




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


__SCREAMING_SNAKE_CASE      :				Optional[Any]				      =get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class      A     (						_lowerCAmelCase     ,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		str       =ErnieMTokenizer
     _snake_case:		List[Any]       =False
     _snake_case:		str       =True
     _snake_case:		Tuple       =False
     _snake_case:		int       =False




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

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




     def 	__snake_case				(      self					:				int		,					A__					:				Any     )			->    List[Any]:
           lowerCamelCase__ 					=			'this is a test'
           lowerCamelCase__ 					=			'this is a test'
           return input_text, output_text




     def 	__snake_case				(      self					:				int     )			->    List[Any]:
           lowerCamelCase__ 					=			'<pad>'
           lowerCamelCase__ 					=			0

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




     def 	__snake_case				(      self					:				Optional[Any]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			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(A__     )		,					3_0_0_0_0     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    List[str]:
           self.assertEqual(self.get_tokenizer().vocab_size		,					3_0_0_0_0     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    Union[str, Any]:
           if not self.test_rust_tokenizer:
                 return

           lowerCamelCase__ 					=			self.get_tokenizer()
           lowerCamelCase__ 					=			self.get_rust_tokenizer()

           lowerCamelCase__ 					=			'I was born in 92000, and this is falsé.'

           lowerCamelCase__ 					=			tokenizer.tokenize(A__     )
           lowerCamelCase__ 					=			rust_tokenizer.tokenize(A__     )
           self.assertListEqual(A__		,					A__     )

           lowerCamelCase__ 					=			tokenizer.encode(A__		,					add_special_tokens=A__     )
           lowerCamelCase__ 					=			rust_tokenizer.encode(A__		,					add_special_tokens=A__     )
           self.assertListEqual(A__		,					A__     )

           lowerCamelCase__ 					=			self.get_rust_tokenizer()
           lowerCamelCase__ 					=			tokenizer.encode(A__     )
           lowerCamelCase__ 					=			rust_tokenizer.encode(A__     )
           self.assertListEqual(A__		,					A__     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			ErnieMTokenizer(A__		,					do_lower_case=A__		,					unk_token='<unk>'		,					pad_token='<pad>'     )

           lowerCamelCase__ 					=			tokenizer.tokenize('This is a test'     )
           self.assertListEqual(A__		,					['▁this', '▁is', '▁a', '▁test']     )

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

           lowerCamelCase__ 					=			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(
               A__		,					['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']     )
           lowerCamelCase__ 					=			tokenizer.convert_tokens_to_ids(A__     )
           self.assertListEqual(A__		,					[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]     )

           lowerCamelCase__ 					=			tokenizer.convert_ids_to_tokens(A__     )
           self.assertListEqual(
               A__		,					['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']		,					)




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[Any]:
           lowerCamelCase__ 					=			ErnieMTokenizer(A__		,					unk_token='<unk>'		,					pad_token='<pad>'     )

           lowerCamelCase__ 					=			tokenizer.encode('sequence builders'     )
           lowerCamelCase__ 					=			tokenizer.encode('multi-sequence build'     )

           lowerCamelCase__ 					=			tokenizer.build_inputs_with_special_tokens(A__     )
           lowerCamelCase__ 					=			tokenizer.build_inputs_with_special_tokens(A__		,					A__     )

           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					:				Dict     )			->    str:
           # fmt: off
           lowerCamelCase__ 					=			{'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=A__		,					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


__SCREAMING_SNAKE_CASE      :				int				      ={"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __SCREAMING_SNAKE_CASE      :				Optional[int]				      =[
          """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

      __SCREAMING_SNAKE_CASE      :				Any				      =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)