:%zu\n", sol->first, sol->second);
}}
@endcode

@subsection TextPipelineDefineTheTaskGraph Define the Task Graph

To build up the taskflow graph for the pipeline,
we create a module task out of the pipeline structure and connect it with
two tasks that outputs messages before and after the pipeline:

@code{.cpp}
tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })
                        .name("starting pipeline");
tf::Task task = taskflow.composed_of(pl)
                        .name("pipeline");
tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })
                        .name("pipeline stopped");
init.precede(task);
task.precede(stop);
@endcode

@subsection TextPipelineSubmitTheTaskGraph Submit the Task Graph

Finally, we submit the taskflow to the execution and run it once:

@code{.cpp}
executor.run(taskflow).wait();
@endcode

As the second stage is a parallel pipe, the output may interleave.
One possible result is shown below:

@code{.bash}
ready
stage 1: input token = abade
stage 1: input token = ddddf
stage 2: map = f:1 d:4 
stage 2: map = e:1 d:1 a:2 b:1 
stage 3: a:2
stage 1: input token = eefge
stage 2: map = g:1 e:3 f:1 
stage 3: d:4
stage 1: input token = xyzzd
stage 3: e:3
stage 1: input token = ijjjj
stage 2: map = z:2 x:1 d:1 y:1 
stage 3: z:2
stage 1: input token = jiiii
stage 2: map = j:4 i:1 
stage 3: j:4
stage 2: map = i:4 j:1 
stage 1: input token = kkijk
stage 3: i:4
stage 2: map = j:1 k:3 i:1 
stage 3: k:3
stopped
@endcode

We can see seven outputs at the third stage that show the most frequent character
for each of the seven strings in order (`a:2`, `d:4`, `e:3`, `z:2`, `j:4`, `i:4`, `k:3`).
The taskflow graph of this pipeline workload is shown below:

@dotfile images/text_processing_pipeline.dot

*/

}



