# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Script to shard into separate train and test dataset files

Reference: https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT
"""

import multiprocessing
import os
import statistics
from collections import defaultdict
from itertools import islice

import nltk

nltk.download('punkt')


class Sharding:
    def __init__(
        self,
        input_files,
        output_name_prefix,
        n_training_shards,
        n_test_shards,
        fraction_test_set,
    ):
        assert (
            len(input_files) > 0
        ), 'The input file list must contain at least one file.'
        assert n_training_shards > 0, 'There must be at least one output shard.'
        assert n_test_shards > 0, 'There must be at least one output shard.'

        self.n_training_shards = n_training_shards
        self.n_test_shards = n_test_shards
        self.fraction_test_set = fraction_test_set

        self.input_files = input_files

        self.output_name_prefix = output_name_prefix
        self.output_training_identifier = '_training'
        self.output_test_identifier = '_test'
        self.output_file_extension = '.txt'

        self.articles = {}  # key: integer identifier, value: list of articles
        self.sentences = {}  # key: integer identifier, value: list of sentences
        self.output_training_files = (
            {}
        )  # key: filename, value: list of articles to go into file
        self.output_test_files = (
            {}
        )  # key: filename, value: list of articles to go into file

        self.init_output_files()

    # Remember, the input files contain one article per line (the whitespace check is to skip extraneous blank lines)
    def load_articles(self):
        print(f"Start: Loading Articles")

        global_article_count = 0
        for input_file in self.input_files:
            print('input file:', input_file)
            with open(input_file, mode='r', newline='\n') as f:
                for i, line in enumerate(f):
                    if line.strip():
                        self.articles[global_article_count] = line.rstrip()
                        global_article_count += 1

        print(
            f"End: Loading Articles: There are {len(self.articles)} articles."
        )

    def segment_articles_into_sentences(self, segmenter):
        print(f"Start: Sentence Segmentation")
        if len(self.articles) is 0:
            self.load_articles()

        assert (
            len(self.articles) is not 0
        ), 'Please check that input files are present and contain data.'

        use_multiprocessing = 'serial'

        def chunks(data, size=len(self.articles)):
            it = iter(data)
            for i in range(0, len(data), size):
                yield {k: data[k] for k in islice(it, size)}

        if use_multiprocessing == 'manager':
            manager = multiprocessing.Manager()
            return_dict = manager.dict()
            jobs = []
            n_processes = 7  # in addition to the main process, total = n_proc+1

            def work(articles, return_dict):
                sentences = {}
                for i, article in enumerate(articles):
                    sentences[i] = segmenter.segment_string(articles[article])

                    if i % 5000 == 0:
                        print(f"Segmenting article {i}")

                return_dict.update(sentences)

            for item in chunks(self.articles, len(self.articles)):
                p = multiprocessing.Process(
                    target=work, args=(item, return_dict)
                )

                # Busy wait
                while len(jobs) >= n_processes:
                    pass

                jobs.append(p)
                p.start()

            for proc in jobs:
                proc.join()

        elif use_multiprocessing == 'queue':
            work_queue = multiprocessing.Queue()
            jobs = []

            for item in chunks(self.articles, len(self.articles)):
                pass

        else:  # serial option
            for i, article in enumerate(self.articles):
                self.sentences[i] = segmenter.segment_string(
                    self.articles[article]
                )

                if i % 5000 == 0:
                    print(f"Segmenting article {i}")

        print(f"End: Sentence Segmentation")

    def init_output_files(self):
        print(f"Start: Init Output Files")
        assert (
            len(self.output_training_files) is 0
        ), 'Internal storage \
            self.output_files already contains data. This function is \
                intended to be used by the constructor only.'
        assert (
            len(self.output_test_files) is 0
        ), 'Internal storage \
            self.output_files already contains data. \
                This function is intended to be used by the constructor only.'

        for i in range(self.n_training_shards):
            name = (
                self.output_name_prefix
                + self.output_training_identifier
                + '_'
                + str(i)
                + self.output_file_extension
            )
            self.output_training_files[name] = []

        for i in range(self.n_test_shards):
            name = (
                self.output_name_prefix
                + self.output_test_identifier
                + '_'
                + str(i)
                + self.output_file_extension
            )
            self.output_test_files[name] = []

        print('End: Init Output Files')

    def get_sentences_per_shard(self, shard):
        result = 0
        for article_id in shard:
            result += len(self.sentences[article_id])

        return result

    def distribute_articles_over_shards(self):
        print(f"Start: Distribute Articles Over Shards")
        assert (
            len(self.articles) >= self.n_training_shards + self.n_test_shards
        ), 'There are fewer articles than shards. \
            Please add more data or reduce the number of shards requested.'

        # Create dictionary with - key: sentence count per article, value: article id number
        sentence_counts = defaultdict(lambda: [])

        max_sentences = 0
        total_sentences = 0

        for article_id in self.sentences:
            current_length = len(self.sentences[article_id])
            sentence_counts[current_length].append(article_id)
            max_sentences = max(max_sentences, current_length)
            total_sentences += current_length

        n_sentences_assigned_to_training = int(
            (1 - self.fraction_test_set) * total_sentences
        )
        nominal_sentences_per_training_shard = (
            n_sentences_assigned_to_training // self.n_training_shards
        )
        nominal_sentences_per_test_shard = (
            total_sentences - n_sentences_assigned_to_training
        ) // self.n_test_shards

        consumed_article_set = set({})
        unused_article_set = set(self.articles.keys())

        # Make first pass and add one article worth of lines per file
        for file in self.output_training_files:
            current_article_id = sentence_counts[max_sentences][-1]
            sentence_counts[max_sentences].pop(-1)
            self.output_training_files[file].append(current_article_id)
            consumed_article_set.add(current_article_id)
            unused_article_set.remove(current_article_id)

            # Maintain the max sentence count
            while (
                len(sentence_counts[max_sentences]) == 0 and max_sentences > 0
            ):
                max_sentences -= 1

            if (
                len(self.sentences[current_article_id])
                > nominal_sentences_per_training_shard
            ):
                nominal_sentences_per_training_shard = len(
                    self.sentences[current_article_id]
                )
                print(
                    f"Warning: A single article contains more"
                    f" than the nominal number of sentences per training shard."
                )

        for file in self.output_test_files:
            current_article_id = sentence_counts[max_sentences][-1]
            sentence_counts[max_sentences].pop(-1)
            self.output_test_files[file].append(current_article_id)
            consumed_article_set.add(current_article_id)
            unused_article_set.remove(current_article_id)

            # Maintain the max sentence count
            while (
                len(sentence_counts[max_sentences]) == 0 and max_sentences > 0
            ):
                max_sentences -= 1

            if (
                len(self.sentences[current_article_id])
                > nominal_sentences_per_test_shard
            ):
                nominal_sentences_per_test_shard = len(
                    self.sentences[current_article_id]
                )
                print(
                    f"Warning: A single article contains more \
                        than the nominal number of sentences per test shard."
                )

        training_counts = []
        test_counts = []

        for shard in self.output_training_files:
            training_counts.append(
                self.get_sentences_per_shard(self.output_training_files[shard])
            )

        for shard in self.output_test_files:
            test_counts.append(
                self.get_sentences_per_shard(self.output_test_files[shard])
            )

        training_median = statistics.median(training_counts)
        test_median = statistics.median(test_counts)

        # Make subsequent passes over files to find articles to add without going over limit
        history_remaining = []
        n_history_remaining = 4

        while len(consumed_article_set) < len(self.articles):
            for fidx, file in enumerate(self.output_training_files):
                nominal_next_article_size = min(
                    nominal_sentences_per_training_shard
                    - training_counts[fidx],
                    max_sentences,
                )

                # Maintain the max sentence count
                while (
                    len(sentence_counts[max_sentences]) == 0
                    and max_sentences > 0
                ):
                    max_sentences -= 1

                while (
                    len(sentence_counts[nominal_next_article_size]) == 0
                    and nominal_next_article_size > 0
                ):
                    nominal_next_article_size -= 1

                if (
                    nominal_next_article_size not in sentence_counts
                    or nominal_next_article_size is 0
                    or training_counts[fidx] > training_median
                ):
                    continue
                # skip adding to this file,
                # will come back later if no file can accept unused articles

                current_article_id = sentence_counts[nominal_next_article_size][
                    -1
                ]
                sentence_counts[nominal_next_article_size].pop(-1)

                self.output_training_files[file].append(current_article_id)
                consumed_article_set.add(current_article_id)
                unused_article_set.remove(current_article_id)

            for fidx, file in enumerate(self.output_test_files):
                nominal_next_article_size = min(
                    nominal_sentences_per_test_shard - test_counts[fidx],
                    max_sentences,
                )

                # Maintain the max sentence count
                while (
                    len(sentence_counts[max_sentences]) == 0
                    and max_sentences > 0
                ):
                    max_sentences -= 1

                while (
                    len(sentence_counts[nominal_next_article_size]) == 0
                    and nominal_next_article_size > 0
                ):
                    nominal_next_article_size -= 1

                if (
                    nominal_next_article_size not in sentence_counts
                    or nominal_next_article_size is 0
                    or test_counts[fidx] > test_median
                ):
                    continue
                # skip adding to this file,
                # will come back later if no file can accept unused articles

                current_article_id = sentence_counts[nominal_next_article_size][
                    -1
                ]
                sentence_counts[nominal_next_article_size].pop(-1)

                self.output_test_files[file].append(current_article_id)
                consumed_article_set.add(current_article_id)
                unused_article_set.remove(current_article_id)

            # If unable to place articles a few times,
            # bump up nominal sizes by fraction until articles get placed
            if len(history_remaining) == n_history_remaining:
                history_remaining.pop(0)
            history_remaining.append(len(unused_article_set))

            history_same = True
            for i in range(1, len(history_remaining)):
                history_same = history_same and (
                    history_remaining[i - 1] == history_remaining[i]
                )

            if history_same:
                nominal_sentences_per_training_shard += 1
                # nominal_sentences_per_test_shard += 1

            training_counts = []
            test_counts = []
            for shard in self.output_training_files:
                training_counts.append(
                    self.get_sentences_per_shard(
                        self.output_training_files[shard]
                    )
                )

            for shard in self.output_test_files:
                test_counts.append(
                    self.get_sentences_per_shard(self.output_test_files[shard])
                )

            training_median = statistics.median(training_counts)
            test_median = statistics.median(test_counts)

            print(
                f"Distributing data over shards: {len(unused_article_set)} articles remaining."
            )

        if len(unused_article_set) != 0:
            print(f"Warning: Some articles did not make it into output files.")

        for shard in self.output_training_files:
            print(
                f"Training shard sentences: {self.get_sentences_per_shard(self.output_training_files[shard])}"
            )

        for shard in self.output_test_files:
            print(
                f"Test shard sentences:{self.get_sentences_per_shard(self.output_test_files[shard])}"
            )

        print(f"End: Distribute Articles Over Shards")

    def write_shards_to_disk(self):
        print('Start: Write Shards to Disk')
        for shard in self.output_training_files:
            self.write_single_shard(
                shard, self.output_training_files[shard], 'training'
            )

        for shard in self.output_test_files:
            self.write_single_shard(
                shard, self.output_test_files[shard], 'test'
            )

        print(f"End: Write Shards to Disk")

    def write_single_shard(self, shard_name, shard, split):
        shard_split = os.path.split(shard_name)
        shard_name = shard_split[0] + '/' + split + '/' + shard_split[1]

        with open(shard_name, mode='w', newline='\n') as f:
            for article_id in shard:
                for line in self.sentences[article_id]:
                    f.write(line + '\n')

                f.write('\n')  # Line break between articles


class NLTKSegmenter:
    def __init__(self):
        pass

    def segment_string(self, article):
        return nltk.tokenize.sent_tokenize(article)
