





"""simple docstring"""
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      __lowerCamelCase   (							a_     :		List[str]							)			->      Optional[int]:
      return {"hash": hashlib.mda(re.sub(a_ ,   '''''' ,   example['''content''']							).encode('''utf-8'''							)							).hexdigest()}



def      __lowerCamelCase   (							a_     :		Union[str, Any]							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  [len(a_							) for line in example['''content'''].splitlines()]
      return {"line_mean": np.mean(a_							), "line_max": max(a_							)}



def      __lowerCamelCase   (							a_     :		Tuple							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.mean([c.isalnum() for c in example['''content''']]							)
      return {"alpha_frac": alpha_frac}



def      __lowerCamelCase   (							a_     :		Optional[Any] ,   a_     :		str							)			->      Optional[Any]:
      if example["hash"] in uniques:
            uniques.remove(example['''hash''']							)
            return True
      else:
            return False



def      __lowerCamelCase   (							a_     :		int ,   a_     :		Optional[int]=5							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  ['''auto-generated''', '''autogenerated''', '''automatically generated''']
      __SCREAMING_SNAKE_CASE   :int						=  example['''content'''].splitlines()
      for _, line in zip(range(a_							) ,   a_							):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"autogenerated": True}
      else:
            return {"autogenerated": False}



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Dict=5 ,   a_     :		Any=0.05							)			->      Any:
      __SCREAMING_SNAKE_CASE   :Tuple						=  ['''unit tests''', '''test file''', '''configuration file''']
      __SCREAMING_SNAKE_CASE   :List[Any]						=  example['''content'''].splitlines()
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  0
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  0
      # first test
      for _, line in zip(range(a_							) ,   a_							):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"config_or_test": True}
    # second test
      __SCREAMING_SNAKE_CASE   :List[Any]						=  example['''content'''].count('''\n'''							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  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      __lowerCamelCase   (							a_     :		Dict							)			->      int:
      __SCREAMING_SNAKE_CASE   :str						=  ['''def ''', '''class ''', '''for ''', '''while ''']
      __SCREAMING_SNAKE_CASE   :List[str]						=  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      __lowerCamelCase   (							a_     :		Any ,   a_     :		str=4							)			->      Dict:
      __SCREAMING_SNAKE_CASE   :Any						=  example['''content'''].splitlines()
      __SCREAMING_SNAKE_CASE   :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      __lowerCamelCase   (							a_     :		Tuple							)			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :Any						=  tokenizer(example['''content'''] ,   truncation=a_							)['''input_ids''']
      __SCREAMING_SNAKE_CASE   :List[str]						=  len(example['''content''']							) / len(a_							)
      return {"ratio": ratio}



def      __lowerCamelCase   (							a_     :		Dict							)			->      int:
      __SCREAMING_SNAKE_CASE   :Dict						=  {}
      results.update(get_hash(a_							)							)
      results.update(line_stats(a_							)							)
      results.update(alpha_stats(a_							)							)
      results.update(char_token_ratio(a_							)							)
      results.update(is_autogenerated(a_							)							)
      results.update(is_config_or_test(a_							)							)
      results.update(has_no_keywords(a_							)							)
      results.update(has_few_assignments(a_							)							)
      return results



def      __lowerCamelCase   (							a_     :		Union[str, Any] ,   a_     :		Tuple ,   a_     :		str							)			->      Any:
      if not check_uniques(a_ ,   a_							):
            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      __lowerCamelCase   (							a_     :		Dict							)			->      List[str]:
      with open(a_ ,   '''rb'''							) as f_in:
            with gzip.open(str(a_							) + '''.gz''' ,   '''wb''' ,   compresslevel=6							) as f_out:
                  shutil.copyfileobj(a_ ,   a_							)
      os.unlink(a_							)


# 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}')






"""simple docstring"""
def      __lowerCamelCase   (							a_     :		Union[str, Any]							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :List[str]						=  1
      __SCREAMING_SNAKE_CASE   :Dict						=  2
      while i * i <= n:
            __SCREAMING_SNAKE_CASE   :Tuple						=  0
            while n % i == 0:
                  n //= i
                  multiplicity += 1
            n_divisors *= multiplicity + 1
            i += 1
      if n > 1:
            n_divisors *= 2
      return n_divisors



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :Dict						=  1
      __SCREAMING_SNAKE_CASE   :Dict						=  1

      while True:
            i += 1
            t_num += i

            if count_divisors(a_							) > 5_00:
                  break

      return t_num


if __name__ == "__main__":
  print(solution())