





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__:    List[str]  	=	re.compile(R'''\s+''')



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



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Union[str, Any]					=     [len(A_) for line in example["content"].splitlines()]
    return {"line_mean": np.mean(A_), "line_max": max(A_)}



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Tuple					=     np.mean([c.isalnum() for c in example["content"]])
    return {"alpha_frac": alpha_frac}



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



def 				lowerCAmelCase_ (							A_    ,A_=5):
    UpperCamelCase__:	List[Any]					=     ["auto-generated", "autogenerated", "automatically generated"]
    UpperCamelCase__:	List[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 				lowerCAmelCase_ (							A_    ,A_=5    ,A_=0.05):
    UpperCamelCase__:	str					=     ["unit tests", "test file", "configuration file"]
    UpperCamelCase__:	str					=     example["content"].splitlines()
    UpperCamelCase__:	Dict					=     0
    UpperCamelCase__:	Tuple					=     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__:	Any					=     example["content"].count("\n")
    UpperCamelCase__:	Optional[Any]					=     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 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Optional[Any]					=     ["def ", "class ", "for ", "while "]
    UpperCamelCase__:	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 				lowerCAmelCase_ (							A_    ,A_=4):
    UpperCamelCase__:	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 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Union[str, Any]					=     tokenizer(example["content"]    ,truncation=A_)["input_ids"]
    UpperCamelCase__:	Union[str, Any]					=     len(example["content"]) / len(A_)
    return {"ratio": ratio}



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Optional[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 				lowerCAmelCase_ (							A_    ,A_    ,A_):
    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 				lowerCAmelCase_ (							A_):
    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__:    Dict  	=	HfArgumentParser(PreprocessingArguments)
A__:    Dict  	=	parser.parse_args()
if args.num_workers is None:
   A__:    Tuple  	=	multiprocessing.cpu_count()
A__:    int  	=	AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
A__:    Any  	=	time.time()
A__:    Any  	=	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__:    Tuple  	=	time.time()
   A__	,		A__:    List[Any]  	=	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__:    str  	=	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__:    int  	=	output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

A__:    Dict  	=	time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   A__:    Dict  	=	str(data_dir / f"file-{file_number+1:012}.json")
   A__:    Any  	=	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 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	str					=     [1]
    for i in range(2    ,A_):
        factorials.append(factorials[-1] * i)
    assert 0 <= k < factorials[-1] * n, "k out of bounds"

    UpperCamelCase__:	Optional[int]					=     []
    UpperCamelCase__:	Optional[Any]					=     list(range(A_))

    # Find permutation
    while factorials:
        UpperCamelCase__:	Dict					=     factorials.pop()
        UpperCamelCase__				, UpperCamelCase__:	Optional[int]					=     divmod(A_    ,A_)
        permutation.append(elements[number])
        elements.remove(elements[number])
    permutation.append(elements[0])

    return permutation


if __name__ == "__main__":
   import doctest

   doctest.testmod()

