




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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Any )    ->				Tuple:
    return {"hash": hashlib.mda(re.sub(lowercase__			,	''''''			,	example['''content'''] ).encode('''utf-8''' ) ).hexdigest()}




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




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




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Optional[Any]			,	__UpperCamelCase     :		Union[str, Any] )    ->				Optional[int]:
    if example["hash"] in uniques:
        uniques.remove(example['''hash'''] )
        return True
    else:
        return False




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		List[str]			,	__UpperCamelCase     :		Tuple=5 )    ->				Optional[int]:
    UpperCAmelCase_									=	['''auto-generated''', '''autogenerated''', '''automatically generated''']
    UpperCAmelCase_									=	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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Dict			,	__UpperCamelCase     :		Any=5			,	__UpperCamelCase     :		int=0.05 )    ->				List[str]:
    UpperCAmelCase_									=	['''unit tests''', '''test file''', '''configuration file''']
    UpperCAmelCase_									=	example['''content'''].splitlines()
    UpperCAmelCase_									=	0
    UpperCAmelCase_									=	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_									=	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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Dict )    ->				str:
    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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Union[str, Any]			,	__UpperCamelCase     :		Tuple=4 )    ->				Dict:
    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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Union[str, Any] )    ->				Any:
    UpperCAmelCase_									=	tokenizer(example['''content''']			,	truncation=lowercase__ )['''input_ids''']
    UpperCAmelCase_									=	len(example['''content'''] ) / len(lowercase__ )
    return {"ratio": ratio}




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Any )    ->				Union[str, Any]:
    UpperCAmelCase_									=	{}
    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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		List[str]			,	__UpperCamelCase     :		Optional[int]			,	__UpperCamelCase     :		Optional[int] )    ->				List[Any]:
    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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		int )    ->				Optional[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
_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}")








from __future__ import annotations





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if (direction == 1 and array[indexa] > array[indexa]) or (
	    direction == 0 and array[indexa] < array[indexa]
	):
		snake_case_		,							snake_case_   		=			array[indexa], array[indexa]





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if length > 1:
		snake_case_   		=			int(length / 2						)
		for i in range(lowercase__      ,			low + middle						):
			comp_and_swap(lowercase__      ,			lowercase__      ,			i + middle      ,			lowercase__						)
		bitonic_merge(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)
		bitonic_merge(lowercase__      ,			low + middle      ,			lowercase__      ,			lowercase__						)





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if length > 1:
		snake_case_   		=			int(length / 2						)
		bitonic_sort(lowercase__      ,			lowercase__      ,			lowercase__      ,			1						)
		bitonic_sort(lowercase__      ,			low + middle      ,			lowercase__      ,			0						)
		bitonic_merge(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						)


if __name__ == "__main__":
		A									=  input('Enter numbers separated by a comma:\n').strip()
		A									=  [int(item.strip()) for item in user_input.split(',')]

		bitonic_sort(unsorted, 0, len(unsorted), 1)
		print('\nSorted array in ascending order is: ', end='')
		print(*unsorted, sep=', ')

		bitonic_merge(unsorted, 0, len(unsorted), 0)
		print('Sorted array in descending order is: ', end='')
		print(*unsorted, sep=', ')
