





'''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}""")










'''simple docstring'''

from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   Tuple:
        return [
            {"col_1": 3, "col_2": "a"},
            {"col_1": 2, "col_2": "b"},
            {"col_1": 1, "col_2": "c"},
            {"col_1": 0, "col_2": "d"},
        ]


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
        return Dataset.from_dict(A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertListEqual(dset.column_names ,		["""col_1""", """col_2"""]       )
        for i, r in enumerate(A       ):
            self.assertDictEqual(A ,		example_records[i]       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        _SCREAMING_SNAKE_CASE									=			Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
        self.assertEqual(dset.info ,		dset_from_dict.info       )


    def 			snake_case_(		self       )		->   Union[str, Any]:  # checks what happens with missing columns
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": 1}, {"""col_2""": """x"""}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertDictEqual(dset[0] ,		{"""col_1""": 1}       )
        self.assertDictEqual(dset[1] ,		{"""col_1""": None}       )  # NB: first record is used for columns


    def 			snake_case_(		self       )		->   Optional[Any]:  # checks if the type can be inferred from the second record
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": []}, {"""col_1""": [1, 2]}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertEqual(dset.info.features["""col_1"""] ,		Sequence(Value("""int64"""       )       )       )





    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list([]       )
        self.assertEqual(len(A       ) ,		0       )
        self.assertListEqual(dset.column_names ,		[]       )



