
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							:Optional[Any]							 =				re.compile(R'\s+')


def 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_						)		->	Dict:







				"""simple docstring"""



				__A   			= [len(__lowerCamelCase						) for line in example["content"].splitlines()]
				return {"line_mean": np.mean(__lowerCamelCase						), "line_max": max(__lowerCamelCase						)}


def 						UpperCAmelCase			(	a_						)		->	Any:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_						)		->	Union[str, Any]:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_		,   a_=5						)		->	str:







				"""simple docstring"""



				__A   			= ["auto-generated", "autogenerated", "automatically generated"]
				__A   			= 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 						UpperCAmelCase			(	a_		,   a_=5		,   a_=0.05						)		->	Dict:







				"""simple docstring"""



				__A   			= ["unit tests", "test file", "configuration file"]
				__A   			= example["content"].splitlines()
				__A   			= 0
				__A   			= 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
				__A   			= example["content"].count("\n"						)
				__A   			= 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_						)		->	str:







				"""simple docstring"""



				__A   			= ["def ", "class ", "for ", "while "]
				__A   			= 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						)		->	List[Any]:







				"""simple docstring"""



				__A   			= example["content"].splitlines()
				__A   			= 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_						)		->	str:







				"""simple docstring"""



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


def 						UpperCAmelCase			(	a_						)		->	List[Any]:







				"""simple docstring"""



				__A   			= {}
				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 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				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 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				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
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				HfArgumentParser(PreprocessingArguments)
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()
if args.num_workers is None:
					SCREAMING_SNAKE_CASE							:Optional[Any]							 =				multiprocessing.cpu_count()
SCREAMING_SNAKE_CASE							:List[Any]							 =				AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				time.time()
SCREAMING_SNAKE_CASE							:List[str]							 =				ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
SCREAMING_SNAKE_CASE							:int							 =				set(ds.unique('hash'))
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				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							:int							 =				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							:Union[str, Any]							 =				time.time()
					SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE							: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
SCREAMING_SNAKE_CASE							:Tuple							 =				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							:Optional[int]							 =				output_dir / 'data'
data_dir.mkdir(exist_ok=True)

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



