






"""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_				     =     get_tests_dir('fixtures/test_sentencepiece.model')


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

lowercase_				     =     25_0004
lowercase_				     =     25_0020


@require_sentencepiece
@require_tokenizers
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	List[Any]						 = MBartTokenizer
   A_						:	Tuple						 = MBartTokenizerFast
   A_						:	Optional[int]						 = True
   A_						:	Union[str, Any]						 = True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     super().setUp()

     # We have a SentencePiece fixture for testing
     __A									= MBartTokenizer(_lowerCamelCase,       keep_accents=_lowerCamelCase  )
     tokenizer.save_pretrained(self.tmpdirname  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= MBartTokenizer(_lowerCamelCase,       keep_accents=_lowerCamelCase  )

     __A									= 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]],       )

     __A									= 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''',
             '''.''',
         ],       )
     __A									= 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]
             #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
         ],       )

     __A									= 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 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     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(_lowerCamelCase,       **_lowerCamelCase  )
         __A									= self.tokenizer_class.from_pretrained(_lowerCamelCase,       **_lowerCamelCase  )

         __A									= tempfile.mkdtemp()

         __A									= tokenizer_r.save_pretrained(_lowerCamelCase  )
         __A									= 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  )  )
         __A									= 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
         __A									= tokenizer_r.from_pretrained(_lowerCamelCase  )
         __A									= 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  )  )
           # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
           # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

         shutil.rmtree(_lowerCamelCase  )

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

         __A									= tokenizer_r.save_pretrained(_lowerCamelCase,       legacy_format=_lowerCamelCase  )
         __A									= tokenizer_p.save_pretrained(_lowerCamelCase  )

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

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

         __A									= tokenizer_r.save_pretrained(_lowerCamelCase,       legacy_format=_lowerCamelCase  )
         __A									= 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
         __A									= tokenizer_r.from_pretrained(_lowerCamelCase  )
         __A									= 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
@require_sentencepiece
@require_tokenizers
class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   A_						:	Union[str, Any]						 = "facebook/mbart-large-en-ro"
   A_						:	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.",
   ]
   A_						:	Optional[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.",
   ]
   A_						:	Tuple						 = [8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2, EN_CODE]


   @classmethod
   def 				_SCREAMING_SNAKE_CASE				(	cls :     List[str]  ):

     '''simple docstring'''
     __A									= MBartTokenizer.from_pretrained(
         cls.checkpoint_name,       src_lang='''en_XX''',       tgt_lang='''ro_RO'''  )
     __A									= 1
     return cls


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     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 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
     self.assertListEqual(self.expected_src_tokens,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     self.assertIn(_lowerCamelCase,       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(_lowerCamelCase,       skip_special_tokens=_lowerCamelCase  )
     __A									= self.tokenizer.decode(generated_ids[1:],       skip_special_tokens=_lowerCamelCase  )
     self.assertEqual(_lowerCamelCase,       _lowerCamelCase  )
     self.assertNotIn(self.tokenizer.eos_token,       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= ['''this is gunna be a long sentence ''' * 20]
     assert isinstance(src_text[0],       _lowerCamelCase  )
     __A									= 10
     __A									= self.tokenizer(_lowerCamelCase,       max_length=_lowerCamelCase,       truncation=_lowerCamelCase  ).input_ids[0]
     self.assertEqual(ids[-2],       2  )
     self.assertEqual(ids[-1],       _lowerCamelCase  )
     self.assertEqual(len(_lowerCamelCase  ),       _lowerCamelCase  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']  ),       [25_00_26, 25_00_01]  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= tempfile.mkdtemp()
     __A									= self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(_lowerCamelCase  )
     __A									= MBartTokenizer.from_pretrained(_lowerCamelCase  )
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids,       _lowerCamelCase  )


   @require_torch
   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     __A									= self.tokenizer(self.src_text,       text_target=self.tgt_text,       padding=_lowerCamelCase,       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 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.tokenizer(
         self.src_text,       text_target=self.tgt_text,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       max_length=len(self.expected_src_tokens  ),       return_tensors='''pt''',       )

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

     self.assertIsInstance(_lowerCamelCase,       _lowerCamelCase  )

     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,       _lowerCamelCase  )
     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 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     __A									= self.tokenizer(self.src_text,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       max_length=3,       return_tensors='''pt'''  )
     __A									= self.tokenizer(
         text_target=self.tgt_text,       padding=_lowerCamelCase,       truncation=_lowerCamelCase,       max_length=10,       return_tensors='''pt'''  )
     __A									= targets['''input_ids''']
     __A									= shift_tokens_right(_lowerCamelCase,       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 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     __A									= self.tokenizer._build_translation_inputs(
         '''A test''',       return_tensors='''pt''',       src_lang='''en_XX''',       tgt_lang='''ar_AR'''  )

     self.assertEqual(
         nested_simplify(_lowerCamelCase  ),       {
             # 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"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=13,       _lowerCamelCase :     Any=3,       _lowerCamelCase :     Optional[int]=2_24,       _lowerCamelCase :     str=30,       _lowerCamelCase :     Dict=4_00,       _lowerCamelCase :     Union[str, Any]=True,       _lowerCamelCase :     Any=None,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Any=[0.5, 0.5, 0.5],       _lowerCamelCase :     List[str]=[0.5, 0.5, 0.5],       ):

     '''simple docstring'''
     __A									= size if size is not None else {'''height''': 18, '''width''': 18}
     __A									= parent
     __A									= batch_size
     __A									= num_channels
     __A									= image_size
     __A									= min_resolution
     __A									= max_resolution
     __A									= do_resize
     __A									= size
     __A									= do_normalize
     __A									= image_mean
     __A									= image_std






   def 				_SCREAMING_SNAKE_CASE				(	self :     int  ):

     '''simple docstring'''
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
     }





@require_torch
@require_vision
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	str						 = ViTImageProcessor if is_vision_available() else None


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= EfficientFormerImageProcessorTester(self  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     return self.image_proc_tester.prepare_image_processor_dict()


   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any]  ):

     '''simple docstring'''
     __A									= self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,       '''size'''  )  )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     pass


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any]  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       Image.Image  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       np.ndarray  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )






   def 				_SCREAMING_SNAKE_CASE				(	self :     str  ):

     '''simple docstring'''
     # Initialize image_processor
     __A									= self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     __A									= prepare_image_inputs(self.image_proc_tester,       equal_resolution=_lowerCamelCase,       torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,       torch.Tensor  )

     # Test not batched input
     __A									= image_processor(image_inputs[0],       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             1,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )

     # Test batched
     __A									= image_processor(_lowerCamelCase,       return_tensors='''pt'''  ).pixel_values
     self.assertEqual(
         encoded_images.shape,       (
             self.image_proc_tester.batch_size,
             self.image_proc_tester.num_channels,
             self.image_proc_tester.size['''height'''],
             self.image_proc_tester.size['''width'''],
         ),       )




