


'''simple docstring'''






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


a_					:    List[Any]          =    get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

a_					:    Optional[Any]          =    2_5_0_0_0_4
a_					:    Optional[int]          =    2_5_0_0_2_0




@require_sentencepiece
@require_tokenizers
class  a     (     _SCREAMING_SNAKE_CASE  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	MBartTokenizer
 _lowerCAmelCase 					=	MBartTokenizerFast
 _lowerCAmelCase 					=	True
 _lowerCAmelCase 					=	True
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      super().setUp()

      # We have a SentencePiece fixture for testing
      _a			      =							MBartTokenizer(__magic_name__      ,				keep_accents=__magic_name__						)
      tokenizer.save_pretrained(self.tmpdirname						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							MBartTokenizer(__magic_name__      ,				keep_accents=__magic_name__						)

      _a			      =							tokenizer.tokenize('This is a test'						)
      self.assertListEqual(__magic_name__      ,				['▁This', '▁is', '▁a', '▁t', 'est']						)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__magic_name__						)      ,				[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(
          __magic_name__      ,				[
              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(__magic_name__						)
      self.assertListEqual(
          __magic_name__      ,				[
              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]
              #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
          ]      ,				)

      _a			      =							tokenizer.convert_ids_to_tokens(__magic_name__						)
      self.assertListEqual(
          __magic_name__      ,				[
              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						)					->							Union[str, Any]:
      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

      _a			      =							(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})'						):
                _a			      =							self.rust_tokenizer_class.from_pretrained(__magic_name__      ,				**__magic_name__						)
                _a			      =							self.tokenizer_class.from_pretrained(__magic_name__      ,				**__magic_name__						)

                _a			      =							tempfile.mkdtemp()

                _a			      =							tokenizer_r.save_pretrained(__magic_name__						)
                _a			      =							tokenizer_p.save_pretrained(__magic_name__						)

                # 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(__magic_name__      ,				__magic_name__						)

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

                # Check special tokens are set accordingly on Rust and Python
                for key in tokenizer_pp.special_tokens_map:
                     self.assertTrue(hasattr(__magic_name__      ,				__magic_name__						)						)
                     # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                     # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                shutil.rmtree(__magic_name__						)

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

                _a			      =							tokenizer_r.save_pretrained(__magic_name__      ,				legacy_format=__magic_name__						)
                _a			      =							tokenizer_p.save_pretrained(__magic_name__						)

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

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

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

                shutil.rmtree(__magic_name__						)

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

                _a			      =							tokenizer_r.save_pretrained(__magic_name__      ,				legacy_format=__magic_name__						)
                _a			      =							tokenizer_p.save_pretrained(__magic_name__						)

                # 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(__magic_name__						)
                _a			      =							tokenizer_p.from_pretrained(__magic_name__						)

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

                shutil.rmtree(__magic_name__						)





@require_torch
@require_sentencepiece
@require_tokenizers
class  a     (     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 							__UpperCAmelCase		( cls						)					->							str:
      _a			      =							MBartTokenizer.from_pretrained(
          cls.checkpoint_name      ,				src_lang='en_XX'      ,				tgt_lang='ro_RO'						)
      _a			      =							1
      return cls
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']      ,				25_00_01						)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']      ,				25_00_04						)
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']      ,				25_00_20						)
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
      self.assertListEqual(self.expected_src_tokens      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      self.assertIn(__magic_name__      ,				self.tokenizer.all_special_ids						)
      _a			      =							[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
      _a			      =							self.tokenizer.decode(__magic_name__      ,				skip_special_tokens=__magic_name__						)
      _a			      =							self.tokenizer.decode(generated_ids[1:]      ,				skip_special_tokens=__magic_name__						)
      self.assertEqual(__magic_name__      ,				__magic_name__						)
      self.assertNotIn(self.tokenizer.eos_token      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Optional[int]:
      _a			      =							['this is gunna be a long sentence ' * 20]
      assert isinstance(src_text[0]      ,				__magic_name__						)
      _a			      =							10
      _a			      =							self.tokenizer(__magic_name__      ,				max_length=__magic_name__      ,				truncation=__magic_name__						).input_ids[0]
      self.assertEqual(ids[-2]      ,				2						)
      self.assertEqual(ids[-1]      ,				__magic_name__						)
      self.assertEqual(len(__magic_name__						)      ,				__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							str:
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']						)      ,				[25_00_26, 25_00_01]						)
 def 							__UpperCAmelCase		( self						)					->							int:
      _a			      =							tempfile.mkdtemp()
      _a			      =							self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(__magic_name__						)
      _a			      =							MBartTokenizer.from_pretrained(__magic_name__						)
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,				__magic_name__						)
 @require_torch
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							self.tokenizer(self.src_text      ,				text_target=self.tgt_text      ,				padding=__magic_name__      ,				return_tensors='pt'						)
      _a			      =							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 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.tokenizer(
          self.src_text      ,				text_target=self.tgt_text      ,				padding=__magic_name__      ,				truncation=__magic_name__      ,				max_length=len(self.expected_src_tokens						)      ,				return_tensors='pt'      ,				)

      _a			      =							shift_tokens_right(batch['labels']      ,				self.tokenizer.pad_token_id						)

      self.assertIsInstance(__magic_name__      ,				__magic_name__						)

      self.assertEqual((2, 14)      ,				batch.input_ids.shape						)
      self.assertEqual((2, 14)      ,				batch.attention_mask.shape						)
      _a			      =							batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens      ,				__magic_name__						)
      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 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							self.tokenizer(self.src_text      ,				padding=__magic_name__      ,				truncation=__magic_name__      ,				max_length=3      ,				return_tensors='pt'						)
      _a			      =							self.tokenizer(
          text_target=self.tgt_text      ,				padding=__magic_name__      ,				truncation=__magic_name__      ,				max_length=10      ,				return_tensors='pt'						)
      _a			      =							targets['input_ids']
      _a			      =							shift_tokens_right(__magic_name__      ,				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 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							self.tokenizer._build_translation_inputs(
          'A test'      ,				return_tensors='pt'      ,				src_lang='en_XX'      ,				tgt_lang='ar_AR'						)

      self.assertEqual(
          nested_simplify(__magic_name__						)      ,				{
              # A, test, EOS, en_XX
              'input_ids': [[62, 30_34, 2, 25_00_04]],
              'attention_mask': [[1, 1, 1, 1]],
              # ar_AR
              'forced_bos_token_id': 25_00_01,
          }      ,				)





'''simple docstring'''






from typing import TYPE_CHECKING

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


a_					:    int          =    {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    List[Any]          =    ["ViTFeatureExtractor"]
 a_					:    Optional[int]          =    ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Optional[int]          =    [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Any          =    [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_					:    Dict          =    [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

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

