

"""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


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




def   _A  (  UpperCamelCase_     :       List[str])	->						List[str]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       Any)	->						str:
   '''simple docstring'''


   __lowercase								=						[len(lowerCAmelCase__) for line in example['''content'''].splitlines()]
   return {"line_mean": np.mean(lowerCAmelCase__), "line_max": max(lowerCAmelCase__)}




def   _A  (  UpperCamelCase_     :       int)	->						int:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       List[str],			UpperCamelCase_     :       List[Any])	->						Optional[int]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       List[str]=5)	->						Tuple:
   '''simple docstring'''


   __lowercase								=						['''auto-generated''', '''autogenerated''', '''automatically generated''']
   __lowercase								=						example['''content'''].splitlines()
   for _, line in zip(range(lowerCAmelCase__),			lowerCAmelCase__):
      for keyword in keywords:
         if keyword in line.lower():
            return {"autogenerated": True}
   else:
      return {"autogenerated": False}




def   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       List[Any]=5,			UpperCamelCase_     :       int=0.05)	->						Tuple:
   '''simple docstring'''


   __lowercase								=						['''unit tests''', '''test file''', '''configuration file''']
   __lowercase								=						example['''content'''].splitlines()
   __lowercase								=						0
   __lowercase								=						0
   # first test
   for _, line in zip(range(lowerCAmelCase__),			lowerCAmelCase__):
      for keyword in keywords:
         if keyword in line.lower():
            return {"config_or_test": True}
            # second test
   __lowercase								=						example['''content'''].count("\n")
   __lowercase								=						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   _A  (  UpperCamelCase_     :       int)	->						str:
   '''simple docstring'''


   __lowercase								=						['''def ''', '''class ''', '''for ''', '''while ''']
   __lowercase								=						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   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Any=4)	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						example['''content'''].splitlines()
   __lowercase								=						0
   for line in lines:
      counter += line.lower().count("=")
      if counter > minimum:
         return {"has_few_assignments": False}
   return {"has_few_assignments": True}




def   _A  (  UpperCamelCase_     :       Tuple)	->						Optional[int]:
   '''simple docstring'''


   __lowercase								=						tokenizer(example["content"],			truncation=lowerCAmelCase__)['''input_ids''']
   __lowercase								=						len(example["content"]) / len(lowerCAmelCase__)
   return {"ratio": ratio}




def   _A  (  UpperCamelCase_     :       List[str])	->						Optional[int]:
   '''simple docstring'''


   __lowercase								=						{}
   results.update(get_hash(lowerCAmelCase__))
   results.update(line_stats(lowerCAmelCase__))
   results.update(alpha_stats(lowerCAmelCase__))
   results.update(char_token_ratio(lowerCAmelCase__))
   results.update(is_autogenerated(lowerCAmelCase__))
   results.update(is_config_or_test(lowerCAmelCase__))
   results.update(has_no_keywords(lowerCAmelCase__))
   results.update(has_few_assignments(lowerCAmelCase__))
   return results




def   _A  (  UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Any)	->						List[Any]:
   '''simple docstring'''


   if not check_uniques(lowerCAmelCase__,			lowerCAmelCase__):
      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   _A  (  UpperCamelCase_     :       List[str])	->						int:
   '''simple docstring'''


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


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

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

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

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

# Deduplicate data and apply heuristics
_a       =							time.time()
_a       =							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:
   _a       =							time.time()
   _a      ,						_a       =							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
_a       =							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)

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

_a       =							time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   _a       =							str(data_dir / F"file-{file_number+1:012}.json")
   _a       =							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}")






from __future__ import annotations




def    __UpperCamelCase     (				lowerCAmelCase__							:				list[float]  ,   lowerCAmelCase__							:				list[float]  ):
 __a :  Dict						=  sorted(numsa + numsa  )
 __a				,       __a :  Optional[Any]						=  divmod(len(lowerCAmelCase__  )  ,   2  )
 if mod == 1:
  return all_numbers[div]
 else:
  return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      lowercase__       =[float(x) for x in input('Enter the elements of first array: ').split()]
      lowercase__       =[float(x) for x in input('Enter the elements of second array: ').split()]
      print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")
