
'''simple docstring'''







import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    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


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


if is_torch_available():
  from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowerCAmelCase         =    2_5_6_0_4_7
__lowerCAmelCase         =    2_5_6_1_4_5







@require_sentencepiece
@require_tokenizers
class UpperCAmelCase__	( lowercase__    ,				unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Dict   =					NllbTokenizer
  __UpperCAmelCase				:				Tuple   =					NllbTokenizerFast
  __UpperCAmelCase				:				str   =					True
  __UpperCAmelCase				:				Tuple   =					True
  __UpperCAmelCase				:				Optional[Any]   =					{}


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     super().setUp()

     # We have a SentencePiece fixture for testing
     _a				:	int			=   NllbTokenizer(_a							,keep_accents=_a       )
     tokenizer.save_pretrained(self.tmpdirname       )


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	List[str]			=   NllbTokenizer(_a							,keep_accents=_a       )

     _a				:	List[Any]			=   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				:	Dict			=   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				:	List[str]			=   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				:	List[str]			=   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   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     _a				:	List[Any]			=   (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
           _a				:	Tuple			=   self.rust_tokenizer_class.from_pretrained(_a							,**_a       )
           _a				:	List[Any]			=   self.tokenizer_class.from_pretrained(_a							,**_a       )

           _a				:	Optional[int]			=   tempfile.mkdtemp()

           _a				:	Optional[int]			=   tokenizer_r.save_pretrained(_a       )
           _a				:	Tuple			=   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				:	Dict			=   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				:	Tuple			=   tokenizer_r.from_pretrained(_a       )
           _a				:	int			=   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=True
           _a				:	Tuple			=   tempfile.mkdtemp()

           _a				:	List[str]			=   tokenizer_r.save_pretrained(_a							,legacy_format=_a       )
           _a				:	str			=   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				:	Optional[Any]			=   tokenizer_r.from_pretrained(_a       )
           _a				:	int			=   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				:	int			=   tempfile.mkdtemp()

           _a				:	Tuple			=   tokenizer_r.save_pretrained(_a							,legacy_format=_a       )
           _a				:	Tuple			=   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				:	List[str]			=   tokenizer_r.from_pretrained(_a       )
           _a				:	Optional[Any]			=   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
  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     if not self.test_seqaseq:
        return

     _a				:	Dict			=   self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
           # Longer text that will definitely require truncation.
           _a				:	Tuple			=   [
               ' 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.',
           ]
           _a				:	int			=   [
               '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.',
           ]
           try:
              _a				:	Dict			=   tokenizer.prepare_seqaseq_batch(
                  src_texts=_a							,tgt_texts=_a							,max_length=3							,max_target_length=10							,return_tensors='pt'							,src_lang='eng_Latn'							,tgt_lang='ron_Latn'							,)
           except NotImplementedError:
              return
           self.assertEqual(batch.input_ids.shape[1]							,3       )
           self.assertEqual(batch.labels.shape[1]							,10       )
           # max_target_length will default to max_length if not specified
           _a				:	Tuple			=   tokenizer.prepare_seqaseq_batch(
               _a							,tgt_texts=_a							,max_length=3							,return_tensors='pt'       )
           self.assertEqual(batch.input_ids.shape[1]							,3       )
           self.assertEqual(batch.labels.shape[1]							,3       )

           _a				:	Union[str, Any]			=   tokenizer.prepare_seqaseq_batch(
               src_texts=_a							,max_length=3							,max_target_length=10							,return_tensors='pt'       )
           self.assertEqual(batch_encoder_only.input_ids.shape[1]							,3       )
           self.assertEqual(batch_encoder_only.attention_mask.shape[1]							,3       )
           self.assertNotIn('decoder_input_ids'							,_a       )


  @unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'       )
  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     pass


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
           _a				:	int			=   [AddedToken('<special>'							,lstrip=_a       )]

           _a				:	Optional[Any]			=   self.rust_tokenizer_class.from_pretrained(
               _a							,additional_special_tokens=_a							,**_a       )
           _a				:	Any			=   tokenizer_r.encode('Hey this is a <special> token'       )

           _a				:	Union[str, Any]			=   tokenizer_r.encode('<special>'							,add_special_tokens=_a       )[0]

           self.assertTrue(special_token_id in r_output       )

           if self.test_slow_tokenizer:
              _a				:	Union[str, Any]			=   self.rust_tokenizer_class.from_pretrained(
                  _a							,additional_special_tokens=_a							,**_a							,)
              _a				:	str			=   self.tokenizer_class.from_pretrained(
                  _a							,additional_special_tokens=_a							,**_a       )

              _a				:	List[str]			=   tokenizer_p.encode('Hey this is a <special> token'       )

              _a				:	Union[str, Any]			=   tokenizer_cr.encode('Hey this is a <special> token'       )

              self.assertEqual(_a							,_a       )
              self.assertEqual(_a							,_a       )
              self.assertTrue(special_token_id in p_output       )
              self.assertTrue(special_token_id in cr_output       )







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





  """simple docstring"""

  __UpperCAmelCase				:				Tuple   =					'''facebook/nllb-200-distilled-600M'''
  __UpperCAmelCase				:				Optional[Any]   =					[
      ''' 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				:				Optional[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.''',
  ]
  __UpperCAmelCase				:				List[Any]   =					[
      25_6047,
      1_6297,
      13_4408,
      8165,
      24_8066,
      1_4734,
      950,
      1135,
      10_5721,
      3573,
      83,
      2_7352,
      108,
      4_9486,
      2,
  ]


  @classmethod
  def   __lowercase		(		cls      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	NllbTokenizer			=   NllbTokenizer.from_pretrained(
         cls.checkpoint_name							,src_lang='eng_Latn'							,tgt_lang='ron_Latn'       )
     _a				:	str			=   1
     return cls


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']							,25_6001       )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']							,25_6002       )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']							,25_6057       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     self.assertIn(_a							,self.tokenizer.all_special_ids       )
     # fmt: off
     _a				:	Optional[int]			=   [RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
     # fmt: on

     _a				:	List[str]			=   self.tokenizer.decode(_a							,skip_special_tokens=_a       )
     _a				:	Optional[int]			=   self.tokenizer.decode(generated_ids[1:]							,skip_special_tokens=_a       )
     self.assertEqual(_a							,_a       )
     self.assertNotIn(self.tokenizer.eos_token							,_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   ['this is gunna be a long sentence ' * 20]
     assert isinstance(src_text[0]							,_a       )
     _a				:	Any			=   10
     _a				:	Optional[Any]			=   self.tokenizer(_a							,max_length=_a							,truncation=_a       ).input_ids[0]
     self.assertEqual(ids[-1]							,2       )
     self.assertEqual(ids[0]							,_a       )
     self.assertEqual(len(_a       )							,_a       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']       )							,[25_6203, 3]       )


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	int			=   tempfile.mkdtemp()
     _a				:	int			=   self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(_a       )
     _a				:	List[Any]			=   NllbTokenizer.from_pretrained(_a       )
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,_a       )


  @require_torch
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Any			=   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				:	Optional[Any]			=   shift_tokens_right(
         batch['labels']							,self.tokenizer.pad_token_id							,self.tokenizer.lang_code_to_id['ron_Latn']       )

     self.assertIsInstance(_a							,_a       )

     self.assertEqual((2, 15)							,batch.input_ids.shape       )
     self.assertEqual((2, 15)							,batch.attention_mask.shape       )
     _a				:	Any			=   batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens							,_a       )
     self.assertEqual(_a							,batch.decoder_input_ids[0, 0]       )  # EOS
     # 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   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   self.tokenizer(self.src_text							,padding=_a							,truncation=_a							,max_length=3							,return_tensors='pt'       )
     _a				:	Optional[int]			=   self.tokenizer(
         text_target=self.tgt_text							,padding=_a							,truncation=_a							,max_length=10							,return_tensors='pt'       )
     _a				:	Optional[int]			=   targets['input_ids']
     _a				:	List[str]			=   shift_tokens_right(
         _a							,self.tokenizer.pad_token_id							,decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]							,)

     self.assertEqual(batch.input_ids.shape[1]							,3       )
     self.assertEqual(batch.decoder_input_ids.shape[1]							,10       )


  @require_torch
  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   self.tokenizer._build_translation_inputs(
         'A test'							,return_tensors='pt'							,src_lang='eng_Latn'							,tgt_lang='fra_Latn'       )

     self.assertEqual(
         nested_simplify(_a       )							,{
             # A, test, EOS, en_XX
             'input_ids': [[25_6047, 70, 7356, 2]],
             'attention_mask': [[1, 1, 1, 1]],
             # ar_AR
             'forced_bos_token_id': 25_6057,
         }							,)


  @require_torch
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	str			=   True
     _a				:	Tuple			=   self.tokenizer(
         'UN Chief says there is no military solution in Syria'							,src_lang='eng_Latn'							,tgt_lang='fra_Latn'       )
     self.assertEqual(
         inputs.input_ids							,[1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047]       )

     _a				:	Tuple			=   False
     _a				:	Dict			=   self.tokenizer(
         'UN Chief says there is no military solution in Syria'							,src_lang='eng_Latn'							,tgt_lang='fra_Latn'       )
     self.assertEqual(
         inputs.input_ids							,[25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2]       )





'''simple docstring'''







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   {}


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     print(self.vertex       )
     for i in self.vertex:
        print(_a							,' -> '							,' -> '.join([str(_a       ) for j in self.vertex[i]]       )       )


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if from_vertex in self.vertex:
        self.vertex[from_vertex].append(_a       )
     else:
        # else make a new vertex
        _a				:	int			=   [to_vertex]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Tuple			=   [False] * len(self.vertex       )

     # call the recursive helper function
     for i in range(len(self.vertex       )       ):
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     list       ):


     '''simple docstring'''






     _a				:	List[Any]			=   True

     print(_a							,end=' '       )

     # Recur for all the vertices that are adjacent to this node
     for i in self.vertex:
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


if __name__ == "__main__":
  __lowerCAmelCase         =    Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3



