





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 :    Union[str, Any]  							= re.compile(r'''\s+''')




def _a (  SCREAMING_SNAKE_CASE__      :  List[str]       )	->    List[Any]:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  List[str]       )	->    Union[str, Any]:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  Any       )	->    Optional[int]:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    Dict:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  List[str]=5       )	->    List[str]:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  Any=5			,				SCREAMING_SNAKE_CASE__      :  List[str]=0.0_5       )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Tuple     =						["unit tests", "test file", "configuration file"]
							SCREAMING_SNAKE_CASE__       :      Any     =						example["content"].splitlines()
							SCREAMING_SNAKE_CASE__       :      str     =						0
							SCREAMING_SNAKE_CASE__       :      Any     =						0
							# first test
							for _, line in zip(range(SCREAMING_SNAKE_CASE__       )			,				SCREAMING_SNAKE_CASE__       ):
														for keyword in keywords:
																					if keyword in line.lower():
																												return {"config_or_test": True}
    # second test
							SCREAMING_SNAKE_CASE__       :      int     =						example["content"].count("\n"       )
							SCREAMING_SNAKE_CASE__       :      Any     =						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 (  SCREAMING_SNAKE_CASE__      :  Dict       )	->    List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Any     =						["def ", "class ", "for ", "while "]
							SCREAMING_SNAKE_CASE__       :      str     =						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 (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  str=4       )	->    List[Any]:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    Optional[Any]:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]       )	->    Union[str, Any]:

							'''simple docstring'''






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




def _a (  SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    List[Any]:

							'''simple docstring'''






							if not check_uniques(SCREAMING_SNAKE_CASE__			,				SCREAMING_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 (  SCREAMING_SNAKE_CASE__      :  Dict       )	->    Optional[int]:

							'''simple docstring'''






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


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

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

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

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

# Deduplicate data and apply heuristics
_lowerCamelCase :    Optional[Any]  							= 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 :    List[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 :    Dict  							= 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 :    Dict  							= 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 :    List[str]  							= str(data_dir / f"file-{file_number+1:012}.json")
		_lowerCamelCase :    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}")













def _a (  SCREAMING_SNAKE_CASE__      :  int       )	->    str:

							'''simple docstring'''






							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														raise TypeError("'float' object cannot be interpreted as an integer"       )
							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														raise TypeError("'str' object cannot be interpreted as an integer"       )

							if num == 0:
														return "0b0"

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						False

							if num < 0:
														SCREAMING_SNAKE_CASE__       :      Any     =						True
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						-num

							SCREAMING_SNAKE_CASE__       :      list[int]     =						[]
							while num > 0:
														binary.insert(0			,				num % 2       )
														num >>= 1

							if negative:
														return "-0b" + "".join(str(SCREAMING_SNAKE_CASE__       ) for e in binary       )

							return "0b" + "".join(str(SCREAMING_SNAKE_CASE__       ) for e in binary       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






