




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


lowercase__							:	str											=		re.compile(R"\s+")


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





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


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	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'''





	snake_case_						       =	["auto-generated", "autogenerated", "automatically generated"]
	snake_case_						       =	example["content"].splitlines()
	for _, line in zip(range(_A				)	,      _A				):
		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'''





	snake_case_						       =	["unit tests", "test file", "configuration file"]
	snake_case_						       =	example["content"].splitlines()
	snake_case_						       =	0
	snake_case_						       =	0
	# first test
	for _, line in zip(range(_A				)	,      _A				):
		for keyword in keywords:
			if keyword in line.lower():
				return {"config_or_test": True}
		# second test
	snake_case_						       =	example["content"].count("\n"				)
	snake_case_						       =	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'''





	snake_case_						       =	["def ", "class ", "for ", "while "]
	snake_case_						       =	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'''





	snake_case_						       =	example["content"].splitlines()
	snake_case_						       =	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'''





	snake_case_						       =	tokenizer(example["content"]	,      truncation=_A				)["input_ids"]
	snake_case_						       =	len(example["content"]				) / len(_A				)
	return {"ratio": ratio}


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	{}
	results.update(get_hash(_A				)				)
	results.update(line_stats(_A				)				)
	results.update(alpha_stats(_A				)				)
	results.update(char_token_ratio(_A				)				)
	results.update(is_autogenerated(_A				)				)
	results.update(is_config_or_test(_A				)				)
	results.update(has_no_keywords(_A				)				)
	results.update(has_few_assignments(_A				)				)
	return results


def 		lowerCamelCase__       (						_A	,      _A	,      _A				):







	'''simple docstring'''





	if not check_uniques(_A	,      _A				):
		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(_A	,      "rb"				) as f_in:
		with gzip.open(str(_A				) + ".gz"	,      "wb"	,      compresslevel=6				) as f_out:
			shutil.copyfileobj(_A	,      _A				)
	os.unlink(_A				)


# Settings
lowercase__							:	Dict											=		HfArgumentParser(PreprocessingArguments)
lowercase__							:	List[str]											=		parser.parse_args()
if args.num_workers is None:
		lowercase__							:	Optional[Any]											=		multiprocessing.cpu_count()
lowercase__							:	List[str]											=		AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
lowercase__							:	Union[str, Any]											=		time.time()
lowercase__							:	Dict											=		load_dataset(args.dataset_name, split="train")
print(f'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
lowercase__							:	int											=		time.time()
lowercase__							:	Dict											=		ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
lowercase__							:	str											=		set(ds.unique("hash"))
lowercase__							:	Any											=		len(uniques) / len(ds)
print(f'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
lowercase__							:	str											=		time.time()
lowercase__							:	List[Any]											=		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:
		lowercase__							:	int											=		time.time()
		lowercase__						,			lowercase__							:	int											=		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
lowercase__							:	Any											=		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)

lowercase__							:	Optional[int]											=		output_dir / "data"
data_dir.mkdir(exist_ok=True)

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


from collections.abc import Generator






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _UpperCAmelCase			,					_UpperCAmelCase    			=   0, 1
  while True:
    _UpperCAmelCase			,					_UpperCAmelCase    			=   b, a + b
    yield b






def      __UpperCAmelCase       (      lowercase = 10_00		):







  """simple docstring"""
  _UpperCAmelCase    			=   1
  _UpperCAmelCase    			=   fibonacci_generator()
  while len(str(next(lowercase		)		)		) < n:
    answer += 1
  return answer + 1


if __name__ == "__main__":
    print(solution(int(str(input()).strip())))
