





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	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a     ,       _a=5	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= ["auto-generated", "autogenerated", "automatically generated"]
		lowerCAmelCase__       :      Optional[int]   					= 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 					lowerCamelCase_   (	_a     ,       _a=5     ,       _a=0.05	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= ["unit tests", "test file", "configuration file"]
		lowerCAmelCase__       :      Dict   					= example["content"].splitlines()
		lowerCAmelCase__       :      Tuple   					= 0
		lowerCAmelCase__       :      Optional[Any]   					= 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
		lowerCAmelCase__       :      str   					= example["content"].count('''\n'''	)
		lowerCAmelCase__       :      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	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[int]   					= ["def ", "class ", "for ", "while "]
		lowerCAmelCase__       :      Optional[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 					lowerCamelCase_   (	_a     ,       _a=4	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= example["content"].splitlines()
		lowerCAmelCase__       :      int   					= 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	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Tuple   					= {}
		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 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""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 					lowerCamelCase_   (	_a	):




		"""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
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}''')








import random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_   (	_a     ,       _a=1_000	):




		"""simple docstring"""






		if n < 2:
				return False

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

		# this means n is odd
		lowerCAmelCase__       :      int   					= n - 1
		lowerCAmelCase__       :      Any   					= 0
		while d % 2 == 0:
				d /= 2
				exp += 1

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


if __name__ == "__main__":
		lowerCamelCase						=		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)))




