





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


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


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

snake_case_				=			256047
snake_case_				=			256145

@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Any							=    NllbTokenizer
      A_						:				Tuple							=    NllbTokenizerFast
      A_						:				Union[str, Any]							=    True
      A_						:				List[str]							=    True
      A_						:				int							=    {}


      def    a (self       :      Any      ):





         """simple docstring"""



         super().setUp()

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


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   NllbTokenizer(a__				,			keep_accents=a__      )

         __snake_case							       =   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]]				,			)

         __snake_case							       =   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''',
                 '''.''',
             ]				,			)
         __snake_case							       =   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]
             ]				,			)

         __snake_case							       =   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    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   (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})"""      ):
               __snake_case							       =   self.rust_tokenizer_class.from_pretrained(a__				,			**a__      )
               __snake_case							       =   self.tokenizer_class.from_pretrained(a__				,			**a__      )

               __snake_case							       =   tempfile.mkdtemp()

               __snake_case							       =   tokenizer_r.save_pretrained(a__      )
               __snake_case							       =   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      )      )
               __snake_case							       =   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
               __snake_case							       =   tokenizer_r.from_pretrained(a__      )
               __snake_case							       =   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
               __snake_case							       =   tempfile.mkdtemp()

               __snake_case							       =   tokenizer_r.save_pretrained(a__				,			legacy_format=a__      )
               __snake_case							       =   tokenizer_p.save_pretrained(a__      )

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

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

               __snake_case							       =   tokenizer_r.save_pretrained(a__				,			legacy_format=a__      )
               __snake_case							       =   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
               __snake_case							       =   tokenizer_r.from_pretrained(a__      )
               __snake_case							       =   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    a (self       :      Optional[Any]      ):





         """simple docstring"""



         if not self.test_seqaseq:
            return

         __snake_case							       =   self.get_tokenizers()
         for tokenizer in tokenizers:
            with self.subTest(f"""{tokenizer.__class__.__name__}"""      ):
               # Longer text that will definitely require truncation.
               __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.''',
               ]
               __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.''',
               ]
               try:
                  __snake_case							       =   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
               __snake_case							       =   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      )

               __snake_case							       =   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    a (self       :      List[str]      ):





         """simple docstring"""



         pass


      def    a (self       :      int      ):





         """simple docstring"""



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

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

               __snake_case							       =   tokenizer_r.encode('''<special>'''				,			add_special_tokens=a__      )[0]

               self.assertTrue(special_token_id in r_output      )

               if self.test_slow_tokenizer:
                  __snake_case							       =   self.rust_tokenizer_class.from_pretrained(
                      a__				,			additional_special_tokens=a__				,			**a__				,			)
                  __snake_case							       =   self.tokenizer_class.from_pretrained(
                      a__				,			additional_special_tokens=a__				,			**a__      )

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

                  __snake_case							       =   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 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):
      A_						:				List[Any]							=    'facebook/nllb-200-distilled-600M'
      A_						:				List[str]							=    [
          ' 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_						:				List[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_						:				List[Any]							=    [
          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       :      Tuple      ):





         """simple docstring"""



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


      def    a (self       :      Optional[int]      ):





         """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    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens				,			a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         self.assertIn(a__				,			self.tokenizer.all_special_ids      )
         # fmt: off
         __snake_case							       =   [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

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


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   ['''this is gunna be a long sentence ''' * 20]
         assert isinstance(src_text[0]				,			a__      )
         __snake_case							       =   10
         __snake_case							       =   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    a (self       :      Tuple      ):





         """simple docstring"""



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


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   tempfile.mkdtemp()
         __snake_case							       =   self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(a__      )
         __snake_case							       =   NllbTokenizer.from_pretrained(a__      )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,			a__      )


      @require_torch
      def    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer(
             self.src_text				,			text_target=self.tgt_text				,			padding=a__				,			truncation=a__				,			max_length=len(self.expected_src_tokens      )				,			return_tensors='''pt'''				,			)
         __snake_case							       =   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      )
         __snake_case							       =   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    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer(self.src_text				,			padding=a__				,			truncation=a__				,			max_length=3				,			return_tensors='''pt'''      )
         __snake_case							       =   self.tokenizer(
             text_target=self.tgt_text				,			padding=a__				,			truncation=a__				,			max_length=10				,			return_tensors='''pt'''      )
         __snake_case							       =   targets['''input_ids''']
         __snake_case							       =   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    a (self       :      str      ):





         """simple docstring"""



         __snake_case							       =   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    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   True
         __snake_case							       =   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]      )

         __snake_case							       =   False
         __snake_case							       =   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]      )








from math import pi






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  int       )      ->				float:
   return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
    print(arc_length(90, 10))

