'''simple docstring'''




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__UpperCAmelCase					=logging.getLogger(__name__)




@dataclass
class        a__ (  A__					):
     lowerCamelCase    :	Optional[float]				=field(
         default=0.0				,  metadata={"help": "The label smoothing epsilon to apply (if not zero)."}					)
     lowerCamelCase    :	bool				=field(default=A__				,  metadata={"help": "Whether to SortishSamler or not."}					)
     lowerCamelCase    :	bool				=field(
         default=A__				,  metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}					)
     lowerCamelCase    :	bool				=field(default=A__				,  metadata={"help": "whether to use adafactor"}					)
     lowerCamelCase    :	Optional[float]				=field(
         default=A__				,  metadata={"help": "Encoder layer dropout probability. Goes into model.config."}					)
     lowerCamelCase    :	Optional[float]				=field(
         default=A__				,  metadata={"help": "Decoder layer dropout probability. Goes into model.config."}					)
     lowerCamelCase    :	Optional[float]				=field(default=A__				,  metadata={"help": "Dropout probability. Goes into model.config."}					)
     lowerCamelCase    :	Optional[float]				=field(
         default=A__				,  metadata={"help": "Attention dropout probability. Goes into model.config."}					)
     lowerCamelCase    :	Optional[str]				=field(
         default="linear"				,  metadata={"help": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''}				,  )



"""simple docstring"""




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      ):
			lowerCamelCase__ :		List[str]       		=				PhobertTokenizer
			lowerCamelCase__ :		Union[str, Any]       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Any:
						super().setUp()

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

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]							)
						SCREAMING_SNAKE_CASE__										=   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(__UpperCAmelCase							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					**__UpperCAmelCase					:    int							) ->     Optional[int]:
						kwargs.update(self.special_tokens_map							)
						return PhobertTokenizer.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   """Tôi là VinAI Research"""
						SCREAMING_SNAKE_CASE__										=   """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
						return input_text, output_text





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

						SCREAMING_SNAKE_CASE__										=   tokens + [tokenizer.unk_token]

						SCREAMING_SNAKE_CASE__										=   [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(__UpperCAmelCase							)     ,					__UpperCAmelCase							)


