import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase							= get_tests_dir('''fixtures/spiece.model''')




@require_sentencepiece
@require_tokenizers
class       __lowerCAmelCase     (				a_,		unittest.TestCase  ):



 '''simple docstring'''
 _lowerCamelCase  :		str 			=       ErnieMTokenizer
 _lowerCamelCase  :		str 			=       False
 _lowerCamelCase  :		Optional[int] 			=       True
 _lowerCamelCase  :		Optional[Any] 			=       False
 _lowerCamelCase  :		List[Any] 			=       False

 def     __snake_case    (	self				:						Optional[Any]     )       ->							List[Any]:
        super().setUp()

        # We have a SentencePiece fixture for testing
        _UpperCamelCase   :				Any       =     ErnieMTokenizer(lowerCamelCase				,	unk_token='<unk>'				,	pad_token='<pad>'     )
        tokenizer.save_pretrained(self.tmpdirname     )

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						int     )       ->							Dict:
        _UpperCamelCase   :				str       =     'this is a test'
        _UpperCamelCase   :				Dict       =     'this is a test'
        return input_text, output_text

 def     __snake_case    (	self				:						List[Any]     )       ->							str:
        _UpperCamelCase   :				Tuple       =     '<pad>'
        _UpperCamelCase   :				Union[str, Any]       =     0

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

 def     __snake_case    (	self				:						str     )       ->							Dict:
        _UpperCamelCase   :				Optional[int]       =     list(self.get_tokenizer().get_vocab().keys()     )

        self.assertEqual(vocab_keys[0]				,	'<pad>'     )
        self.assertEqual(vocab_keys[1]				,	'<unk>'     )
        self.assertEqual(vocab_keys[-1]				,	'▁eloquent'     )
        self.assertEqual(len(lowerCamelCase     )				,	3_0_0_0_0     )

 def     __snake_case    (	self				:						int     )       ->							Dict:
        self.assertEqual(self.get_tokenizer().vocab_size				,	3_0_0_0_0     )

 def     __snake_case    (	self				:						int     )       ->							List[str]:
        if not self.test_rust_tokenizer:
               return

        _UpperCamelCase   :				str       =     self.get_tokenizer()
        _UpperCamelCase   :				List[str]       =     self.get_rust_tokenizer()

        _UpperCamelCase   :				Union[str, Any]       =     'I was born in 92000, and this is falsé.'

        _UpperCamelCase   :				int       =     tokenizer.tokenize(lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     rust_tokenizer.tokenize(lowerCamelCase     )
        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

        _UpperCamelCase   :				Union[str, Any]       =     tokenizer.encode(lowerCamelCase				,	add_special_tokens=lowerCamelCase     )
        _UpperCamelCase   :				List[Any]       =     rust_tokenizer.encode(lowerCamelCase				,	add_special_tokens=lowerCamelCase     )
        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

        _UpperCamelCase   :				Dict       =     self.get_rust_tokenizer()
        _UpperCamelCase   :				Any       =     tokenizer.encode(lowerCamelCase     )
        _UpperCamelCase   :				Optional[Any]       =     rust_tokenizer.encode(lowerCamelCase     )
        self.assertListEqual(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						int     )       ->							Dict:
        _UpperCamelCase   :				Union[str, Any]       =     ErnieMTokenizer(lowerCamelCase				,	do_lower_case=lowerCamelCase				,	unk_token='<unk>'				,	pad_token='<pad>'     )

        _UpperCamelCase   :				int       =     tokenizer.tokenize('This is a test'     )
        self.assertListEqual(lowerCamelCase				,	['▁this', '▁is', '▁a', '▁test']     )

        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase     )				,	[4_8, 2_5, 2_1, 1_2_8_9]     )

        _UpperCamelCase   :				str       =     tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
        # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
        self.assertListEqual(
            lowerCamelCase				,	['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']     )
        _UpperCamelCase   :				List[Any]       =     tokenizer.convert_tokens_to_ids(lowerCamelCase     )
        self.assertListEqual(lowerCamelCase				,	[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]     )

        _UpperCamelCase   :				Union[str, Any]       =     tokenizer.convert_ids_to_tokens(lowerCamelCase     )
        self.assertListEqual(
            lowerCamelCase				,	['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']				,	)

 def     __snake_case    (	self				:						int     )       ->							Union[str, Any]:
        _UpperCamelCase   :				Dict       =     ErnieMTokenizer(lowerCamelCase				,	unk_token='<unk>'				,	pad_token='<pad>'     )

        _UpperCamelCase   :				Union[str, Any]       =     tokenizer.encode('sequence builders'     )
        _UpperCamelCase   :				Tuple       =     tokenizer.encode('multi-sequence build'     )

        _UpperCamelCase   :				List[Any]       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase     )
        _UpperCamelCase   :				str       =     tokenizer.build_inputs_with_special_tokens(lowerCamelCase				,	lowerCamelCase     )

        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
            tokenizer.sep_token_id
        ] + text_a + [tokenizer.sep_token_id]

 @slow
 def     __snake_case    (	self				:						Union[str, Any]     )       ->							Optional[int]:
        # fmt: off
        _UpperCamelCase   :				Optional[int]       =     {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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, 1, 1, 1]]}
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=lowerCamelCase				,	model_name='susnato/ernie-m-base_pytorch'				,	sequences=[
                'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
                'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
                'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
                'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
                'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
                'conditioning on both left and right context in all layers.',
                'The quick brown fox jumps over the lazy dog.',
            ]				,	)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase							= {'''configuration_nat''': ['''NAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NatConfig''']}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase							= [
     '''NAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NatForImageClassification''',
     '''NatModel''',
     '''NatPreTrainedModel''',
     '''NatBackbone''',
 ]

if TYPE_CHECKING:
 from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nat import (
      NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      NatBackbone,
      NatForImageClassification,
      NatModel,
      NatPreTrainedModel,
  )

else:
 import sys

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



