





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


SCREAMING_SNAKE_CASE_						       =	re.compile(r"""\s+""")



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


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



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[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       (				_SCREAMING_SNAKE_CASE     )			->       Union[str, Any]:


      '''simple docstring'''


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



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


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=5     )			->       str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						["""auto-generated""", """autogenerated""", """automatically generated"""]
      SCREAMING_SNAKE_CASE		=						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       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=5			,						_SCREAMING_SNAKE_CASE=0.05     )			->       Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						["""unit tests""", """test file""", """configuration file"""]
      SCREAMING_SNAKE_CASE		=						example["""content"""].splitlines()
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						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
      SCREAMING_SNAKE_CASE		=						example["""content"""].count("""\n"""     )
      SCREAMING_SNAKE_CASE		=						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       (				_SCREAMING_SNAKE_CASE     )			->       Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						["""def """, """class """, """for """, """while """]
      SCREAMING_SNAKE_CASE		=						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       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=4     )			->       int:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						{}
      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       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      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       (				_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      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
SCREAMING_SNAKE_CASE_						       =	HfArgumentParser(PreprocessingArguments)
SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
if args.num_workers is None:
      SCREAMING_SNAKE_CASE_						       =	multiprocessing.cpu_count()
SCREAMING_SNAKE_CASE_						       =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
SCREAMING_SNAKE_CASE_						       =	time.time()
SCREAMING_SNAKE_CASE_						       =	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:
      SCREAMING_SNAKE_CASE_						       =	time.time()
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_						       =	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
SCREAMING_SNAKE_CASE_						       =	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)

SCREAMING_SNAKE_CASE_						       =	output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Image:


      '''simple docstring'''


      def brightness(_SCREAMING_SNAKE_CASE     ) -> float:
            return 1_28 + level + (c - 1_28)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""     )
      return img.point(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            SCREAMING_SNAKE_CASE_						       =	change_brightness(img, 1_0_0)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


