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


SCREAMING_SNAKE_CASE :	str							     =		get_tests_dir("""fixtures/test_sentencepiece.model""")


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

SCREAMING_SNAKE_CASE :	Optional[int]							     =		25_6047
SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		25_6145
@require_sentencepiece
@require_tokenizers
class    A_ (       lowercase_			,       unittest.TestCase ):
      _SCREAMING_SNAKE_CASE    				=			NllbTokenizer
      _SCREAMING_SNAKE_CASE    				=			NllbTokenizerFast
      _SCREAMING_SNAKE_CASE    				=			True
      _SCREAMING_SNAKE_CASE    				=			True
      _SCREAMING_SNAKE_CASE    				=			{}

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

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

      def 		_UpperCAmelCase   (		self :      List[str]       ):
       __a       =  NllbTokenizer(lowerCamelCase_     ,							keep_accents=lowerCamelCase_       )

       __a       =  tokenizer.tokenize("This is a test"       )
       self.assertListEqual(lowerCamelCase_     ,							["▁This", "▁is", "▁a", "▁t", "est"]       )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(lowerCamelCase_       )     ,							[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]     ,							)

       __a       =  tokenizer.tokenize("I was born in 92000, and this is falsé."       )
       self.assertListEqual(
           lowerCamelCase_     ,							[
               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(lowerCamelCase_       )
       self.assertListEqual(
           lowerCamelCase_     ,							[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]     ,							)

       __a       =  tokenizer.convert_ids_to_tokens(lowerCamelCase_       )
       self.assertListEqual(
           lowerCamelCase_     ,							[
               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 		_UpperCAmelCase   (		self :      List[str]       ):
       __a       =  (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       =  self.rust_tokenizer_class.from_pretrained(lowerCamelCase_     ,							**lowerCamelCase_       )
         __a       =  self.tokenizer_class.from_pretrained(lowerCamelCase_     ,							**lowerCamelCase_       )

         __a       =  tempfile.mkdtemp()

         __a       =  tokenizer_r.save_pretrained(lowerCamelCase_       )
         __a       =  tokenizer_p.save_pretrained(lowerCamelCase_       )

         # 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(lowerCamelCase_     ,							lowerCamelCase_       )

         # Checks everything loads correctly in the same way
         __a       =  tokenizer_r.from_pretrained(lowerCamelCase_       )
         __a       =  tokenizer_p.from_pretrained(lowerCamelCase_       )

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

         shutil.rmtree(lowerCamelCase_       )

         # Save tokenizer rust, legacy_format=True
         __a       =  tempfile.mkdtemp()

         __a       =  tokenizer_r.save_pretrained(lowerCamelCase_     ,							legacy_format=lowerCamelCase_       )
         __a       =  tokenizer_p.save_pretrained(lowerCamelCase_       )

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

         # Checks everything loads correctly in the same way
         __a       =  tokenizer_r.from_pretrained(lowerCamelCase_       )
         __a       =  tokenizer_p.from_pretrained(lowerCamelCase_       )

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

         shutil.rmtree(lowerCamelCase_       )

         # Save tokenizer rust, legacy_format=False
         __a       =  tempfile.mkdtemp()

         __a       =  tokenizer_r.save_pretrained(lowerCamelCase_     ,							legacy_format=lowerCamelCase_       )
         __a       =  tokenizer_p.save_pretrained(lowerCamelCase_       )

         # 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(lowerCamelCase_       )
         __a       =  tokenizer_p.from_pretrained(lowerCamelCase_       )

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

         shutil.rmtree(lowerCamelCase_       )

      @require_torch
      def 		_UpperCAmelCase   (		self :      Dict       ):



       if not self.test_seqaseq:
        return

       __a       =  self.get_tokenizers()
       for tokenizer in tokenizers:
        with self.subTest(f"""{tokenizer.__class__.__name__}"""       ):
         # Longer text that will definitely require truncation.
         __a       =  [
             """ 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       =  [
             """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       =  tokenizer.prepare_seqaseq_batch(
              src_texts=lowerCamelCase_     ,							tgt_texts=lowerCamelCase_     ,							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       =  tokenizer.prepare_seqaseq_batch(
             lowerCamelCase_     ,							tgt_texts=lowerCamelCase_     ,							max_length=3     ,							return_tensors="pt"       )
         self.assertEqual(batch.input_ids.shape[1]     ,							3       )
         self.assertEqual(batch.labels.shape[1]     ,							3       )

         __a       =  tokenizer.prepare_seqaseq_batch(
             src_texts=lowerCamelCase_     ,							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"     ,							lowerCamelCase_       )

      @unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."       )
      def 		_UpperCAmelCase   (		self :      str       ):
       pass

      def 		_UpperCAmelCase   (		self :      Optional[int]       ):



       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
         __a       =  [AddedToken("<special>"     ,							lstrip=lowerCamelCase_       )]

         __a       =  self.rust_tokenizer_class.from_pretrained(
             lowerCamelCase_     ,							additional_special_tokens=lowerCamelCase_     ,							**lowerCamelCase_       )
         __a       =  tokenizer_r.encode("Hey this is a <special> token"       )

         __a       =  tokenizer_r.encode("<special>"     ,							add_special_tokens=lowerCamelCase_       )[0]

         self.assertTrue(special_token_id in r_output       )

         if self.test_slow_tokenizer:
          __a       =  self.rust_tokenizer_class.from_pretrained(
              lowerCamelCase_     ,							additional_special_tokens=lowerCamelCase_     ,							**lowerCamelCase_     ,							)
          __a       =  self.tokenizer_class.from_pretrained(
              lowerCamelCase_     ,							additional_special_tokens=lowerCamelCase_     ,							**lowerCamelCase_       )

          __a       =  tokenizer_p.encode("Hey this is a <special> token"       )

          __a       =  tokenizer_cr.encode("Hey this is a <special> token"       )

          self.assertEqual(lowerCamelCase_     ,							lowerCamelCase_       )
          self.assertEqual(lowerCamelCase_     ,							lowerCamelCase_       )
          self.assertTrue(special_token_id in p_output       )
          self.assertTrue(special_token_id in cr_output       )
@require_torch
@require_sentencepiece
@require_tokenizers
class    A_ (       unittest.TestCase ):
      _SCREAMING_SNAKE_CASE    				=			"""facebook/nllb-200-distilled-600M"""
      _SCREAMING_SNAKE_CASE    				=			[
          """ 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    				=			[
          """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    				=			[
          25_60_47,
          1_62_97,
          13_44_08,
          81_65,
          24_80_66,
          1_47_34,
          9_50,
          11_35,
          10_57_21,
          35_73,
          83,
          2_73_52,
          1_08,
          4_94_86,
          2,
      ]

      @classmethod
      def 		_UpperCAmelCase   (		cls :      Tuple       ):
       __a       =  NllbTokenizer.from_pretrained(
           cls.checkpoint_name     ,							src_lang="eng_Latn"     ,							tgt_lang="ron_Latn"       )
       __a       =  1
       return cls

      def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]     ,							25_60_01       )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]     ,							25_60_02       )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]     ,							25_60_57       )

      def 		_UpperCAmelCase   (		self :      int       ):
       __a       =  self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens     ,							lowerCamelCase_       )

      def 		_UpperCAmelCase   (		self :      Tuple       ):
       self.assertIn(lowerCamelCase_     ,							self.tokenizer.all_special_ids       )
       # fmt: off
       __a       =  [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
       # fmt: on

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

      def 		_UpperCAmelCase   (		self :      Any       ):
       __a       =  ["""this is gunna be a long sentence """ * 20]
       assert isinstance(src_text[0]     ,							lowerCamelCase_       )
       __a       =  10
       __a       =  self.tokenizer(lowerCamelCase_     ,							max_length=lowerCamelCase_     ,							truncation=lowerCamelCase_       ).input_ids[0]
       self.assertEqual(ids[-1]     ,							2       )
       self.assertEqual(ids[0]     ,							lowerCamelCase_       )
       self.assertEqual(len(lowerCamelCase_       )     ,							lowerCamelCase_       )

      def 		_UpperCAmelCase   (		self :      List[Any]       ):
       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]       )     ,							[25_62_03, 3]       )

      def 		_UpperCAmelCase   (		self :      List[Any]       ):
       __a       =  tempfile.mkdtemp()
       __a       =  self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(lowerCamelCase_       )
       __a       =  NllbTokenizer.from_pretrained(lowerCamelCase_       )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,							lowerCamelCase_       )

      @require_torch
      def 		_UpperCAmelCase   (		self :      Dict       ):
       __a       =  self.tokenizer(
           self.src_text     ,							text_target=self.tgt_text     ,							padding=lowerCamelCase_     ,							truncation=lowerCamelCase_     ,							max_length=len(self.expected_src_tokens       )     ,							return_tensors="pt"     ,							)
       __a       =  shift_tokens_right(
           batch["labels"]     ,							self.tokenizer.pad_token_id     ,							self.tokenizer.lang_code_to_id["ron_Latn"]       )

       self.assertIsInstance(lowerCamelCase_     ,							lowerCamelCase_       )

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

       self.assertEqual(
           nested_simplify(lowerCamelCase_       )     ,							{
               # A, test, EOS, en_XX
               "input_ids": [[25_60_47, 70, 73_56, 2]],
               "attention_mask": [[1, 1, 1, 1]],
               # ar_AR
               "forced_bos_token_id": 25_60_57,
           }     ,							)

      @require_torch
      def 		_UpperCAmelCase   (		self :      Tuple       ):
       __a       =  True
       __a       =  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_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]       )

       __a       =  False
       __a       =  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_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]       )



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  [i - 1 for i in range(limit + 1					)]

 for i in range(2							,				limit + 1					):
  if phi[i] == i - 1:
   for j in range(2 * i							,				limit + 1							,				_A					):
    phi[j] -= phi[j] // i

 return sum(phi[2 : limit + 1]					)


if __name__ == "__main__":
  print(solution())


