



import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (    __snake_case,  unittest.TestCase	):







					'''simple docstring'''







					lowerCAmelCase_					:     Optional[int]					       =	PhobertTokenizer
					lowerCAmelCase_					:     Optional[int]					       =	False



					def  A__		(      self  ):
							super().setUp()

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

							UpperCAmelCase_						       =					os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["vocab_file"]  )
							UpperCAmelCase_						       =					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(__lowerCamelCase  )  )



					def  A__		(      self     ,						**lowerCAmelCase  ):
							kwargs.update(self.special_tokens_map  )
							return PhobertTokenizer.from_pretrained(self.tmpdirname     ,						**__lowerCamelCase  )



					def  A__		(      self     ,						lowerCAmelCase  ):
							UpperCAmelCase_						       =					"Tôi là VinAI Research"
							UpperCAmelCase_						       =					"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
							return input_text, output_text



					def  A__		(      self  ):
							UpperCAmelCase_						       =					PhobertTokenizer(self.vocab_file     ,						self.merges_file     ,						**self.special_tokens_map  )
							UpperCAmelCase_						       =					"Tôi là VinAI Research"
							UpperCAmelCase_						       =					"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
							UpperCAmelCase_						       =					tokenizer.tokenize(__lowerCamelCase  )
							print(__lowerCamelCase  )
							self.assertListEqual(__lowerCamelCase     ,						__lowerCamelCase  )

							UpperCAmelCase_						       =					tokens + [tokenizer.unk_token]

							UpperCAmelCase_						       =					[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(__lowerCamelCase  )     ,						__lowerCamelCase  )




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			:					Optional[Any]						=			logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__		)
class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      field(default=__snake_case					,       metadata={"help": "Whether to use SortishSampler or not."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
   lowerCamelCase__				=      field(
       default=__snake_case					,       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."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       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."
           )
       }					,       )
   lowerCamelCase__				=      field(
       default=__snake_case					,       metadata={
           "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
       }					,       )





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   super().to_dict()
       for k, v in d.items():
           if isinstance(__lowerCamelCase     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   v.to_dict()
       return d