'''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


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







class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					BartphoTokenizer
 _SCREAMING_SNAKE_CASE     :       Dict      	=					False
 _SCREAMING_SNAKE_CASE     :       int      	=					True
 def a					(      self   :	Tuple							)  ->    Optional[Any]:
  super().setUp()

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

  __lowerCAmelCase											=						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											=						BartphoTokenizer(SCREAMING_SNAKE_CASE__      ,       self.monolingual_vocab_file      ,       **self.special_tokens_map							)
  tokenizer.save_pretrained(self.tmpdirname							)
 def a					(      self   :	str      ,       **SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Tuple:
  kwargs.update(self.special_tokens_map							)
  return BartphoTokenizer.from_pretrained(self.tmpdirname      ,       **SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    Tuple:
  __lowerCAmelCase											=						"""This is a là test"""
  __lowerCAmelCase											=						"""This is a<unk><unk> test"""
  return input_text, output_text





 def a					(      self   :	List[str]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						BartphoTokenizer(SCREAMING_SNAKE_CASE__      ,       self.monolingual_vocab_file      ,       **self.special_tokens_map							)
  __lowerCAmelCase											=						"""This is a là test"""
  __lowerCAmelCase											=						"""▁This ▁is ▁a ▁l à ▁t est""".split()
  __lowerCAmelCase											=						tokenizer.tokenize(SCREAMING_SNAKE_CASE__							)
  self.assertListEqual(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)

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







'''simple docstring'''

import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast







@dataclass
class 				_lowercase     (				datasets.BuilderConfig			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[datasets.Features]      	=					None







class 				_lowercase     (				datasets.ArrowBasedBuilder			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					PandasConfig
 def a					(      self   :	Union[str, Any]							)  ->    int:
  return datasets.DatasetInfo(features=self.config.features							)
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    str:
  if not self.config.data_files:
   raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
  __lowerCAmelCase											=						dl_manager.download_and_extract(self.config.data_files							)
  if isinstance(SCREAMING_SNAKE_CASE__      ,       (str, list, tuple)							):
   __lowerCAmelCase											=						data_files
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,       gen_kwargs={"""files""": files}							)]
  __lowerCAmelCase											=						[]
  for split_name, files in data_files.items():
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__      ,       gen_kwargs={"""files""": files}							)							)
  return splits
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	pa.Table							)  ->    pa.Table:
  if self.config.features is not None:
   # more expensive cast to support nested features with keys in a different order
   # allows str <-> int/float or str to Audio for example
   __lowerCAmelCase											=						table_cast(SCREAMING_SNAKE_CASE__      ,       self.config.features.arrow_schema							)
  return pa_table





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    List[Any]:
  for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__							)							):
   with open(SCREAMING_SNAKE_CASE__      ,       """rb"""							) as f:
    __lowerCAmelCase											=						pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__							)							)
    yield i, self._cast_table(SCREAMING_SNAKE_CASE__							)






