





'''simple docstring'''






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   BarthezTokenizer
	snake_case_      =   BarthezTokenizerFast
	snake_case_      =   True
	snake_case_      =   True



	def   _lowerCamelCase    (   self	):
			super().setUp()

			lowerCAmelCase     :    str						=   BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"	)
			tokenizer.save_pretrained(self.tmpdirname	)
			tokenizer.save_pretrained(self.tmpdirname		,      legacy_format=a_	)
			lowerCAmelCase     :    int						=   tokenizer



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   "<pad>"
			lowerCAmelCase     :    Any						=   1

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



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

			self.assertEqual(vocab_keys[0]		,      "<s>"	)
			self.assertEqual(vocab_keys[1]		,      "<pad>"	)
			self.assertEqual(vocab_keys[-1]		,      "<mask>"	)
			self.assertEqual(len(a_	)		,      101_122	)



	def   _lowerCamelCase    (   self	):
			self.assertEqual(self.get_tokenizer().vocab_size		,      101_122	)



	@require_torch
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   ["A long paragraph for summarization.", "Another paragraph for summarization."]
			lowerCAmelCase     :    int						=   [0, 57, 3_018, 70_307, 91, 2]

			lowerCAmelCase     :    Optional[int]						=   self.tokenizer(
			    a_		,      max_length=len(a_	)		,      padding=a_		,      truncation=a_		,      return_tensors="pt"	)
			self.assertIsInstance(a_		,      a_	)

			self.assertEqual((2, 6)		,      batch.input_ids.shape	)
			self.assertEqual((2, 6)		,      batch.attention_mask.shape	)
			lowerCAmelCase     :    str						=   batch.input_ids.tolist()[0]
			self.assertListEqual(a_		,      a_	)



	def   _lowerCamelCase    (   self	):
			if not self.test_rust_tokenizer:
					return

			lowerCAmelCase     :    int						=   self.get_tokenizer()
			lowerCAmelCase     :    Union[str, Any]						=   self.get_rust_tokenizer()

			lowerCAmelCase     :    Optional[int]						=   "I was born in 92000, and this is falsé."

			lowerCAmelCase     :    Any						=   tokenizer.tokenize(a_	)
			lowerCAmelCase     :    Optional[Any]						=   rust_tokenizer.tokenize(a_	)
			self.assertListEqual(a_		,      a_	)

			lowerCAmelCase     :    Optional[Any]						=   tokenizer.encode(a_		,      add_special_tokens=a_	)
			lowerCAmelCase     :    Dict						=   rust_tokenizer.encode(a_		,      add_special_tokens=a_	)
			self.assertListEqual(a_		,      a_	)

			lowerCAmelCase     :    str						=   self.get_rust_tokenizer()
			lowerCAmelCase     :    int						=   tokenizer.encode(a_	)
			lowerCAmelCase     :    Dict						=   rust_tokenizer.encode(a_	)
			self.assertListEqual(a_		,      a_	)





	@slow
	def   _lowerCamelCase    (   self	):
			# fmt: off
			lowerCAmelCase     :    Optional[int]						=   {"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], "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, 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], [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

			# moussaKam/mbarthez is a french model. So we also use french texts.
			lowerCAmelCase     :    Any						=   [
			    "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
			    "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
			    "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
			    "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
			    "telles que la traduction et la synthèse de texte.",
			]

			self.tokenizer_integration_test_util(
			    expected_encoding=a_		,      model_name="moussaKam/mbarthez"		,      revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"		,      sequences=a_		,      )










'''simple docstring'''






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def      __A						(							a_						:  List[str]      ,a_						:  Dict      ,a_						:  List[Any]      ,a_						:  Optional[int]=1_0_2_4	):
		lowerCAmelCase	,		lowerCAmelCase     :    Dict						=   [], []

		lowerCAmelCase     :    Optional[Any]						=   list(zip(a_      ,a_	)	)
		lowerCAmelCase	,		lowerCAmelCase     :    Any						=   sorted_examples[0]

		def is_too_big(a_						:  Any	):
				return tok(a_      ,return_tensors="pt"	).input_ids.shape[1] > max_tokens

		for src, tgt in tqdm(sorted_examples[1:]	):
				lowerCAmelCase     :    Optional[int]						=   new_src + " " + src
				lowerCAmelCase     :    Union[str, Any]						=   new_tgt + " " + tgt
				if is_too_big(a_	) or is_too_big(a_	):  # cant fit, finalize example
						finished_src.append(a_	)
						finished_tgt.append(a_	)
						lowerCAmelCase	,		lowerCAmelCase     :    str						=   src, tgt
				else:  # can fit, keep adding
						lowerCAmelCase	,		lowerCAmelCase     :    List[str]						=   cand_src, cand_tgt

    # cleanup
		if new_src:
				assert new_tgt
				finished_src.append(a_	)
				finished_tgt.append(a_	)
		return finished_src, finished_tgt






def      __A						(							a_						:  List[str]      ,a_						:  Path      ,a_						:  Any      ,a_						:  int	):
		lowerCAmelCase     :    Optional[int]						=   Path(a_	)
		save_path.mkdir(exist_ok=a_	)
		for split in ["train"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				lowerCAmelCase     :    Tuple						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase     :    str						=   [x.rstrip() for x in Path(a_	).open().readlines()]
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   pack_examples(a_      ,a_      ,a_      ,a_	)
				print(f'''packed {split} split from {len(a_	)} examples -> {len(a_	)}.'''	)
				Path(save_path / f'''{split}.source'''	).open("w"	).write("\n".join(a_	)	)
				Path(save_path / f'''{split}.target'''	).open("w"	).write("\n".join(a_	)	)
		for split in ["val", "test"]:
				lowerCAmelCase	,		lowerCAmelCase     :    Optional[Any]						=   data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
				shutil.copyfile(a_      ,save_path / f'''{split}.source'''	)
				shutil.copyfile(a_      ,save_path / f'''{split}.target'''	)






def      __A						(							):
		lowerCAmelCase     :    List[str]						=   argparse.ArgumentParser()
		parser.add_argument("--tok_name"      ,type=a_      ,help="like facebook/bart-large-cnn,t5-base, etc."	)
		parser.add_argument("--max_seq_len"      ,type=a_      ,default=1_2_8	)
		parser.add_argument("--data_dir"      ,type=a_	)
		parser.add_argument("--save_path"      ,type=a_	)
		lowerCAmelCase     :    Optional[Any]						=   parser.parse_args()
		lowerCAmelCase     :    List[str]						=   AutoTokenizer.from_pretrained(args.tok_name	)
		return pack_data_dir(a_      ,Path(args.data_dir	)      ,args.max_seq_len      ,args.save_path	)


if __name__ == "__main__":
					packer_cli()



