

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           =					re.compile(r'''\s+''')




def 			UpperCAmelCase__				(			_A      : Tuple							):





  '''simple docstring'''







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




def 			UpperCAmelCase__				(			_A      : List[str]							):





  '''simple docstring'''







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




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







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




def 			UpperCAmelCase__				(			_A      : str	,      _A      : Optional[int]							):





  '''simple docstring'''







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




def 			UpperCAmelCase__				(			_A      : int	,      _A      : Tuple=5							):





  '''simple docstring'''







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




def 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : Any=5	,      _A      : Optional[int]=0.05							):





  '''simple docstring'''







  a__  =['''unit tests''', '''test file''', '''configuration file''']
  a__  =example['''content'''].splitlines()
  a__  =0
  a__  =0
  # first test
  for _, line in zip(range(_A							)	,      _A							):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  a__  =example['''content'''].count('''\n'''							)
  a__  =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__				(			_A      : Any							):





  '''simple docstring'''







  a__  =['''def ''', '''class ''', '''for ''', '''while ''']
  a__  =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__				(			_A      : Any	,      _A      : int=4							):





  '''simple docstring'''







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




def 			UpperCAmelCase__				(			_A      : Tuple							):





  '''simple docstring'''







  a__  =tokenizer(example['''content''']	,      truncation=_A							)['''input_ids''']
  a__  =len(example['''content''']							) / len(_A							)
  return {"ratio": ratio}




def 			UpperCAmelCase__				(			_A      : str							):





  '''simple docstring'''







  a__  ={}
  results.update(get_hash(_A							)							)
  results.update(line_stats(_A							)							)
  results.update(alpha_stats(_A							)							)
  results.update(char_token_ratio(_A							)							)
  results.update(is_autogenerated(_A							)							)
  results.update(is_config_or_test(_A							)							)
  results.update(has_no_keywords(_A							)							)
  results.update(has_few_assignments(_A							)							)
  return results




def 			UpperCAmelCase__				(			_A      : str	,      _A      : Tuple	,      _A      : Optional[int]							):





  '''simple docstring'''







  if not check_uniques(_A	,      _A							):
    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__				(			_A      : Union[str, Any]							):





  '''simple docstring'''







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


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

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

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

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

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

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



from __future__ import annotations




def 			UpperCAmelCase__				(			_A      : float	,      _A      : float	,      _A      : float	,      ):





  '''simple docstring'''







  if (stress, tangential_force, area).count(0							) != 1:
    raise ValueError('''You cannot supply more or less than 2 values'''							)
  elif stress < 0:
    raise ValueError('''Stress cannot be negative'''							)
  elif tangential_force < 0:
    raise ValueError('''Tangential Force cannot be negative'''							)
  elif area < 0:
    raise ValueError('''Area cannot be negative'''							)
  elif stress == 0:
    return (
        "stress",
        tangential_force / area,
    )
  elif tangential_force == 0:
    return (
        "tangential_force",
        stress * area,
    )
  else:
    return (
        "area",
        tangential_force / stress,
    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
