

'''simple docstring'''







import shutil
import tempfile
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


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

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

__lowerCamelCase						    =      25_0004
__lowerCamelCase						    =      25_0020


@require_sentencepiece
@require_tokenizers
class    A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						MBartaaTokenizer
    lowercase		   =						MBartaaTokenizerFast
    lowercase		   =						True
    lowercase		   =						True



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           super().setUp()

           # We have a SentencePiece fixture for testing
           A_		   = MBartaaTokenizer(__a						,			src_lang="""en_XX"""						,			tgt_lang="""ro_RO"""						,			keep_accents=__a    )
           tokenizer.save_pretrained(self.tmpdirname    )



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





           '''simple docstring'''
           A_		   = """<s>"""
           A_		   = 0

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



    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           A_		   = 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    )						,			1054    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           self.assertEqual(self.get_tokenizer().vocab_size						,			1054    )



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = MBartaaTokenizer(__a						,			src_lang="""en_XX"""						,			tgt_lang="""ro_RO"""						,			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]
               ]						,			)

           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>""", """."""]						,			)



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





           '''simple docstring'''
           A_		   = {"""input_ids""": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 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, 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], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 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, 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]], """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, 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], [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, 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]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__a						,			model_name="""facebook/mbart-large-50"""						,			revision="""d3913889c59cd5c9e456b269c376325eabad57e2"""						,			)







    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           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-mbart50""", {})
           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    A__ ( unittest.TestCase     ):
    lowercase		   =						"facebook/mbart-large-50-one-to-many-mmt"
    lowercase		   =						[
        " 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.",
    ]
    lowercase		   =						[
        "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.",
    ]
    lowercase		   =						[EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]



    @classmethod
    def    snake_case_     (   cls    )  -> Tuple:





           '''simple docstring'''
           A_		   = MBartaaTokenizer.from_pretrained(
               cls.checkpoint_name						,			src_lang="""en_XX"""						,			tgt_lang="""ro_RO"""    )
           A_		   = 1
           return cls



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





           '''simple docstring'''
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ar_AR"""]						,			250001    )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""en_EN"""]						,			250004    )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ro_RO"""]						,			250020    )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""mr_IN"""]						,			250038    )



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





           '''simple docstring'''
           A_		   = self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens						,			__a    )



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





           '''simple docstring'''
           self.assertIn(__a						,			self.tokenizer.all_special_ids    )
           A_		   = [RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 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    )  -> Any:





           '''simple docstring'''
           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[0]						,			__a    )
           self.assertEqual(ids[-1]						,			2    )
           self.assertEqual(len(__a    )						,			__a    )



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





           '''simple docstring'''
           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]    )						,			[250053, 250001]    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = tempfile.mkdtemp()
           A_		   = self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(__a    )
           A_		   = MBartaaTokenizer.from_pretrained(__a    )
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,			__a    )



    @require_torch
    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           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][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == RO_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



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





           '''simple docstring'''
           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, 0]    )  # decoder_start_token_id
           # Test that special tokens are reset
           self.assertEqual(self.tokenizer.prefix_tokens						,			[EN_CODE]    )
           self.assertEqual(self.tokenizer.suffix_tokens						,			[self.tokenizer.eos_token_id]    )



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





           '''simple docstring'''
           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    )  -> Dict:





           '''simple docstring'''
           A_		   = self.tokenizer._build_translation_inputs(
               """A test"""						,			return_tensors="""pt"""						,			src_lang="""en_XX"""						,			tgt_lang="""ar_AR"""    )

           self.assertEqual(
               nested_simplify(__a    )						,			{
                   # en_XX, A, test, EOS
                   """input_ids""": [[250004, 62, 3034, 2]],
                   """attention_mask""": [[1, 1, 1, 1]],
                   # ar_AR
                   """forced_bos_token_id""": 250001,
               }						,			)








'''simple docstring'''







def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__			)     ->     bool:
       A_		   = len(UpperCAmelCase__			)
       A_		   = [[False] * (required_sum + 1) for _ in range(arr_len + 1			)]

       # for each arr value, a sum of zero(0) can be formed by not taking any element
       # hence True/1
       for i in range(arr_len + 1			):
              A_		   = True

       # sum is not zero and set is empty then false
       for i in range(1,    required_sum + 1			):
              A_		   = False

       for i in range(1,    arr_len + 1			):
              for j in range(1,    required_sum + 1			):
                     if arr[i - 1] > j:
                            A_		   = subset[i - 1][j]
                     if arr[i - 1] <= j:
                            A_		   = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

       return subset[arr_len][required_sum]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
