
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
    from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
    from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowerCamelCase      =1_2_8_0_2_2
lowerCamelCase      =1_2_8_0_2_8




@require_sentencepiece
class 				_lowerCamelCase (      snake_case__	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MaMaaaTokenizer
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  False
     SCREAMING_SNAKE_CASE_		   =  True






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




           """simple docstring"""







           super().setUp()

           UpperCamelCase__    :   Any					=				["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
           UpperCamelCase__    :   Optional[Any]					=				dict(zip(lowercase_						,						range(len(lowercase_  )  )  )  )
           UpperCamelCase__    :   Tuple					=				Path(self.tmpdirname  )
           save_json(lowercase_						,						save_dir / VOCAB_FILES_NAMES['''vocab_file''']  )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(lowercase_						,						save_dir / VOCAB_FILES_NAMES['''spm_file''']  )

           UpperCamelCase__    :   List[str]					=				MaMaaaTokenizer.from_pretrained(self.tmpdirname  )
           tokenizer.save_pretrained(self.tmpdirname  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           return MaMaaaTokenizer.from_pretrained(self.tmpdirname						,						**lowercase_  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           return (
               "This is a test",
               "This is a test",
           )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   int					=				"</s>"
           UpperCamelCase__    :   int					=				0

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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.get_tokenizer()
           UpperCamelCase__    :   Optional[Any]					=				list(tokenizer.get_vocab().keys()  )

           self.assertEqual(vocab_keys[0]						,						'''</s>'''  )
           self.assertEqual(vocab_keys[1]						,						'''<unk>'''  )
           self.assertEqual(vocab_keys[-1]						,						'''<s>'''  )
           self.assertEqual(len(lowercase_  )						,						tokenizer.vocab_size + len(tokenizer.get_added_vocab()  )  )






     @unittest.skip('''Skip this test while all models are still to be uploaded.'''  )
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				self.get_tokenizer()

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

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(lowercase_  )						,						[2, 3, 4, 5, 6]						,						)

           UpperCamelCase__    :   int					=				tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]  )
           self.assertListEqual(lowercase_						,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']  )

           UpperCamelCase__    :   Optional[Any]					=				tokenizer.convert_tokens_to_string(lowercase_  )
           self.assertEqual(lowercase_						,						'''This is a test'''  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				{"input_ids": [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_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_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 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, 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, 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], [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, 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=lowercase_						,						model_name='''facebook/m2m100_418M'''						,						revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''						,						)




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_lowerCamelCase (      unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''facebook/m2m100_418M'''
     SCREAMING_SNAKE_CASE_		   =  [
         '''In my opinion, there are two levels of response from the French government.''',
         '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
     ]
     SCREAMING_SNAKE_CASE_		   =  [
         '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
         '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
     ]

     # fmt: off
     SCREAMING_SNAKE_CASE_		   =  [EN_CODE, 593, 1949, 11_5781, 4, 7_1586, 4234, 6_0633, 12_6233, 432, 12_3808, 1_5592, 1197, 11_7132, 12_0618, 5, 2]






     @classmethod
     def    __SCREAMING_SNAKE_CASE			(							cls  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   MaMaaaTokenizer					=				MaMaaaTokenizer.from_pretrained(
               cls.checkpoint_name						,						src_lang='''en'''						,						tgt_lang='''fr'''  )
           UpperCamelCase__    :   int					=				1
           return cls






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




           """simple docstring"""







           self.assertEqual(self.tokenizer.get_lang_id('''ar'''  )						,						1_2_8_0_0_6  )
           self.assertEqual(self.tokenizer.get_lang_id('''en'''  )						,						1_2_8_0_2_2  )
           self.assertEqual(self.tokenizer.get_lang_id('''ro'''  )						,						1_2_8_0_7_6  )
           self.assertEqual(self.tokenizer.get_lang_id('''mr'''  )						,						1_2_8_0_6_3  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.tokenizer.get_vocab()
           self.assertEqual(len(lowercase_  )						,						self.tokenizer.vocab_size  )
           self.assertEqual(vocab['''<unk>''']						,						3  )
           self.assertIn(self.tokenizer.get_lang_token('''en'''  )						,						lowercase_  )






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




           """simple docstring"""







           UpperCamelCase__    :   int					=				"en"
           UpperCamelCase__    :   Any					=				self.tokenizer.batch_encode_plus(self.src_text  ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens						,						lowercase_  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           self.assertIn(lowercase_						,						self.tokenizer.all_special_ids  )
           # fmt: off
           UpperCamelCase__    :   Optional[int]					=				[FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
           # fmt: on
           UpperCamelCase__    :   Any					=				self.tokenizer.decode(lowercase_						,						skip_special_tokens=lowercase_  )
           UpperCamelCase__    :   str					=				self.tokenizer.decode(generated_ids[1:]						,						skip_special_tokens=lowercase_  )
           self.assertEqual(lowercase_						,						lowercase_  )
           self.assertNotIn(self.tokenizer.eos_token						,						lowercase_  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				tempfile.mkdtemp()
           UpperCamelCase__    :   Any					=				self.tokenizer.lang_token_to_id
           self.tokenizer.save_pretrained(lowercase_  )
           UpperCamelCase__    :   int					=				MaMaaaTokenizer.from_pretrained(lowercase_  )
           self.assertDictEqual(new_tok.lang_token_to_id						,						lowercase_  )






     @require_torch
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				"en"
           UpperCamelCase__    :   Union[str, Any]					=				"fr"

           UpperCamelCase__    :   Union[str, Any]					=				self.tokenizer(self.src_text						,						text_target=self.tgt_text						,						padding=lowercase_						,						return_tensors='''pt'''  )

           UpperCamelCase__    :   str					=				shift_tokens_right(
               batch['''labels''']						,						self.tokenizer.pad_token_id						,						self.tokenizer.eos_token_id  )

           for k in batch:
                 UpperCamelCase__    :   Optional[int]					=				batch[k].tolist()
           # batch = {k: v.tolist() for k,v in batch.items()}
           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           # batch.decoder_inputs_ids[0][0] ==
           assert batch.input_ids[1][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == FR_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






     @require_torch
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				"mr"
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id('''mr'''  )]  )
           self.assertListEqual(self.tokenizer.suffix_tokens						,						[self.tokenizer.eos_token_id]  )

           UpperCamelCase__    :   int					=				"zh"
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id('''zh'''  )]  )
           self.assertListEqual(self.tokenizer.suffix_tokens						,						[self.tokenizer.eos_token_id]  )






     @require_torch
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				"mr"
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id('''mr'''  )]  )
           self.assertListEqual(self.tokenizer.suffix_tokens						,						[self.tokenizer.eos_token_id]  )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id(self.tokenizer.src_lang  )]  )

           UpperCamelCase__    :   str					=				"zh"
           self.tokenizer._switch_to_target_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id('''zh'''  )]  )
           self.assertListEqual(self.tokenizer.suffix_tokens						,						[self.tokenizer.eos_token_id]  )
           self.tokenizer._switch_to_input_mode()
           self.assertListEqual(self.tokenizer.prefix_tokens						,						[self.tokenizer.get_lang_id(self.tokenizer.src_lang  )]  )






     @require_torch
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.tokenizer._build_translation_inputs('''A test'''						,						return_tensors='''pt'''						,						src_lang='''en'''						,						tgt_lang='''ar'''  )

           self.assertEqual(
               nested_simplify(lowercase_  )						,						{
                   # en_XX, A, test, EOS
                   '''input_ids''': [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
                   '''attention_mask''': [[1, 1, 1, 1]],
                   # ar_AR
                   '''forced_bos_token_id''': 1_2_8_0_0_6,
               }						,						)




import torch

from transformers import AutoModel




class 				_lowerCamelCase (      torch.nn.Module ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"  )  ->   Optional[Any]:




           """simple docstring"""







           super(__SCREAMING_SNAKE_CASE						,						self  ).__init__()

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






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




           """simple docstring"""







           return self.bert(**__SCREAMING_SNAKE_CASE  ).last_hidden_state






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE  )  ->   Dict:




           """simple docstring"""







           return token_embeddings.sum(2						,						keepdim=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=1  )  ->   str:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				W_supports['''sizes'''].tolist()
           UpperCamelCase__    :   Tuple					=				W_supports['''start_token_id'''].item()
           UpperCamelCase__    :   int					=				W_supports['''end_token_id'''].item()

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

           UpperCamelCase__    :   Optional[int]					=				self.BERT(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.BERT(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Dict					=				None

           UpperCamelCase__    :   Any					=				W_supports['''input_ids'''] == start_token_id
           UpperCamelCase__    :   Optional[int]					=				W_supports['''input_ids'''] == end_token_id

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

                 UpperCamelCase__    :   List[Any]					=				S[s : s + size][start_token_masks[s : s + size]]
                 UpperCamelCase__    :   Optional[Any]					=				S[s : s + size][end_token_masks[s : s + size]]

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

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

           return p_starts, p_ends


