


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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
       __UpperCamelCase   							=[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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
       __UpperCamelCase   							=np.mean([c.isalnum() for c in example['content']]    )
       return {"alpha_frac": alpha_frac}

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : Any    ):
       if example["hash"] in uniques:
              uniques.remove(example['hash']    )
              return True
       else:
              return False

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

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
       __UpperCamelCase   							=tokenizer(example['content']      ,					truncation=SCREAMING_SNAKE_CASE__    )['input_ids']
       __UpperCamelCase   							=len(example['content']    ) / len(SCREAMING_SNAKE_CASE__    )
       return {"ratio": ratio}

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]    ):
       __UpperCamelCase   							={}
       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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : List[str]    ):
       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 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Any    ):
       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
_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	,					_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}""")









import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812

def 						_UpperCAmelCase    (      ):
     print('Making key files...'    )
     make_key_files('rsa'      ,					10_24    )
     print('Key files generation successful.'    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : int    ):
     print('Generating prime p...'    )
     __UpperCamelCase   							=rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__    )
     print('Generating prime q...'    )
     __UpperCamelCase   							=rabinMiller.generate_large_prime(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=p * q

     print('Generating e that is relatively prime to (p - 1) * (q - 1)...'    )
     while True:
          __UpperCamelCase   							=random.randrange(2 ** (key_size - 1)      ,					2 ** (key_size)    )
          if cryptoMath.gcd(SCREAMING_SNAKE_CASE__      ,					(p - 1) * (q - 1)    ) == 1:
               break

     print('Calculating d that is mod inverse of e...'    )
     __UpperCamelCase   							=cryptoMath.find_mod_inverse(SCREAMING_SNAKE_CASE__      ,					(p - 1) * (q - 1)    )

     __UpperCamelCase   							=(n, e)
     __UpperCamelCase   							=(n, d)
     return (public_key, private_key)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : int    ):
     if os.path.exists(F'{name}_pubkey.txt'    ) or os.path.exists(F'{name}_privkey.txt'    ):
          print('\nWARNING:'    )
          print(
              F'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
              'Use a different name or delete these files and re-run this program.'    )
          sys.exit()

     __UpperCamelCase							,				__UpperCamelCase   							=generate_key(SCREAMING_SNAKE_CASE__    )
     print(F'\nWriting public key to file {name}_pubkey.txt...'    )
     with open(F'{name}_pubkey.txt'      ,					'w'    ) as out_file:
          out_file.write(F'{key_size},{public_key[0]},{public_key[1]}'    )

     print(F'Writing private key to file {name}_privkey.txt...'    )
     with open(F'{name}_privkey.txt'      ,					'w'    ) as out_file:
          out_file.write(F'{key_size},{private_key[0]},{private_key[1]}'    )


if __name__ == "__main__":
   main()





