



import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin



class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Any       					=  PhobertTokenizer
       UpperCAmelCase__    : str       					=  False
       def  snake_case_	(      self	)					->		List[Any]:
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCamelCase   :  Optional[Any]						     =     ['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
          UpperCamelCase   :  Union[str, Any]						     =     dict(zip(SCREAMING_SNAKE_CASE_,      range(len(SCREAMING_SNAKE_CASE_	)	)	)	)
          UpperCamelCase   :  List[str]						     =     ['#version: 0.2', 'l à</w>']
          UpperCamelCase   :  Optional[Any]						     =     {'unk_token': '<unk>'}

          UpperCamelCase   :  Tuple						     =     os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES['vocab_file']	)
          UpperCamelCase   :  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(SCREAMING_SNAKE_CASE_	)	)
       def  snake_case_	(      self,      **SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          kwargs.update(self.special_tokens_map	)
          return PhobertTokenizer.from_pretrained(self.tmpdirname,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          UpperCamelCase   :  List[str]						     =     'Tôi là VinAI Research'
          UpperCamelCase   :  Tuple						     =     'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
          return input_text, output_text






       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     PhobertTokenizer(self.vocab_file,      self.merges_file,      **self.special_tokens_map	)
          UpperCamelCase   :  Optional[Any]						     =     'Tôi là VinAI Research'
          UpperCamelCase   :  Optional[Any]						     =     'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
          UpperCamelCase   :  Optional[int]						     =     tokenizer.tokenize(SCREAMING_SNAKE_CASE_	)
          print(SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     tokens + [tokenizer.unk_token]

          UpperCamelCase   :  List[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(SCREAMING_SNAKE_CASE_	),      SCREAMING_SNAKE_CASE_	)








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class     lowerCAmelCase_		(  a__							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = True,      SCREAMING_SNAKE_CASE_ = "arrow",      **SCREAMING_SNAKE_CASE_,      )					->		int:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      keep_in_memory=SCREAMING_SNAKE_CASE_,      streaming=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )
          UpperCamelCase   :  Any						     =     load_from_cache_file
          UpperCamelCase   :  Any						     =     file_format
          UpperCamelCase   :  str						     =     Spark(
              df=SCREAMING_SNAKE_CASE_,      features=SCREAMING_SNAKE_CASE_,      cache_dir=SCREAMING_SNAKE_CASE_,      working_dir=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )






       def  snake_case_	(      self	)					->		Tuple:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split	)
          UpperCamelCase   :  Dict						     =     None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,      file_format=self._file_format,      )
          return self.builder.as_dataset(split=self.split	)



