import os
import re
import subprocess
from typing import Set, Tuple

# Third-party imports
import spacy
import jieba

import gzip
import json
from pathlib import Path
import logging
from datatrove.executor.local import LocalPipelineExecutor
from datatrove.pipeline.filters import (
    RegexFilter,
    LanguageFilter,
    GopherQualityFilter,
    C4QualityFilter,
)
from datatrove.pipeline.dedup import (
    MinhashDedupCluster,
    MinhashDedupFilter,
    MinhashDedupSignature,
    MinhashConfig,
    MinhashDedupBuckets,
)
from datatrove.pipeline.writers.jsonl import JsonlWriter
from datatrove.pipeline.writers.parquet import ParquetWriter
from datatrove.pipeline.readers import ParquetReader
from datatrove.pipeline.tokens import TokensCounter
from datatrove.pipeline.formatters import PIIFormatter

# Constants
MAIN_OUTPUT_PATH = "./output"
FILTERING_OUTPUT_PATH = f"{MAIN_OUTPUT_PATH}/base_processing"
DUMP = "CC-MAIN-2024-26"
S3_MINHASH_BASE_PATH = f"{MAIN_OUTPUT_PATH}/minhash"
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# Configuration
minhash_config = MinhashConfig(
    num_buckets=14,
    hashes_per_bucket=8,
    n_grams=2,
)

# Chinese stop words
chinese_stop_words = [
    "的", "了", "和", "是", "就", "都", "而", "及", "與", "這", "其", "但", "並", "個", "我",
    "你", "他", "她", "它", "們", "我們", "你們", "他們", "她們", "它們", "在", "有", "人",
    "這個", "那個", "如果", "因為", "所以", "可以", "沒有", "很", "非常", "得", "著", "過", "為", "再",
    "吧", "呢", "啊", "哪", "那", "麼", "什麼", "誰", "哪裡", "哪裡", "怎麼", "怎麼樣", "為什麼", "將"
]
import shutil

def clear_previous_outputs():
    directories_to_clear = [
        f"{MAIN_OUTPUT_PATH}/base_processing",
        f"{MAIN_OUTPUT_PATH}/filtered_output",
        f"{S3_MINHASH_BASE_PATH}/{DUMP}/signatures",
        f"{S3_MINHASH_BASE_PATH}/{DUMP}/buckets",
        f"{S3_MINHASH_BASE_PATH}/{DUMP}/remove_ids",
        f"{S3_MINHASH_BASE_PATH}/{DUMP}/deduped_output",
        f"{MAIN_OUTPUT_PATH}/logs",
    ]
    
    for directory in directories_to_clear:
        if os.path.exists(directory):
            print(f"Removing: {directory}")
            shutil.rmtree(directory)
        os.makedirs(directory, exist_ok=True)

def read_character_mappings(file_path: str) -> Tuple[Set[str], Set[str]]:
    traditional = set()
    simplified = set()
    with open(file_path, 'r', encoding='utf-8') as f:
        for line in f:
            chars = line.strip().split('\t')
            if len(chars) >= 2:
                trad, simps = chars[0], chars[1].split()
                traditional.add(trad)
                simplified.update(simps)
    return traditional, simplified

def read_traditional_characters(file_path: str) -> Set[str]:
    with open(file_path, 'r', encoding='utf-8') as f:
        return set(f.read().strip())

def create_simplified_filter(simplified_only: Set[str]) -> RegexFilter:
    simplified_pattern = '|'.join(re.escape(char) for char in simplified_only)
    return RegexFilter(
        regex_exp=simplified_pattern,
        exclusion_writer=JsonlWriter(
            f"{FILTERING_OUTPUT_PATH}/removed/simplified/{DUMP}",
            output_filename="${rank}.jsonl.gz"
        )
    )

def initial_filtering_pipeline(simplified_filter: RegexFilter) -> Tuple[LocalPipelineExecutor, LocalPipelineExecutor, LocalPipelineExecutor]:
    # Initial filtering pipeline - Part 1
    initial_executor_part1 = LocalPipelineExecutor(
        pipeline=[
            ParquetReader("./input/hugg-init/out-1720278098.parquet"),
            LanguageFilter(languages=["zh"], language_threshold=0.65),
            simplified_filter,
            ParquetWriter(f"{MAIN_OUTPUT_PATH}/temp_output_part1")
        ],
        tasks=8,
        workers=8,
        logging_dir=f"{MAIN_OUTPUT_PATH}/logs/base_processing/{DUMP}/part1",
    )

    # Initial filtering pipeline - Part 2
    initial_executor_part2 = LocalPipelineExecutor(
        pipeline=[
            ParquetReader(f"{MAIN_OUTPUT_PATH}/temp_output_part1"),
            GopherQualityFilter(
                min_doc_words=15,
                max_doc_words=10000,
                min_avg_word_length=1,
                max_avg_word_length=4,
                max_symbol_word_ratio=0.1,
                max_bullet_lines_ratio=0.9,
                max_ellipsis_lines_ratio=0.3,
                min_stop_words=1,
                stop_words=chinese_stop_words,
                exclusion_writer=JsonlWriter(f"{FILTERING_OUTPUT_PATH}/removed/4_gopher_qual/{DUMP}"),
                language="zh",
            ),
            ParquetWriter(f"{MAIN_OUTPUT_PATH}/temp_output_part2")
        ],
        tasks=8,
        workers=8,
        logging_dir=f"{MAIN_OUTPUT_PATH}/logs/base_processing/{DUMP}/part2",
    )

    # Initial filtering pipeline - Part 3
    initial_executor_part3 = LocalPipelineExecutor(
        pipeline=[
            ParquetReader(f"{MAIN_OUTPUT_PATH}/temp_output_part2"),
            C4QualityFilter(
                split_paragraph=False,
                remove_citations=False,
                filter_no_terminal_punct=False,
                min_num_sentences=-1,
                min_words_per_line=-1,
                max_word_length=-1,
                filter_lorem_ipsum=False,
                filter_javascript=True,
                filter_curly_bracket=True,
                filter_policy=True,
                language="zh",
                exclusion_writer=JsonlWriter(f"{FILTERING_OUTPUT_PATH}/removed/5_c4/{DUMP}"),
            ),
            ParquetWriter(f"{MAIN_OUTPUT_PATH}/filtered_output")
        ],
        tasks=8,
        workers=8,
        logging_dir=f"{MAIN_OUTPUT_PATH}/logs/base_processing/{DUMP}/part3",
    )
    return initial_executor_part1, initial_executor_part2, initial_executor_part3 

def deduplication_pipeline() -> Tuple[LocalPipelineExecutor, LocalPipelineExecutor, LocalPipelineExecutor, LocalPipelineExecutor]:
    stage1 = LocalPipelineExecutor(
        pipeline=[
            ParquetReader(f"{MAIN_OUTPUT_PATH}/filtered_output"),
            MinhashDedupSignature(
                output_folder=f"{S3_MINHASH_BASE_PATH}/{DUMP}/signatures",
                config=minhash_config,
                language="zh",
            ),
        ],
        tasks=8,
        workers=8,
        logging_dir=f"{MAIN_OUTPUT_PATH}/logs/minhash/signatures",
    )

    stage2 = LocalPipelineExecutor(
        pipeline=[
            MinhashDedupBuckets(
                input_folder=f"{S3_MINHASH_BASE_PATH}/{DUMP}/signatures",
                output_folder=f"{S3_MINHASH_BASE_PATH}/{DUMP}/buckets",
                config=minhash_config,
            ),
        ],
        tasks=minhash_config.num_buckets,
        workers=8,
        logging_dir=f"{MAIN_OUTPUT_PATH}/logs/minhash/buckets",
    )

    stage3 = LocalPipelineExecutor(
        pipeline=[
            MinhashDedupCluster(
                input_folder=f"{S3_MINHASH_BASE_PATH}/{DUMP}/buckets",
                output_folder=f"{S3_MINHASH_BASE_PATH}/{DUMP}/remove_ids",
                config=minhash_config,
            ),
        ],
        tasks=1,
        workers=1,
        logging_dir=f"{MAIN_OUTPUT_PATH}/logs/minhash/clustering",
    )

    stage4 = LocalPipelineExecutor(
        pipeline=[
            ParquetReader(f"{MAIN_OUTPUT_PATH}/filtered_output"),
            TokensCounter(),
            MinhashDedupFilter(input_folder=f"{S3_MINHASH_BASE_PATH}/{DUMP}/remove_ids"),
            PIIFormatter(),
            ParquetWriter(f"{S3_MINHASH_BASE_PATH}/{DUMP}/deduped_output"),
        ],
        tasks=8,
        workers=8,
        logging_dir=f"{MAIN_OUTPUT_PATH}/logs/minhash/filtering",
    )

    return stage1, stage2, stage3, stage4

def analyze_removed_documents(removed_dir: str, simplified_pattern: str, output_file: str):
    removed_files = Path(removed_dir).glob('*.jsonl.gz')
    simplified_regex = re.compile(f'[{simplified_pattern}]')
    
    total_documents = 0
    documents_with_simplified = 0
    
    with gzip.open(output_file, 'wt', encoding='utf-8') as out_f:
        for file in removed_files:
            logger.info(f"Analyzing file: {file.name}")
            with gzip.open(file, 'rt', encoding='utf-8') as in_f:
                for line in in_f:
                    total_documents += 1
                    document = json.loads(line)
                    text = document.get('text', '')
                    
                    matches = list(set(simplified_regex.findall(text)))
                    if matches:
                        documents_with_simplified += 1
                        result = {
                            "document_number": total_documents,
                            "simplified_characters": matches,
                            "text_snippet": text  
                        }
                        out_f.write(json.dumps(result, ensure_ascii=False) + '\n')
    
    logger.info(f"Analysis complete. Results written to {output_file}")
    logger.info(f"Total documents analyzed: {total_documents}")
    logger.info(f"Documents with simplified characters: {documents_with_simplified}")

def main(restart=False):
    if restart:
        clear_previous_outputs()

    # Read character mappings
    ts_traditional, ts_simplified = read_character_mappings("./input/ts-map/TSCharacters.txt")
    st_traditional, st_simplified = read_character_mappings("./input/ts-map/STCharacters.txt")

    # Combine sets
    traditional = ts_traditional.union(st_traditional)
    simplified = ts_simplified.union(st_simplified)

    # Additional processing
    white_list = set("床峰群秘霉庄痴雇简体踪")
    additional_set = read_traditional_characters("./input/dict-tradi2/trad.txt")
    print(f"Number of additional traditional characters: {len(additional_set)}")

    simplified_only = simplified - traditional - white_list

    # Create simplified filter
    simplified_filter = create_simplified_filter(simplified_only)

    # Run initial filtering pipeline

    initial_executor_part1, initial_executor_part2, initial_executor_part3 = initial_filtering_pipeline(simplified_filter)
    initial_executor_part1.run()
    initial_executor_part2.run()
    initial_executor_part3.run()

    logger.info("Initial filtering complete. Starting analysis of removed documents.")
    removed_dir = f"{FILTERING_OUTPUT_PATH}/removed/simplified/{DUMP}"
    output_file = f"{MAIN_OUTPUT_PATH}/simplified_analysis_results.jsonl.gz"
    
    # Create the simplified pattern
    simplified_pattern = ''.join(simplified_only)
    
    analyze_removed_documents(removed_dir, simplified_pattern, output_file)

    # Run deduplication pipeline
    # stage1, stage2, stage3, stage4 = deduplication_pipeline()
    # stage1.run()
    # stage2.run()
    # stage3.run()
    # stage4.run()
    

if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description="Run Chinese text processing pipeline")
    parser.add_argument("--restart", action="store_true", help="Clear previous outputs and restart processing")
    args = parser.parse_args()
    
    main(restart=args.restart)