/* Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved. */

/* Helper methods for fast index mapping builds */

#include <algorithm>
#include <iostream>
#include <limits>
#include <math.h>
#include <stdexcept>
#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <random>

namespace py = pybind11;
using namespace std;

const int32_t LONG_SENTENCE_LEN = 512;


void build_blending_indices(py::array_t<int16_t>& dataset_index,
			    py::array_t<int64_t>& dataset_sample_index,
			    const py::array_t<double>& weights,
			    const int32_t num_datasets,
			    const int64_t size, const bool verbose) {
  /* Given multiple datasets and a weighting array, build samples
   such that it follows those wieghts.*/

  if (verbose) {
    std::cout << "> building indices for blendable datasets ..." << std::endl;
  }

  // Get the pointer access without the checks.
  auto dataset_index_ptr = dataset_index.mutable_unchecked<1>();
  auto dataset_sample_index_ptr = dataset_sample_index.mutable_unchecked<1>();
  auto weights_ptr = weights.unchecked<1>();

  // Initialize buffer for number of samples used for each dataset.
  int64_t current_samples[num_datasets];
  for(int64_t i = 0; i < num_datasets; ++i) {
    current_samples[i] = 0;
  }

  // For each sample:
  for(int64_t sample_idx = 0; sample_idx < size; ++sample_idx) {

    // Determine where the max error in sampling is happening.
    auto sample_idx_double = std::max(static_cast<double>(sample_idx), 1.0);
    int64_t max_error_index = 0;
    double max_error = weights_ptr[0] * sample_idx_double -
      static_cast<double>(current_samples[0]);
    for (int64_t dataset_idx = 1; dataset_idx < num_datasets; ++dataset_idx) {
      double error = weights_ptr[dataset_idx] * sample_idx_double -
	static_cast<double>(current_samples[dataset_idx]);
      if (error > max_error) {
	max_error = error;
	max_error_index = dataset_idx;
      }
    }

    // Populate the indices.
    dataset_index_ptr[sample_idx] = static_cast<int16_t>(max_error_index);
    dataset_sample_index_ptr[sample_idx] = current_samples[max_error_index];

    // Update the total samples.
    current_samples[max_error_index] += 1;
    
  }

  // print info
  if (verbose) {
    std::cout << " > sample ratios:" << std::endl;
    for (int64_t dataset_idx = 0; dataset_idx < num_datasets; ++dataset_idx) {
      auto ratio = static_cast<double>(current_samples[dataset_idx]) /
	static_cast<double>(size);
      std::cout << "   dataset " << dataset_idx << ", input: " <<
	weights_ptr[dataset_idx] << ", achieved: " << ratio << std::endl; 
    }
  }

}


py::array build_sample_idx(const py::array_t<int32_t>& sizes_,
			   const py::array_t<int32_t>& doc_idx_,
			   const int32_t seq_length,
			   const int32_t num_epochs,
			   const int64_t tokens_per_epoch) {
    /* Sample index (sample_idx) is used for gpt2 like dataset for which
       the documents are flattened and the samples are built based on this
       1-D flatten array. It is a 2D array with sizes [number-of-samples + 1, 2]
       where [..., 0] contains the index into `doc_idx` and [..., 1] is the
       starting offset in that document.*/

    // Consistency checks.
    assert(seq_length > 1);
    assert(num_epochs > 0);
    assert(tokens_per_epoch > 1);

    // Remove bound checks.
    auto sizes = sizes_.unchecked<1>();
    auto doc_idx = doc_idx_.unchecked<1>();

    // Mapping and it's length (1D).
    int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length;
    int32_t* sample_idx = new int32_t[2*(num_samples+1)];

    cout << "    using:" << endl << std::flush;
    cout << "     number of documents:       " <<
      doc_idx_.shape(0) / num_epochs << endl << std::flush;
    cout << "     number of epochs:          " << num_epochs <<
      endl << std::flush;
    cout << "     sequence length:           " << seq_length <<
      endl << std::flush;
    cout << "     total number of samples:   " << num_samples <<
      endl << std::flush;

    // Index into sample_idx.
    int64_t sample_index = 0;
    // Index into doc_idx.
    int64_t doc_idx_index = 0;
    // Begining offset for each document.
    int32_t doc_offset = 0;
    // Start with first document and no offset.
    sample_idx[2 * sample_index] = doc_idx_index;
    sample_idx[2 * sample_index + 1] = doc_offset;
    ++sample_index;

    while (sample_index <= num_samples) {
        // Start with a fresh sequence.
      int32_t remaining_seq_length = seq_length + 1;
      while (remaining_seq_length != 0) {
            // Get the document length.
	auto doc_id = doc_idx[doc_idx_index];
	auto doc_length = sizes[doc_id] - doc_offset;
	// And add it to the current sequence.
	remaining_seq_length -= doc_length;
	// If we have more than a full sequence, adjust offset and set
	// remaining length to zero so we return from the while loop.
	// Note that -1 here is for the same reason we have -1 in
	// `_num_epochs` calculations.
	if (remaining_seq_length <= 0) {
	  doc_offset += (remaining_seq_length + doc_length - 1);
	  remaining_seq_length = 0;
	} else {
	  // Otherwise, start from the begining of the next document.
	  ++doc_idx_index;
	  doc_offset = 0;
	}
      }
      // Record the sequence.
      sample_idx[2 * sample_index] = doc_idx_index;
      sample_idx[2 * sample_index + 1] = doc_offset;
      ++sample_index;
    }

    // Method to deallocate memory.
    py::capsule free_when_done(sample_idx, [](void *mem_) {
	int32_t *mem = reinterpret_cast<int32_t*>(mem_);
	delete[] mem;
      });

    // Return the numpy array.
    const auto byte_size = sizeof(int32_t);
    return py::array(std::vector<int64_t>{num_samples+1, 2}, // shape
                     {2*byte_size, byte_size}, // C-style contiguous strides
                     sample_idx, // the data pointer
                     free_when_done); // numpy array references
    
}


inline int32_t get_target_sample_len(const int32_t short_seq_ratio,
				     const int32_t max_length,
				     std::mt19937& rand32_gen) {
    /* Training sample length. */
    if (short_seq_ratio == 0) {
      return max_length;
    }
    const auto random_number = rand32_gen();
    if ((random_number % short_seq_ratio) == 0) {
      return 2 + random_number % (max_length - 1);
    }
    return max_length;
}


template<typename DocIdx>
py::array build_mapping_impl(const py::array_t<int64_t>& docs_,
                             const py::array_t<int32_t>& sizes_,
                             const int32_t num_epochs,
                             const uint64_t max_num_samples,
                             const int32_t max_seq_length,
                             const double short_seq_prob,
                             const int32_t seed,
			     const bool verbose,
			     const int32_t min_num_sent) {
    /* Build a mapping of (start-index, end-index, sequence-length) where
       start and end index are the indices of the sentences in the sample
       and sequence-length is the target sequence length.
    */

    // Consistency checks.
    assert(num_epochs > 0);
    assert(max_seq_length > 1);
    assert(short_seq_prob >= 0.0);
    assert(short_seq_prob <= 1.0);
    assert(seed > 0);

    // Remove bound checks.
    auto docs = docs_.unchecked<1>();
    auto sizes = sizes_.unchecked<1>();

    // For efficiency, convert probability to ratio. Note: rand() generates int.
    int32_t short_seq_ratio = 0;
    if (short_seq_prob > 0) {
      short_seq_ratio = static_cast<int32_t>(round(1.0 / short_seq_prob));
    }

    if (verbose) {
        const auto sent_start_index = docs[0];
	const auto sent_end_index = docs[docs_.shape(0) - 1];
	const auto num_sentences = sent_end_index - sent_start_index;
	cout << "    using:" << endl << std::flush;
	cout << "     number of documents:            " << docs_.shape(0) - 1 <<
	  endl << std::flush;
	cout << "     sentences range:                [" << sent_start_index <<
	", " << sent_end_index << ")" << endl << std::flush;
	cout << "     total number of sentences:      " << num_sentences <<
	  endl << std::flush;
	cout << "     number of epochs:               " << num_epochs <<
	  endl << std::flush;
	cout << "     maximum number of samples:      " << max_num_samples <<
	  endl << std::flush;
	cout << "     maximum sequence length:        " << max_seq_length <<
	  endl << std::flush;
	cout << "     short sequence probability:     " << short_seq_prob <<
	endl << std::flush;
	cout << "     short sequence ration (1/prob): " << short_seq_ratio <<
	  endl << std::flush;
	cout << "     seed:                           " << seed << endl <<
	  std::flush;
    }

    // Mapping and it's length (1D).
    int64_t num_samples = -1;
    DocIdx* maps = NULL;

    // Perform two iterations, in the first iteration get the size
    // and allocate memory and in the second iteration populate the map.
    bool second = false;
    for (int32_t iteration=0; iteration<2; ++iteration) {

        // Set the seed so both iterations produce the same results.
        std::mt19937 rand32_gen(seed);

        // Set the flag on second iteration.
        second = (iteration == 1);

        // Counters:
        uint64_t empty_docs = 0;
        uint64_t one_sent_docs = 0;
	uint64_t long_sent_docs = 0;

        // Current map index.
        uint64_t map_index = 0;

        // For each epoch:
        for (int32_t epoch=0; epoch<num_epochs; ++epoch) {
            if (map_index >= max_num_samples) {
	        if (verbose && (!second)) {
		  cout << "    reached " << max_num_samples << " samples after "
		       << epoch << " epochs ..." << endl << std::flush;
		}
                break;
            }
            // For each document:
            for (int32_t doc=0; doc<(docs.shape(0) - 1); ++doc) {

                // Document sentences are in [sent_index_first, sent_index_last)
                const auto sent_index_first = docs[doc];
                const auto sent_index_last = docs[doc + 1];

                // At the begining of the document previous index is the
		// start index.
                auto prev_start_index = sent_index_first;

                // Remaining documents.
                auto num_remain_sent = sent_index_last - sent_index_first;

                // Some bookkeeping
                if ((epoch == 0) && (!second)) {
                    if (num_remain_sent == 0) {
		        ++empty_docs;
                    }
                    if (num_remain_sent == 1) {
		        ++one_sent_docs;
                    }
                }

		// Detect documents with long sentences.
		bool contains_long_sentence = false;
		if (num_remain_sent > 1) {
		    for (auto sent_index=sent_index_first;
			 sent_index < sent_index_last; ++sent_index) {
		        if (sizes[sent_index] > LONG_SENTENCE_LEN){
			    if ((epoch == 0) && (!second)) {
			        ++long_sent_docs;
			    }
			    contains_long_sentence = true;
			    break;
			}
		    }
		}

                // If we have more than two sentences.
                if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) {

                    // Set values.
                    auto seq_len = int32_t{0};
                    auto num_sent = int32_t{0};
                    auto target_seq_len = get_target_sample_len(short_seq_ratio,
								max_seq_length,
								rand32_gen);

                    // Loop through sentences.
                    for (auto sent_index=sent_index_first;
                         sent_index < sent_index_last; ++sent_index) {

		        // Add the size and number of sentences.
		        seq_len += sizes[sent_index];
		        ++num_sent;
			--num_remain_sent;

			// If we have reached the target length.
			// and if not only one sentence is left in the document.
			// and if we have at least two sentneces.
			// and if we have reached end of the document.
			if (((seq_len >= target_seq_len) &&
			     (num_remain_sent > 1) &&
			     (num_sent >= min_num_sent) ) || (num_remain_sent == 0)) {

			    // Check for overflow.
			    if ((3 * map_index + 2) >
				std::numeric_limits<int64_t>::max()) {
			        cout << "number of samples exceeded maximum "
				     << "allowed by type int64: "
				     << std::numeric_limits<int64_t>::max()
				     << endl;
				throw std::overflow_error("Number of samples");
			    }

			    // Populate the map.
			    if (second) {
			        const auto map_index_0 = 3 * map_index;
				maps[map_index_0] = static_cast<DocIdx>(prev_start_index);
				maps[map_index_0 + 1] = static_cast<DocIdx>(sent_index + 1);
				maps[map_index_0 + 2] = static_cast<DocIdx>(target_seq_len);
			    }

			    // Update indices / counters.
			    ++map_index;
			    prev_start_index = sent_index + 1;
			    target_seq_len = get_target_sample_len(short_seq_ratio,
								   max_seq_length,
								   rand32_gen);
			    seq_len = 0;
			    num_sent = 0;
			}

                    } // for (auto sent_index=sent_index_first; ...
                } // if (num_remain_sent > 1) {
            } // for (int doc=0; doc < num_docs; ++doc) {
        } // for (int epoch=0; epoch < num_epochs; ++epoch) {

        if (!second) {
	    if (verbose) {
	        cout << "   number of empty documents: " << empty_docs <<
		  endl << std::flush;
		cout << "   number of documents with one sentence: " <<
		  one_sent_docs << endl << std::flush;
		cout << "   number of documents with long sentences: " <<
		  long_sent_docs << endl << std::flush;
		cout << "   will create mapping for " << map_index <<
		  " samples" << endl << std::flush;
	    }
	    assert(maps == NULL);
	    assert(num_samples < 0);
            maps = new DocIdx[3*map_index];
            num_samples = static_cast<int64_t>(map_index);
        }

    } // for (int iteration=0; iteration < 2; ++iteration) {

    // Shuffle.
    // We need a 64 bit random number generator as we might have more
    // than 2 billion samples.
    std::mt19937_64 rand64_gen(seed + 1);
    for (auto i=(num_samples - 1); i > 0; --i) {
      const auto j = static_cast<int64_t>(rand64_gen() % (i + 1));
      const auto i0 = 3 * i;
      const auto j0 = 3 * j;
      // Swap values.
      swap(maps[i0], maps[j0]);
      swap(maps[i0 + 1], maps[j0 + 1]);
      swap(maps[i0 + 2], maps[j0 + 2]);
    }

    // Method to deallocate memory.
    py::capsule free_when_done(maps, [](void *mem_) {
            DocIdx *mem = reinterpret_cast<DocIdx*>(mem_);
	    delete[] mem;
        });

    // Return the numpy array.
    const auto byte_size = sizeof(DocIdx);
    return py::array(std::vector<int64_t>{num_samples, 3}, // shape
                     {3*byte_size, byte_size}, // C-style contiguous strides
                     maps, // the data pointer
                     free_when_done); // numpy array references

}


py::array build_mapping(const py::array_t<int64_t>& docs_,
                        const py::array_t<int>& sizes_,
                        const int num_epochs,
                        const uint64_t max_num_samples,
                        const int max_seq_length,
                        const double short_seq_prob,
                        const int seed,
			const bool verbose,
			const int32_t min_num_sent) {

    if (sizes_.size() > std::numeric_limits<uint32_t>::max()) {
        if (verbose) {
	   cout << "    using uint64 for data mapping..." << endl << std::flush;
	}
	return build_mapping_impl<uint64_t>(docs_, sizes_, num_epochs,
					    max_num_samples, max_seq_length,
					    short_seq_prob, seed, verbose,
					    min_num_sent);
    } else {
       if (verbose) {
	   cout << "    using uint32 for data mapping..." << endl << std::flush;
       }
       return build_mapping_impl<uint32_t>(docs_, sizes_, num_epochs,
					   max_num_samples, max_seq_length,
					   short_seq_prob, seed, verbose,
					   min_num_sent);
    }
}

template<typename DocIdx>
py::array build_blocks_mapping_impl(const py::array_t<int64_t>& docs_,
                                    const py::array_t<int32_t>& sizes_,
                                    const py::array_t<int32_t>& titles_sizes_,
                                    const int32_t num_epochs,
                                    const uint64_t max_num_samples,
                                    const int32_t max_seq_length,
                                    const int32_t seed,
                                    const bool verbose,
                                    const bool use_one_sent_blocks) {
    /* Build a mapping of (start-index, end-index, sequence-length) where
       start and end index are the indices of the sentences in the sample
       and sequence-length is the target sequence length.
    */

    // Consistency checks.
    assert(num_epochs > 0);
    assert(max_seq_length > 1);
    assert(seed > 0);

    // Remove bound checks.
    auto docs = docs_.unchecked<1>();
    auto sizes = sizes_.unchecked<1>();
    auto titles_sizes = titles_sizes_.unchecked<1>();

    if (verbose) {
        const auto sent_start_index = docs[0];
        const auto sent_end_index = docs[docs_.shape(0) - 1];
        const auto num_sentences = sent_end_index - sent_start_index;
        cout << "    using:" << endl << std::flush;
        cout << "     number of documents:            " << docs_.shape(0) - 1 <<
          endl << std::flush;
        cout << "     sentences range:                [" << sent_start_index <<
        ", " << sent_end_index << ")" << endl << std::flush;
        cout << "     total number of sentences:      " << num_sentences <<
          endl << std::flush;
        cout << "     number of epochs:               " << num_epochs <<
          endl << std::flush;
        cout << "     maximum number of samples:      " << max_num_samples <<
          endl << std::flush;
        cout << "     maximum sequence length:        " << max_seq_length <<
          endl << std::flush;
        cout << "     seed:                           " << seed << endl <<
          std::flush;
    }

    // Mapping and its length (1D).
    int64_t num_samples = -1;
    DocIdx* maps = NULL;

    // Acceptable number of sentences per block.
    int min_num_sent = 2;
    if (use_one_sent_blocks) {
        min_num_sent = 1;
    }

    // Perform two iterations, in the first iteration get the size
    // and allocate memory and in the second iteration populate the map.
    bool second = false;
    for (int32_t iteration=0; iteration<2; ++iteration) {

        // Set the flag on second iteration.
        second = (iteration == 1);

        // Current map index.
        uint64_t map_index = 0;

        uint64_t empty_docs = 0;
        uint64_t one_sent_docs = 0;
        uint64_t long_sent_docs = 0;
        // For each epoch:
        for (int32_t epoch=0; epoch<num_epochs; ++epoch) {
            // assign every block a unique id
            int32_t block_id = 0;

            if (map_index >= max_num_samples) {
                if (verbose && (!second)) {
                cout << "    reached " << max_num_samples << " samples after "
                     << epoch << " epochs ..." << endl << std::flush;
                }
                break;
            }
            // For each document:
            for (int32_t doc=0; doc<(docs.shape(0) - 1); ++doc) {

                // Document sentences are in [sent_index_first, sent_index_last)
                const auto sent_index_first = docs[doc];
                const auto sent_index_last = docs[doc + 1];
                const auto target_seq_len = max_seq_length - titles_sizes[doc];

                // At the begining of the document previous index is the
                // start index.
                auto prev_start_index = sent_index_first;

                // Remaining documents.
                auto num_remain_sent = sent_index_last - sent_index_first;

                // Some bookkeeping
                if ((epoch == 0) && (!second)) {
                    if (num_remain_sent == 0) {
		                ++empty_docs;
                    }
                    if (num_remain_sent == 1) {
		                ++one_sent_docs;
                    }
                }
                // Detect documents with long sentences.
                bool contains_long_sentence = false;
                if (num_remain_sent >= min_num_sent) {
                    for (auto sent_index=sent_index_first;
                    sent_index < sent_index_last; ++sent_index) {
                        if (sizes[sent_index] > LONG_SENTENCE_LEN){
                            if ((epoch == 0) && (!second)) {
                                ++long_sent_docs;
                            }
                            contains_long_sentence = true;
                            break;
                        }
                    }
                }
                // If we have enough sentences and no long sentences.
                if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) {

                    // Set values.
                    auto seq_len = int32_t{0};
                    auto num_sent = int32_t{0};

                    // Loop through sentences.
                    for (auto sent_index=sent_index_first;
                         sent_index < sent_index_last; ++sent_index) {

                            // Add the size and number of sentences.
                            seq_len += sizes[sent_index];
                            ++num_sent;
                            --num_remain_sent;

                        // If we have reached the target length.
                        // and there are an acceptable number of sentences left
                        // and if we have at least the minimum number of sentences.
                        // or if we have reached end of the document.
                        if (((seq_len >= target_seq_len) &&
                             (num_remain_sent >= min_num_sent) &&
                             (num_sent >= min_num_sent) ) || (num_remain_sent == 0)) {

                            // Populate the map.
                            if (second) {
                                const auto map_index_0 = 4 * map_index;
                                // Each sample has 4 items: the starting sentence index, ending sentence index,
                                // the index of the document from which the block comes (used for fetching titles)
                                // and the unique id of the block (used for creating block indexes)

                                maps[map_index_0] = static_cast<DocIdx>(prev_start_index);
                                maps[map_index_0 + 1] = static_cast<DocIdx>(sent_index + 1);
                                maps[map_index_0 + 2] = static_cast<DocIdx>(doc);
                                maps[map_index_0 + 3] = static_cast<DocIdx>(block_id);
                            }

                            // Update indices / counters.
                            ++map_index;
                            ++block_id;
                            prev_start_index = sent_index + 1;
                            seq_len = 0;
                            num_sent = 0;
                        }
                    } // for (auto sent_index=sent_index_first; ...
                } // if (num_remain_sent > 1) {
            } // for (int doc=0; doc < num_docs; ++doc) {
        } // for (int epoch=0; epoch < num_epochs; ++epoch) {

        if (!second) {
            if (verbose) {
	        cout << "   number of empty documents: " << empty_docs <<
              endl << std::flush;
            cout << "   number of documents with one sentence: " <<
              one_sent_docs << endl << std::flush;
            cout << "   number of documents with long sentences: " <<
              long_sent_docs << endl << std::flush;
            cout << "   will create mapping for " << map_index <<
              " samples" << endl << std::flush;
            }
            assert(maps == NULL);
            assert(num_samples < 0);
            maps = new DocIdx[4*map_index];
            num_samples = static_cast<int64_t>(map_index);
        }

    } // for (int iteration=0; iteration < 2; ++iteration) {

    // Shuffle.
    // We need a 64 bit random number generator as we might have more
    // than 2 billion samples.
    std::mt19937_64 rand64_gen(seed + 1);
    for (auto i=(num_samples - 1); i > 0; --i) {
        const auto j = static_cast<int64_t>(rand64_gen() % (i + 1));
        const auto i0 = 4 * i;
        const auto j0 = 4 * j;
        // Swap values.
        swap(maps[i0], maps[j0]);
        swap(maps[i0 + 1], maps[j0 + 1]);
        swap(maps[i0 + 2], maps[j0 + 2]);
        swap(maps[i0 + 3], maps[j0 + 3]);
    }

    // Method to deallocate memory.
    py::capsule free_when_done(maps, [](void *mem_) {
            DocIdx *mem = reinterpret_cast<DocIdx*>(mem_);
	    delete[] mem;
        });

    // Return the numpy array.
    const auto byte_size = sizeof(DocIdx);
    return py::array(std::vector<int64_t>{num_samples, 4}, // shape
                     {4*byte_size, byte_size}, // C-style contiguous strides
                     maps, // the data pointer
                     free_when_done); // numpy array references

}

py::array build_blocks_mapping(const py::array_t<int64_t>& docs_,
                               const py::array_t<int>& sizes_,
                               const py::array_t<int>& titles_sizes_,
                               const int num_epochs,
                               const uint64_t max_num_samples,
                               const int max_seq_length,
                               const int seed,
                    const bool verbose,
                    const bool use_one_sent_blocks) {

    if (sizes_.size() > std::numeric_limits<uint32_t>::max()) {
        if (verbose) {
	   cout << "    using uint64 for data mapping..." << endl << std::flush;
	}
	return build_blocks_mapping_impl<uint64_t>(docs_, sizes_, titles_sizes_,
	                    num_epochs, max_num_samples, max_seq_length, seed, verbose, use_one_sent_blocks);
    } else {
       if (verbose) {
	   cout << "    using uint32 for data mapping..." << endl << std::flush;
       }
       return build_blocks_mapping_impl<uint32_t>(docs_, sizes_, titles_sizes_,
                        num_epochs, max_num_samples, max_seq_length, seed, verbose, use_one_sent_blocks);
    }
}

PYBIND11_MODULE(helpers, m) {
    m.def("build_mapping", &build_mapping);
    m.def("build_blocks_mapping", &build_blocks_mapping);
    m.def("build_sample_idx", &build_sample_idx);
    m.def("build_blending_indices", &build_blending_indices);
}
