
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											=    25_0004
_lowercase											=    25_0020


@require_sentencepiece
@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						MBartTokenizer
       __lowerCamelCase				       =						MBartTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						True


       def 	_snake_case				(    self				)						->    List[str]:
              super().setUp()

              # We have a SentencePiece fixture for testing
              SCREAMING_SNAKE_CASE_							:				Dict		=MBartTokenizer(__A						,       keep_accents=__A				)
              tokenizer.save_pretrained(self.tmpdirname				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=MBartTokenizer(__A						,       keep_accents=__A				)

              SCREAMING_SNAKE_CASE_							:				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]]						,       )

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=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''',
                      '''.''',
                  ]						,       )
              SCREAMING_SNAKE_CASE_							:				Tuple		=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 ^
                  ]						,       )

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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 	_snake_case				(    self				)						->    Tuple:
              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

              SCREAMING_SNAKE_CASE_							:				int		=(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})'				):
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.rust_tokenizer_class.from_pretrained(__A						,       **__A				)
                            SCREAMING_SNAKE_CASE_							:				Any		=self.tokenizer_class.from_pretrained(__A						,       **__A				)

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

                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer_r.save_pretrained(__A				)
                            SCREAMING_SNAKE_CASE_							:				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				)				)
                            SCREAMING_SNAKE_CASE_							:				Any		=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
                            SCREAMING_SNAKE_CASE_							:				int		=tokenizer_r.from_pretrained(__A				)
                            SCREAMING_SNAKE_CASE_							:				Union[str, 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				)				)
                                   # 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
                            SCREAMING_SNAKE_CASE_							:				int		=tempfile.mkdtemp()

                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer_r.save_pretrained(__A						,       legacy_format=__A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=tokenizer_p.save_pretrained(__A				)

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

                            # Checks everything loads correctly in the same way
                            SCREAMING_SNAKE_CASE_							:				int		=tokenizer_r.from_pretrained(__A				)
                            SCREAMING_SNAKE_CASE_							:				str		=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
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tempfile.mkdtemp()

                            SCREAMING_SNAKE_CASE_							:				int		=tokenizer_r.save_pretrained(__A						,       legacy_format=__A				)
                            SCREAMING_SNAKE_CASE_							:				Any		=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
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer_r.from_pretrained(__A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[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				)






@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowercase_		(      unittest.TestCase       ):
       __lowerCamelCase				       =						"facebook/mbart-large-en-ro"
       __lowerCamelCase				       =						[
           " 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				       =						[
           "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				       =						[8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]


       @classmethod
       def 	_snake_case				(    cls				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				MBartTokenizer		=MBartTokenizer.from_pretrained(
                  cls.checkpoint_name						,       src_lang='''en_XX'''						,       tgt_lang='''ro_RO'''				)
              SCREAMING_SNAKE_CASE_							:				str		=1
              return cls


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              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 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
              self.assertListEqual(self.expected_src_tokens						,       __A				)


       def 	_snake_case				(    self				)						->    Optional[int]:
              self.assertIn(__A						,       self.tokenizer.all_special_ids				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.tokenizer.decode(__A						,       skip_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				Any		=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				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=['''this is gunna be a long sentence ''' * 20]
              assert isinstance(src_text[0]						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=10
              SCREAMING_SNAKE_CASE_							:				List[Any]		=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 	_snake_case				(    self				)						->    List[Any]:
              self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']				)						,       [250_026, 250_001]				)


       def 	_snake_case				(    self				)						->    List[str]:
              SCREAMING_SNAKE_CASE_							:				Any		=tempfile.mkdtemp()
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.tokenizer.fairseq_tokens_to_ids
              self.tokenizer.save_pretrained(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=MBartTokenizer.from_pretrained(__A				)
              self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,       __A				)


       @require_torch
       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.tokenizer(self.src_text						,       text_target=self.tgt_text						,       padding=__A						,       return_tensors='''pt'''				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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 	_snake_case				(    self				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.tokenizer(
                  self.src_text						,       text_target=self.tgt_text						,       padding=__A						,       truncation=__A						,       max_length=len(self.expected_src_tokens				)						,       return_tensors='''pt'''						,       )

              SCREAMING_SNAKE_CASE_							:				Tuple		=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				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=self.tokenizer(self.src_text						,       padding=__A						,       truncation=__A						,       max_length=3						,       return_tensors='''pt'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.tokenizer(
                  text_target=self.tgt_text						,       padding=__A						,       truncation=__A						,       max_length=10						,       return_tensors='''pt'''				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=targets['''input_ids''']
              SCREAMING_SNAKE_CASE_							:				List[str]		=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				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Dict		=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,
                  }						,       )


from math import isqrt, loga
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						list[int]:
       SCREAMING_SNAKE_CASE_							:				int		=[True] * max_number
       for i in range(2			,							isqrt(max_number - 1				) + 1				):
              if is_prime[i]:
                     for j in range(i**2			,							UpperCAmelCase_			,							UpperCAmelCase_				):
                            SCREAMING_SNAKE_CASE_							:				Dict		=False

       return [i for i in range(2			,							UpperCAmelCase_				) if is_prime[i]]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8_0_0_8_0_0			,							UpperCAmelCase_	:     int = 8_0_0_8_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				str		=degree * loga(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=int(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				int		=calculate_prime_numbers(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				int		=0
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0
       SCREAMING_SNAKE_CASE_							:				Any		=len(UpperCAmelCase_				) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]				)
                  + prime_numbers[left] * loga(prime_numbers[right]				)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


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