




'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:   Union[str, Any]      		= get_tests_dir("fixtures/test_sentencepiece_bpe.model")




class  _snake_case		(	lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      List[str]						       =    BartphoTokenizer
			lowerCAmelCase_     :      Dict						       =    False
			lowerCAmelCase_     :      str						       =    True




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										super().setUp()

										snake_case_						=      ["▁This", "▁is", "▁a", "▁t", "est"]
										snake_case_						=      dict(zip(a__				,							range(len(a__							)							)							)							)
										snake_case_						=      {"unk_token": "<unk>"}

										snake_case_						=      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'							)

										snake_case_						=      BartphoTokenizer(a__				,							self.monolingual_vocab_file				,							**self.special_tokens_map							)
										tokenizer.save_pretrained(self.tmpdirname							)




			def    lowerCAmelCase__				(					self				,							**a__							)				->				Any:

										'''simple docstring'''




										kwargs.update(self.special_tokens_map							)
										return BartphoTokenizer.from_pretrained(self.tmpdirname				,							**a__							)




			def    lowerCAmelCase__				(					self				,							a__							)				->				int:

										'''simple docstring'''




										snake_case_						=      "This is a là test"
										snake_case_						=      "This is a<unk><unk> test"
										return input_text, output_text




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      BartphoTokenizer(a__				,							self.monolingual_vocab_file				,							**self.special_tokens_map							)
										snake_case_						=      "This is a là test"
										snake_case_						=      "▁This ▁is ▁a ▁l à ▁t est".split()
										snake_case_						=      tokenizer.tokenize(a__							)
										self.assertListEqual(a__				,							a__							)

										snake_case_						=      tokens + [tokenizer.unk_token]
										snake_case_						=      [4, 5, 6, 3, 3, 7, 8, 3]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(a__							)				,							a__							)











from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     def __init__(							self						:    Union[str, Any]						,lowerCamelCase__						:    Callable						,lowerCamelCase__						:    Optional[Features] = None						,lowerCamelCase__						:    str = None						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    Optional[dict] = None						,lowerCamelCase__						:    Optional[int] = None						,**lowerCamelCase__						:    Optional[Any]						,)     ->		List[str]:




           '''simple docstring'''


           super().__init__(
               features=lowerCamelCase__						,cache_dir=lowerCamelCase__						,keep_in_memory=lowerCamelCase__						,streaming=lowerCamelCase__						,num_proc=lowerCamelCase__						,**lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						Generator(
               cache_dir=lowerCamelCase__						,features=lowerCamelCase__						,generator=lowerCamelCase__						,gen_kwargs=lowerCamelCase__						,**lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           if self.streaming:
                 SCREAMING_SNAKE_CASE		=						self.builder.as_streaming_dataset(split="""train"""       )
           # Build regular (map-style) dataset
           else:
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None

                 self.builder.download_and_prepare(
                     download_config=lowerCamelCase__						,download_mode=lowerCamelCase__						,verification_mode=lowerCamelCase__						,base_path=lowerCamelCase__						,num_proc=self.num_proc						,)
                 SCREAMING_SNAKE_CASE		=						self.builder.as_dataset(
                     split="""train"""						,verification_mode=lowerCamelCase__						,in_memory=self.keep_in_memory       )
           return dataset


