






'''simple docstring'''




import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase   :					int   							=					get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
		from transformers.models.plbart.modeling_plbart import shift_tokens_right

__lowerCamelCase   :					Any   							=					50003
__lowerCamelCase   :					List[str]   							=					50002







@require_sentencepiece
@require_tokenizers
class    A_	(a_						,					unittest.TestCase							):
							"""simple docstring"""




							a__   		=	PLBartTokenizer
							a__   		=	None
							a__   		=	False

							def 							_A	(		self			:List[Any]				)				->  Optional[int]:


										'''simple docstring'''




										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case_				:					int   =       PLBartTokenizer(lowerCAmelCase__       ,							language_codes="base"       ,							keep_accents=lowerCAmelCase__				)
										tokenizer.save_pretrained(self.tmpdirname				)

							def 							_A	(		self			:Tuple				)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       PLBartTokenizer(lowerCAmelCase__       ,							language_codes="base"       ,							keep_accents=lowerCAmelCase__				)

										snake_case_				:					List[str]   =       tokenizer.tokenize("This is a test"				)
										self.assertListEqual(lowerCAmelCase__       ,							["▁This", "▁is", "▁a", "▁t", "est"]				)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)       ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,							)

										snake_case_				:					int   =       tokenizer.tokenize("I was born in 92000, and this is falsé."				)
										self.assertListEqual(
										    lowerCAmelCase__       ,							[
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "9",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "é",
										        ".",
										    ]       ,							)
										snake_case_				:					str   =       tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
										self.assertListEqual(
										    lowerCAmelCase__       ,							[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										    ]       ,							)

										snake_case_				:					int   =       tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
										self.assertListEqual(
										    lowerCAmelCase__       ,							[
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "<unk>",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "<unk>",
										        ".",
										    ]       ,							)

										snake_case_				:					int   =       tokenizer.vocab_size
										snake_case_				:					List[str]   =       [tokenizer.convert_ids_to_tokens(lowerCAmelCase__				) for x in range(end - 4       ,							lowerCAmelCase__				)]

										self.assertListEqual(lowerCAmelCase__       ,							["__java__", "__python__", "__en_XX__", "<mask>"]				)

										snake_case_				:					List[str]   =       "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
										snake_case_				:					List[Any]   =       tokenizer(lowerCAmelCase__				).input_ids
										self.assertEqual(
										    tokenizer.decode(lowerCAmelCase__       ,							skip_special_tokens=lowerCAmelCase__       ,							clean_up_tokenization_spaces=lowerCAmelCase__				)       ,							lowerCAmelCase__       ,							)






							def 							_A	(		self			:str				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       PLBartTokenizer(lowerCAmelCase__       ,							language_codes="multi"       ,							keep_accents=lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       tokenizer.tokenize("This is a test"				)
										self.assertListEqual(lowerCAmelCase__       ,							["▁This", "▁is", "▁a", "▁t", "est"]				)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)       ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,							)

										snake_case_				:					Optional[int]   =       tokenizer.tokenize("I was born in 92000, and this is falsé."				)
										self.assertListEqual(
										    lowerCAmelCase__       ,							[
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "9",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "é",
										        ".",
										    ]       ,							)
										snake_case_				:					str   =       tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
										self.assertListEqual(
										    lowerCAmelCase__       ,							[
										        value + tokenizer.fairseq_offset
										        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
										    ]       ,							)

										snake_case_				:					List[Any]   =       tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
										self.assertListEqual(
										    lowerCAmelCase__       ,							[
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "<unk>",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "<unk>",
										        ".",
										    ]       ,							)
										snake_case_				:					Dict   =       tokenizer.vocab_size
										snake_case_				:					List[str]   =       [tokenizer.convert_ids_to_tokens(lowerCAmelCase__				) for x in range(end - 7       ,							lowerCAmelCase__				)]

										self.assertListEqual(
										    lowerCAmelCase__       ,							["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]				)
										snake_case_				:					Any   =       "java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
										snake_case_				:					Optional[Any]   =       tokenizer(lowerCAmelCase__				).input_ids
										self.assertEqual(
										    tokenizer.decode(lowerCAmelCase__       ,							skip_special_tokens=lowerCAmelCase__       ,							clean_up_tokenization_spaces=lowerCAmelCase__				)       ,							lowerCAmelCase__       ,							)







@require_torch
@require_sentencepiece
@require_tokenizers
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							a__   		=	'''uclanlp/plbart-python-en_XX'''
							a__   		=	[
							    '''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
							    '''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
							]
							a__   		=	[
							    '''Returns the maximum value of a b c.''',
							    '''Sums the values of a b c.''',
							]
							a__   		=	[
							    134,
							    5452,
							    33460,
							    33441,
							    33463,
							    33465,
							    33463,
							    33449,
							    988,
							    20,
							    33456,
							    19,
							    33456,
							    771,
							    39,
							    4258,
							    889,
							    3318,
							    33441,
							    33463,
							    33465,
							    33463,
							    33449,
							    2471,
							    2,
							    PYTHON_CODE,
							]

							@classmethod
							def 							_A	(		cls			:int				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					PLBartTokenizer   =       PLBartTokenizer.from_pretrained(
										    cls.checkpoint_name       ,							language_codes="base"       ,							src_lang="python"       ,							tgt_lang="en_XX"				)
										snake_case_				:					Union[str, Any]   =       1
										return cls

							def 							_A	(		self			:str				)				->  Optional[Any]:


										'''simple docstring'''




										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]       ,							50_001				)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]       ,							50_002				)
										self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]       ,							50_003				)

							def 							_A	(		self			:List[Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
										self.assertListEqual(self.expected_src_tokens       ,							lowerCAmelCase__				)

							def 							_A	(		self			:List[str]				)				->  Optional[int]:


										'''simple docstring'''




										self.assertIn(lowerCAmelCase__       ,							self.tokenizer.all_special_ids				)
										snake_case_				:					List[str]   =       [EN_CODE, 9_037, 33_442, 57, 752, 153, 14, 56, 18, 9, 2]
										snake_case_				:					str   =       self.tokenizer.decode(lowerCAmelCase__       ,							skip_special_tokens=lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       self.tokenizer.decode(generated_ids[1:]       ,							skip_special_tokens=lowerCAmelCase__				)
										self.assertEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertNotIn(self.tokenizer.eos_token       ,							lowerCAmelCase__				)

							def 							_A	(		self			:Optional[int]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       ["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
										self.assertIsInstance(src_text[0]       ,							lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       10
										snake_case_				:					int   =       self.tokenizer(lowerCAmelCase__       ,							max_length=lowerCAmelCase__       ,							truncation=lowerCAmelCase__				).input_ids[0]
										self.assertEqual(ids[-2]       ,							2				)
										self.assertEqual(ids[-1]       ,							lowerCAmelCase__				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							lowerCAmelCase__				)

							def 							_A	(		self			:str				)				->  List[str]:


										'''simple docstring'''




										self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]				)       ,							[50_004, 50_001]				)

							def 							_A	(		self			:Tuple				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[str]   =       tempfile.mkdtemp()
										snake_case_				:					Union[str, Any]   =       self.tokenizer.fairseq_tokens_to_ids
										self.tokenizer.save_pretrained(lowerCAmelCase__				)
										snake_case_				:					Tuple   =       PLBartTokenizer.from_pretrained(lowerCAmelCase__				)
										self.assertDictEqual(new_tok.fairseq_tokens_to_ids       ,							lowerCAmelCase__				)

							@require_torch
							def 							_A	(		self			:int				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					List[str]   =       self.tokenizer(self.src_text       ,							text_target=self.tgt_text       ,							padding=lowerCAmelCase__       ,							return_tensors="pt"				)
										snake_case_				:					Any   =       shift_tokens_right(batch["labels"]       ,							self.tokenizer.pad_token_id				)

										# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
										self.assertEqual(batch.input_ids[1][-2:].tolist()       ,							[2, PYTHON_CODE]				)
										self.assertEqual(batch.decoder_input_ids[1][0]       ,							lowerCAmelCase__				)
										self.assertEqual(batch.decoder_input_ids[1][-1]       ,							2				)
										self.assertEqual(batch.labels[1][-2:].tolist()       ,							[2, EN_CODE]				)

							@require_torch
							def 							_A	(		self			:Tuple				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       self.tokenizer(
										    self.src_text       ,							text_target=self.tgt_text       ,							padding=lowerCAmelCase__       ,							truncation=lowerCAmelCase__       ,							max_length=len(self.expected_src_tokens				)       ,							return_tensors="pt"       ,							)
										snake_case_				:					Tuple   =       shift_tokens_right(batch["labels"]       ,							self.tokenizer.pad_token_id				)

										self.assertIsInstance(lowerCAmelCase__       ,							lowerCAmelCase__				)

										self.assertEqual((2, 26)       ,							batch.input_ids.shape				)
										self.assertEqual((2, 26)       ,							batch.attention_mask.shape				)
										snake_case_				:					Optional[int]   =       batch.input_ids.tolist()[0]
										self.assertListEqual(self.expected_src_tokens       ,							lowerCAmelCase__				)
										self.assertEqual(2       ,							batch.decoder_input_ids[0, -1]				)  # EOS
										# Test that special tokens are reset
										self.assertEqual(self.tokenizer.prefix_tokens       ,							[]				)
										self.assertEqual(self.tokenizer.suffix_tokens       ,							[self.tokenizer.eos_token_id, PYTHON_CODE]				)

							def 							_A	(		self			:int				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       self.tokenizer(self.src_text       ,							padding=lowerCAmelCase__       ,							truncation=lowerCAmelCase__       ,							max_length=3       ,							return_tensors="pt"				)
										snake_case_				:					List[str]   =       self.tokenizer(
										    text_target=self.tgt_text       ,							padding=lowerCAmelCase__       ,							truncation=lowerCAmelCase__       ,							max_length=10       ,							return_tensors="pt"				)
										snake_case_				:					int   =       targets["input_ids"]
										snake_case_				:					List[Any]   =       shift_tokens_right(lowerCAmelCase__       ,							self.tokenizer.pad_token_id				)

										self.assertEqual(batch.input_ids.shape[1]       ,							3				)
										self.assertEqual(batch.decoder_input_ids.shape[1]       ,							10				)






							@require_torch
							def 							_A	(		self			:Dict				)				->  str:


										'''simple docstring'''




										snake_case_				:					str   =       self.tokenizer._build_translation_inputs(
										    "A test"       ,							return_tensors="pt"       ,							src_lang="en_XX"       ,							tgt_lang="java"				)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__				)       ,							{
										        # A, test, EOS, en_XX
										        "input_ids": [[150, 242, 2, 50_003]],
										        "attention_mask": [[1, 1, 1, 1]],
										        # java
										        "forced_bos_token_id": 50_001,
										    }       ,							)












'''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


__lowerCamelCase   :					List[str]   							=					re.compile(R'''\s+''')

def 	__UpperCAmelCase       ( __magic_name__						)->    Union[str, Any]:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__						)->    str:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__						)->    int:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					List[str]   =       ["auto-generated", "autogenerated", "automatically generated"]
			snake_case_				:					Optional[Any]   =       example["content"].splitlines()
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"autogenerated": True}
			else:
						return {"autogenerated": False}

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=5     ,__magic_name__=0.05						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       ["unit tests", "test file", "configuration file"]
			snake_case_				:					int   =       example["content"].splitlines()
			snake_case_				:					Optional[Any]   =       0
			snake_case_				:					Any   =       0
			# first test
			for _, line in zip(range(__magic_name__						)     ,__magic_name__						):
						for keyword in keywords:
									if keyword in line.lower():
												return {"config_or_test": True}
    # second test
			snake_case_				:					Tuple   =       example["content"].count("\n"						)
			snake_case_				:					int   =       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       ( __magic_name__						)->    str:



			"""simple docstring"""



			snake_case_				:					List[Any]   =       ["def ", "class ", "for ", "while "]
			snake_case_				:					Optional[Any]   =       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       ( __magic_name__     ,__magic_name__=4						)->    Optional[int]:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       tokenizer(example["content"]     ,truncation=__magic_name__						)["input_ids"]
			snake_case_				:					int   =       len(example["content"]						) / len(__magic_name__						)
			return {"ratio": ratio}

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Union[str, Any]   =       {}
			results.update(get_hash(__magic_name__						)						)
			results.update(line_stats(__magic_name__						)						)
			results.update(alpha_stats(__magic_name__						)						)
			results.update(char_token_ratio(__magic_name__						)						)
			results.update(is_autogenerated(__magic_name__						)						)
			results.update(is_config_or_test(__magic_name__						)						)
			results.update(has_no_keywords(__magic_name__						)						)
			results.update(has_few_assignments(__magic_name__						)						)
			return results

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Tuple:



			"""simple docstring"""



			if not check_uniques(__magic_name__     ,__magic_name__						):
						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       ( __magic_name__						)->    Dict:



			"""simple docstring"""



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


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

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

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

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

# Deduplicate data and apply heuristics
__lowerCamelCase   :					List[str]   							=					time.time()
__lowerCamelCase   :					Tuple   							=					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:
		__lowerCamelCase   :					List[str]   							=					time.time()
		__lowerCamelCase							,      __lowerCamelCase   :					Tuple   							=					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
__lowerCamelCase   :					List[Any]   							=					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)

__lowerCamelCase   :					List[str]   							=					output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

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




