





"""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


a		:					str	      =     re.compile(R'''\s+''')






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict   )					->Optional[int]:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str   )					->Optional[Any]:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]   )					->Optional[Any]:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Dict   )					->int:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       Union[str, Any]=5   )					->Any:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       Optional[int]=5       ,			_lowercase		:       Any=0.05   )					->Optional[Any]:






	'''simple docstring'''






	a :						List[str]      =							["""unit tests""", """test file""", """configuration file"""]
	a :						int      =							example["""content"""].splitlines()
	a :						int      =							0
	a :						List[Any]      =							0
	# first test
	for _, line in zip(range(__lowerCAmelCase   )       ,			__lowerCAmelCase   ):
		for keyword in keywords:
			if keyword in line.lower():
				return {"config_or_test": True}
    # second test
	a :						List[Any]      =							example["""content"""].count("\n"   )
	a :						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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]   )					->Optional[Any]:






	'''simple docstring'''






	a :						Dict      =							["""def """, """class """, """for """, """while """]
	a :						Union[str, 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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       int       ,			_lowercase		:       Optional[Any]=4   )					->int:






	'''simple docstring'''






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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict   )					->str:






	'''simple docstring'''






	a :						List[Any]      =							tokenizer(example["content"]       ,			truncation=__lowerCAmelCase   )["""input_ids"""]
	a :						Optional[int]      =							len(example["content"]   ) / len(__lowerCAmelCase   )
	return {"ratio": ratio}






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]   )					->Dict:






	'''simple docstring'''






	a :						Tuple      =							{}
	results.update(get_hash(__lowerCAmelCase   )   )
	results.update(line_stats(__lowerCAmelCase   )   )
	results.update(alpha_stats(__lowerCAmelCase   )   )
	results.update(char_token_ratio(__lowerCAmelCase   )   )
	results.update(is_autogenerated(__lowerCAmelCase   )   )
	results.update(is_config_or_test(__lowerCAmelCase   )   )
	results.update(has_no_keywords(__lowerCAmelCase   )   )
	results.update(has_few_assignments(__lowerCAmelCase   )   )
	return results






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]       ,			_lowercase		:       Optional[Any]       ,			_lowercase		:       List[str]   )					->int:






	'''simple docstring'''






	if not check_uniques(__lowerCAmelCase       ,			__lowerCAmelCase   ):
		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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[int]   )					->Any:






	'''simple docstring'''






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


# Settings
a		:					List[Any]	      =     HfArgumentParser(PreprocessingArguments)
a		:					Union[str, Any]	      =     parser.parse_args()
if args.num_workers is None:
		a		:					Dict	      =     multiprocessing.cpu_count()
a		:					Optional[int]	      =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

# Deduplicate hashes
a		:					Tuple	      =     set(ds.unique('''hash'''))
a		:					Any	      =     len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
a		:					Dict	      =     time.time()
a		:					Optional[Any]	      =     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		:					Optional[Any]	      =     time.time()
		a      ,					a		:					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
a		:					Dict	      =     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		:					int	      =     output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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










from __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



