


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			:			Tuple												=					re.compile(R'''\s+''')


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







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


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=5	):


			'''simple docstring'''







			__lowercase			       = ['''auto-generated''', '''autogenerated''', '''automatically generated''']
			__lowercase			       = example['''content'''].splitlines()
			for _, line in zip(range(_UpperCamelCase	)							,      _UpperCamelCase	):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=5							,      _UpperCamelCase=0.05	):


			'''simple docstring'''







			__lowercase			       = ['''unit tests''', '''test file''', '''configuration file''']
			__lowercase			       = example['''content'''].splitlines()
			__lowercase			       = 0
			__lowercase			       = 0
			# first test
			for _, line in zip(range(_UpperCamelCase	)							,      _UpperCamelCase	):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			__lowercase			       = example['''content'''].count('''\n'''	)
			__lowercase			       = 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 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = ['''def ''', '''class ''', '''for ''', '''while ''']
			__lowercase			       = 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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=4	):


			'''simple docstring'''







			__lowercase			       = example['''content'''].splitlines()
			__lowercase			       = 0
			for line in lines:
						counter += line.lower().count('''='''	)
						if counter > minimum:
									return {"has_few_assignments": False}
			return {"has_few_assignments": True}


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = tokenizer(example['''content''']							,      truncation=_UpperCamelCase	)['''input_ids''']
			__lowercase			       = len(example['''content''']	) / len(_UpperCamelCase	)
			return {"ratio": ratio}


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = {}
			results.update(get_hash(_UpperCamelCase	)	)
			results.update(line_stats(_UpperCamelCase	)	)
			results.update(alpha_stats(_UpperCamelCase	)	)
			results.update(char_token_ratio(_UpperCamelCase	)	)
			results.update(is_autogenerated(_UpperCamelCase	)	)
			results.update(is_config_or_test(_UpperCamelCase	)	)
			results.update(has_no_keywords(_UpperCamelCase	)	)
			results.update(has_few_assignments(_UpperCamelCase	)	)
			return results


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			if not check_uniques(_UpperCamelCase							,      _UpperCamelCase	):
						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 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			with open(_UpperCamelCase							,      '''rb'''	) as f_in:
						with gzip.open(str(_UpperCamelCase	) + '''.gz'''							,      '''wb'''							,      compresslevel=6	) as f_out:
									shutil.copyfileobj(_UpperCamelCase							,      _UpperCamelCase	)
			os.unlink(_UpperCamelCase	)


# Settings
a			:			Tuple												=					HfArgumentParser(PreprocessingArguments)
a			:			Optional[int]												=					parser.parse_args()
if args.num_workers is None:
	a			:			str												=					multiprocessing.cpu_count()
a			:			List[str]												=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
a			:			Union[str, Any]												=					time.time()
a			:			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:
	a			:			Dict												=					time.time()
	a						,				a			:			Dict												=					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			:			Dict												=					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			:			List[str]												=					output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								super().__init__(
								    snake_case_			,							split=snake_case_			,							features=snake_case_			,							cache_dir=snake_case_			,							keep_in_memory=snake_case_			,							streaming=snake_case_			,							num_proc=snake_case_			,							**snake_case_			,							)
								__lowercase			       = path_or_paths if isinstance(snake_case_			,							snake_case_    ) else {self.split: path_or_paths}
								__lowercase			       = Text(
								    cache_dir=snake_case_			,							data_files=snake_case_			,							features=snake_case_			,							**snake_case_			,							)






					def A   (					self    ) ->      int:







								'''simple docstring'''

								if self.streaming:
											__lowercase			       = self.builder.as_streaming_dataset(split=self.split    )
								# Build regular (map-style) dataset
								else:
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None

											self.builder.download_and_prepare(
											    download_config=snake_case_			,							download_mode=snake_case_			,							verification_mode=snake_case_			,							base_path=snake_case_			,							num_proc=self.num_proc			,							)
											__lowercase			       = self.builder.as_dataset(
											    split=self.split			,							verification_mode=snake_case_			,							in_memory=self.keep_in_memory    )
								return dataset


