




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      :  List[Any]      						=						re.compile(R"\s+")




def 							_UpperCAmelCase  (UpperCamelCase_ : Union[str, Any]       ):
				'''simple docstring'''
				return {"hash": hashlib.mda(re.sub(UpperCamelCase_      ,    """"""      ,    example["""content"""]       ).encode("""utf-8"""       )       ).hexdigest()}




def 							_UpperCAmelCase  (UpperCamelCase_ : List[str]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      [len(UpperCamelCase_       ) for line in example["""content"""].splitlines()]
				return {"line_mean": np.mean(UpperCamelCase_       ), "line_max": max(UpperCamelCase_       )}




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      np.mean([c.isalnum() for c in example["""content"""]]       )
				return {"alpha_frac": alpha_frac}




def 							_UpperCAmelCase  (UpperCamelCase_ : int      ,    UpperCamelCase_ : str       ):
				'''simple docstring'''
				if example["hash"] in uniques:
								uniques.remove(example["""hash"""]       )
								return True
				else:
								return False




def 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : Union[str, Any]=5       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[Any]										=      ["""auto-generated""", """autogenerated""", """automatically generated"""]
				_lowerCAmelCase    :    List[Any]										=      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 							_UpperCAmelCase  (UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : Union[str, Any]=5      ,    UpperCamelCase_ : str=0.05       ):
				'''simple docstring'''
				_lowerCAmelCase    :    str										=      ["""unit tests""", """test file""", """configuration file"""]
				_lowerCAmelCase    :    int										=      example["""content"""].splitlines()
				_lowerCAmelCase    :    List[str]										=      0
				_lowerCAmelCase    :    str										=      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    :    Optional[Any]										=      example["""content"""].count("""\n"""       )
				_lowerCAmelCase    :    Tuple										=      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 							_UpperCAmelCase  (UpperCamelCase_ : int       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Dict										=      ["""def """, """class """, """for """, """while """]
				_lowerCAmelCase    :    Optional[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 							_UpperCAmelCase  (UpperCamelCase_ : Union[str, Any]      ,    UpperCamelCase_ : List[Any]=4       ):
				'''simple docstring'''
				_lowerCAmelCase    :    str										=      example["""content"""].splitlines()
				_lowerCAmelCase    :    Union[str, Any]										=      0
				for line in lines:
								counter += line.lower().count("""="""       )
								if counter > minimum:
												return {"has_few_assignments": False}
				return {"has_few_assignments": True}




def 							_UpperCAmelCase  (UpperCamelCase_ : Any       ):
				'''simple docstring'''
				_lowerCAmelCase    :    int										=      tokenizer(example["""content"""]      ,    truncation=UpperCamelCase_       )["""input_ids"""]
				_lowerCAmelCase    :    Any										=      len(example["""content"""]       ) / len(UpperCamelCase_       )
				return {"ratio": ratio}




def 							_UpperCAmelCase  (UpperCamelCase_ : List[Any]       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[str]										=      {}
				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 							_UpperCAmelCase  (UpperCamelCase_ : Union[str, Any]      ,    UpperCamelCase_ : str      ,    UpperCamelCase_ : str       ):
				'''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 							_UpperCAmelCase  (UpperCamelCase_ : Dict       ):
				'''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
_lowerCamelCase      :  Tuple      						=						HfArgumentParser(PreprocessingArguments)
_lowerCamelCase      :  str      						=						parser.parse_args()
if args.num_workers is None:
	_lowerCamelCase      :  int      						=						multiprocessing.cpu_count()
_lowerCamelCase      :  List[str]      						=						AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

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

_lowerCamelCase      :  Any      						=						output_dir / "data"
data_dir.mkdir(exist_ok=True)

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







import numpy as np




def 							_UpperCAmelCase  (UpperCamelCase_ : np.array       ):
				'''simple docstring'''
				return (2 / (1 + np.exp(-2 * vector       ))) - 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

