


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


UpperCAmelCase_      : List[Any]  					=       re.compile(R'\s+')







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict				)	->		Optional[Any]:

       """simple docstring"""

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







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		Dict:

       """simple docstring"""

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







def        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		Optional[Any]:

       """simple docstring"""

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







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]  ,    __A :			Optional[int]				)	->		Optional[int]:

       """simple docstring"""

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







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[Any]  ,    __A :			int=5				)	->		Any:

       """simple docstring"""

       a_					:		Dict							=  ['auto-generated', 'autogenerated', 'automatically generated']
       a_					:		Optional[int]							=  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        SCREAMING_SNAKE_CASE_						(			__A :			Union[str, Any]  ,    __A :			Tuple=5  ,    __A :			List[str]=0.05				)	->		List[Any]:

       """simple docstring"""

       a_					:		List[Any]							=  ['unit tests', 'test file', 'configuration file']
       a_					:		Union[str, Any]							=  example['content'].splitlines()
       a_					:		Union[str, Any]							=  0
       a_					:		Optional[Any]							=  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_					:		Optional[Any]							=  example['content'].count('\n'				)
       a_					:		int							=  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_						(			__A :			str				)	->		List[str]:

       """simple docstring"""

       a_					:		str							=  ['def ', 'class ', 'for ', 'while ']
       a_					:		Optional[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_						(			__A :			Any  ,    __A :			List[Any]=4				)	->		Optional[Any]:

       """simple docstring"""

       a_					:		Any							=  example['content'].splitlines()
       a_					:		List[Any]							=  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_						(			__A :			Optional[Any]				)	->		List[str]:

       """simple docstring"""

       a_					:		Optional[int]							=  tokenizer(example['content']  ,    truncation=__A				)['input_ids']
       a_					:		Union[str, Any]							=  len(example['content']				) / len(__A				)
       return {"ratio": ratio}







def        SCREAMING_SNAKE_CASE_						(			__A :			Optional[int]				)	->		Optional[Any]:

       """simple docstring"""

       a_					:		List[Any]							=  {}
       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        SCREAMING_SNAKE_CASE_						(			__A :			str  ,    __A :			str  ,    __A :			Union[str, Any]				)	->		Optional[Any]:

       """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        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		str:

       """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
UpperCAmelCase_      : Union[str, Any]  					=       HfArgumentParser(PreprocessingArguments)
UpperCAmelCase_      : Optional[Any]  					=       parser.parse_args()
if args.num_workers is None:
   UpperCAmelCase_      : Tuple  					=       multiprocessing.cpu_count()
UpperCAmelCase_      : Optional[Any]  					=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
UpperCAmelCase_      : Optional[Any]  					=       time.time()
UpperCAmelCase_      : Optional[int]  					=       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:
   UpperCAmelCase_      : Optional[int]  					=       time.time()
   UpperCAmelCase_					,   UpperCAmelCase_      : int  					=       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
UpperCAmelCase_      : int  					=       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)

UpperCAmelCase_      : Dict  					=       output_dir / 'data'
data_dir.mkdir(exist_ok=True)

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




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase					   =5_0_0_0_0_0

_lowerCamelCase   ,  _lowerCamelCase					   =os.path.split(__file__)
_lowerCamelCase					   =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.map(**lowerCamelCase							)





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.filter(**lowerCamelCase							)





def     _a					(						):
 lowerCamelCase		:	Optional[Any]							=      {"""num examples""": SPEED_TEST_N_EXAMPLES}
 with tempfile.TemporaryDirectory() as tmp_dir:
  lowerCamelCase		:	Any							=      datasets.Features({"""text""": datasets.Value("""string"""							), """numbers""": datasets.Value("""float32"""							)}							)
  lowerCamelCase		:	Tuple							=      generate_example_dataset(
      os.path.join(lowerCamelCase,	"""dataset.arrow"""							),	lowerCamelCase,	num_examples=lowerCamelCase							)

  lowerCamelCase		:	Tuple							=      transformers.AutoTokenizer.from_pretrained("""bert-base-cased""",	use_fast=lowerCamelCase							)

  def tokenize(lowerCamelCase							):
   return tokenizer(examples["""text"""]							)

  lowerCamelCase		:	List[str]							=      map(lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""numpy"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""pandas"""							):
   lowerCamelCase		:	List[str]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""torch""",	columns="""numbers"""							):
   lowerCamelCase		:	List[str]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""tensorflow""",	columns="""numbers"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	Union[str, Any]							=      filter(lowerCamelCase							)

  # Activate later when tokenizer support batched inputs
  # with dataset.formatted_as(type='numpy'):
  #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

 with open(lowerCamelCase,	"""wb"""							) as f:
  f.write(json.dumps(lowerCamelCase							).encode("""utf-8"""							)							)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_map_filter()




