


import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


__lowerCAmelCase		       =							re.compile(r'''\s+''')




def    snake_case_     (  snake_case     )  ->				Optional[int]:
       return {"hash": hashlib.mda(re.sub(snake_case			,							''			,							example['content']     ).encode('utf-8'     )     ).hexdigest()}




def    snake_case_     (  snake_case     )  ->				str:
       lowercase__:   str  			=   [len(snake_case     ) for line in example['content'].splitlines()]
       return {"line_mean": np.mean(snake_case     ), "line_max": max(snake_case     )}




def    snake_case_     (  snake_case     )  ->				str:
       lowercase__:   Optional[Any]  			=   np.mean([c.isalnum() for c in example['content']]     )
       return {"alpha_frac": alpha_frac}




def    snake_case_     (  snake_case			,							snake_case     )  ->				List[Any]:
       if example["hash"] in uniques:
              uniques.remove(example['hash']     )
              return True
       else:
              return False




def    snake_case_     (  snake_case			,							snake_case=5     )  ->				Tuple:
       lowercase__:   Any  			=   ['auto-generated', 'autogenerated', 'automatically generated']
       lowercase__:   str  			=   example['content'].splitlines()
       for _, line in zip(range(snake_case     )			,							snake_case     ):
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"autogenerated": True}
       else:
              return {"autogenerated": False}




def    snake_case_     (  snake_case			,							snake_case=5			,							snake_case=0.0_5     )  ->				int:
       lowercase__:   int  			=   ['unit tests', 'test file', 'configuration file']
       lowercase__:   Optional[Any]  			=   example['content'].splitlines()
       lowercase__:   str  			=   0
       lowercase__:   Tuple  			=   0
       # first test
       for _, line in zip(range(snake_case     )			,							snake_case     ):
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"config_or_test": True}
    # second test
       lowercase__:   List[Any]  			=   example['content'].count('\n'     )
       lowercase__:   int  			=   int(coeff * nlines     )
       for line in lines:
              count_config += line.lower().count('config'     )
              count_test += line.lower().count('test'     )
              if count_config > threshold or count_test > threshold:
                     return {"config_or_test": True}
       return {"config_or_test": False}




def    snake_case_     (  snake_case     )  ->				int:
       lowercase__:   List[str]  			=   ['def ', 'class ', 'for ', 'while ']
       lowercase__:   int  			=   example['content'].splitlines()
       for line in lines:
              for keyword in keywords:
                     if keyword in line.lower():
                            return {"has_no_keywords": False}
       return {"has_no_keywords": True}




def    snake_case_     (  snake_case			,							snake_case=4     )  ->				Tuple:
       lowercase__:   Dict  			=   example['content'].splitlines()
       lowercase__:   List[str]  			=   0
       for line in lines:
              counter += line.lower().count('='     )
              if counter > minimum:
                     return {"has_few_assignments": False}
       return {"has_few_assignments": True}




def    snake_case_     (  snake_case     )  ->				List[str]:
       lowercase__:   str  			=   tokenizer(example['content']			,							truncation=snake_case     )['input_ids']
       lowercase__:   Union[str, Any]  			=   len(example['content']     ) / len(snake_case     )
       return {"ratio": ratio}




def    snake_case_     (  snake_case     )  ->				int:
       lowercase__:   Optional[int]  			=   {}
       results.update(get_hash(snake_case     )     )
       results.update(line_stats(snake_case     )     )
       results.update(alpha_stats(snake_case     )     )
       results.update(char_token_ratio(snake_case     )     )
       results.update(is_autogenerated(snake_case     )     )
       results.update(is_config_or_test(snake_case     )     )
       results.update(has_no_keywords(snake_case     )     )
       results.update(has_few_assignments(snake_case     )     )
       return results




def    snake_case_     (  snake_case			,							snake_case			,							snake_case     )  ->				Optional[int]:
       if not check_uniques(snake_case			,							snake_case     ):
              return False
       elif example["autogenerated"]:
              return False
       elif example["line_max"] > args.line_max:
              return False
       elif example["line_mean"] > args.line_mean:
              return False
       elif example["alpha_frac"] < args.alpha_frac:
              return False
       elif example["ratio"] < args.min_token_ratio:
              return False
       elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
              return False
       elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
              return False
       elif example["has_few_assignments"]:
              return False
       else:
              return True




def    snake_case_     (  snake_case     )  ->				Dict:
       with open(snake_case			,							'rb'     ) as f_in:
              with gzip.open(str(snake_case     ) + '.gz'			,							'wb'			,							compresslevel=6     ) as f_out:
                     shutil.copyfileobj(snake_case			,							snake_case     )
       os.unlink(snake_case     )


# Settings
__lowerCAmelCase		       =							HfArgumentParser(PreprocessingArguments)
__lowerCAmelCase		       =							parser.parse_args()
if args.num_workers is None:
 __lowerCAmelCase		       =							multiprocessing.cpu_count()
__lowerCAmelCase		       =							AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__lowerCAmelCase		       =							time.time()
__lowerCAmelCase		       =							load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
__lowerCAmelCase		       =							time.time()
__lowerCAmelCase		       =							ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
__lowerCAmelCase		       =							set(ds.unique('''hash'''))
__lowerCAmelCase		       =							len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
__lowerCAmelCase		       =							time.time()
__lowerCAmelCase		       =							ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
 __lowerCAmelCase		       =							time.time()
 __lowerCAmelCase       ,__lowerCAmelCase		       =							deduplicate_dataset(ds_filter, args.jaccard_threshold)
 print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
 print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
__lowerCAmelCase		       =							Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
 with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
  json.dump(duplicate_clusters, f)

__lowerCAmelCase		       =							output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

__lowerCAmelCase		       =							time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
 __lowerCAmelCase		       =							str(data_dir / F'''file-{file_number+1:012}.json''')
 __lowerCAmelCase		       =							min(len(ds_filter), index + args.samples_per_file)
 ds_filter.select(list(range(index, end_index))).to_json(file_path)
 compress_file(file_path)




print(F'''Time to save dataset: {time.time()-t_start:.2f}''')






def    snake_case_     (  snake_case     )  ->				int:
       if n == 1 or not isinstance(snake_case			,							snake_case     ):
              return 0
       elif n == 2:
              return 1
       else:
              lowercase__:   Optional[Any]  			=   [0, 1]
              for i in range(2			,							n + 1     ):
                     sequence.append(sequence[i - 1] + sequence[i - 2]     )

              return sequence[n]




def    snake_case_     (  snake_case     )  ->				int:
       lowercase__:   int  			=   0
       lowercase__:   int  			=   2

       while digits < n:
              index += 1
              lowercase__:   Tuple  			=   len(str(fibonacci(snake_case     )     )     )

       return index




def    snake_case_     (  snake_case = 10_00     )  ->				int:
       return fibonacci_digits_index(snake_case     )


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))


