
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


_A       =					get_tests_dir("fixtures/spiece.model")







@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase						(    A_				,					unittest.TestCase    ):
     UpperCAmelCase__							:				Any 	=						ErnieMTokenizer
     UpperCAmelCase__							:				Optional[int] 	=						False
     UpperCAmelCase__							:				List[str] 	=						True
     UpperCAmelCase__							:				str 	=						False
     UpperCAmelCase__							:				Optional[int] 	=						False





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

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





     def 						UpperCAmelCase(self :       Optional[Any]							,       _A :       str  )		->		int:
         snake_case						=   "this is a test"
         snake_case						=   "this is a test"
         return input_text, output_text





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Dict:
         snake_case						=   "<pad>"
         snake_case						=   0

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





     def 						UpperCAmelCase(self :       Tuple  )		->		Optional[Any]:
         snake_case						=   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 						UpperCAmelCase(self :       str  )		->		Tuple:
         self.assertEqual(self.get_tokenizer().vocab_size							,       3_0_0_0_0  )





     def 						UpperCAmelCase(self :       Tuple  )		->		str:
         if not self.test_rust_tokenizer:
             return

         snake_case						=   self.get_tokenizer()
         snake_case						=   self.get_rust_tokenizer()

         snake_case						=   "I was born in 92000, and this is falsé."

         snake_case						=   tokenizer.tokenize(_A  )
         snake_case						=   rust_tokenizer.tokenize(_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   tokenizer.encode(_A							,       add_special_tokens=_A  )
         snake_case						=   rust_tokenizer.encode(_A							,       add_special_tokens=_A  )
         self.assertListEqual(_A							,       _A  )

         snake_case						=   self.get_rust_tokenizer()
         snake_case						=   tokenizer.encode(_A  )
         snake_case						=   rust_tokenizer.encode(_A  )
         self.assertListEqual(_A							,       _A  )





     def 						UpperCAmelCase(self :       str  )		->		Tuple:
         snake_case						=   ErnieMTokenizer(_A							,       do_lower_case=_A							,       unk_token="<unk>"							,       pad_token="<pad>"  )

         snake_case						=   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]  )

         snake_case						=   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", "."]  )
         snake_case						=   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]  )

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





     def 						UpperCAmelCase(self :       int  )		->		List[Any]:
         snake_case						=   ErnieMTokenizer(_A							,       unk_token="<unk>"							,       pad_token="<pad>"  )

         snake_case						=   tokenizer.encode("sequence builders"  )
         snake_case						=   tokenizer.encode("multi-sequence build"  )

         snake_case						=   tokenizer.build_inputs_with_special_tokens(_A  )
         snake_case						=   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 						UpperCAmelCase(self :       Optional[Any]  )		->		Any:
         # fmt: off
         snake_case						=   {"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.",
             ]							,       )



import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


_A       =					logging.get_logger(__name__)







class 	lowerCamelCase						(    A_    ):





     def __init__(self :       Any							,       *_A :       Optional[Any]							,       **_A :       List[Any]  )		->		None:
         warnings.warn(
             "The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
             " use LevitImageProcessor instead."							,       _A							,       )
         super().__init__(*_A							,       **_A  )

