


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		       =						get_tests_dir('fixtures/test_sentencepiece.model')


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

_A		       =						25_6047
_A		       =						25_6145






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



    """simple docstring"""






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






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

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






    def _a					(			self    )  ->       Optional[int]:
         __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 [285, 46, 10, 170, 382]]					,   )

         __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, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 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 _a					(			self    )  ->       int:
         __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 _a					(			self    )  ->       List[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=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
                   __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=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[Any]:
         pass






    def _a					(			self    )  ->       List[str]:
         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     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "facebook/nllb-200-distilled-600M"
    UpperCAmelCase__						:		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.",
    ]
    UpperCAmelCase__						:		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.",
    ]
    UpperCAmelCase__						:		Optional[Any]      =  [
        2_5_6_0_4_7,
        1_6_2_9_7,
        1_3_4_4_0_8,
        8_1_6_5,
        2_4_8_0_6_6,
        1_4_7_3_4,
        9_5_0,
        1_1_3_5,
        1_0_5_7_2_1,
        3_5_7_3,
        8_3,
        2_7_3_5_2,
        1_0_8,
        4_9_4_8_6,
        2,
    ]






    @classmethod
    def _a					(			cls    )  ->       List[Any]:
         __UpperCamelCase    							=NllbTokenizer.from_pretrained(
             cls.checkpoint_name					,   src_lang='eng_Latn'					,   tgt_lang='ron_Latn'    )
         __UpperCamelCase    							=1
         return cls






    def _a					(			self    )  ->       Tuple:
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']					,   256001    )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']					,   256002    )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']					,   256057    )






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






    def _a					(			self    )  ->       List[str]:
         self.assertIn(A_					,   self.tokenizer.all_special_ids    )
         # fmt: off
         __UpperCamelCase    							=[RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
         # 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 _a					(			self    )  ->       Any:
         __UpperCamelCase    							=['this is gunna be a long sentence ' * 20]
         assert isinstance(src_text[0]					,   A_    )
         __UpperCamelCase    							=10
         __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 _a					(			self    )  ->       List[str]:
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']    )					,   [256203, 3]    )






    def _a					(			self    )  ->       Optional[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 _a					(			self    )  ->       Dict:
         __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, 15)					,   batch.input_ids.shape    )
         self.assertEqual((2, 15)					,   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 _a					(			self    )  ->       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=10					,   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]					,   10    )






    @require_torch
    def _a					(			self    )  ->       Optional[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': [[256047, 70, 7356, 2]],
                 'attention_mask': [[1, 1, 1, 1]],
                 # ar_AR
                 'forced_bos_token_id': 256057,
             }					,   )






    @require_torch
    def _a					(			self    )  ->       Optional[int]:
         __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					,   [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047]    )

         __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					,   [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2]    )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A		       =						{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMAEForPreTraining',
       'ViTMAELayer',
       'ViTMAEModel',
       'ViTMAEPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TFViTMAEForPreTraining',
       'TFViTMAEModel',
       'TFViTMAEPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_mae import (
          VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMAEForPreTraining,
          ViTMAELayer,
          ViTMAEModel,
          ViTMAEPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





