






'''simple docstring'''







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class _lowercase	(				_lowercase				,      unittest.TestCase		):
							a				      =       PhobertTokenizer
							a				      =       False




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														lowerCamelCase__					:	List[Any]         =       ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
														lowerCamelCase__					:	str         =       dict(zip(UpperCamelCase__			,							range(len(UpperCamelCase__     )     )     )     )
														lowerCamelCase__					:	Optional[int]         =       ["""#version: 0.2""", """l à</w>"""]
														lowerCamelCase__					:	Tuple         =       {"""unk_token""": """<unk>"""}

														lowerCamelCase__					:	str         =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""vocab_file"""]     )
														lowerCamelCase__					:	Optional[Any]         =       os.path.join(self.tmpdirname			,							VOCAB_FILES_NAMES["""merges_file"""]     )

														with open(self.vocab_file			,							"""w"""			,							encoding="""utf-8"""     ) as fp:
																					for token in vocab_tokens:
																												fp.write(F'''{token} {vocab_tokens[token]}\n'''     )
														with open(self.merges_file			,							"""w"""			,							encoding="""utf-8"""     ) as fp:
																					fp.write("""\n""".join(UpperCamelCase__     )     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							**UpperCamelCase__:				int     ):
														kwargs.update(self.special_tokens_map     )
														return PhobertTokenizer.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Any			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	int         =       """Tôi là VinAI Research"""
														lowerCamelCase__					:	Tuple         =       """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
														return input_text, output_text




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Dict         =       PhobertTokenizer(self.vocab_file			,							self.merges_file			,							**self.special_tokens_map     )
														lowerCamelCase__					:	Optional[Any]         =       """Tôi là VinAI Research"""
														lowerCamelCase__					:	List[str]         =       """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
														lowerCamelCase__					:	str         =       tokenizer.tokenize(UpperCamelCase__     )
														print(UpperCamelCase__     )
														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       tokens + [tokenizer.unk_token]

														lowerCamelCase__					:	Any         =       [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__     )			,							UpperCamelCase__     )





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		["image_processor", "tokenizer"]
   __A :      Dict     		=		"BridgeTowerImageProcessor"
   __A :      Optional[int]     		=		("RobertaTokenizer", "RobertaTokenizerFast")






   def __init__(		self						,				__A						,				__A ):






     """simple docstring"""





     super().__init__(__A						,				__A )






   def __call__(		self						,				__A						,				__A = None						,				__A = True						,				__A = False						,				__A = None						,				__A = None						,				__A = 0						,				__A = None						,				__A = None						,				__A = None						,				__A = False						,				__A = False						,				__A = False						,				__A = False						,				__A = True						,				__A = None						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					str    =				self.tokenizer(
         text=__A						,				add_special_tokens=__A						,				padding=__A						,				truncation=__A						,				max_length=__A						,				stride=__A						,				pad_to_multiple_of=__A						,				return_token_type_ids=__A						,				return_attention_mask=__A						,				return_overflowing_tokens=__A						,				return_special_tokens_mask=__A						,				return_offsets_mapping=__A						,				return_length=__A						,				verbose=__A						,				return_tensors=__A						,				**__A						,				)
     # add pixel_values + pixel_mask
     lowerCamelCase :					int    =				self.image_processor(
         __A						,				return_tensors=__A						,				do_normalize=__A						,				do_center_crop=__A						,				**__A )
     encoding.update(__A )

     return encoding






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				self.tokenizer.model_input_names
     lowerCamelCase :					int    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



