






"""simple docstring"""


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer






class 						snake_case  (  tf.keras.layers.Layer	):





    def __init__(							self     : Any    ,		A     : Dict[str, int]    ,		A     : List[str]    ,		A     : int = None    ,		A     : int = None      ):


      '''simple docstring'''




      super().__init__()
      a					:      Tuple    =       pad_token_id
      a					:      Any    =       max_length
      a					:      Any    =       vocab
      a					:      Any    =       merges
      a					:      Any    =       BytePairTokenizer(A    ,		A    ,		sequence_length=A      )





    @classmethod
    def     lowerCamelCase__				(							cls     : int    ,		A     : GPTaTokenizer    ,		*A     : Tuple    ,		**A     : str      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       [' '.join(A      ) for m in tokenizer.bpe_ranks.keys()]
      a					:      Optional[int]    =       tokenizer.get_vocab()
      return cls(A    ,		A    ,		*A    ,		**A      )





    @classmethod
    def     lowerCamelCase__				(							cls     : Dict    ,		A     : Union[str, os.PathLike]    ,		*A     : Tuple    ,		**A     : Any      ):


      '''simple docstring'''




      a					:      str    =       GPTaTokenizer.from_pretrained(A    ,		*A    ,		**A      )
      return cls.from_tokenizer(A    ,		*A    ,		**A      )





    @classmethod
    def     lowerCamelCase__				(							cls     : Any    ,		A     : List[Any]      ):


      '''simple docstring'''




      return cls(**A      )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      return {
          "vocab": self.vocab,
          "merges": self.merges,
          "max_length": self.max_length,
          "pad_token_id": self.pad_token_id,
      }





    def     lowerCamelCase__				(							self     : int    ,		A     : Tuple    ,		A     : int = None      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       self.tf_tokenizer(A      )
      a					:      str    =       tf.ones_like(A      )

      if self.pad_token_id is not None:
        # pad the tokens up to max length
        a					:      Optional[int]    =       max_length if max_length is not None else self.max_length

        if max_length is not None:
          a,					a					:      List[str]    =       pad_model_inputs(
              A    ,		max_seq_length=A    ,		pad_value=self.pad_token_id      )

      return {"attention_mask": attention_mask, "input_ids": input_ids}












"""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


_UpperCamelCase    :   int   			=						get_tests_dir('fixtures/test_sentencepiece_bpe.model')






class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					BartphoTokenizer
    __magic_name__   					=					False
    __magic_name__   					=					True





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      super().setUp()

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

      a					:      Tuple    =       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'''      )

      a					:      Optional[int]    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      tokenizer.save_pretrained(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Dict    ,		**A     : str      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       'This is a là test'
      a					:      List[Any]    =       'This is a<unk><unk> test'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      a					:      int    =       'This is a là test'
      a					:      int    =       '▁This ▁is ▁a ▁l à ▁t est'.split()
      a					:      str    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      Union[str, Any]    =       tokens + [tokenizer.unk_token]
      a					:      Dict    =       [4, 5, 6, 3, 3, 7, 8, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		A      )




