




'''simple docstring'''
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, 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


_SCREAMING_SNAKE_CASE			=						get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE			=						25_00_04
_SCREAMING_SNAKE_CASE			=						25_00_20
@require_sentencepiece
@require_tokenizers
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		MBartTokenizer
					_UpperCamelCase							=		MBartTokenizerFast
					_UpperCamelCase							=		True
					_UpperCamelCase							=		True



					def     __snake_case						(	self     :						Tuple)    ->							Tuple:
								super().setUp()

								# We have a SentencePiece fixture for testing
								A_									= MBartTokenizer(A_      ,       keep_accents=A_)
								tokenizer.save_pretrained(self.tmpdirname)



					def     __snake_case						(	self     :						int)    ->							Optional[Any]:
								A_									= MBartTokenizer(A_      ,       keep_accents=A_)

								A_									= tokenizer.tokenize('This is a test')
								self.assertListEqual(A_      ,       ['▁This', '▁is', '▁a', '▁t', 'est'])

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

								A_									= tokenizer.tokenize('I was born in 92000, and this is falsé.')
								self.assertListEqual(
								    A_      ,       [
								        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',
								        '.',
								    ]      ,       )
								A_									= tokenizer.convert_tokens_to_ids(A_)
								self.assertListEqual(
								    A_      ,       [
								        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]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ]      ,       )

								A_									= tokenizer.convert_ids_to_tokens(A_)
								self.assertListEqual(
								    A_      ,       [
								        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>',
								        '.',
								    ]      ,       )



					def     __snake_case						(	self     :						Optional[int])    ->							Optional[Any]:
								if not self.test_slow_tokenizer:
											# as we don't have a slow version, we can't compare the outputs between slow and fast versions
											return

								A_									= (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart', {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
														A_									= self.rust_tokenizer_class.from_pretrained(A_      ,       **A_)
														A_									= self.tokenizer_class.from_pretrained(A_      ,       **A_)

														A_									= tempfile.mkdtemp()

														A_									= tokenizer_r.save_pretrained(A_)
														A_									= tokenizer_p.save_pretrained(A_)

														# Checks it save with the same files + the tokenizer.json file for the fast one
														self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))
														A_									= tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
														self.assertSequenceEqual(A_      ,       A_)

														# Checks everything loads correctly in the same way
														A_									= tokenizer_r.from_pretrained(A_)
														A_									= tokenizer_p.from_pretrained(A_)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A_      ,       A_))
																	# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																	# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

														shutil.rmtree(A_)

														# Save tokenizer rust, legacy_format=True
														A_									= tempfile.mkdtemp()

														A_									= tokenizer_r.save_pretrained(A_      ,       legacy_format=A_)
														A_									= tokenizer_p.save_pretrained(A_)

														# Checks it save with the same files
														self.assertSequenceEqual(A_      ,       A_)

														# Checks everything loads correctly in the same way
														A_									= tokenizer_r.from_pretrained(A_)
														A_									= tokenizer_p.from_pretrained(A_)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A_      ,       A_))

														shutil.rmtree(A_)

														# Save tokenizer rust, legacy_format=False
														A_									= tempfile.mkdtemp()

														A_									= tokenizer_r.save_pretrained(A_      ,       legacy_format=A_)
														A_									= tokenizer_p.save_pretrained(A_)

														# Checks it saved the tokenizer.json file
														self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))

														# Checks everything loads correctly in the same way
														A_									= tokenizer_r.from_pretrained(A_)
														A_									= tokenizer_p.from_pretrained(A_)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																	self.assertTrue(hasattr(A_      ,       A_))

														shutil.rmtree(A_)





@require_torch
@require_sentencepiece
@require_tokenizers
class   __UpperCAmelCase				(     unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""facebook/mbart-large-en-ro"""
					_UpperCamelCase							=		[
					    """ UN Chief Says There Is No Military Solution in Syria""",
					    """ Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
					]
					_UpperCamelCase							=		[
					    """Şeful ONU declară că nu există o soluţie militară în Siria""",
					    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
					    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
					    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
					]
					_UpperCamelCase							=		[8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2, EN_CODE]



					@classmethod
					def     __snake_case						(	cls     :						Tuple)    ->							int:
								A_									= MBartTokenizer.from_pretrained(
								    cls.checkpoint_name      ,       src_lang='en_XX'      ,       tgt_lang='ro_RO')
								A_									= 1
								return cls



					def     __snake_case						(	self     :						List[str])    ->							Any:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']      ,       250_001)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']      ,       250_004)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']      ,       250_020)



					def     __snake_case						(	self     :						Dict)    ->							List[str]:
								A_									= self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
								self.assertListEqual(self.expected_src_tokens      ,       A_)



					def     __snake_case						(	self     :						Optional[Any])    ->							Any:
								self.assertIn(A_      ,       self.tokenizer.all_special_ids)
								A_									= [RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
								A_									= self.tokenizer.decode(A_      ,       skip_special_tokens=A_)
								A_									= self.tokenizer.decode(generated_ids[1:]      ,       skip_special_tokens=A_)
								self.assertEqual(A_      ,       A_)
								self.assertNotIn(self.tokenizer.eos_token      ,       A_)



					def     __snake_case						(	self     :						Tuple)    ->							Optional[int]:
								A_									= ['this is gunna be a long sentence ' * 20]
								assert isinstance(src_text[0]      ,       A_)
								A_									= 10
								A_									= self.tokenizer(A_      ,       max_length=A_      ,       truncation=A_).input_ids[0]
								self.assertEqual(ids[-2]      ,       2)
								self.assertEqual(ids[-1]      ,       A_)
								self.assertEqual(len(A_)      ,       A_)



					def     __snake_case						(	self     :						List[Any])    ->							Tuple:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])      ,       [250_026, 250_001])



					def     __snake_case						(	self     :						List[str])    ->							Dict:
								A_									= tempfile.mkdtemp()
								A_									= self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(A_)
								A_									= MBartTokenizer.from_pretrained(A_)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,       A_)



					@require_torch
					def     __snake_case						(	self     :						int)    ->							Tuple:
								A_									= self.tokenizer(self.src_text      ,       text_target=self.tgt_text      ,       padding=A_      ,       return_tensors='pt')
								A_									= shift_tokens_right(batch['labels']      ,       self.tokenizer.pad_token_id)

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
								assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
								assert batch.decoder_input_ids[1][-1] == 2
								assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



					@require_torch
					def     __snake_case						(	self     :						Any)    ->							List[Any]:
								A_									= self.tokenizer(
								    self.src_text      ,       text_target=self.tgt_text      ,       padding=A_      ,       truncation=A_      ,       max_length=len(self.expected_src_tokens)      ,       return_tensors='pt'      ,       )

								A_									= shift_tokens_right(batch['labels']      ,       self.tokenizer.pad_token_id)

								self.assertIsInstance(A_      ,       A_)

								self.assertEqual((2, 14)      ,       batch.input_ids.shape)
								self.assertEqual((2, 14)      ,       batch.attention_mask.shape)
								A_									= batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens      ,       A_)
								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, EN_CODE])



					def     __snake_case						(	self     :						Union[str, Any])    ->							List[str]:
								A_									= self.tokenizer(self.src_text      ,       padding=A_      ,       truncation=A_      ,       max_length=3      ,       return_tensors='pt')
								A_									= self.tokenizer(
								    text_target=self.tgt_text      ,       padding=A_      ,       truncation=A_      ,       max_length=10      ,       return_tensors='pt')
								A_									= targets['input_ids']
								A_									= shift_tokens_right(A_      ,       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     __snake_case						(	self     :						Union[str, Any])    ->							int:
								A_									= self.tokenizer._build_translation_inputs(
								    'A test'      ,       return_tensors='pt'      ,       src_lang='en_XX'      ,       tgt_lang='ar_AR')

								self.assertEqual(
								    nested_simplify(A_)      ,       {
								        # A, test, EOS, en_XX
								        'input_ids': [[62, 3_034, 2, 250_004]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 250_001,
								    }      ,       )











import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A=1024     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=[], []

   UpperCAmelCase__						=list(zip(A				,							A     )     )
   UpperCAmelCase__			,							UpperCAmelCase__						=sorted_examples[0]

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

   for src, tgt in tqdm(sorted_examples[1:]     ):
      UpperCAmelCase__						=new_src + " " + src
      UpperCAmelCase__						=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     )
         UpperCAmelCase__			,							UpperCAmelCase__						=src, tgt
      else:  # can fit, keep adding
         UpperCAmelCase__			,							UpperCAmelCase__						=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 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=Path(A     )
   save_path.mkdir(exist_ok=A     )
   for split in ["train"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=data_dir / F"""{split}.source""", data_dir / F"""{split}.target"""
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__						=[x.rstrip() for x in Path(A     ).open().readlines()]
      UpperCAmelCase__			,							UpperCAmelCase__						=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"]:
      UpperCAmelCase__			,							UpperCAmelCase__						=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 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=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=128     )
   parser.add_argument("--data_dir"				,							type=A     )
   parser.add_argument("--save_path"				,							type=A     )
   UpperCAmelCase__						=parser.parse_args()
   UpperCAmelCase__						=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()



