

"""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_				:      Union[str, Any]           =			re.compile(R"\s+")

def 						A							(  snake_case__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__		,					snake_case__=5     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   ["""auto-generated""", """autogenerated""", """automatically generated"""]
			SCREAMING_SNAKE_CASE__										=   example["""content"""].splitlines()
			for _, line in zip(range(snake_case__     )		,					snake_case__     ):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}

def 						A							(  snake_case__		,					snake_case__=5		,					snake_case__=0.05     ):




			'''simple docstring'''


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




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   ["""def """, """class """, """for """, """while """]
			SCREAMING_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 						A							(  snake_case__		,					snake_case__=4     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   example["""content"""].splitlines()
			SCREAMING_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 						A							(  snake_case__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			if not check_uniques(snake_case__		,					snake_case__     ):
						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 						A							(  snake_case__     ):




			'''simple docstring'''


			with open(snake_case__		,					"""rb"""     ) as f_in:
						with gzip.open(str(snake_case__     ) + """.gz"""		,					"""wb"""		,					compresslevel=6     ) as f_out:
									shutil.copyfileobj(snake_case__		,					snake_case__     )
			os.unlink(snake_case__     )


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

# Load dataset
A_				:      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_				:      Optional[int]           =			time.time()
A_				:      Optional[int]           =			ds.map(preprocess, num_proc=args.num_workers)
print(F'Time to preprocess dataset: {time.time()-t_start:.2f}')

# Deduplicate hashes
A_				:      Optional[int]           =			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_				:      int           =			time.time()
A_				:      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:
						A_				:      Any           =			time.time()
						A_       ,						A_				:      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
A_				:      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)

A_				:      str           =			output_dir / "data"
data_dir.mkdir(exist_ok=True)

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




import numpy as np

def 						A							(  snake_case__     ):




			'''simple docstring'''


			return (2 / (1 + np.exp(-2 * vector     ))) - 1


if __name__ == "__main__":
						import doctest

						doctest.testmod()


