





"""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 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

      return {"hash": hashlib.mda(re.sub(lowercase_		,""		,example["content"]			).encode("utf-8"			)			).hexdigest()}




def 					lowercase__    (					lowercase_			)					->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       [len(lowercase_			) for line in example["content"].splitlines()]
      return {"line_mean": np.mean(lowercase_			), "line_max": max(lowercase_			)}




def 					lowercase__    (					lowercase_			)					->		int:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       np.mean([c.isalnum() for c in example["content"]]			)
      return {"alpha_frac": alpha_frac}




def 					lowercase__    (					lowercase_		,lowercase_			)					->		List[Any]:




      """simple docstring"""

      if example["hash"] in uniques:
            uniques.remove(example["hash"]			)
            return True
      else:
            return False




def 					lowercase__    (					lowercase_		,lowercase_=5			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       ["auto-generated", "autogenerated", "automatically generated"]
      _UpperCamelCase      :	Union[str, Any]						=       example["content"].splitlines()
      for _, line in zip(range(lowercase_			)		,lowercase_			):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"autogenerated": True}
      else:
            return {"autogenerated": False}




def 					lowercase__    (					lowercase_		,lowercase_=5		,lowercase_=0.05			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       ["unit tests", "test file", "configuration file"]
      _UpperCamelCase      :	List[str]						=       example["content"].splitlines()
      _UpperCamelCase      :	Optional[int]						=       0
      _UpperCamelCase      :	Union[str, Any]						=       0
      # first test
      for _, line in zip(range(lowercase_			)		,lowercase_			):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"config_or_test": True}
    # second test
      _UpperCamelCase      :	Dict						=       example["content"].count("\n"			)
      _UpperCamelCase      :	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 					lowercase__    (					lowercase_			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       ["def ", "class ", "for ", "while "]
      _UpperCamelCase      :	Dict						=       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 					lowercase__    (					lowercase_		,lowercase_=4			)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       example["content"].splitlines()
      _UpperCamelCase      :	int						=       0
      for line in lines:
            counter += line.lower().count("="			)
            if counter > minimum:
                  return {"has_few_assignments": False}
      return {"has_few_assignments": True}




def 					lowercase__    (					lowercase_			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       tokenizer(example["content"]		,truncation=lowercase_			)["input_ids"]
      _UpperCamelCase      :	Optional[Any]						=       len(example["content"]			) / len(lowercase_			)
      return {"ratio": ratio}




def 					lowercase__    (					lowercase_			)					->		Tuple:




      """simple docstring"""

      _UpperCamelCase      :	Union[str, Any]						=       {}
      results.update(get_hash(lowercase_			)			)
      results.update(line_stats(lowercase_			)			)
      results.update(alpha_stats(lowercase_			)			)
      results.update(char_token_ratio(lowercase_			)			)
      results.update(is_autogenerated(lowercase_			)			)
      results.update(is_config_or_test(lowercase_			)			)
      results.update(has_no_keywords(lowercase_			)			)
      results.update(has_few_assignments(lowercase_			)			)
      return results




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      if not check_uniques(lowercase_		,lowercase_			):
            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 					lowercase__    (					lowercase_			)					->		str:




      """simple docstring"""

      with open(lowercase_		,"rb"			) as f_in:
            with gzip.open(str(lowercase_			) + ".gz"		,"wb"		,compresslevel=6			) as f_out:
                  shutil.copyfileobj(lowercase_		,lowercase_			)
      os.unlink(lowercase_			)


# 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"""


class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       Optional[Any]					,   __a   :       list[int]	)		->				None:
        _UpperCamelCase      :	Tuple						=       len(__a	)
        _UpperCamelCase      :	Dict						=       [0] * len_array

        if len_array > 0:
              _UpperCamelCase      :	Optional[Any]						=       array[0]

        for i in range(1					,   __a	):
              _UpperCamelCase      :	Tuple						=       self.prefix_sum[i - 1] + array[i]

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       int					,   __a   :       int	)		->				int:
        if start == 0:
              return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       int	)		->				bool:
        _UpperCamelCase      :	int						=       {0}
        for sum_item in self.prefix_sum:
              if sum_item - target_sum in sums:
                    return True

              sums.add(__a	)

        return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()
