




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase    :		Tuple			   =       get_tests_dir("fixtures/test_sentencepiece.model")

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

lowerCamelCase    :		Any			   =       250_004
lowerCamelCase    :		str			   =       250_020







@require_sentencepiece
@require_tokenizers
class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						MBartaaTokenizer
 UpperCamelCase						       =						MBartaaTokenizerFast
 UpperCamelCase						       =						True
 UpperCamelCase						       =						True

 def     a__			(    self :  Dict  )       ->     Optional[Any]:

       """simple docstring"""






       super().setUp()

       # We have a SentencePiece fixture for testing
       lowerCamelCase_							=   MBartaaTokenizer(A_						,							src_lang='en_XX'						,							tgt_lang='ro_RO'						,							keep_accents=A_  )
       tokenizer.save_pretrained(self.tmpdirname  )

 def     a__			(    self :  int  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   '<s>'
       lowerCamelCase_							=   0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_  )						,							A_  )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_  )						,							A_  )

 def     a__			(    self :  Optional[int]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]						,							'<s>'  )
       self.assertEqual(vocab_keys[1]						,							'<pad>'  )
       self.assertEqual(vocab_keys[-1]						,							'<mask>'  )
       self.assertEqual(len(A_  )						,							1054  )

 def     a__			(    self :  List[str]  )       ->     Dict:

       """simple docstring"""






       self.assertEqual(self.get_tokenizer().vocab_size						,							1054  )

 def     a__			(    self :  List[str]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   MBartaaTokenizer(A_						,							src_lang='en_XX'						,							tgt_lang='ro_RO'						,							keep_accents=A_  )

       lowerCamelCase_							=   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]]						,							)

       lowerCamelCase_							=   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', '.']						,							)
       lowerCamelCase_							=   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]
           ]						,							)

       lowerCamelCase_							=   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>', '.']						,							)

 @slow
 def     a__			(    self :  int  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   {'input_ids': [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_						,							model_name='facebook/mbart-large-50'						,							revision='d3913889c59cd5c9e456b269c376325eabad57e2'						,							)





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

       lowerCamelCase_							=   (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                   lowerCamelCase_							=   self.rust_tokenizer_class.from_pretrained(A_						,							**A_  )
                   lowerCamelCase_							=   self.tokenizer_class.from_pretrained(A_						,							**A_  )

                   lowerCamelCase_							=   tempfile.mkdtemp()

                   lowerCamelCase_							=   tokenizer_r.save_pretrained(A_  )
                   lowerCamelCase_							=   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  )  )
                   lowerCamelCase_							=   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
                   lowerCamelCase_							=   tokenizer_r.from_pretrained(A_  )
                   lowerCamelCase_							=   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_  )  )
                         # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                         # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                   shutil.rmtree(A_  )

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

                   lowerCamelCase_							=   tokenizer_r.save_pretrained(A_						,							legacy_format=A_  )
                   lowerCamelCase_							=   tokenizer_p.save_pretrained(A_  )

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

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

                   lowerCamelCase_							=   tokenizer_r.save_pretrained(A_						,							legacy_format=A_  )
                   lowerCamelCase_							=   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
                   lowerCamelCase_							=   tokenizer_r.from_pretrained(A_  )
                   lowerCamelCase_							=   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
@require_sentencepiece
@require_tokenizers
class   A(						unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''facebook/mbart-large-50-one-to-many-mmt'''
 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.''',
 ]
 UpperCamelCase						       =						[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]

 @classmethod
 def     a__			(    cls :  Tuple  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   MBartaaTokenizer.from_pretrained(
           cls.checkpoint_name						,							src_lang='en_XX'						,							tgt_lang='ro_RO'  )
       lowerCamelCase_							=   1
       return cls

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

       """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  )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']						,							250038  )

 def     a__			(    self :  int  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens						,							A_  )

 def     a__			(    self :  Dict  )       ->     Optional[int]:

       """simple docstring"""






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

 def     a__			(    self :  int  )       ->     str:

       """simple docstring"""






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

 def     a__			(    self :  Any  )       ->     str:

       """simple docstring"""






       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']  )						,							[250053, 250001]  )

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

       """simple docstring"""






       lowerCamelCase_							=   tempfile.mkdtemp()
       lowerCamelCase_							=   self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(A_  )
       lowerCamelCase_							=   MBartaaTokenizer.from_pretrained(A_  )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,							A_  )

 @require_torch
 def     a__			(    self :  Tuple  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.tokenizer(self.src_text						,							text_target=self.tgt_text						,							padding=A_						,							return_tensors='pt'  )
       lowerCamelCase_							=   shift_tokens_right(batch['labels']						,							self.tokenizer.pad_token_id  )

       # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
       assert batch.input_ids[1][0] == EN_CODE
       assert batch.input_ids[1][-1] == 2
       assert batch.labels[1][0] == RO_CODE
       assert batch.labels[1][-1] == 2
       assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

 @require_torch
 def     a__			(    self :  Optional[int]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   self.tokenizer(
           self.src_text						,							text_target=self.tgt_text						,							padding=A_						,							truncation=A_						,							max_length=len(self.expected_src_tokens  )						,							return_tensors='pt'						,							)
       lowerCamelCase_							=   shift_tokens_right(batch['labels']						,							self.tokenizer.pad_token_id  )

       self.assertIsInstance(A_						,							A_  )

       self.assertEqual((2, 14)						,							batch.input_ids.shape  )
       self.assertEqual((2, 14)						,							batch.attention_mask.shape  )
       lowerCamelCase_							=   batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens						,							A_  )
       self.assertEqual(2						,							batch.decoder_input_ids[0, 0]  )  # decoder_start_token_id
       # 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 :  Optional[Any]  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.tokenizer(self.src_text						,							padding=A_						,							truncation=A_						,							max_length=3						,							return_tensors='pt'  )
       lowerCamelCase_							=   self.tokenizer(
           text_target=self.tgt_text						,							padding=A_						,							truncation=A_						,							max_length=10						,							return_tensors='pt'  )
       lowerCamelCase_							=   targets['input_ids']
       lowerCamelCase_							=   shift_tokens_right(A_						,							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     a__			(    self :  List[str]  )       ->     Union[str, Any]:

       """simple docstring"""






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

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









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
 from .autoencoder_kl import AutoencoderKL
 from .controlnet import ControlNetModel
 from .dual_transformer_ad import DualTransformeraDModel
 from .modeling_utils import ModelMixin
 from .prior_transformer import PriorTransformer
 from .ta_film_transformer import TaFilmDecoder
 from .transformer_ad import TransformeraDModel
 from .unet_ad import UNetaDModel
 from .unet_ad import UNetaDModel
 from .unet_ad_condition import UNetaDConditionModel
 from .unet_ad_condition import UNetaDConditionModel
 from .vq_model import VQModel

if is_flax_available():
 from .controlnet_flax import FlaxControlNetModel
 from .unet_ad_condition_flax import FlaxUNetaDConditionModel
 from .vae_flax import FlaxAutoencoderKL



