"""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  =				re.compile(R'''\s+''')


def    lowercase_   (   _lowerCamelCase:	Union[str, Any]      ) ->      str:



       '''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	List[Any]      ) ->      Dict:



       '''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	List[Any]      ) ->      Union[str, Any]:



       '''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	int      ) ->      Tuple:



       '''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Optional[int]=5      ) ->      Tuple:



       '''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	Optional[int]=5						,   _lowerCamelCase:	Any=0.05      ) ->      Dict:



       '''simple docstring'''

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



       '''simple docstring'''

       __lowerCamelCase					:		Union[str, Any]		   =     ["def ", "class ", "for ", "while "]
       __lowerCamelCase					:		List[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    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	Optional[Any]=4      ) ->      List[str]:



       '''simple docstring'''

       __lowerCamelCase					:		Optional[int]		   =     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    lowercase_   (   _lowerCamelCase:	int      ) ->      Dict:



       '''simple docstring'''

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


def    lowercase_   (   _lowerCamelCase:	Optional[Any]      ) ->      List[str]:



       '''simple docstring'''

       __lowerCamelCase					:		List[Any]		   =     {}
       results.update(get_hash(lowerCAmelCase__      )      )
       results.update(line_stats(lowerCAmelCase__      )      )
       results.update(alpha_stats(lowerCAmelCase__      )      )
       results.update(char_token_ratio(lowerCAmelCase__      )      )
       results.update(is_autogenerated(lowerCAmelCase__      )      )
       results.update(is_config_or_test(lowerCAmelCase__      )      )
       results.update(has_no_keywords(lowerCAmelCase__      )      )
       results.update(has_few_assignments(lowerCAmelCase__      )      )
       return results


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	Union[str, Any]						,   _lowerCamelCase:	str      ) ->      int:



       '''simple docstring'''

       if not check_uniques(lowerCAmelCase__						,   lowerCAmelCase__      ):
              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    lowercase_   (   _lowerCamelCase:	List[Any]      ) ->      List[Any]:



       '''simple docstring'''

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


# Settings
__A  =				HfArgumentParser(PreprocessingArguments)
__A  =				parser.parse_args()
if args.num_workers is None:
    __A  =				multiprocessing.cpu_count()
__A  =				AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
__A  =				time.time()
__A  =				ds.map(preprocess, num_proc=args.num_workers)
print(F"""Time to preprocess dataset: {time.time()-t_start:.2f}""")

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

# Deduplicate data and apply heuristics
__A  =				time.time()
__A  =				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  =				time.time()
    __A  =				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  =				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  =				output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

__A  =				time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
    __A  =				str(data_dir / F"""file-{file_number+1:012}.json""")
    __A  =				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"""





def    lowercase_   (   _lowerCamelCase:	int = 600851475143      ) ->      int:



							'''simple docstring'''

							try:
														__lowerCamelCase					:		Optional[Any]		   =     int(_lowerCamelCase      )
							except (TypeError, ValueError):
														raise TypeError("Parameter n must be int or castable to int."      )
							if n <= 0:
														raise ValueError("Parameter n must be greater than or equal to one."      )
							__lowerCamelCase					:		Union[str, Any]		   =     2
							__lowerCamelCase					:		int		   =     0
							if n == 2:
														return 2
							while n > 2:
														while n % i != 0:
																					i += 1
														__lowerCamelCase					:		Dict		   =     i
														while n % i == 0:
																					__lowerCamelCase					:		Union[str, Any]		   =     n // i
														i += 1
							return int(_lowerCamelCase      )


if __name__ == "__main__":
				print(F"""{solution() = }""")