






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


__UpperCamelCase      :      int     						=						re.compile(r"""\s+""")





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase=5					):




	'''simple docstring'''







	__lowercase             =  ["""auto-generated""", """autogenerated""", """automatically generated"""]
	__lowercase             =  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 				snake_case  (     lowerCamelCase				,  lowerCamelCase=5				,  lowerCamelCase=0.05					):




	'''simple docstring'''







	__lowercase             =  ["""unit tests""", """test file""", """configuration file"""]
	__lowercase             =  example["""content"""].splitlines()
	__lowercase             =  0
	__lowercase             =  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
	__lowercase             =  example["""content"""].count("""\n"""					)
	__lowercase             =  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 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  ["""def """, """class """, """for """, """while """]
	__lowercase             =  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 				snake_case  (     lowerCamelCase				,  lowerCamelCase=4					):




	'''simple docstring'''







	__lowercase             =  example["""content"""].splitlines()
	__lowercase             =  0
	for line in lines:
		counter += line.lower().count("""="""					)
		if counter > minimum:
			return {"has_few_assignments": False}
	return {"has_few_assignments": True}





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







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





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {}
	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 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	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 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	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
__UpperCamelCase      :      Optional[Any]     						=						HfArgumentParser(PreprocessingArguments)
__UpperCamelCase      :      Tuple     						=						parser.parse_args()
if args.num_workers is None:
						__UpperCamelCase      :      Union[str, Any]     						=						multiprocessing.cpu_count()
__UpperCamelCase      :      str     						=						AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
__UpperCamelCase      :      List[str]     						=						time.time()
__UpperCamelCase      :      Tuple     						=						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:
						__UpperCamelCase      :      Optional[int]     						=						time.time()
						__UpperCamelCase						,					__UpperCamelCase      :      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
__UpperCamelCase      :      Optional[int]     						=						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)

__UpperCamelCase      :      List[Any]     						=						output_dir / """data"""
data_dir.mkdir(exist_ok=True)

__UpperCamelCase      :      List[Any]     						=						time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
						__UpperCamelCase      :      List[Any]     						=						str(data_dir / F'''file-{file_number+1:012}.json''')
						__UpperCamelCase      :      str     						=						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 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	if len(lowerCamelCase					) < 2:
		raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""					)
	if any(i <= 0 for i in nums					):
		raise ValueError("""All values must be greater than 0"""					)
	__lowercase             =  nums.copy()
	copy_nums.sort()
	return copy_nums[-1] < sum(copy_nums[:-1]					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

