


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


__UpperCAmelCase : List[str] 			=				get_tests_dir("fixtures/test_sentencepiece.model")


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

__UpperCAmelCase : str 			=				250004
__UpperCAmelCase : int 			=				250020



@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase_	(							snake_case__,     unittest.TestCase):
       __UpperCamelCase      :     Any							=					MBartTokenizer
       __UpperCamelCase      :     List[str]							=					MBartTokenizerFast
       __UpperCamelCase      :     Dict							=					True
       __UpperCamelCase      :     Any							=					True






       def  _lowercase   (     self						):




              """simple docstring"""




              super().setUp()

              # We have a SentencePiece fixture for testing
              UpperCamelCase    :	Optional[int]   	=		MBartTokenizer(_A ,					keep_accents=_A						)
              tokenizer.save_pretrained(self.tmpdirname						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		MBartTokenizer(_A ,					keep_accents=_A						)

              UpperCamelCase    :	List[Any]   	=		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]] ,					)

              UpperCamelCase    :	Optional[int]   	=		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''',
                      '''.''',
                  ] ,					)
              UpperCamelCase    :	str   	=		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]
                      #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                  ] ,					)

              UpperCamelCase    :	Dict   	=		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>''',
                      '''.''',
                  ] ,					)






       def  _lowercase   (     self						):




              """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    :	Optional[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})"""						):
                            UpperCamelCase    :	List[Any]   	=		self.rust_tokenizer_class.from_pretrained(_A ,					**_A						)
                            UpperCamelCase    :	str   	=		self.tokenizer_class.from_pretrained(_A ,					**_A						)

                            UpperCamelCase    :	List[Any]   	=		tempfile.mkdtemp()

                            UpperCamelCase    :	Dict   	=		tokenizer_r.save_pretrained(_A						)
                            UpperCamelCase    :	Tuple   	=		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						)						)
                            UpperCamelCase    :	Optional[Any]   	=		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
                            UpperCamelCase    :	Tuple   	=		tokenizer_r.from_pretrained(_A						)
                            UpperCamelCase    :	List[Any]   	=		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
                            UpperCamelCase    :	Optional[Any]   	=		tempfile.mkdtemp()

                            UpperCamelCase    :	Tuple   	=		tokenizer_r.save_pretrained(_A ,					legacy_format=_A						)
                            UpperCamelCase    :	Union[str, Any]   	=		tokenizer_p.save_pretrained(_A						)

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

                            # Checks everything loads correctly in the same way
                            UpperCamelCase    :	int   	=		tokenizer_r.from_pretrained(_A						)
                            UpperCamelCase    :	str   	=		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
                            UpperCamelCase    :	List[str]   	=		tempfile.mkdtemp()

                            UpperCamelCase    :	Optional[Any]   	=		tokenizer_r.save_pretrained(_A ,					legacy_format=_A						)
                            UpperCamelCase    :	Dict   	=		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
                            UpperCamelCase    :	List[str]   	=		tokenizer_r.from_pretrained(_A						)
                            UpperCamelCase    :	List[Any]   	=		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    UpperCAmelCase_	(							unittest.TestCase):
       __UpperCamelCase      :     str							=					'''facebook/mbart-large-en-ro'''
       __UpperCamelCase      :     List[str]							=					[
           ''' 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      :     Tuple							=					[
           '''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      :     Dict							=					[8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]






       @classmethod
       def  _lowercase   (     cls						):




              """simple docstring"""




              UpperCamelCase    :	MBartTokenizer   	=		MBartTokenizer.from_pretrained(
                  cls.checkpoint_name ,					src_lang='''en_XX''' ,					tgt_lang='''ro_RO'''						)
              UpperCamelCase    :	Union[str, Any]   	=		1
              return cls






       def  _lowercase   (     self						):




              """simple docstring"""




              self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR'''] ,					250_001						)
              self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN'''] ,					250_004						)
              self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO'''] ,					250_020						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		self.tokenizer.batch_encode_plus(self.src_text						).input_ids[0]
              self.assertListEqual(self.expected_src_tokens ,					_A						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.assertIn(_A ,					self.tokenizer.all_special_ids						)
              UpperCamelCase    :	Tuple   	=		[RO_CODE, 884, 9_019, 96, 9, 916, 86_792, 36, 18_743, 15_596, 5, 2]
              UpperCamelCase    :	int   	=		self.tokenizer.decode(_A ,					skip_special_tokens=_A						)
              UpperCamelCase    :	List[str]   	=		self.tokenizer.decode(generated_ids[1:] ,					skip_special_tokens=_A						)
              self.assertEqual(_A ,					_A						)
              self.assertNotIn(self.tokenizer.eos_token ,					_A						)






       def  _lowercase   (     self						):




              """simple docstring"""




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






       def  _lowercase   (     self						):




              """simple docstring"""




              self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']						) ,					[250_026, 250_001]						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		tempfile.mkdtemp()
              UpperCamelCase    :	Tuple   	=		self.tokenizer.fairseq_tokens_to_ids
              self.tokenizer.save_pretrained(_A						)
              UpperCamelCase    :	Any   	=		MBartTokenizer.from_pretrained(_A						)
              self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,					_A						)






       @require_torch
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.tokenizer(self.src_text ,					text_target=self.tgt_text ,					padding=_A ,					return_tensors='''pt'''						)
              UpperCamelCase    :	Union[str, 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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.tokenizer(
                  self.src_text ,					text_target=self.tgt_text ,					padding=_A ,					truncation=_A ,					max_length=len(self.expected_src_tokens						) ,					return_tensors='''pt''' ,					)

              UpperCamelCase    :	Tuple   	=		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						)
              UpperCamelCase    :	List[str]   	=		batch.input_ids.tolist()[0]
              self.assertListEqual(self.expected_src_tokens ,					_A						)
              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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		self.tokenizer(self.src_text ,					padding=_A ,					truncation=_A ,					max_length=3 ,					return_tensors='''pt'''						)
              UpperCamelCase    :	Optional[Any]   	=		self.tokenizer(
                  text_target=self.tgt_text ,					padding=_A ,					truncation=_A ,					max_length=10 ,					return_tensors='''pt'''						)
              UpperCamelCase    :	int   	=		targets['input_ids']
              UpperCamelCase    :	Optional[Any]   	=		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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self.tokenizer._build_translation_inputs(
                  '''A test''' ,					return_tensors='''pt''' ,					src_lang='''en_XX''' ,					tgt_lang='''ar_AR'''						)

              self.assertEqual(
                  nested_simplify(_A						) ,					{
                      # A, test, EOS, en_XX
                      '''input_ids''': [[62, 3_034, 2, 250_004]],
                      '''attention_mask''': [[1, 1, 1, 1]],
                      # ar_AR
                      '''forced_bos_token_id''': 250_001,
                  } ,					)





import torch

from transformers import AutoModel



class    UpperCAmelCase_	(							torch.nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"						):




              """simple docstring"""




              super(__SCREAMING_SNAKE_CASE ,					self						).__init__()

              UpperCamelCase    :	List[str]   	=		AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE ,					return_dict=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		torch.nn.CosineSimilarity(3 ,					1e-08						)
              UpperCamelCase    :	List[Any]   	=		torch.nn.Softmax(dim=1						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return self.bert(**__SCREAMING_SNAKE_CASE						).last_hidden_state






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return token_embeddings.sum(2 ,					keepdim=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=1						):




              """simple docstring"""




              return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		W_supports['''sizes'''].tolist()
              UpperCamelCase    :	Optional[int]   	=		W_supports['''start_token_id'''].item()
              UpperCamelCase    :	Any   	=		W_supports['''end_token_id'''].item()

              del W_supports["sizes"]
              del W_supports["start_token_id"]
              del W_supports["end_token_id"]

              UpperCamelCase    :	Union[str, Any]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		self.BERT(**__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		None
              UpperCamelCase    :	Any   	=		None

              UpperCamelCase    :	Optional[Any]   	=		W_supports['''input_ids'''] == start_token_id
              UpperCamelCase    :	Any   	=		W_supports['''input_ids'''] == end_token_id

              for i, size in enumerate(__SCREAMING_SNAKE_CASE						):
                     if i == 0:
                            UpperCamelCase    :	Optional[int]   	=		0
                     else:
                            UpperCamelCase    :	Tuple   	=		support_sizes[i - 1]

                     UpperCamelCase    :	Tuple   	=		S[s : s + size][start_token_masks[s : s + size]]
                     UpperCamelCase    :	List[str]   	=		S[s : s + size][end_token_masks[s : s + size]]

                     UpperCamelCase    :	Dict   	=		torch.matmul(q[i] ,					s_start.T						).sum(1						).softmax(0						)
                     UpperCamelCase    :	Tuple   	=		torch.matmul(q[i] ,					s_end.T						).sum(1						).softmax(0						)

                     if p_starts is not None:
                            UpperCamelCase    :	List[str]   	=		torch.vstack((p_starts, p_start)						)
                            UpperCamelCase    :	Union[str, Any]   	=		torch.vstack((p_ends, p_end)						)
                     else:
                            UpperCamelCase    :	str   	=		p_start
                            UpperCamelCase    :	Optional[int]   	=		p_end

              return p_starts, p_ends

