






'''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


UpperCamelCase      					=re.compile(R"\s+")



def 				snake_case       (  a_				:			Tuple						)							->					List[str]:





      """simple docstring"""

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



def 				snake_case       (  a_				:			Union[str, Any]						)							->					Tuple:





      """simple docstring"""

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



def 				snake_case       (  a_				:			Dict						)							->					int:





      """simple docstring"""

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



def 				snake_case       (  a_				:			List[str]     , a_				:			Any						)							->					Tuple:





      """simple docstring"""

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



def 				snake_case       (  a_				:			Optional[Any]     , a_				:			Any=5						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				["""auto-generated""", """autogenerated""", """automatically generated"""]
      UpperCamelCase_					: Any            =				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 				snake_case       (  a_				:			Any     , a_				:			List[Any]=5     , a_				:			Any=0.05						)							->					int:





      """simple docstring"""

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





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				["""def """, """class """, """for """, """while """]
      UpperCamelCase_					: str            =				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 				snake_case       (  a_				:			str     , a_				:			Optional[int]=4						)							->					Optional[int]:





      """simple docstring"""

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



def 				snake_case       (  a_				:			int						)							->					int:





      """simple docstring"""

      UpperCamelCase_					: List[str]            =				tokenizer(example["""content"""]     , truncation=a_						)["""input_ids"""]
      UpperCamelCase_					: Optional[int]            =				len(example["""content"""]						) / len(a_						)
      return {"ratio": ratio}



def 				snake_case       (  a_				:			Union[str, Any]						)							->					List[str]:





      """simple docstring"""

      UpperCamelCase_					: str            =				{}
      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 				snake_case       (  a_				:			Optional[Any]     , a_				:			List[str]     , a_				:			List[str]						)							->					List[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 				snake_case       (  a_				:			Optional[int]						)							->					List[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      					=HfArgumentParser(PreprocessingArguments)
UpperCamelCase      					=parser.parse_args()
if args.num_workers is None:
      UpperCamelCase      					=multiprocessing.cpu_count()
UpperCamelCase      					=AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
UpperCamelCase      					=time.time()
UpperCamelCase      					=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      					=time.time()
      UpperCamelCase					,		UpperCamelCase      					=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      					=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      					=output_dir / "data"
data_dir.mkdir(exist_ok=True)

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

def 				snake_case       (  a_				:			str     , a_				:			Optional[int]						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				(boundary[1] - boundary[0]) / steps
      UpperCamelCase_					: Dict            =				boundary[0]
      UpperCamelCase_					: Any            =				boundary[1]
      UpperCamelCase_					: Union[str, Any]            =				make_points(a_     , a_     , a_						)
      UpperCamelCase_					: Any            =				0.0
      y += (h / 2.0) * f(a_						)
      for i in x_i:
            # print(i)
            y += h * f(a_						)
      y += (h / 2.0) * f(a_						)
      return y



def 				snake_case       (  a_				:			Tuple     , a_				:			Any     , a_				:			Tuple						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Optional[int]            =				a + h
      while x < (b - h):
            yield x
            UpperCamelCase_					: List[str]            =				x + h



def 				snake_case       (  a_				:			List[str]						)							->					Tuple:  # enter your function here





      """simple docstring"""

      UpperCamelCase_					: int            =				(x - 0) * (x - 0)
      return y



def 				snake_case       (  )							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				0.0  # Lower bound of integration
      UpperCamelCase_					: Optional[int]            =				1.0  # Upper bound of integration
      UpperCamelCase_					: Optional[Any]            =				10.0  # define number of steps or resolution
      UpperCamelCase_					: Optional[Any]            =				[a, b]  # define boundary of integration
      UpperCamelCase_					: Any            =				method_a(a_     , a_						)
      print(f"y = {y}"						)


if __name__ == "__main__":
      main()






