



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


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


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

UpperCamelCase_										=					256047
UpperCamelCase_										=					256145







@require_sentencepiece
@require_tokenizers
class 	_snake_case							(			__a      ,				unittest.TestCase      ):






   '''simple docstring'''






   A__	:      int    =		NllbTokenizer
   A__	:      Optional[int]    =		NllbTokenizerFast
   A__	:      int    =		True
   A__	:      Optional[Any]    =		True
   A__	:      Union[str, Any]    =		{}


   def 	A__  (  self:			Tuple				)     ->	List[Any]:
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCAmelCase_			:    List[str]   						= NllbTokenizer(_UpperCamelCase   ,keep_accents=_UpperCamelCase				)
       tokenizer.save_pretrained(self.tmpdirname				)


   def 	A__  (  self:			Tuple				)     ->	Union[str, Any]:
       UpperCAmelCase_			:    str   						= NllbTokenizer(_UpperCamelCase   ,keep_accents=_UpperCamelCase				)

       UpperCAmelCase_			:    List[str]   						= tokenizer.tokenize("""This is a test"""				)
       self.assertListEqual(_UpperCamelCase   ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

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

       UpperCAmelCase_			:    int   						= tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
       self.assertListEqual(
           _UpperCamelCase   ,[
               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""",
               """.""",
           ]   ,)
       UpperCAmelCase_			:    Tuple   						= tokenizer.convert_tokens_to_ids(_UpperCamelCase				)
       self.assertListEqual(
           _UpperCamelCase   ,[
               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]
           ]   ,)

       UpperCAmelCase_			:    List[str]   						= tokenizer.convert_ids_to_tokens(_UpperCamelCase				)
       self.assertListEqual(
           _UpperCamelCase   ,[
               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:			str				)     ->	int:
       UpperCAmelCase_			:    str   						= (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})'''				):
               UpperCAmelCase_			:    str   						= self.rust_tokenizer_class.from_pretrained(_UpperCamelCase   ,**_UpperCamelCase				)
               UpperCAmelCase_			:    Optional[Any]   						= self.tokenizer_class.from_pretrained(_UpperCamelCase   ,**_UpperCamelCase				)

               UpperCAmelCase_			:    str   						= tempfile.mkdtemp()

               UpperCAmelCase_			:    Union[str, Any]   						= tokenizer_r.save_pretrained(_UpperCamelCase				)
               UpperCAmelCase_			:    Optional[Any]   						= tokenizer_p.save_pretrained(_UpperCamelCase				)

               # 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				)				)
               UpperCAmelCase_			:    Optional[Any]   						= tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f				)
               self.assertSequenceEqual(_UpperCamelCase   ,_UpperCamelCase				)

               # Checks everything loads correctly in the same way
               UpperCAmelCase_			:    int   						= tokenizer_r.from_pretrained(_UpperCamelCase				)
               UpperCAmelCase_			:    List[Any]   						= tokenizer_p.from_pretrained(_UpperCamelCase				)

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

               shutil.rmtree(_UpperCamelCase				)

               # Save tokenizer rust, legacy_format=True
               UpperCAmelCase_			:    Tuple   						= tempfile.mkdtemp()

               UpperCAmelCase_			:    Optional[int]   						= tokenizer_r.save_pretrained(_UpperCamelCase   ,legacy_format=_UpperCamelCase				)
               UpperCAmelCase_			:    Any   						= tokenizer_p.save_pretrained(_UpperCamelCase				)

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

               # Checks everything loads correctly in the same way
               UpperCAmelCase_			:    Union[str, Any]   						= tokenizer_r.from_pretrained(_UpperCamelCase				)
               UpperCAmelCase_			:    Any   						= tokenizer_p.from_pretrained(_UpperCamelCase				)

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

               shutil.rmtree(_UpperCamelCase				)

               # Save tokenizer rust, legacy_format=False
               UpperCAmelCase_			:    Tuple   						= tempfile.mkdtemp()

               UpperCAmelCase_			:    Any   						= tokenizer_r.save_pretrained(_UpperCamelCase   ,legacy_format=_UpperCamelCase				)
               UpperCAmelCase_			:    List[Any]   						= tokenizer_p.save_pretrained(_UpperCamelCase				)

               # 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
               UpperCAmelCase_			:    Union[str, Any]   						= tokenizer_r.from_pretrained(_UpperCamelCase				)
               UpperCAmelCase_			:    int   						= tokenizer_p.from_pretrained(_UpperCamelCase				)

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

               shutil.rmtree(_UpperCamelCase				)


   @require_torch
   def 	A__  (  self:			Optional[Any]				)     ->	Any:
       if not self.test_seqaseq:
           return

       UpperCAmelCase_			:    int   						= self.get_tokenizers()
       for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
               # Longer text that will definitely require truncation.
               UpperCAmelCase_			:    Union[str, 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.""",
               ]
               try:
                   UpperCAmelCase_			:    List[str]   						= tokenizer.prepare_seqaseq_batch(
                       src_texts=_UpperCamelCase   ,tgt_texts=_UpperCamelCase   ,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
               UpperCAmelCase_			:    List[str]   						= tokenizer.prepare_seqaseq_batch(
                   _UpperCamelCase   ,tgt_texts=_UpperCamelCase   ,max_length=3   ,return_tensors="""pt"""				)
               self.assertEqual(batch.input_ids.shape[1]   ,3				)
               self.assertEqual(batch.labels.shape[1]   ,3				)

               UpperCAmelCase_			:    List[Any]   						= tokenizer.prepare_seqaseq_batch(
                   src_texts=_UpperCamelCase   ,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"""   ,_UpperCamelCase				)


   @unittest.skip("""Unfortunately way too slow to build a BPE with SentencePiece."""				)
   def 	A__  (  self:			Optional[Any]				)     ->	str:
       pass






   def 	A__  (  self:			List[str]				)     ->	List[str]:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
               UpperCAmelCase_			:    Optional[int]   						= [AddedToken("""<special>"""   ,lstrip=_UpperCamelCase				)]

               UpperCAmelCase_			:    Union[str, Any]   						= self.rust_tokenizer_class.from_pretrained(
                   _UpperCamelCase   ,additional_special_tokens=_UpperCamelCase   ,**_UpperCamelCase				)
               UpperCAmelCase_			:    Optional[Any]   						= tokenizer_r.encode("""Hey this is a <special> token"""				)

               UpperCAmelCase_			:    Any   						= tokenizer_r.encode("""<special>"""   ,add_special_tokens=_UpperCamelCase				)[0]

               self.assertTrue(special_token_id in r_output				)

               if self.test_slow_tokenizer:
                   UpperCAmelCase_			:    str   						= self.rust_tokenizer_class.from_pretrained(
                       _UpperCamelCase   ,additional_special_tokens=_UpperCamelCase   ,**_UpperCamelCase   ,)
                   UpperCAmelCase_			:    Tuple   						= self.tokenizer_class.from_pretrained(
                       _UpperCamelCase   ,additional_special_tokens=_UpperCamelCase   ,**_UpperCamelCase				)

                   UpperCAmelCase_			:    List[Any]   						= tokenizer_p.encode("""Hey this is a <special> token"""				)

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

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







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






   '''simple docstring'''






   A__	:      List[Any]    =		"facebook/nllb-200-distilled-600M"
   A__	:      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.",
   ]
   A__	:      Union[str, 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.",
   ]
   A__	:      str    =		[
       256_047,
       16_297,
       134_408,
       8_165,
       248_066,
       14_734,
       950,
       1_135,
       105_721,
       3_573,
       83,
       27_352,
       108,
       49_486,
       2,
   ]


   @classmethod
   def 	A__  (  cls:			str				)     ->	Dict:
       UpperCAmelCase_			:    str   						= NllbTokenizer.from_pretrained(
           cls.checkpoint_name   ,src_lang="""eng_Latn"""   ,tgt_lang="""ron_Latn"""				)
       UpperCAmelCase_			:    Any   						= 1
       return cls


   def 	A__  (  self:			str				)     ->	int:
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Arab"""]   ,256001				)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""ace_Latn"""]   ,256002				)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["""fra_Latn"""]   ,256057				)


   def 	A__  (  self:			int				)     ->	List[str]:
       UpperCAmelCase_			:    str   						= self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
       self.assertListEqual(self.expected_src_tokens   ,_UpperCamelCase				)


   def 	A__  (  self:			Dict				)     ->	List[str]:
       self.assertIn(_UpperCamelCase   ,self.tokenizer.all_special_ids				)
       # fmt: off
       UpperCAmelCase_			:    Union[str, Any]   						= [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
       # fmt: on

       UpperCAmelCase_			:    int   						= self.tokenizer.decode(_UpperCamelCase   ,skip_special_tokens=_UpperCamelCase				)
       UpperCAmelCase_			:    Optional[Any]   						= self.tokenizer.decode(generated_ids[1:]   ,skip_special_tokens=_UpperCamelCase				)
       self.assertEqual(_UpperCamelCase   ,_UpperCamelCase				)
       self.assertNotIn(self.tokenizer.eos_token   ,_UpperCamelCase				)


   def 	A__  (  self:			Tuple				)     ->	List[str]:
       UpperCAmelCase_			:    Tuple   						= ["""this is gunna be a long sentence """ * 20]
       assert isinstance(src_text[0]   ,_UpperCamelCase				)
       UpperCAmelCase_			:    List[Any]   						= 10
       UpperCAmelCase_			:    Dict   						= self.tokenizer(_UpperCamelCase   ,max_length=_UpperCamelCase   ,truncation=_UpperCamelCase				).input_ids[0]
       self.assertEqual(ids[-1]   ,2				)
       self.assertEqual(ids[0]   ,_UpperCamelCase				)
       self.assertEqual(len(_UpperCamelCase				)   ,_UpperCamelCase				)


   def 	A__  (  self:			List[Any]				)     ->	Dict:
       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]				)   ,[256203, 3]				)


   def 	A__  (  self:			List[str]				)     ->	List[str]:
       UpperCAmelCase_			:    List[Any]   						= tempfile.mkdtemp()
       UpperCAmelCase_			:    Any   						= self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(_UpperCamelCase				)
       UpperCAmelCase_			:    Any   						= NllbTokenizer.from_pretrained(_UpperCamelCase				)
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,_UpperCamelCase				)


   @require_torch
   def 	A__  (  self:			Union[str, Any]				)     ->	int:
       UpperCAmelCase_			:    Tuple   						= self.tokenizer(
           self.src_text   ,text_target=self.tgt_text   ,padding=_UpperCamelCase   ,truncation=_UpperCamelCase   ,max_length=len(self.expected_src_tokens				)   ,return_tensors="""pt"""   ,)
       UpperCAmelCase_			:    Union[str, Any]   						= shift_tokens_right(
           batch["""labels"""]   ,self.tokenizer.pad_token_id   ,self.tokenizer.lang_code_to_id["""ron_Latn"""]				)

       self.assertIsInstance(_UpperCamelCase   ,_UpperCamelCase				)

       self.assertEqual((2, 15)   ,batch.input_ids.shape				)
       self.assertEqual((2, 15)   ,batch.attention_mask.shape				)
       UpperCAmelCase_			:    Union[str, Any]   						= batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens   ,_UpperCamelCase				)
       self.assertEqual(_UpperCamelCase   ,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 	A__  (  self:			str				)     ->	Any:
       UpperCAmelCase_			:    str   						= self.tokenizer(self.src_text   ,padding=_UpperCamelCase   ,truncation=_UpperCamelCase   ,max_length=3   ,return_tensors="""pt"""				)
       UpperCAmelCase_			:    List[str]   						= self.tokenizer(
           text_target=self.tgt_text   ,padding=_UpperCamelCase   ,truncation=_UpperCamelCase   ,max_length=10   ,return_tensors="""pt"""				)
       UpperCAmelCase_			:    Optional[Any]   						= targets["""input_ids"""]
       UpperCAmelCase_			:    List[str]   						= shift_tokens_right(
           _UpperCamelCase   ,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 	A__  (  self:			Optional[Any]				)     ->	str:
       UpperCAmelCase_			:    Optional[int]   						= self.tokenizer._build_translation_inputs(
           """A test"""   ,return_tensors="""pt"""   ,src_lang="""eng_Latn"""   ,tgt_lang="""fra_Latn"""				)

       self.assertEqual(
           nested_simplify(_UpperCamelCase				)   ,{
               # A, test, EOS, en_XX
               """input_ids""": [[256047, 70, 7356, 2]],
               """attention_mask""": [[1, 1, 1, 1]],
               # ar_AR
               """forced_bos_token_id""": 256057,
           }   ,)






   @require_torch
   def 	A__  (  self:			Any				)     ->	Any:
       UpperCAmelCase_			:    Dict   						= True
       UpperCAmelCase_			:    int   						= 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   ,[16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]				)

       UpperCAmelCase_			:    Optional[Any]   						= False
       UpperCAmelCase_			:    Optional[int]   						= 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   ,[256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]				)


import functools






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	len(__lowerCAmelCase				)
			lowerCAmelCase_						=	len(__lowerCAmelCase				)

			@functools.cache
			def min_distance(__lowerCAmelCase  :  int    ,							__lowerCAmelCase  :  int				) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						lowerCAmelCase_						=	int(worda[indexa] != worda[indexa]				)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1    ,							__lowerCAmelCase				)    ,							1 + min_distance(__lowerCAmelCase    ,							indexa + 1				)    ,							diff + min_distance(indexa + 1    ,							indexa + 1				)    ,							)

			return min_distance(0    ,							0				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



