



import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase	       =	'''▁'''

__UpperCAmelCase	       =	get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Tuple       					=  BertGenerationTokenizer
       UpperCAmelCase__    : Tuple       					=  False
       UpperCAmelCase__    : str       					=  True
       def  snake_case_	(      self	)					->		int:
          super().setUp()

          UpperCamelCase   :  str						     =     BertGenerationTokenizer(SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_	)
          tokenizer.save_pretrained(self.tmpdirname	)
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Dict						     =     '<s>'
          UpperCamelCase   :  Optional[Any]						     =     1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_	)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  List[Any]						     =     list(self.get_tokenizer().get_vocab().keys()	)

          self.assertEqual(vocab_keys[0],      '<unk>'	)
          self.assertEqual(vocab_keys[1],      '<s>'	)
          self.assertEqual(vocab_keys[-1],      '<pad>'	)
          self.assertEqual(len(SCREAMING_SNAKE_CASE_	),      1002	)
       def  snake_case_	(      self	)					->		Tuple:
          self.assertEqual(self.get_tokenizer().vocab_size,      1000	)
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  int						     =     BertGenerationTokenizer(SCREAMING_SNAKE_CASE_,      keep_accents=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Tuple						     =     tokenizer.tokenize('This is a test'	)
          self.assertListEqual(SCREAMING_SNAKE_CASE_,      ['▁This', '▁is', '▁a', '▁t', 'est']	)

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	),      [285, 46, 10, 170, 382],      )

          UpperCamelCase   :  int						     =     tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
          self.assertListEqual(
              SCREAMING_SNAKE_CASE_,      [
                  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(SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(
              SCREAMING_SNAKE_CASE_,      [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],      )

          UpperCamelCase   :  str						     =     tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(
              SCREAMING_SNAKE_CASE_,      [
                  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>',
                  '.',
              ],      )
       @cached_property
       def  snake_case_	(      self	)					->		Tuple:
          return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'	)
       @slow
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Dict						     =     'Hello World!'
          UpperCamelCase   :  Union[str, Any]						     =     [1_8536, 2260, 101]

          self.assertListEqual(SCREAMING_SNAKE_CASE_,      self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_	)	)
       @slow
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     (
              'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
              ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
          )
          UpperCamelCase   :  List[str]						     =     [
              871,
              419,
              358,
              946,
              991,
              2521,
              452,
              358,
              1357,
              387,
              7751,
              3536,
              112,
              985,
              456,
              126,
              865,
              938,
              5400,
              5734,
              458,
              1368,
              467,
              786,
              2462,
              5246,
              1159,
              633,
              865,
              4519,
              457,
              582,
              852,
              2557,
              427,
              916,
              508,
              405,
              3_4324,
              497,
              391,
              408,
              1_1342,
              1244,
              385,
              100,
              938,
              985,
              456,
              574,
              362,
              1_2597,
              3200,
              3129,
              1172,
          ]

          self.assertListEqual(SCREAMING_SNAKE_CASE_,      self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_	)	)
       @require_torch
       @slow
       def  snake_case_	(      self	)					->		Dict:
          import torch

          from transformers import BertGenerationConfig, BertGenerationEncoder

          # Build sequence
          UpperCamelCase   :  Union[str, Any]						     =     list(self.big_tokenizer.get_vocab().keys()	)[:10]
          UpperCamelCase   :  Optional[Any]						     =     ' '.join(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE_,      return_tensors='pt',      return_token_type_ids=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     self.big_tokenizer.batch_encode_plus(
              [sequence + ' ' + sequence],      return_tensors='pt',      return_token_type_ids=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     BertGenerationConfig()
          UpperCamelCase   :  List[str]						     =     BertGenerationEncoder(SCREAMING_SNAKE_CASE_	)

          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
             model(**SCREAMING_SNAKE_CASE_	)
             model(**SCREAMING_SNAKE_CASE_	)






       @slow
       def  snake_case_	(      self	)					->		Optional[int]:
          # fmt: off
          UpperCamelCase   :  str						     =     {'input_ids': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 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], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 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]], '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, 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, 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=SCREAMING_SNAKE_CASE_,      model_name='google/bert_for_seq_generation_L-24_bbc_encoder',      revision='c817d1fd1be2ffa69431227a1fe320544943d4db',      )








import re
def 		UpperCamelCase     (       snake_case__	:   str     )     ->					str:

   if len(re.findall('[ATCG]'	,					snake_case__     )     ) != len(snake_case__     ):
      raise ValueError('Invalid Strand'     )

   return dna.translate(dna.maketrans('ATCG'	,					'TAGC'     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()



