





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

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

def   a_    ( lowerCAmelCase_      :			List[Any]	):
   __lowerCAmelCase								=	[len(UpperCamelCase__	) for line in example['content'].splitlines()]
   return {"line_mean": np.mean(UpperCamelCase__	), "line_max": max(UpperCamelCase__	)}

def   a_    ( lowerCAmelCase_      :			List[Any]	):
   __lowerCAmelCase								=	np.mean([c.isalnum() for c in example['content']]	)
   return {"alpha_frac": alpha_frac}

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			Optional[Any]	):
   if example["hash"] in uniques:
      uniques.remove(example['hash']	)
      return True
   else:
      return False

def   a_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			List[Any]=5	):
   __lowerCAmelCase								=	['auto-generated', 'autogenerated', 'automatically generated']
   __lowerCAmelCase								=	example['content'].splitlines()
   for _, line in zip(range(UpperCamelCase__	),	UpperCamelCase__	):
      for keyword in keywords:
         if keyword in line.lower():
            return {"autogenerated": True}
   else:
      return {"autogenerated": False}

def   a_    ( lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Tuple=5,	lowerCAmelCase_      :			List[str]=0.05	):
   __lowerCAmelCase								=	['unit tests', 'test file', 'configuration file']
   __lowerCAmelCase								=	example['content'].splitlines()
   __lowerCAmelCase								=	0
   __lowerCAmelCase								=	0
   # first test
   for _, line in zip(range(UpperCamelCase__	),	UpperCamelCase__	):
      for keyword in keywords:
         if keyword in line.lower():
            return {"config_or_test": True}
    # second test
   __lowerCAmelCase								=	example['content'].count('\n'	)
   __lowerCAmelCase								=	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   a_    ( lowerCAmelCase_      :			int	):
   __lowerCAmelCase								=	['def ', 'class ', 'for ', 'while ']
   __lowerCAmelCase								=	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   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Tuple=4	):
   __lowerCAmelCase								=	example['content'].splitlines()
   __lowerCAmelCase								=	0
   for line in lines:
      counter += line.lower().count('='	)
      if counter > minimum:
         return {"has_few_assignments": False}
   return {"has_few_assignments": True}

def   a_    ( lowerCAmelCase_      :			Tuple	):
   __lowerCAmelCase								=	tokenizer(example['content'],	truncation=UpperCamelCase__	)['input_ids']
   __lowerCAmelCase								=	len(example['content']	) / len(UpperCamelCase__	)
   return {"ratio": ratio}

def   a_    ( lowerCAmelCase_      :			Dict	):
   __lowerCAmelCase								=	{}
   results.update(get_hash(UpperCamelCase__	)	)
   results.update(line_stats(UpperCamelCase__	)	)
   results.update(alpha_stats(UpperCamelCase__	)	)
   results.update(char_token_ratio(UpperCamelCase__	)	)
   results.update(is_autogenerated(UpperCamelCase__	)	)
   results.update(is_config_or_test(UpperCamelCase__	)	)
   results.update(has_no_keywords(UpperCamelCase__	)	)
   results.update(has_few_assignments(UpperCamelCase__	)	)
   return results

def   a_    ( lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			int	):
   if not check_uniques(UpperCamelCase__,	UpperCamelCase__	):
      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   a_    ( lowerCAmelCase_      :			int	):
   with open(UpperCamelCase__,	'rb'	) as f_in:
      with gzip.open(str(UpperCamelCase__	) + '.gz',	'wb',	compresslevel=6	) as f_out:
         shutil.copyfileobj(UpperCamelCase__,	UpperCamelCase__	)
   os.unlink(UpperCamelCase__	)


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

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

# Run preprocessing
_snake_case  :    Union[str, 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  :    Dict						    =  set(ds.unique('hash'))
_snake_case  :    List[Any]						    =  len(uniques) / len(ds)
print(F"""Fraction of duplicates: {1-frac:.2%}""")

# Deduplicate data and apply heuristics
_snake_case  :    Union[str, Any]						    =  time.time()
_snake_case  :    str						    =  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  :    Any						    =  time.time()
     _snake_case  :    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  :    List[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  :    Dict						    =  output_dir / 'data'
data_dir.mkdir(exist_ok=True)

_snake_case  :    int						    =  time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
     _snake_case  :    Union[str, Any]						    =  str(data_dir / F"""file-{file_number+1:012}.json""")
     _snake_case  :    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}""")








def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Dict:
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       A__											=						len(UpperCamelCase__  )
       A__											=						max(UpperCamelCase__  )
       A__											=						min(UpperCamelCase__  )

       # create the counting array
       A__											=						coll_max + 1 - coll_min
       A__											=						[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,     UpperCamelCase__  ):
              A__											=						counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       A__											=						[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,     UpperCamelCase__  )  ):
              A__											=						collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int]  )->Tuple:
       return "".join([chr(UpperCamelCase__  ) for i in counting_sort([ord(UpperCamelCase__  ) for c in string]  )]  )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       a__:      Dict									=		input('Enter numbers separated by a comma:\n').strip()
       a__:      Any									=		[int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))





