






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__	(					A_:						int		)	->				Any:







       """simple docstring"""






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







def       lowercase__	(					A_:						int		)	->				Optional[int]:







       """simple docstring"""






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







def       lowercase__	(					A_:						Any		)	->				int:







       """simple docstring"""






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







def       lowercase__	(					A_:						List[Any]  ,      A_:						Tuple		)	->				str:







       """simple docstring"""






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







def       lowercase__	(					A_:						List[str]  ,      A_:						Dict=5		)	->				Optional[Any]:







       """simple docstring"""






       __UpperCAmelCase     				=["""auto-generated""", """autogenerated""", """automatically generated"""]
       __UpperCAmelCase     				=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       lowercase__	(					A_:						str  ,      A_:						List[Any]=5  ,      A_:						List[Any]=0.0_5		)	->				List[str]:







       """simple docstring"""






       __UpperCAmelCase     				=["""unit tests""", """test file""", """configuration file"""]
       __UpperCAmelCase     				=example["""content"""].splitlines()
       __UpperCAmelCase     				=0
       __UpperCAmelCase     				=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
       __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       lowercase__	(					A_:						Any		)	->				Union[str, Any]:







       """simple docstring"""






       __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       lowercase__	(					A_:						Optional[int]  ,      A_:						List[Any]=4		)	->				Any:







       """simple docstring"""






       __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       lowercase__	(					A_:						List[Any]		)	->				Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase     				=tokenizer(example["""content"""]  ,      truncation=A_		)["""input_ids"""]
       __UpperCAmelCase     				=len(example["""content"""]		) / len(A_		)
       return {"ratio": ratio}







def       lowercase__	(					A_:						int		)	->				str:







       """simple docstring"""






       __UpperCAmelCase     				={}
       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       lowercase__	(					A_:						Dict  ,      A_:						Any  ,      A_:						List[str]		)	->				str:







       """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       lowercase__	(					A_:						List[Any]		)	->				Tuple:







       """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
__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}""")











'''simple docstring'''




from __future__ import annotations

import math


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			size
          # approximate the overall size of segment tree with given value
          __A		:					Optional[Any]						=			[0 for i in range(0		,      4 * size)]
          # create array to store lazy update
          __A		:					Optional[Any]						=			[0 for i in range(0		,      4 * size)]
          __A		:					str						=			[0 for i in range(0		,      4 * size)]  # flag for lazy update


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return idx * 2


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return idx * 2 + 1


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if left_element == right_element:
               __A		:					List[Any]						=			a[left_element - 1]
          else:
               __A		:					List[str]						=			(left_element + right_element) // 2
               self.build(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
               self.build(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase)
               __A		:					Any						=			max(
                   self.segment_tree[self.left(_UpperCAmelCase)]		,      self.segment_tree[self.right(_UpperCAmelCase)])


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if self.flag[idx] is True:
               __A		:					Optional[Any]						=			self.lazy[idx]
               __A		:					Optional[Any]						=			False
               if left_element != right_element:
                    __A		:					List[Any]						=			self.lazy[idx]
                    __A		:					Dict						=			self.lazy[idx]
                    __A		:					Tuple						=			True
                    __A		:					Union[str, Any]						=			True

          if right_element < a or left_element > b:
               return True
          if left_element >= a and right_element <= b:
               __A		:					Optional[int]						=			val
               if left_element != right_element:
                    __A		:					Tuple						=			val
                    __A		:					Any						=			val
                    __A		:					Tuple						=			True
                    __A		:					Union[str, Any]						=			True
               return True
          __A		:					str						=			(left_element + right_element) // 2
          self.update(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          self.update(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					int						=			max(
              self.segment_tree[self.left(_UpperCAmelCase)]		,      self.segment_tree[self.right(_UpperCAmelCase)])
          return True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if self.flag[idx] is True:
               __A		:					Union[str, Any]						=			self.lazy[idx]
               __A		:					List[str]						=			False
               if left_element != right_element:
                    __A		:					Union[str, Any]						=			self.lazy[idx]
                    __A		:					Optional[int]						=			self.lazy[idx]
                    __A		:					str						=			True
                    __A		:					Union[str, Any]						=			True
          if right_element < a or left_element > b:
               return -math.inf
          if left_element >= a and right_element <= b:
               return self.segment_tree[idx]
          __A		:					Any						=			(left_element + right_element) // 2
          __A		:					int						=			self.query(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					Union[str, Any]						=			self.query(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          return max(_UpperCAmelCase		,      _UpperCAmelCase)


     def __str__(		self):

          '''simple docstring'''






          return str([self.query(1		,      1		,      self.size		,      _UpperCAmelCase		,      _UpperCAmelCase) for i in range(1		,      self.size + 1)])


if __name__ == "__main__":
  lowercase__       : Union[str, Any]         =       [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
  lowercase__       : str         =       15
  lowercase__       : List[Any]         =       SegmentTree(size)
  segt.build(1, 1, size, A)
  print(segt.query(1, 1, size, 4, 6))
  print(segt.query(1, 1, size, 7, 11))
  print(segt.query(1, 1, size, 7, 12))
  segt.update(1, 1, size, 1, 3, 1_11)
  print(segt.query(1, 1, size, 1, 15))
  segt.update(1, 1, size, 7, 8, 2_35)
  print(segt)