



"""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


lowercase__					:						Dict												=			get_tests_dir('''fixtures/test_sentencepiece.model''')


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

lowercase__					:						Dict												=			25_00_04
lowercase__					:						Any												=			25_00_20




@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    int	      =	MBartTokenizer
 _lowerCAmelCase     :    int	      =	MBartTokenizerFast
 _lowerCAmelCase     :    str	      =	True
 _lowerCAmelCase     :    Union[str, Any]	      =	True

 def 		_snake_case      (  self	: Dict       ):
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_				:  str       =			MBartTokenizer(lowercase_						,  keep_accents=lowercase_       )
     tokenizer.save_pretrained(self.tmpdirname       )

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  str       =			MBartTokenizer(lowercase_						,  keep_accents=lowercase_       )

     snake_case_				:  str       =			tokenizer.tokenize('''This is a test'''       )
     self.assertListEqual(lowercase_						,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowercase_       )						,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,  )

     snake_case_				:  str       =			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
     self.assertListEqual(
         lowercase_						,  [
             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_				:  List[str]       =			tokenizer.convert_tokens_to_ids(lowercase_       )
     self.assertListEqual(
         lowercase_						,  [
             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]
             #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
         ]						,  )

     snake_case_				:  str       =			tokenizer.convert_ids_to_tokens(lowercase_       )
     self.assertListEqual(
         lowercase_						,  [
             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 		_snake_case      (  self	: str       ):
     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

     snake_case_				:  int       =			(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})"       ):
             snake_case_				:  Optional[int]       =			self.rust_tokenizer_class.from_pretrained(lowercase_						,  **lowercase_       )
             snake_case_				:  Tuple       =			self.tokenizer_class.from_pretrained(lowercase_						,  **lowercase_       )

             snake_case_				:  Union[str, Any]       =			tempfile.mkdtemp()

             snake_case_				:  List[str]       =			tokenizer_r.save_pretrained(lowercase_       )
             snake_case_				:  List[str]       =			tokenizer_p.save_pretrained(lowercase_       )

             # 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_				:  int       =			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f       )
             self.assertSequenceEqual(lowercase_						,  lowercase_       )

             # Checks everything loads correctly in the same way
             snake_case_				:  str       =			tokenizer_r.from_pretrained(lowercase_       )
             snake_case_				:  Tuple       =			tokenizer_p.from_pretrained(lowercase_       )

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

             shutil.rmtree(lowercase_       )

             # Save tokenizer rust, legacy_format=True
             snake_case_				:  List[Any]       =			tempfile.mkdtemp()

             snake_case_				:  Dict       =			tokenizer_r.save_pretrained(lowercase_						,  legacy_format=lowercase_       )
             snake_case_				:  Optional[int]       =			tokenizer_p.save_pretrained(lowercase_       )

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

             # Checks everything loads correctly in the same way
             snake_case_				:  Optional[int]       =			tokenizer_r.from_pretrained(lowercase_       )
             snake_case_				:  Tuple       =			tokenizer_p.from_pretrained(lowercase_       )

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

             shutil.rmtree(lowercase_       )

             # Save tokenizer rust, legacy_format=False
             snake_case_				:  List[str]       =			tempfile.mkdtemp()

             snake_case_				:  Any       =			tokenizer_r.save_pretrained(lowercase_						,  legacy_format=lowercase_       )
             snake_case_				:  Dict       =			tokenizer_p.save_pretrained(lowercase_       )

             # 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_				:  Tuple       =			tokenizer_r.from_pretrained(lowercase_       )
             snake_case_				:  Union[str, Any]       =			tokenizer_p.from_pretrained(lowercase_       )

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

             shutil.rmtree(lowercase_       )





@require_torch
@require_sentencepiece
@require_tokenizers
class  _UpperCAmelCase (    unittest.TestCase):
 _lowerCAmelCase     :    Dict	      =	"""facebook/mbart-large-en-ro"""
 _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     :    int	      =	[
     """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     :    Tuple	      =	[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 		_snake_case      (  cls	: List[Any]       ):
     snake_case_				:  MBartTokenizer       =			MBartTokenizer.from_pretrained(
         cls.checkpoint_name						,  src_lang='''en_XX'''						,  tgt_lang='''ro_RO'''       )
     snake_case_				:  List[str]       =			1
     return cls

 def 		_snake_case      (  self	: List[Any]       ):
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']						,  250001       )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']						,  250004       )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']						,  250020       )

 def 		_snake_case      (  self	: Optional[int]       ):
     snake_case_				:  Optional[Any]       =			self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
     self.assertListEqual(self.expected_src_tokens						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     self.assertIn(lowercase_						,  self.tokenizer.all_special_ids       )
     snake_case_				:  Dict       =			[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
     snake_case_				:  Optional[int]       =			self.tokenizer.decode(lowercase_						,  skip_special_tokens=lowercase_       )
     snake_case_				:  Tuple       =			self.tokenizer.decode(generated_ids[1:]						,  skip_special_tokens=lowercase_       )
     self.assertEqual(lowercase_						,  lowercase_       )
     self.assertNotIn(self.tokenizer.eos_token						,  lowercase_       )

 def 		_snake_case      (  self	: List[str]       ):
     snake_case_				:  Tuple       =			['''this is gunna be a long sentence ''' * 20]
     assert isinstance(src_text[0]						,  lowercase_       )
     snake_case_				:  int       =			10
     snake_case_				:  Optional[Any]       =			self.tokenizer(lowercase_						,  max_length=lowercase_						,  truncation=lowercase_       ).input_ids[0]
     self.assertEqual(ids[-2]						,  2       )
     self.assertEqual(ids[-1]						,  lowercase_       )
     self.assertEqual(len(lowercase_       )						,  lowercase_       )

 def 		_snake_case      (  self	: Union[str, Any]       ):
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']       )						,  [250026, 250001]       )

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  Dict       =			tempfile.mkdtemp()
     snake_case_				:  List[Any]       =			self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(lowercase_       )
     snake_case_				:  Union[str, Any]       =			MBartTokenizer.from_pretrained(lowercase_       )
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,  lowercase_       )

 @require_torch
 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  List[str]       =			self.tokenizer(self.src_text						,  text_target=self.tgt_text						,  padding=lowercase_						,  return_tensors='''pt'''       )
     snake_case_				:  Any       =			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 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  List[Any]       =			self.tokenizer(
         self.src_text						,  text_target=self.tgt_text						,  padding=lowercase_						,  truncation=lowercase_						,  max_length=len(self.expected_src_tokens       )						,  return_tensors='''pt'''						,  )

     snake_case_				:  Union[str, Any]       =			shift_tokens_right(batch['''labels''']						,  self.tokenizer.pad_token_id       )

     self.assertIsInstance(lowercase_						,  lowercase_       )

     self.assertEqual((2, 14)						,  batch.input_ids.shape       )
     self.assertEqual((2, 14)						,  batch.attention_mask.shape       )
     snake_case_				:  List[str]       =			batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens						,  lowercase_       )
     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 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  int       =			self.tokenizer(self.src_text						,  padding=lowercase_						,  truncation=lowercase_						,  max_length=3						,  return_tensors='''pt'''       )
     snake_case_				:  Dict       =			self.tokenizer(
         text_target=self.tgt_text						,  padding=lowercase_						,  truncation=lowercase_						,  max_length=10						,  return_tensors='''pt'''       )
     snake_case_				:  Optional[int]       =			targets['''input_ids''']
     snake_case_				:  str       =			shift_tokens_right(lowercase_						,  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 		_snake_case      (  self	: str       ):
     snake_case_				:  List[Any]       =			self.tokenizer._build_translation_inputs(
         '''A test'''						,  return_tensors='''pt'''						,  src_lang='''en_XX'''						,  tgt_lang='''ar_AR'''       )

     self.assertEqual(
         nested_simplify(lowercase_       )						,  {
             # A, test, EOS, en_XX
             '''input_ids''': [[62, 3034, 2, 250004]],
             '''attention_mask''': [[1, 1, 1, 1]],
             # ar_AR
             '''forced_bos_token_id''': 250001,
         }						,  )










"""simple docstring"""

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[Any]       =			AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     snake_case_				:  List[Any]       =			AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     model.to(lowercase_       )

     from datasets import load_dataset

     snake_case_				:  str       =			load_dataset('''nielsr/rvlcdip-demo'''       )

     snake_case_				:  Tuple       =			dataset['''train'''][0]['''image'''].convert('''RGB'''       )

     snake_case_				:  Union[str, Any]       =			image_processor(lowercase_						,  return_tensors='''pt'''       ).to(lowercase_       )

     # forward pass
     with torch.no_grad():
         snake_case_				:  List[Any]       =			model(**lowercase_       )
         snake_case_				:  int       =			outputs.logits

     snake_case_				:  Any       =			torch.Size((1, 16)       )
     self.assertEqual(logits.shape						,  lowercase_       )

     snake_case_				:  Any       =			torch.tensor(
         [-0.41_58, -0.40_92, -0.43_47]						,  device=lowercase_						,  dtype=torch.float						,  )
     self.assertTrue(torch.allclose(logits[0, :3]						,  lowercase_						,  atol=1E-4       )       )





