



"""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


__snake_case							:     Optional[Any]   =					re.compile(R'\s+')


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


def     _lowercase       (						__snake_case			)		->     Union[str, Any]:
   __lowerCAmelCase   :						List[Any]				= [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     _lowercase       (						__snake_case			)		->     Optional[Any]:
   __lowerCAmelCase   :						int				= np.mean([c.isalnum() for c in example["content"]]			)
   return {"alpha_frac": alpha_frac}


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Union[str, Any]:
   if example["hash"] in uniques:
      uniques.remove(example["hash"]			)
      return True
   else:
      return False


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


def     _lowercase       (						__snake_case			)		->     Tuple:
   __lowerCAmelCase   :						Union[str, Any]				= tokenizer(example["content"]      ,truncation=SCREAMING_SNAKE_CASE_			)["input_ids"]
   __lowerCAmelCase   :						Any				= len(example["content"]			) / len(SCREAMING_SNAKE_CASE_			)
   return {"ratio": ratio}


def     _lowercase       (						__snake_case			)		->     Tuple:
   __lowerCAmelCase   :						Dict				= {}
   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     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     int:
   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     _lowercase       (						__snake_case			)		->     int:
   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							:     List[str]   =					HfArgumentParser(PreprocessingArguments)
__snake_case							:     List[Any]   =					parser.parse_args()
if args.num_workers is None:
 __snake_case							:     Optional[int]   =					multiprocessing.cpu_count()
__snake_case							:     List[Any]   =					AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__snake_case							:     Union[str, Any]   =					time.time()
__snake_case							:     Optional[Any]   =					load_dataset(args.dataset_name, split='train')
print(F"""Time to load dataset: {time.time()-t_start:.2f}""")

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

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

# Deduplicate data and apply heuristics
__snake_case							:     Optional[Any]   =					time.time()
__snake_case							:     Tuple   =					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							:     Optional[int]   =					time.time()
 __snake_case							:     List[Any]   =					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							:     str   =					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							:     Tuple   =					output_dir / "data"
data_dir.mkdir(exist_ok=True)

__snake_case							:     Optional[Any]   =					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							:     Optional[int]   =					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       (						__snake_case = 3      ,__snake_case = 7      ,__snake_case = 1_000_000			)		->     int:
			__lowerCAmelCase   :						Optional[Any]				= 0
			__lowerCAmelCase   :						List[str]				= 1

			for current_denominator in range(1      ,limit + 1			):
						__lowerCAmelCase   :						Optional[int]				= current_denominator * numerator // denominator
						if current_denominator % denominator == 0:
									current_numerator -= 1
						if current_numerator * max_denominator > current_denominator * max_numerator:
									__lowerCAmelCase   :						Union[str, Any]				= current_numerator
									__lowerCAmelCase   :						int				= current_denominator
			return max_numerator


if __name__ == "__main__":
	print(solution(numerator=3, denominator=7, limit=1_000_000))