






'''simple docstring'''




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


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







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




							a__   		=	XGLMTokenizer
							a__   		=	XGLMTokenizerFast
							a__   		=	True
							a__   		=	True

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


										'''simple docstring'''




										super().setUp()

										# We have a SentencePiece fixture for testing
										snake_case_				:					Optional[int]   =       XGLMTokenizer(lowerCAmelCase__       ,							keep_accents=lowerCAmelCase__				)
										tokenizer.save_pretrained(self.tmpdirname				)

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


										'''simple docstring'''




										snake_case_				:					Any   =       "<pad>"
										snake_case_				:					Optional[int]   =       1

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

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


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       list(self.get_tokenizer().get_vocab().keys()				)

										self.assertEqual(vocab_keys[0]       ,							"<s>"				)
										self.assertEqual(vocab_keys[1]       ,							"<pad>"				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							1_008				)

							def 							_A	(		self			:str				)				->  Tuple:


										'''simple docstring'''




										self.assertEqual(self.get_tokenizer().vocab_size       ,							1_008				)

							def 							_A	(		self			:Tuple				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					List[str]   =       XGLMTokenizer(lowerCAmelCase__       ,							keep_accents=lowerCAmelCase__				)

										snake_case_				:					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_				:					Dict   =       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_				:					Tuple   =       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_				:					Tuple   =       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>",
										        ".",
										    ]       ,							)

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


										'''simple docstring'''




										return XGLMTokenizer.from_pretrained("facebook/xglm-564M"				)

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


										'''simple docstring'''




										with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(lowerCAmelCase__       ,							f.name				)
													snake_case_				:					Union[str, Any]   =       XGLMTokenizer(f.name       ,							keep_accents=lowerCAmelCase__				)
													snake_case_				:					Tuple   =       pickle.dumps(lowerCAmelCase__				)
										pickle.loads(lowerCAmelCase__				)

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


										'''simple docstring'''




										if not self.test_rust_tokenizer:
													return

										snake_case_				:					List[Any]   =       self.get_tokenizer()
										snake_case_				:					int   =       self.get_rust_tokenizer()

										snake_case_				:					List[Any]   =       "I was born in 92000, and this is falsé."

										snake_case_				:					List[Any]   =       tokenizer.tokenize(lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       rust_tokenizer.tokenize(lowerCAmelCase__				)
										self.assertListEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)

										snake_case_				:					List[str]   =       tokenizer.encode(lowerCAmelCase__       ,							add_special_tokens=lowerCAmelCase__				)
										snake_case_				:					Union[str, Any]   =       rust_tokenizer.encode(lowerCAmelCase__       ,							add_special_tokens=lowerCAmelCase__				)
										self.assertListEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       self.get_rust_tokenizer()
										snake_case_				:					Optional[int]   =       tokenizer.encode(lowerCAmelCase__				)
										snake_case_				:					Dict   =       rust_tokenizer.encode(lowerCAmelCase__				)
										self.assertListEqual(lowerCAmelCase__       ,							lowerCAmelCase__				)

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


										'''simple docstring'''




										snake_case_				:					List[str]   =       "Hello World!"
										snake_case_				:					Tuple   =       [2, 31_227, 4_447, 35]

										self.assertListEqual(lowerCAmelCase__       ,							self.big_tokenizer.encode(lowerCAmelCase__				)				)

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


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       (
										    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
										    " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
										)
										# fmt: off
										snake_case_				:					Dict   =       [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
										# fmt: on

										self.assertListEqual(lowerCAmelCase__       ,							self.big_tokenizer.encode(lowerCAmelCase__				)				)






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


										'''simple docstring'''




										snake_case_				:					str   =       {
										    "input_ids": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 5]],
										    "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, 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, 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, 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]]
										}  # noqa: E501
										# fmt: on

										self.tokenizer_integration_test_util(
										    expected_encoding=lowerCAmelCase__       ,							model_name="facebook/xglm-564M"       ,							padding=lowerCAmelCase__       ,							)












'''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}''')




