

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		:Any						=     re.compile(R"""\s+""")




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Union[str, Any]:



    """simple docstring"""






    UpperCamelCase_			  =						[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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=5			)->						Union[str, Any]:



    """simple docstring"""






    UpperCamelCase_			  =						["auto-generated", "autogenerated", "automatically generated"]
    UpperCamelCase_			  =						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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=5			,						SCREAMING_SNAKE_CASE_=0.05			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						["unit tests", "test file", "configuration file"]
    UpperCamelCase_			  =						example["content"].splitlines()
    UpperCamelCase_			  =						0
    UpperCamelCase_			  =						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
    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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Union[str, 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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=4			)->						List[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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Tuple:



    """simple docstring"""






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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						{}
    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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Tuple:



    """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 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """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		:Optional[int]						=     HfArgumentParser(PreprocessingArguments)
SCREAMING_SNAKE_CASE		:int						=     parser.parse_args()
if args.num_workers is None:
 SCREAMING_SNAKE_CASE		:Dict						=     multiprocessing.cpu_count()
SCREAMING_SNAKE_CASE		:str						=     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

# Deduplicate hashes
SCREAMING_SNAKE_CASE		:Union[str, Any]						=     set(ds.unique("""hash"""))
SCREAMING_SNAKE_CASE		:Dict						=     len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
SCREAMING_SNAKE_CASE		:Optional[Any]						=     time.time()
SCREAMING_SNAKE_CASE		:str						=     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		:Tuple						=     time.time()
 SCREAMING_SNAKE_CASE	,							SCREAMING_SNAKE_CASE		:int						=     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		: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)

SCREAMING_SNAKE_CASE		:int						=     output_dir / """data"""
data_dir.mkdir(exist_ok=True)

SCREAMING_SNAKE_CASE		:Any						=     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     str(data_dir / F'''file-{file_number+1:012}.json''')
 SCREAMING_SNAKE_CASE		:str						=     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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    if exponent == 1:
        return base
    if exponent % 2 == 0:
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						exponent // 2			,						SCREAMING_SNAKE_CASE_			) % modulo_value
        return (x * x) % modulo_value
    else:
        return (base * _modexpt(SCREAMING_SNAKE_CASE_			,						exponent - 1			,						SCREAMING_SNAKE_CASE_			)) % modulo_value




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_7_7_7			,						SCREAMING_SNAKE_CASE_ = 1_8_5_5			,						SCREAMING_SNAKE_CASE_ = 8			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						base
    for _ in range(1			,						SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						1_0**digits			)

    return result


if __name__ == "__main__":
 print(F'''{solution() = }''')
