



'''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				:    Optional[Any]     						=re.compile(r'''\s+''')







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	return {"hash": hashlib.mda(re.sub(_lowercase,				'',				example['content']					).encode('utf-8'					)					).hexdigest()}







def 						__UpperCamelCase				(							_lowercase					) ->			Dict:
	_lowercase					:       Tuple       =		[len(_lowercase					) for line in example["""content"""].splitlines()]
	return {"line_mean": np.mean(_lowercase					), "line_max": max(_lowercase					)}







def 						__UpperCamelCase				(							_lowercase					) ->			str:
	_lowercase					:       int       =		np.mean([c.isalnum() for c in example['content']]					)
	return {"alpha_frac": alpha_frac}







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Optional[Any]:







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







def 						__UpperCamelCase				(							_lowercase,				_lowercase=5					) ->			Tuple:
	_lowercase					:       Tuple       =		["""auto-generated""", """autogenerated""", """automatically generated"""]
	_lowercase					:       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 						__UpperCamelCase				(							_lowercase,				_lowercase=5,				_lowercase=0.0_5					) ->			List[str]:
	_lowercase					:       Optional[int]       =		["""unit tests""", """test file""", """configuration file"""]
	_lowercase					:       Tuple       =		example["""content"""].splitlines()
	_lowercase					:       List[str]       =		0
	_lowercase					:       Dict       =		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
	_lowercase					:       Optional[Any]       =		example["""content"""].count('\n'					)
	_lowercase					:       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 						__UpperCamelCase				(							_lowercase					) ->			Optional[Any]:
	_lowercase					:       Any       =		["""def """, """class """, """for """, """while """]
	_lowercase					:       Any       =		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 						__UpperCamelCase				(							_lowercase,				_lowercase=4					) ->			Tuple:
	_lowercase					:       int       =		example["""content"""].splitlines()
	_lowercase					:       Any       =		0
	for line in lines:
		counter += line.lower().count('='					)
		if counter > minimum:
			return {"has_few_assignments": False}
	return {"has_few_assignments": True}







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	_lowercase					:       List[str]       =		tokenizer(example['content'],				truncation=_lowercase					)["""input_ids"""]
	_lowercase					:       str       =		len(example['content']					) / len(_lowercase					)
	return {"ratio": ratio}







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	_lowercase					:       Tuple       =		{}
	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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase					) ->			int:







	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 						__UpperCamelCase				(							_lowercase					) ->			Union[str, Any]:







	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
_A				:    Any     						=HfArgumentParser(PreprocessingArguments)
_A				:    Dict     						=parser.parse_args()
if args.num_workers is None:
		_A				:    Any     						=multiprocessing.cpu_count()
_A				:    Any     						=AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

_A				:    Optional[int]     						=time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
		_A				:    Union[str, Any]     						=str(data_dir / F'''file-{file_number+1:012}.json''')
		_A				:    Dict     						=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 						__UpperCamelCase				(							_lowercase					) ->			bool:
	return str(_lowercase					) == str(_lowercase					)[::-1]







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return int(_lowercase					) + int(str(_lowercase					)[::-1]					)







def 						__UpperCamelCase				(							_lowercase = 1_0000					) ->			int:
	_lowercase					:       List[str]       =		[]
	for num in range(1,				_lowercase					):
		_lowercase					:       Tuple       =		0
		_lowercase					:       Tuple       =		num
		while iterations < 50:
			_lowercase					:       Union[str, Any]       =		sum_reverse(_lowercase					)
			iterations += 1
			if is_palindrome(_lowercase					):
				break
		else:
			lychrel_nums.append(_lowercase					)
	return len(_lowercase					)


if __name__ == "__main__":
		print(F'''{solution() = }''')
