





'''simple docstring'''

import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin

class       UpperCAmelCase (						_lowercase			,       unittest.TestCase  ):
		UpperCAmelCase					:	Any 			=		PhobertTokenizer
		UpperCAmelCase					:	Optional[Any] 			=		False




		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Optional[Any]:
						super().setUp()

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

						lowercase         = os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["vocab_file"]				)
						lowercase         = 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 				UpperCAmelCase__   (self      :	Any							,			**A__      :	Union[str, Any]				)					-> Tuple:
						kwargs.update(self.special_tokens_map				)
						return PhobertTokenizer.from_pretrained(self.tmpdirname							,			**A__				)




		def 				UpperCAmelCase__   (self      :	str							,			A__      :	str				)					-> List[str]:
						lowercase         = "Tôi là VinAI Research"
						lowercase         = "T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
						return input_text, output_text





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

						lowercase         = tokens + [tokenizer.unk_token]

						lowercase         = [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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate

@dataclass(frozen=_lowercase  )
class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	str 			=		field(default='''image-classification'''			,       metadata={'''include_in_asdict_even_if_is_default''': True}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''image''': Image()}  )
		UpperCAmelCase					:	ClassVar[Features] 			=		Features({'''labels''': ClassLabel}  )
		UpperCAmelCase					:	str 			=		"image"
		UpperCAmelCase					:	str 			=		"labels"




		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Union[str, Any]				)					-> Union[str, Any]:
						if self.label_column not in features:
										raise ValueError(f'Column {self.label_column} is not present in features.'				)
						if not isinstance(features[self.label_column]							,			A__				):
										raise ValueError(f'Column {self.label_column} is not a ClassLabel.'				)
						lowercase         = copy.deepcopy(self				)
						lowercase         = self.label_schema.copy()
						lowercase         = features[self.label_column]
						lowercase         = label_schema
						return task_template





		@property
		def 				UpperCAmelCase__   (self      :	List[Any]				)					-> Dict[str, str]:
						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}




