
"""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__           =	re.compile(R"""\s+""")
def 					__lowerCAmelCase       (_UpperCamelCase	):
	return {"hash": hashlib.mda(re.sub(_UpperCamelCase ,						'' ,						example['content']	).encode('utf-8'	)	).hexdigest()}
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		int		    =   [len(_UpperCamelCase	) for line in example['content'].splitlines()]
	return {"line_mean": np.mean(_UpperCamelCase	), "line_max": max(_UpperCamelCase	)}
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   np.mean([c.isalnum() for c in example['content']]	)
	return {"alpha_frac": alpha_frac}
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase	):
	if example["hash"] in uniques:
		uniques.remove(example['hash']	)
		return True
	else:
		return False
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=5	):
	__lowerCAmelCase    :		Optional[Any]		    =   ['auto-generated', 'autogenerated', 'automatically generated']
	__lowerCAmelCase    :		Dict		    =   example['content'].splitlines()
	for _, line in zip(range(_UpperCamelCase	) ,						_UpperCamelCase	):
		for keyword in keywords:
			if keyword in line.lower():
				return {"autogenerated": True}
	else:
		return {"autogenerated": False}
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=5 ,						_UpperCamelCase=0.05	):
	__lowerCAmelCase    :		Optional[int]		    =   ['unit tests', 'test file', 'configuration file']
	__lowerCAmelCase    :		Optional[int]		    =   example['content'].splitlines()
	__lowerCAmelCase    :		List[str]		    =   0
	__lowerCAmelCase    :		List[Any]		    =   0
	# first test
	for _, line in zip(range(_UpperCamelCase	) ,						_UpperCamelCase	):
		for keyword in keywords:
			if keyword in line.lower():
				return {"config_or_test": True}
    # second test
	__lowerCAmelCase    :		List[Any]		    =   example['content'].count('\n'	)
	__lowerCAmelCase    :		int		    =   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       (_UpperCamelCase	):
	__lowerCAmelCase    :		Dict		    =   ['def ', 'class ', 'for ', 'while ']
	__lowerCAmelCase    :		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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase=4	):
	__lowerCAmelCase    :		Tuple		    =   example['content'].splitlines()
	__lowerCAmelCase    :		Dict		    =   0
	for line in lines:
		counter += line.lower().count('='	)
		if counter > minimum:
			return {"has_few_assignments": False}
	return {"has_few_assignments": True}
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   tokenizer(example['content'] ,						truncation=_UpperCamelCase	)['input_ids']
	__lowerCAmelCase    :		str		    =   len(example['content']	) / len(_UpperCamelCase	)
	return {"ratio": ratio}
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		Tuple		    =   {}
	results.update(get_hash(_UpperCamelCase	)	)
	results.update(line_stats(_UpperCamelCase	)	)
	results.update(alpha_stats(_UpperCamelCase	)	)
	results.update(char_token_ratio(_UpperCamelCase	)	)
	results.update(is_autogenerated(_UpperCamelCase	)	)
	results.update(is_config_or_test(_UpperCamelCase	)	)
	results.update(has_no_keywords(_UpperCamelCase	)	)
	results.update(has_few_assignments(_UpperCamelCase	)	)
	return results
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	if not check_uniques(_UpperCamelCase ,						_UpperCamelCase	):
		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       (_UpperCamelCase	):
	with open(_UpperCamelCase ,						'rb'	) as f_in:
		with gzip.open(str(_UpperCamelCase	) + '.gz' ,						'wb' ,						compresslevel=6	) as f_out:
			shutil.copyfileobj(_UpperCamelCase ,						_UpperCamelCase	)
	os.unlink(_UpperCamelCase	)


# 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}')

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()