


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


_lowerCAmelCase:						Tuple   				=       '▁'

_lowerCAmelCase:						List[str]   				=       get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
class       lowercase_					(lowercase__      ,					unittest.TestCase   ):
				snake_case	    =BertGenerationTokenizer
				snake_case	    =False
				snake_case	    =True





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											super().setUp()

											a__      =BertGenerationTokenizer(lowercase_       ,  keep_accents=lowercase_)
											tokenizer.save_pretrained(self.tmpdirname)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      ='<s>'
											a__      =1

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





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =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(lowercase_)       ,  1002)





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											self.assertEqual(self.get_tokenizer().vocab_size       ,  1000)





				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =BertGenerationTokenizer(lowercase_       ,  keep_accents=lowercase_)

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

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

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

											a__      =tokenizer.convert_ids_to_tokens(lowercase_)
											self.assertListEqual(
											    lowercase_       ,  [
											        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 						__UpperCamelCase    (				self)  -> Optional[Any]:
											return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      ='Hello World!'
											a__      =[18536, 2260, 101]

											self.assertListEqual(lowercase_       ,  self.big_tokenizer.encode(lowercase_))





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =(
											    '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'
											)
											a__      =[
											    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,
											    34324,
											    497,
											    391,
											    408,
											    11342,
											    1244,
											    385,
											    100,
											    938,
											    985,
											    456,
											    574,
											    362,
											    12597,
											    3200,
											    3129,
											    1172,
											]

											self.assertListEqual(lowercase_       ,  self.big_tokenizer.encode(lowercase_))





				@require_torch
				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											import torch

											from transformers import BertGenerationConfig, BertGenerationEncoder

											# Build sequence
											a__      =list(self.big_tokenizer.get_vocab().keys())[:10]
											a__      =' '.join(lowercase_)
											a__      =self.big_tokenizer.encode_plus(lowercase_       ,  return_tensors='pt'       ,  return_token_type_ids=lowercase_)
											a__      =self.big_tokenizer.batch_encode_plus(
											    [sequence + ' ' + sequence]       ,  return_tensors='pt'       ,  return_token_type_ids=lowercase_)

											a__      =BertGenerationConfig()
											a__      =BertGenerationEncoder(lowercase_)

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

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





				@slow
				def 						__UpperCamelCase    (				self)  -> List[Any]:
											# fmt: off
											a__      ={'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 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, 21932, 18146, 726, 363, 17032, 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=lowercase_       ,  model_name='google/bert_for_seq_generation_L-24_bbc_encoder'       ,  revision='c817d1fd1be2ffa69431227a1fe320544943d4db'       ,  )





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case   							=						{
    '''configuration_autoformer''': [
        '''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AutoformerConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case   							=						[
        '''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''AutoformerForPrediction''',
        '''AutoformerModel''',
        '''AutoformerPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    __snake_case   							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


