


import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					int      =						BlenderbotSmallTokenizer
 _lowercase     :					Union[str, Any]      =						False

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      super().setUp()

      _lowerCAmelCase   :					List[Any]        =    ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
      _lowerCAmelCase   :					Optional[Any]        =    dict(zip(A_     ,						range(len(A_       )       )       )       )

      _lowerCAmelCase   :					Any        =    ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
      _lowerCAmelCase   :					Dict        =    {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

      _lowerCAmelCase   :					int        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["vocab_file"]       )
      _lowerCAmelCase   :					Dict        =    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 :	Any     ,						**A_ :	Tuple       ):






      '''simple docstring'''




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

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






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    "adapt act apte"
      _lowerCAmelCase   :					Tuple        =    "adapt act apte"
      return input_text, output_text

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






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    BlenderbotSmallTokenizer(self.vocab_file     ,						self.merges_file     ,						**self.special_tokens_map       )
      _lowerCAmelCase   :					Tuple        =    "adapt act apte"
      _lowerCAmelCase   :					Any        =    ["adapt", "act", "ap@@", "te"]
      _lowerCAmelCase   :					int        =    tokenizer.tokenize(A_       )
      self.assertListEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Any        =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

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

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"       )
      assert tok("sam"       ).input_ids == [1_3_8_4]
      _lowerCAmelCase   :					Tuple        =    "I am a small frog."
      _lowerCAmelCase   :					Tuple        =    tok([src_text]     ,						padding=A_     ,						truncation=A_       )["input_ids"]
      _lowerCAmelCase   :					Tuple        =    tok.batch_decode(A_     ,						skip_special_tokens=A_     ,						clean_up_tokenization_spaces=A_       )[0]
      assert src_text != decoded  # I wish it did!
      assert decoded == "i am a small frog ."


 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"       )
      _lowerCAmelCase   :					Optional[int]        =    "I am a small frog ."
      _lowerCAmelCase   :					str        =    "."
      _lowerCAmelCase   :					Optional[Any]        =    tok(A_       )["input_ids"]
      _lowerCAmelCase   :					Tuple        =    tok(A_       )["input_ids"]

      assert encoded[-1] == encoded_dot[0]









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_       )






