

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


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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Optional[int]				=	[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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   str=5      ):



     '''simple docstring'''





     lowercase__   :       int				=	['auto-generated', 'autogenerated', 'automatically generated']
     lowercase__   :       Union[str, Any]				=	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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]=5     ,					SCREAMING_SNAKE_CASE_		:   List[Any]=0.05      ):



     '''simple docstring'''





     lowercase__   :       Any				=	['unit tests', 'test file', 'configuration file']
     lowercase__   :       Union[str, Any]				=	example['content'].splitlines()
     lowercase__   :       Any				=	0
     lowercase__   :       Optional[int]				=	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
     lowercase__   :       Any				=	example['content'].count('\n'      )
     lowercase__   :       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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     lowercase__   :       int				=	['def ', 'class ', 'for ', 'while ']
     lowercase__   :       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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]=4      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	example['content'].splitlines()
     lowercase__   :       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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





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

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     lowercase__   :       int				=	{}
     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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]      ):



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

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

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

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

# Deduplicate data and apply heuristics
snake_case_ :    List[Any]        =			time.time()
snake_case_ :    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:
       snake_case_ :    List[Any]        =			time.time()
       snake_case_  ,		snake_case_ :    Optional[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
snake_case_ :    Tuple        =			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)

snake_case_ :    Union[str, Any]        =			output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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




import math
import sys

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int      ):



     '''simple docstring'''





     if number != int(SCREAMING_SNAKE_CASE_      ):
          raise ValueError('the value of input must be a natural number'      )
     if number < 0:
          raise ValueError('the value of input must not be a negative number'      )
     if number == 0:
          return 1
     lowercase__   :       Tuple				=	[-1] * (number + 1)
     lowercase__   :       Tuple				=	0
     for i in range(1     ,					number + 1      ):
          lowercase__   :       Tuple				=	sys.maxsize
          lowercase__   :       str				=	int(math.sqrt(SCREAMING_SNAKE_CASE_      )      )
          for j in range(1     ,					root + 1      ):
               lowercase__   :       List[Any]				=	1 + answers[i - (j**2)]
               lowercase__   :       str				=	min(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          lowercase__   :       List[str]				=	answer
     return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

