





import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 			_lowerCamelCase	(      lowerCamelCase_	,				unittest.TestCase      ):
      UpperCAmelCase_  : str				      =BertweetTokenizer
      UpperCAmelCase_  : int				      =False







      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        __snake_case				:			Dict            =    ["I", "m", "V@@", "R@@", "r", "e@@"]
        __snake_case				:			Optional[Any]            =    dict(zip(UpperCAmelCase			,  range(len(UpperCAmelCase				)				)				)				)
        __snake_case				:			Tuple            =    ["#version: 0.2", "a m</w>"]
        __snake_case				:			Any            =    {"unk_token": "<unk>"}

        __snake_case				:			Any            =    os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]				)
        __snake_case				:			Optional[int]            =    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   UpperCAmelCase						(	self			,  **UpperCAmelCase				)    ->				Union[str, Any]:






        '''simple docstring'''






        kwargs.update(self.special_tokens_map				)
        return BertweetTokenizer.from_pretrained(self.tmpdirname			,  **UpperCAmelCase				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    "I am VinAI Research"
        __snake_case				:			List[Any]            =    "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
        return input_text, output_text







      def   UpperCAmelCase						(	self				)    ->				int:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    BertweetTokenizer(self.vocab_file			,  self.merges_file			,  **self.special_tokens_map				)
        __snake_case				:			Any            =    "I am VinAI Research"
        __snake_case				:			Optional[int]            =    "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
        __snake_case				:			Union[str, Any]            =    tokenizer.tokenize(UpperCAmelCase				)
        self.assertListEqual(UpperCAmelCase			,  UpperCAmelCase				)

        __snake_case				:			int            =    tokens + [tokenizer.unk_token]

        __snake_case				:			List[str]            =    [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase				)			,  UpperCAmelCase				)








import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


_UpperCamelCase    =	logging.get_logger(__name__)




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      def __init__(	self			,  *UpperCAmelCase			,  **UpperCAmelCase				)    ->				None:






        '''simple docstring'''






        warnings.warn(
            "The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use MaskFormerImageProcessor instead."			,  UpperCAmelCase			,  )
        super().__init__(*UpperCAmelCase			,  **UpperCAmelCase				)

