


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}")









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



       '''simple docstring'''





       UpperCAmelCase__										=     len(__A			)
       for i in range(__A			):
              for j in range(i + 1 ,						__A			):
                     if numbers[j] < numbers[i]:
                            UpperCAmelCase__						,			UpperCAmelCase__										=     numbers[j], numbers[i]
       return numbers


if __name__ == "__main__":
    A      = input("Enter numbers separated by a comma:\n").strip()
    A      = [int(item) for item in user_input.split(",")]
    print(exchange_sort(unsorted))





