"""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__  :  int						     =	re.compile(r'\s+')



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



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



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							np.mean([c.isalnum() for c in example['content']]       )
				return {"alpha_frac": alpha_frac}



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if example["hash"] in uniques:
								uniques.remove(example['hash']       )
								return True
				else:
								return False



def   a_						(					lowerCamelCase					,			lowerCamelCase=5       ):
				UpperCAmelCase__             =							['auto-generated', 'autogenerated', 'automatically generated']
				UpperCAmelCase__             =							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_						(					lowerCamelCase					,			lowerCamelCase=5					,			lowerCamelCase=0.05       ):
				UpperCAmelCase__             =							['unit tests', 'test file', 'configuration file']
				UpperCAmelCase__             =							example['content'].splitlines()
				UpperCAmelCase__             =							0
				UpperCAmelCase__             =							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
				UpperCAmelCase__             =							example['content'].count('\n'       )
				UpperCAmelCase__             =							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_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							['def ', 'class ', 'for ', 'while ']
				UpperCAmelCase__             =							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_						(					lowerCamelCase					,			lowerCamelCase=4       ):
				UpperCAmelCase__             =							example['content'].splitlines()
				UpperCAmelCase__             =							0
				for line in lines:
								counter += line.lower().count('='       )
								if counter > minimum:
												return {"has_few_assignments": False}
				return {"has_few_assignments": True}



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							tokenizer(example['content']					,			truncation=lowerCamelCase       )['input_ids']
				UpperCAmelCase__             =							len(example['content']       ) / len(lowerCamelCase       )
				return {"ratio": ratio}



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							{}
				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_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				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_						(					lowerCamelCase       ):
				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
lowerCAmelCase__  :  Dict						     =	HfArgumentParser(PreprocessingArguments)
lowerCAmelCase__  :  Optional[Any]						     =	parser.parse_args()
if args.num_workers is None:
						lowerCAmelCase__  :  Optional[int]						     =	multiprocessing.cpu_count()
lowerCAmelCase__  :  Tuple						     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
lowerCAmelCase__  :  List[Any]						     =	time.time()
lowerCAmelCase__  :  Optional[int]						     =	load_dataset(args.dataset_name, split='train')
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")

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

# Deduplicate hashes
lowerCAmelCase__  :  Union[str, Any]						     =	set(ds.unique('hash'))
lowerCAmelCase__  :  Optional[Any]						     =	len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
lowerCAmelCase__  :  Optional[Any]						     =	time.time()
lowerCAmelCase__  :  Tuple						     =	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__  :  List[str]						     =	time.time()
						lowerCAmelCase__     ,	lowerCAmelCase__  :  Optional[Any]						     =	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__  :  Dict						     =	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__  :  Optional[int]						     =	output_dir / 'data'
data_dir.mkdir(exist_ok=True)

lowerCAmelCase__  :  Any						     =	time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
						lowerCAmelCase__  :  Optional[int]						     =	str(data_dir / F"""file-{file_number+1:012}.json""")
						lowerCAmelCase__  :  Union[str, Any]						     =	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}""")









import random

from .binary_exp_mod import bin_exp_mod

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000    )	-> List[str]:
       if n < 2:
              return False

       if n % 2 == 0:
              return n == 2

       # this means n is odd
       lowerCamelCase   :      List[Any]			 =					n - 1
       lowerCamelCase   :      Dict			 =					0
       while d % 2 == 0:
              d /= 2
              exp += 1

       # n - 1=d*(2**exp)
       lowerCamelCase   :      Optional[Any]			 =					0
       while count < prec:
              lowerCamelCase   :      str			 =					random.randint(2 ,n - 1    )
              lowerCamelCase   :      Dict			 =					bin_exp_mod(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
              if b != 1:
                     lowerCamelCase   :      str			 =					True
                     for _ in range(_SCREAMING_SNAKE_CASE    ):
                            if b == n - 1:
                                   lowerCamelCase   :      Tuple			 =					False
                                   break
                            lowerCamelCase   :      int			 =					b * b
                            b %= n
                     if flag:
                            return False
                     count += 1
       return True


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[int]  =   abs(int(input('Enter bound : ').strip()))
       print('Here\'s the list of primes:')
       print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))






