

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Any      =						CTRLTokenizer
 _lowercase     :					List[Any]      =						False
 _lowercase     :					int      =						False

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _lowerCAmelCase   :					Optional[Any]        =    ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
      _lowerCAmelCase   :					Any        =    dict(zip(A_     ,						range(len(A_       )       )       )       )
      _lowerCAmelCase   :					List[str]        =    ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
      _lowerCAmelCase   :					List[str]        =    {"unk_token": "<unk>"}

      _lowerCAmelCase   :					int        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["vocab_file"]       )
      _lowerCAmelCase   :					List[str]        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["merges_file"]       )
      with open(self.vocab_file     ,						"w"     ,						encoding="utf-8"       ) as fp:
           fp.write(json.dumps(A_       ) + "\n"       )
      with open(self.merges_file     ,						"w"     ,						encoding="utf-8"       ) as fp:
           fp.write("\n".join(A_       )       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      kwargs.update(self.special_tokens_map       )
      return CTRLTokenizer.from_pretrained(self.tmpdirname     ,						**A_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    "adapt react readapt apt"
      _lowerCAmelCase   :					str        =    "adapt react readapt apt"
      return input_text, output_text


 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    CTRLTokenizer(self.vocab_file     ,						self.merges_file     ,						**self.special_tokens_map       )
      _lowerCAmelCase   :					Dict        =    "adapt react readapt apt"
      _lowerCAmelCase   :					List[str]        =    "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
      _lowerCAmelCase   :					Optional[int]        =    tokenizer.tokenize(A_       )
      self.assertListEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    tokens + [tokenizer.unk_token]

      _lowerCAmelCase   :					str        =    [0, 1, 2, 4, 5, 1, 0, 3, 6]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A_       )     ,						A_       )









import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 def __init__(		self :	Tuple     ,						*A_ :	Optional[int]     ,						**A_ :	int       ):






      '''simple docstring'''




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






