





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_    =    re.compile(r'''\s+''')






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=5			)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:   Tuple           =  ["""auto-generated""", """autogenerated""", """automatically generated"""]
				snake_case_						:   Optional[int]           =  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_						(    _UpperCamelCase			,    _UpperCamelCase=5			,    _UpperCamelCase=0.05			)		->						List[str]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  ["""unit tests""", """test file""", """configuration file"""]
				snake_case_						:   Optional[Any]           =  example["""content"""].splitlines()
				snake_case_						:   List[Any]           =  0
				snake_case_						:   Optional[Any]           =  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
				snake_case_						:   Optional[int]           =  example["""content"""].count('''\n'''			)
				snake_case_						:   Union[str, Any]           =  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_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  ["""def """, """class """, """for """, """while """]
				snake_case_						:   Tuple           =  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_						(    _UpperCamelCase			,    _UpperCamelCase=4			)		->						Union[str, Any]:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Tuple           =  {}
				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_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						List[str]:






				"""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 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""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
lowerCAmelCase_    =    HfArgumentParser(PreprocessingArguments)
lowerCAmelCase_    =    parser.parse_args()
if args.num_workers is None:
		lowerCAmelCase_    =    multiprocessing.cpu_count()
lowerCAmelCase_    =    AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
lowerCAmelCase_    =    time.time()
lowerCAmelCase_    =    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_    =    time.time()
		lowerCAmelCase_    =    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_    =    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_    =    output_dir / "data"
data_dir.mkdir(exist_ok=True)

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







import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_    =    get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')



@require_sentencepiece
@require_tokenizers
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				GPTSwaTokenizer
			lowerCamelCase_					:					str	      =				False
			lowerCamelCase_					:					str	      =				True
			lowerCamelCase_					:					List[Any]	      =				False






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


							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:   Union[str, Any]           =  GPTSwaTokenizer(__magic_name__ ,							eos_token='''<unk>''' ,							bos_token='''<unk>''' ,							pad_token='''<unk>'''   )

							tokenizer.save_pretrained(self.tmpdirname   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  '''This is a test'''
							snake_case_						:   str           =  '''This is a test'''
							return input_text, output_text






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   int           =  '''<s>'''
							snake_case_						:   Any           =  1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__magic_name__   ) ,							__magic_name__   )
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__magic_name__   ) ,							__magic_name__   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  list(self.get_tokenizer().get_vocab().keys()   )

							self.assertEqual(vocab_keys[0] ,							'''<unk>'''   )
							self.assertEqual(vocab_keys[1] ,							'''<s>'''   )
							self.assertEqual(vocab_keys[-1] ,							'''j'''   )
							self.assertEqual(len(__magic_name__   ) ,							2000   )






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


							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size ,							2000   )






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


							'''simple docstring'''



							snake_case_						:   str           =  GPTSwaTokenizer(__magic_name__   )

							snake_case_						:   List[str]           =  tokenizer.tokenize('''This is a test'''   )
							self.assertListEqual(__magic_name__ ,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']   )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__   ) ,							[465, 287, 265, 631, 842]   )

							snake_case_						:   Optional[int]           =  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''   )
							# fmt: off
							self.assertListEqual(
							    __magic_name__ ,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.'''] ,							)
							# fmt: on

							snake_case_						:   int           =  tokenizer.convert_tokens_to_ids(__magic_name__   )
							self.assertListEqual(
							    __magic_name__ ,							[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] ,							)

							snake_case_						:   Dict           =  tokenizer.convert_ids_to_tokens(__magic_name__   )
							# fmt: off
							self.assertListEqual(
							    __magic_name__ ,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']   )
							# fmt: on






			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  GPTSwaTokenizer(__magic_name__   )
							snake_case_						:   Tuple           =  ['''This is a test''', '''I was born in 92000, and this is falsé.''']
							snake_case_						:   Optional[int]           =  [
							    [465, 287, 265, 631, 842],
							    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(__magic_name__ ,							__magic_name__   ):
											self.assertListEqual(tokenizer.encode_fast(__magic_name__   ) ,							__magic_name__   )

							# Test that decode_fast returns the input text
							for text, token_ids in zip(__magic_name__ ,							__magic_name__   ):
											self.assertEqual(tokenizer.decode_fast(__magic_name__   ) ,							__magic_name__   )






			@slow
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   str           =  [
							    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
							    '''Hey there, how are you doing this fine day?''',
							    '''This is a text with a trailing spaces followed by a dot     .''',
							    '''Häj sväjs lillebrör! =)''',
							    '''Det är inget fel på Mr. Cool''',
							]

							# fmt: off
							snake_case_						:   str           =  {'''input_ids''': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=__magic_name__ ,							model_name='''AI-Sweden/gpt-sw3-126m''' ,							sequences=__magic_name__ ,							)
