



import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


A__  :  Dict								=					get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')



class 				__snake_case						(     UpperCamelCase_						,unittest.TestCase						):
							_a			      = BartphoTokenizer
							_a			      = False
							_a			      = True




							def    UpperCAmelCase__			(       self     :       List[str]):
									super().setUp()

									lowerCAmelCase_			:     str									= ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
									lowerCAmelCase_			:     int									= dict(zip(A_				,				range(len(A_))))
									lowerCAmelCase_			:     Any									= {'''unk_token''': '''<unk>'''}

									lowerCAmelCase_			:     Dict									= os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES['''monolingual_vocab_file'''])
									with open(self.monolingual_vocab_file				,				'''w'''				,				encoding='''utf-8''') as fp:
											for token in vocab_tokens:
													fp.write(F"""{token} {vocab_tokens[token]}\n""")

									lowerCAmelCase_			:     List[Any]									= BartphoTokenizer(A_				,				self.monolingual_vocab_file				,				**self.special_tokens_map)
									tokenizer.save_pretrained(self.tmpdirname)




							def    UpperCAmelCase__			(       self     :       Any				,				**A_     :       Union[str, Any]):
									kwargs.update(self.special_tokens_map)
									return BartphoTokenizer.from_pretrained(self.tmpdirname				,				**A_)




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Optional[Any]):
									lowerCAmelCase_			:     Union[str, Any]									= '''This is a là test'''
									lowerCAmelCase_			:     Union[str, Any]									= '''This is a<unk><unk> test'''
									return input_text, output_text







							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Any									= BartphoTokenizer(A_				,				self.monolingual_vocab_file				,				**self.special_tokens_map)
									lowerCAmelCase_			:     Any									= '''This is a là test'''
									lowerCAmelCase_			:     Optional[int]									= '''▁This ▁is ▁a ▁l à ▁t est'''.split()
									lowerCAmelCase_			:     int									= tokenizer.tokenize(A_)
									self.assertListEqual(A_				,				A_)

									lowerCAmelCase_			:     Optional[int]									= tokens + [tokenizer.unk_token]
									lowerCAmelCase_			:     List[str]									= [4, 5, 6, 3, 3, 7, 8, 3]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(A_)				,				A_)












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''				,			a__				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __snake_case							       =   image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''      )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''      )

         super().__init__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """simple docstring"""



         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''      )

         if text is not None:
            __snake_case							       =   self.tokenizer(a__				,			return_tensors=a__				,			**a__      )

         if images is not None:
            __snake_case							       =   self.image_processor(a__				,			return_tensors=a__				,			**a__      )

         if text is not None and images is not None:
            __snake_case							       =   image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__      )				,			tensor_type=a__      )


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__				,			**a__      )


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.tokenizer.decode(*a__				,			**a__      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.model_input_names
         __snake_case							       =   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

