





'''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__		:     Tuple			=							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__		:     List[Any]			=							25_00_20
@require_sentencepiece
@require_tokenizers
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Dict									=       MBartTokenizer
       _snake_case      :	Tuple									=       MBartTokenizerFast
       _snake_case      :	Union[str, Any]									=       True
       _snake_case      :	Tuple									=       True





       def snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            super().setUp()

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





       def snake_case__		(				self  :						Any	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			MBartTokenizer(lowerCAmelCase__			,					keep_accents=lowerCAmelCase__	)

            _UpperCamelCase							       =			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 [285, 46, 10, 170, 382]]			,					)

            _UpperCamelCase							       =			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''',
                    '''.''',
                ]			,					)
            _UpperCamelCase							       =			tokenizer.convert_tokens_to_ids(lowerCAmelCase__	)
            self.assertListEqual(
                lowerCAmelCase__			,					[
                    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 ^
                ]			,					)

            _UpperCamelCase							       =			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 snake_case__		(				self  :						Optional[int]	)       ->	Tuple:

            '''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

            _UpperCamelCase							       =			(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})"""	):
                      _UpperCamelCase							       =			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__			,					**lowerCAmelCase__	)
                      _UpperCamelCase							       =			self.tokenizer_class.from_pretrained(lowerCAmelCase__			,					**lowerCAmelCase__	)

                      _UpperCamelCase							       =			tempfile.mkdtemp()

                      _UpperCamelCase							       =			tokenizer_r.save_pretrained(lowerCAmelCase__	)
                      _UpperCamelCase							       =			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	)	)
                      _UpperCamelCase							       =			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
                      _UpperCamelCase							       =			tokenizer_r.from_pretrained(lowerCAmelCase__	)
                      _UpperCamelCase							       =			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
                      _UpperCamelCase							       =			tempfile.mkdtemp()

                      _UpperCamelCase							       =			tokenizer_r.save_pretrained(lowerCAmelCase__			,					legacy_format=lowerCAmelCase__	)
                      _UpperCamelCase							       =			tokenizer_p.save_pretrained(lowerCAmelCase__	)

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

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

                      _UpperCamelCase							       =			tokenizer_r.save_pretrained(lowerCAmelCase__			,					legacy_format=lowerCAmelCase__	)
                      _UpperCamelCase							       =			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
                      _UpperCamelCase							       =			tokenizer_r.from_pretrained(lowerCAmelCase__	)
                      _UpperCamelCase							       =			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 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Tuple									=       'facebook/mbart-large-en-ro'
       _snake_case      :	List[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.',
       ]
       _snake_case      :	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.',
       ]
       _snake_case      :	Any									=       [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  :						str	)       ->	int:

            '''simple docstring'''




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





       def snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




            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[Any]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
            self.assertListEqual(self.expected_src_tokens			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	int:

            '''simple docstring'''




            self.assertIn(lowerCAmelCase__			,					self.tokenizer.all_special_ids	)
            _UpperCamelCase							       =			[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
            _UpperCamelCase							       =			self.tokenizer.decode(lowerCAmelCase__			,					skip_special_tokens=lowerCAmelCase__	)
            _UpperCamelCase							       =			self.tokenizer.decode(generated_ids[1:]			,					skip_special_tokens=lowerCAmelCase__	)
            self.assertEqual(lowerCAmelCase__			,					lowerCAmelCase__	)
            self.assertNotIn(self.tokenizer.eos_token			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Dict	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			['''this is gunna be a long sentence ''' * 20]
            assert isinstance(src_text[0]			,					lowerCAmelCase__	)
            _UpperCamelCase							       =			10
            _UpperCamelCase							       =			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 snake_case__		(				self  :						Dict	)       ->	List[Any]:

            '''simple docstring'''




            self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']	)			,					[250026, 250001]	)





       def snake_case__		(				self  :						int	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			tempfile.mkdtemp()
            _UpperCamelCase							       =			self.tokenizer.fairseq_tokens_to_ids
            self.tokenizer.save_pretrained(lowerCAmelCase__	)
            _UpperCamelCase							       =			MBartTokenizer.from_pretrained(lowerCAmelCase__	)
            self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,					lowerCAmelCase__	)





       @require_torch
       def snake_case__		(				self  :						int	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer(self.src_text			,					text_target=self.tgt_text			,					padding=lowerCAmelCase__			,					return_tensors='''pt'''	)
            _UpperCamelCase							       =			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  :						Tuple	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer(
                self.src_text			,					text_target=self.tgt_text			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=len(self.expected_src_tokens	)			,					return_tensors='''pt'''			,					)

            _UpperCamelCase							       =			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	)
            _UpperCamelCase							       =			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 snake_case__		(				self  :						Dict	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.tokenizer(self.src_text			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=3			,					return_tensors='''pt'''	)
            _UpperCamelCase							       =			self.tokenizer(
                text_target=self.tgt_text			,					padding=lowerCAmelCase__			,					truncation=lowerCAmelCase__			,					max_length=10			,					return_tensors='''pt'''	)
            _UpperCamelCase							       =			targets['''input_ids''']
            _UpperCamelCase							       =			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 snake_case__		(				self  :						str	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			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, 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
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_torch
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    List[str]							=							XLMRobertaModel.from_pretrained("xlm-roberta-base"							)
        UpperCAmelCase__						:    str							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Tuple							=							torch.Size((1, 1_2, 7_6_8)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[int]							=							torch.tensor(
            [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    int							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)




   @slow
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							XLMRobertaModel.from_pretrained("xlm-roberta-large"							)
        UpperCAmelCase__						:    Tuple							=							torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]							)
        # The dog is cute and lives in the garden house

        UpperCAmelCase__						:    Union[str, Any]							=							torch.Size((1, 1_2, 1_0_2_4)							)  # batch_size, sequence_length, embedding_vector_dim
        UpperCAmelCase__						:    Optional[Any]							=							torch.tensor(
            [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]							)
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
             UpperCAmelCase__						:    Optional[Any]							=							model(snake_case__							)["last_hidden_state"].detach()
        self.assertEqual(output.shape							,			snake_case__							)
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]							,			snake_case__							,			atol=1e-3							)							)


