





'''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


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





def lowerCamelCase						(     __lowerCamelCase :    str    )						->Optional[Any]:
    return {"hash": hashlib.mda(re.sub(__lowerCamelCase					,       """"""					,       example["""content"""]    ).encode("""utf-8"""    )    ).hexdigest()}





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			[len(__lowerCamelCase    ) for line in example["""content"""].splitlines()]
    return {"line_mean": np.mean(__lowerCamelCase    ), "line_max": max(__lowerCamelCase    )}





def lowerCamelCase						(     __lowerCamelCase :    Any    )						->Any:
    _SCREAMING_SNAKE_CASE									=			np.mean([c.isalnum() for c in example["""content"""]]    )
    return {"alpha_frac": alpha_frac}





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[int]    )						->Union[str, Any]:
    if example["hash"] in uniques:
        uniques.remove(example["""hash"""]    )
        return True
    else:
        return False





def lowerCamelCase						(     __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]=5    )						->str:
    _SCREAMING_SNAKE_CASE									=			["""auto-generated""", """autogenerated""", """automatically generated"""]
    _SCREAMING_SNAKE_CASE									=			example["""content"""].splitlines()
    for _, line in zip(range(__lowerCamelCase    )					,       __lowerCamelCase    ):
        for keyword in keywords:
            if keyword in line.lower():
                return {"autogenerated": True}
    else:
        return {"autogenerated": False}





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Union[str, Any]=5					,       __lowerCamelCase :    Union[str, Any]=0.05    )						->Dict:
    _SCREAMING_SNAKE_CASE									=			["""unit tests""", """test file""", """configuration file"""]
    _SCREAMING_SNAKE_CASE									=			example["""content"""].splitlines()
    _SCREAMING_SNAKE_CASE									=			0
    _SCREAMING_SNAKE_CASE									=			0
    # first test
    for _, line in zip(range(__lowerCamelCase    )					,       __lowerCamelCase    ):
        for keyword in keywords:
            if keyword in line.lower():
                return {"config_or_test": True}
    # second test
    _SCREAMING_SNAKE_CASE									=			example["""content"""].count("""\n"""    )
    _SCREAMING_SNAKE_CASE									=			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						(     __lowerCamelCase :    List[str]    )						->str:
    _SCREAMING_SNAKE_CASE									=			["""def """, """class """, """for """, """while """]
    _SCREAMING_SNAKE_CASE									=			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						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[int]=4    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			example["""content"""].splitlines()
    _SCREAMING_SNAKE_CASE									=			0
    for line in lines:
        counter += line.lower().count("""="""    )
        if counter > minimum:
            return {"has_few_assignments": False}
    return {"has_few_assignments": True}





def lowerCamelCase						(     __lowerCamelCase :    str    )						->str:
    _SCREAMING_SNAKE_CASE									=			tokenizer(example["""content"""]					,       truncation=__lowerCamelCase    )["""input_ids"""]
    _SCREAMING_SNAKE_CASE									=			len(example["""content"""]    ) / len(__lowerCamelCase    )
    return {"ratio": ratio}





def lowerCamelCase						(     __lowerCamelCase :    List[Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			{}
    results.update(get_hash(__lowerCamelCase    )    )
    results.update(line_stats(__lowerCamelCase    )    )
    results.update(alpha_stats(__lowerCamelCase    )    )
    results.update(char_token_ratio(__lowerCamelCase    )    )
    results.update(is_autogenerated(__lowerCamelCase    )    )
    results.update(is_config_or_test(__lowerCamelCase    )    )
    results.update(has_no_keywords(__lowerCamelCase    )    )
    results.update(has_few_assignments(__lowerCamelCase    )    )
    return results





def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    int					,       __lowerCamelCase :    str    )						->Tuple:
    if not check_uniques(__lowerCamelCase					,       __lowerCamelCase    ):
        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						(     __lowerCamelCase :    Dict    )						->Optional[int]:
    with open(__lowerCamelCase					,       """rb"""    ) as f_in:
        with gzip.open(str(__lowerCamelCase    ) + """.gz"""					,       """wb"""					,       compresslevel=6    ) as f_out:
            shutil.copyfileobj(__lowerCamelCase					,       __lowerCamelCase    )
    os.unlink(__lowerCamelCase    )


# Settings
lowercase_					= HfArgumentParser(PreprocessingArguments)
lowercase_					= parser.parse_args()
if args.num_workers is None:
      lowercase_					= multiprocessing.cpu_count()
lowercase_					= AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
lowercase_					= time.time()
lowercase_					= 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:
      lowercase_					= time.time()
      lowercase_				,			lowercase_					= 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
lowercase_					= 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)

lowercase_					= output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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





from __future__ import annotations

import typing
from collections import Counter


def     __lowerCAmelCase			(			a__  )       ->  typing.Counter[int]:
				__a										=    Counter()
				for base in range(1						,    max_perimeter + 1  ):
								for perpendicular in range(a__						,    max_perimeter + 1  ):
												__a										=    (base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(a__  ):
																__a										=    int(base + perpendicular + hypotenuse  )
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets


def     __lowerCAmelCase			(			a__ = 1000  )       ->  int:
				__a										=    pythagorean_triple(a__  )
				return triplets.most_common(1  )[0][0]


if __name__ == "__main__":
					print(F"Perimeter {solution()} has maximum solutions")