




"""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_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Dict				  =     PhobertTokenizer
 UpperCAmelCase   :					Dict				  =     False

 def       lowerCAmelCase_							(			self	:	List[str]		):
       super().setUp()

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

       _A        =       os.path.join(self.tmpdirname   ,							VOCAB_FILES_NAMES['vocab_file']		)
       _A        =       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(_a		)		)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	Dict		):
       kwargs.update(self.special_tokens_map		)
       return PhobertTokenizer.from_pretrained(self.tmpdirname   ,							**_a		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Tuple		):
       _A        =       "Tôi là VinAI Research"
       _A        =       "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	:	Dict		):
       _A        =       PhobertTokenizer(self.vocab_file   ,							self.merges_file   ,							**self.special_tokens_map		)
       _A        =       "Tôi là VinAI Research"
       _A        =       "T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
       _A        =       tokenizer.tokenize(_a		)
       print(_a		)
       self.assertListEqual(_a   ,							_a		)

       _A        =       tokens + [tokenizer.unk_token]

       _A        =       [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(_a		)   ,							_a		)







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a         =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					bool				  =     field(default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `max_length` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `num_beams` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[Union[str, Path, GenerationConfig]]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
     }      ,     )

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       super().to_dict()
       for k, v in d.items():
             if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   _A        =       v.to_dict()
       return d

