


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





def __UpperCAmelCase       (		__A			)						->					Tuple:



  '''simple docstring'''





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





def __UpperCAmelCase       (		__A			)						->					Dict:



  '''simple docstring'''





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





def __UpperCAmelCase       (		__A			)						->					str:



  '''simple docstring'''





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





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



  '''simple docstring'''





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





def __UpperCAmelCase       (		__A ,						__A=5			)						->					Any:



  '''simple docstring'''





  UpperCAmelCase__										=     ["auto-generated", "autogenerated", "automatically generated"]
  UpperCAmelCase__										=     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 __UpperCAmelCase       (		__A ,						__A=5 ,						__A=0.05			)						->					Tuple:



  '''simple docstring'''





  UpperCAmelCase__										=     ["unit tests", "test file", "configuration file"]
  UpperCAmelCase__										=     example["content"].splitlines()
  UpperCAmelCase__										=     0
  UpperCAmelCase__										=     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__										=     example["content"].count("\n"			)
  UpperCAmelCase__										=     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 __UpperCAmelCase       (		__A			)						->					Optional[Any]:



  '''simple docstring'''





  UpperCAmelCase__										=     ["def ", "class ", "for ", "while "]
  UpperCAmelCase__										=     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 __UpperCAmelCase       (		__A ,						__A=4			)						->					Optional[Any]:



  '''simple docstring'''





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





def __UpperCAmelCase       (		__A			)						->					Any:



  '''simple docstring'''





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





def __UpperCAmelCase       (		__A			)						->					Optional[int]:



  '''simple docstring'''





  UpperCAmelCase__										=     {}
  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 __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					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 __UpperCAmelCase       (		__A			)						->					Dict:



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

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

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

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

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

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

import tweepy

# Twitter API credentials
A      = ""
A      = ""
A      = ""
A      = ""





def __UpperCAmelCase       (		__A			)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     tweepy.OAuthHandler(__A ,						__A			)
       auth.set_access_token(__A ,						__A			)
       UpperCAmelCase__										=     tweepy.API(__A			)

       # initialize a list to hold all the tweepy Tweets
       UpperCAmelCase__										=     []

       # make initial request for most recent tweets (200 is the maximum allowed count)
       UpperCAmelCase__										=     api.user_timeline(screen_name=__A ,						count=2_0_0			)

       # save most recent tweets
       alltweets.extend(__A			)

       # save the id of the oldest tweet less one
       UpperCAmelCase__										=     alltweets[-1].id - 1

       # keep grabbing tweets until there are no tweets left to grab
       while len(__A			) > 0:
              print(F"""getting tweets before {oldest}"""			)

              # all subsequent requests use the max_id param to prevent duplicates
              UpperCAmelCase__										=     api.user_timeline(
                  screen_name=__A ,						count=2_0_0 ,						max_id=__A			)

              # save most recent tweets
              alltweets.extend(__A			)

              # update the id of the oldest tweet less one
              UpperCAmelCase__										=     alltweets[-1].id - 1

              print(F"""...{len(__A			)} tweets downloaded so far"""			)

       # transform the tweepy tweets into a 2D array that will populate the csv
       UpperCAmelCase__										=     [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

       # write the csv
       with open(F"""new_{screen_name}_tweets.csv""" ,						"w"			) as f:
              UpperCAmelCase__										=     csv.writer(__A			)
              writer.writerow(["id", "created_at", "text"]			)
              writer.writerows(__A			)


if __name__ == "__main__":
    # pass in the username of the account you want to download
    get_all_tweets("FirePing32")





