

'''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


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


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

lowercase_	 =						250_004
lowercase_	 =						250_020






@require_sentencepiece
@require_tokenizers
class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: List[str]    = MBartTokenizer
 __lowerCamelCase					: List[Any]    = MBartTokenizerFast
 __lowerCamelCase					: List[Any]    = True
 __lowerCamelCase					: List[str]    = True



 def       a__						(self )					->  Dict:


      """simple docstring"""



      super().setUp()

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



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      _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       a__						(self )					->  Tuple:


      """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-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 __A    (						unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: int    = 'facebook/mbart-large-en-ro'
 __lowerCamelCase					: Optional[int]    = [
     ' 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.',
 ]
 __lowerCamelCase					: List[Any]    = [
     '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.',
 ]
 __lowerCamelCase					: str    = [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       a__						(cls )					->  Any:


      """simple docstring"""



      _a											=						MBartTokenizer.from_pretrained(
          cls.checkpoint_name				,     src_lang='''en_XX'''				,     tgt_lang='''ro_RO''' )
      _a											=						1
      return cls



 def       a__						(self )					->  Dict:


      """simple docstring"""



      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       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
      self.assertListEqual(self.expected_src_tokens				,     A )



 def       a__						(self )					->  Tuple:


      """simple docstring"""



      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       a__						(self )					->  Optional[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[-2]				,     2 )
      self.assertEqual(ids[-1]				,     A )
      self.assertEqual(len(A )				,     A )



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


      """simple docstring"""



      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR'''] )				,     [250_026, 250_001] )



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


      """simple docstring"""



      _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       a__						(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][-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       a__						(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, -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       a__						(self )					->  Union[str, Any]:


      """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       a__						(self )					->  List[Any]:


      """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 )				,     {
              # 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,
          }				,     )








'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch

  from transformers.generation import DisjunctiveConstraint






@require_torch
class __A    (						unittest.TestCase		):



 '''simple docstring'''







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


      """simple docstring"""



      _a											=						[[1, 2, 4], [1, 2, 3, 4]]
      _a											=						DisjunctiveConstraint(A )
      self.assertTrue(isinstance(dc.token_ids				,     A ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						[[1, 2], [1, 2, 3, 4]]

      with self.assertRaises(A ):
           DisjunctiveConstraint(A )  # fails here



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(3 )
      _a											=						stepped is True and completed is True and reset is False
      self.assertTrue(A )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 3] )



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


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(4 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2, 4] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

      dc.reset()

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 3 )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 2 )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.remaining() == 0 )
      self.assertTrue(dc.current_seq == [1, 2, 5] )



