





'''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


a__							:							Dict =get_tests_dir('''fixtures/test_sentencepiece.model''')


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

a__							:							List[str] =256_047
a__							:							Optional[int] =256_145

@require_sentencepiece
@require_tokenizers
class      snake_case     (    A_			,       unittest.TestCase       ):




 """simple docstring"""



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






 def        _lowerCamelCase					(       self       :							int	):
        super().setUp()

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






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  NllbTokenizer(A_							,	keep_accents=A_	)

        __UpperCamelCase       =  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 [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]							,	)

        __UpperCamelCase       =  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',
                '.',
            ]							,	)
        __UpperCamelCase       =  tokenizer.convert_tokens_to_ids(A_	)
        self.assertListEqual(
            A_							,	[
                value + tokenizer.fairseq_offset
                for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
            ]							,	)

        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  (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       =  self.rust_tokenizer_class.from_pretrained(A_							,	**A_	)
                      __UpperCamelCase       =  self.tokenizer_class.from_pretrained(A_							,	**A_	)

                      __UpperCamelCase       =  tempfile.mkdtemp()

                      __UpperCamelCase       =  tokenizer_r.save_pretrained(A_	)
                      __UpperCamelCase       =  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	)	)
                      __UpperCamelCase       =  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
                      __UpperCamelCase       =  tokenizer_r.from_pretrained(A_	)
                      __UpperCamelCase       =  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
                      __UpperCamelCase       =  tempfile.mkdtemp()

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

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

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

                      __UpperCamelCase       =  tokenizer_r.save_pretrained(A_							,	legacy_format=A_	)
                      __UpperCamelCase       =  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
                      __UpperCamelCase       =  tokenizer_r.from_pretrained(A_	)
                      __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							Optional[Any]	):
        if not self.test_seqaseq:
               return

        __UpperCamelCase       =  self.get_tokenizers()
        for tokenizer in tokenizers:
               with self.subTest(f'''{tokenizer.__class__.__name__}'''	):
                      # Longer text that will definitely require truncation.
                      __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.',
                      ]
                      try:
                             __UpperCamelCase       =  tokenizer.prepare_seqaseq_batch(
                                 src_texts=A_							,	tgt_texts=A_							,	max_length=3							,	max_target_length=1_0							,	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]							,	1_0	)
                      # max_target_length will default to max_length if not specified
                      __UpperCamelCase       =  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	)

                      __UpperCamelCase       =  tokenizer.prepare_seqaseq_batch(
                          src_texts=A_							,	max_length=3							,	max_target_length=1_0							,	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        _lowerCamelCase					(       self       :							str	):
        pass






 def        _lowerCamelCase					(       self       :							Tuple	):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                      __UpperCamelCase       =  [AddedToken('<special>'							,	lstrip=A_	)]

                      __UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(
                          A_							,	additional_special_tokens=A_							,	**A_	)
                      __UpperCamelCase       =  tokenizer_r.encode('Hey this is a <special> token'	)

                      __UpperCamelCase       =  tokenizer_r.encode('<special>'							,	add_special_tokens=A_	)[0]

                      self.assertTrue(special_token_id in r_output	)

                      if self.test_slow_tokenizer:
                             __UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(
                                 A_							,	additional_special_tokens=A_							,	**A_							,	)
                             __UpperCamelCase       =  self.tokenizer_class.from_pretrained(
                                 A_							,	additional_special_tokens=A_							,	**A_	)

                             __UpperCamelCase       =  tokenizer_p.encode('Hey this is a <special> token'	)

                             __UpperCamelCase       =  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      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					="facebook/nllb-200-distilled-600M"
 SCREAMING_SNAKE_CASE_	:					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.",
 ]
 SCREAMING_SNAKE_CASE_	:					List[str]					=[
     "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.",
 ]
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=[
     256047,
     16297,
     134408,
     8165,
     248066,
     14734,
     950,
     1135,
     105721,
     3573,
     83,
     27352,
     108,
     49486,
     2,
 ]






 @classmethod
 def        _lowerCamelCase					(       cls       :							Optional[int]	):
        __UpperCamelCase       =  NllbTokenizer.from_pretrained(
            cls.checkpoint_name							,	src_lang='eng_Latn'							,	tgt_lang='ron_Latn'	)
        __UpperCamelCase       =  1
        return cls






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']							,	2_5_6_0_0_1	)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']							,	2_5_6_0_0_2	)
        self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']							,	2_5_6_0_5_7	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
        self.assertListEqual(self.expected_src_tokens							,	A_	)






 def        _lowerCamelCase					(       self       :							Dict	):
        self.assertIn(A_							,	self.tokenizer.all_special_ids	)
        # fmt: off
        __UpperCamelCase       =  [RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
        # fmt: on

        __UpperCamelCase       =  self.tokenizer.decode(A_							,	skip_special_tokens=A_	)
        __UpperCamelCase       =  self.tokenizer.decode(generated_ids[1:]							,	skip_special_tokens=A_	)
        self.assertEqual(A_							,	A_	)
        self.assertNotIn(self.tokenizer.eos_token							,	A_	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  ['this is gunna be a long sentence ' * 2_0]
        assert isinstance(src_text[0]							,	A_	)
        __UpperCamelCase       =  1_0
        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							List[Any]	):
        self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']	)							,	[2_5_6_2_0_3, 3]	)






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  tempfile.mkdtemp()
        __UpperCamelCase       =  self.tokenizer.fairseq_tokens_to_ids
        self.tokenizer.save_pretrained(A_	)
        __UpperCamelCase       =  NllbTokenizer.from_pretrained(A_	)
        self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,	A_	)






 @require_torch
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  self.tokenizer(
            self.src_text							,	text_target=self.tgt_text							,	padding=A_							,	truncation=A_							,	max_length=len(self.expected_src_tokens	)							,	return_tensors='pt'							,	)
        __UpperCamelCase       =  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, 1_5)							,	batch.input_ids.shape	)
        self.assertEqual((2, 1_5)							,	batch.attention_mask.shape	)
        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  self.tokenizer(self.src_text							,	padding=A_							,	truncation=A_							,	max_length=3							,	return_tensors='pt'	)
        __UpperCamelCase       =  self.tokenizer(
            text_target=self.tgt_text							,	padding=A_							,	truncation=A_							,	max_length=1_0							,	return_tensors='pt'	)
        __UpperCamelCase       =  targets['input_ids']
        __UpperCamelCase       =  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]							,	1_0	)






 @require_torch
 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  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': [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
                'attention_mask': [[1, 1, 1, 1]],
                # ar_AR
                'forced_bos_token_id': 2_5_6_0_5_7,
            }							,	)






 @require_torch
 def        _lowerCamelCase					(       self       :							Any	):
        __UpperCamelCase       =  True
        __UpperCamelCase       =  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_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7]	)

        __UpperCamelCase       =  False
        __UpperCamelCase       =  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							,	[2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2]	)




def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int = 10**12				):
     __UpperCamelCase    							=1
     __UpperCamelCase    							=0

     __UpperCamelCase    							=1
     __UpperCamelCase    							=1

     while numerator <= 2 * min_total - 1:
          prev_numerator += 2 * numerator
          numerator += 2 * prev_numerator

          prev_denominator += 2 * denominator
          denominator += 2 * prev_denominator

     return (denominator + 1) // 2


if __name__ == "__main__":
   print(f"""{solution() = }""")





