


'''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 :  Tuple    							=				get_tests_dir("fixtures/test_sentencepiece.model")


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

__A :  int    							=				25_6047
__A :  Dict    							=				25_6145






@require_sentencepiece
@require_tokenizers
class        __snake_case	(       _SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= NllbTokenizer
    lowercase       			= NllbTokenizerFast
    lowercase       			= True
    lowercase       			= True
    lowercase       			= {}

    def __lowercase       (       self   :  Optional[Any]		)       ->  Any:
      super().setUp()

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

    def __lowercase       (       self   :  Union[str, Any]		)       ->  int:
      lowerCAmelCase_      :				Optional[Any]      =       NllbTokenizer(lowerCamelCase   ,						keep_accents=lowerCamelCase		)

      lowerCAmelCase_      :				Tuple      =       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]]   ,						)

      lowerCAmelCase_      :				List[Any]      =       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""",
              """.""",
          ]   ,						)
      lowerCAmelCase_      :				List[str]      =       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]
          ]   ,						)

      lowerCAmelCase_      :				List[Any]      =       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 __lowercase       (       self   :  Any		)       ->  Optional[Any]:
      lowerCAmelCase_      :				Optional[int]      =       (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})'		):
          lowerCAmelCase_      :				List[str]      =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase   ,						**lowerCamelCase		)
          lowerCAmelCase_      :				List[str]      =       self.tokenizer_class.from_pretrained(lowerCamelCase   ,						**lowerCamelCase		)

          lowerCAmelCase_      :				int      =       tempfile.mkdtemp()

          lowerCAmelCase_      :				Dict      =       tokenizer_r.save_pretrained(lowerCamelCase		)
          lowerCAmelCase_      :				Dict      =       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		)		)
          lowerCAmelCase_      :				Any      =       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
          lowerCAmelCase_      :				Optional[Any]      =       tokenizer_r.from_pretrained(lowerCamelCase		)
          lowerCAmelCase_      :				Any      =       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
          lowerCAmelCase_      :				Tuple      =       tempfile.mkdtemp()

          lowerCAmelCase_      :				Optional[int]      =       tokenizer_r.save_pretrained(lowerCamelCase   ,						legacy_format=lowerCamelCase		)
          lowerCAmelCase_      :				Optional[Any]      =       tokenizer_p.save_pretrained(lowerCamelCase		)

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

          # Checks everything loads correctly in the same way
          lowerCAmelCase_      :				Optional[int]      =       tokenizer_r.from_pretrained(lowerCamelCase		)
          lowerCAmelCase_      :				Dict      =       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
          lowerCAmelCase_      :				List[str]      =       tempfile.mkdtemp()

          lowerCAmelCase_      :				Tuple      =       tokenizer_r.save_pretrained(lowerCamelCase   ,						legacy_format=lowerCamelCase		)
          lowerCAmelCase_      :				int      =       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
          lowerCAmelCase_      :				Optional[Any]      =       tokenizer_r.from_pretrained(lowerCamelCase		)
          lowerCAmelCase_      :				Union[str, Any]      =       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 __lowercase       (       self   :  Tuple		)       ->  Optional[Any]:
      if not self.test_seqaseq:
        return

      lowerCAmelCase_      :				Union[str, Any]      =       self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F'{tokenizer.__class__.__name__}'		):
          # Longer text that will definitely require truncation.
          lowerCAmelCase_      :				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.""",
          ]
          lowerCAmelCase_      :				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:
            lowerCAmelCase_      :				int      =       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
          lowerCAmelCase_      :				Union[str, Any]      =       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		)

          lowerCAmelCase_      :				Tuple      =       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 __lowercase       (       self   :  Tuple		)       ->  Union[str, Any]:
      pass





    def __lowercase       (       self   :  Optional[Any]		)       ->  Dict:
      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'		):
          lowerCAmelCase_      :				Union[str, Any]      =       [AddedToken("""<special>"""   ,						lstrip=lowerCamelCase		)]

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

          lowerCAmelCase_      :				List[str]      =       tokenizer_r.encode("""<special>"""   ,						add_special_tokens=lowerCamelCase		)[0]

          self.assertTrue(special_token_id in r_output		)

          if self.test_slow_tokenizer:
            lowerCAmelCase_      :				Any      =       self.rust_tokenizer_class.from_pretrained(
                lowerCamelCase   ,						additional_special_tokens=lowerCamelCase   ,						**lowerCamelCase   ,						)
            lowerCAmelCase_      :				str      =       self.tokenizer_class.from_pretrained(
                lowerCamelCase   ,						additional_special_tokens=lowerCamelCase   ,						**lowerCamelCase		)

            lowerCAmelCase_      :				int      =       tokenizer_p.encode("""Hey this is a <special> token"""		)

            lowerCAmelCase_      :				str      =       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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    lowercase       			= 'facebook/nllb-200-distilled-600M'
    lowercase       			= [
        ' 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.',
    ]
    lowercase       			= [
        '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.',
    ]
    lowercase       			= [
        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 __lowercase       (       cls   :  Union[str, Any]		)       ->  Optional[Any]:
      lowerCAmelCase_      :				NllbTokenizer      =       NllbTokenizer.from_pretrained(
          cls.checkpoint_name   ,						src_lang="""eng_Latn"""   ,						tgt_lang="""ron_Latn"""		)
      lowerCAmelCase_      :				str      =       1
      return cls

    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, 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 __lowercase       (       self   :  List[str]		)       ->  Any:
      lowerCAmelCase_      :				Any      =       self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
      self.assertListEqual(self.expected_src_tokens   ,						lowerCamelCase		)

    def __lowercase       (       self   :  Dict		)       ->  Any:
      self.assertIn(lowerCamelCase   ,						self.tokenizer.all_special_ids		)
      # fmt: off
      lowerCAmelCase_      :				Tuple      =       [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

      lowerCAmelCase_      :				List[str]      =       self.tokenizer.decode(lowerCamelCase   ,						skip_special_tokens=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       self.tokenizer.decode(generated_ids[1:]   ,						skip_special_tokens=lowerCamelCase		)
      self.assertEqual(lowerCamelCase   ,						lowerCamelCase		)
      self.assertNotIn(self.tokenizer.eos_token   ,						lowerCamelCase		)

    def __lowercase       (       self   :  int		)       ->  List[str]:
      lowerCAmelCase_      :				Dict      =       ["""this is gunna be a long sentence """ * 20]
      assert isinstance(src_text[0]   ,						lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       10
      lowerCAmelCase_      :				Any      =       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 __lowercase       (       self   :  int		)       ->  Optional[Any]:
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["""<mask>""", """ar_AR"""]		)   ,						[25_62_03, 3]		)

    def __lowercase       (       self   :  Any		)       ->  Any:
      lowerCAmelCase_      :				Any      =       tempfile.mkdtemp()
      lowerCAmelCase_      :				Any      =       self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       NllbTokenizer.from_pretrained(lowerCamelCase		)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,						lowerCamelCase		)

    @require_torch
    def __lowercase       (       self   :  Union[str, Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				str      =       self.tokenizer(
          self.src_text   ,						text_target=self.tgt_text   ,						padding=lowerCamelCase   ,						truncation=lowerCamelCase   ,						max_length=len(self.expected_src_tokens		)   ,						return_tensors="""pt"""   ,						)
      lowerCAmelCase_      :				List[Any]      =       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		)
      lowerCAmelCase_      :				Tuple      =       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 __lowercase       (       self   :  str		)       ->  Any:
      lowerCAmelCase_      :				List[Any]      =       self.tokenizer(self.src_text   ,						padding=lowerCamelCase   ,						truncation=lowerCamelCase   ,						max_length=3   ,						return_tensors="""pt"""		)
      lowerCAmelCase_      :				Optional[Any]      =       self.tokenizer(
          text_target=self.tgt_text   ,						padding=lowerCamelCase   ,						truncation=lowerCamelCase   ,						max_length=10   ,						return_tensors="""pt"""		)
      lowerCAmelCase_      :				Tuple      =       targets["""input_ids"""]
      lowerCAmelCase_      :				Union[str, Any]      =       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 __lowercase       (       self   :  Optional[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[int]      =       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 __lowercase       (       self   :  List[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				int      =       True
      lowerCAmelCase_      :				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   ,						[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]		)

      lowerCAmelCase_      :				Optional[int]      =       False
      lowerCAmelCase_      :				List[Any]      =       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]		)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A :  List[str]    							=				{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A :  Union[str, Any]    							=				[
      "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BigBirdPegasusForCausalLM",
      "BigBirdPegasusForConditionalGeneration",
      "BigBirdPegasusForQuestionAnswering",
      "BigBirdPegasusForSequenceClassification",
      "BigBirdPegasusModel",
      "BigBirdPegasusPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_bigbird_pegasus import (
      BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BigBirdPegasusConfig,
      BigBirdPegasusOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
        BigBirdPegasusForCausalLM,
        BigBirdPegasusForConditionalGeneration,
        BigBirdPegasusForQuestionAnswering,
        BigBirdPegasusForSequenceClassification,
        BigBirdPegasusModel,
        BigBirdPegasusPreTrainedModel,
    )


else:
  import sys

  __A :  List[Any]    							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



