


"""simple docstring"""


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 snake_case      (  __UpperCAmelCase     ,				unittest.TestCase):
  __UpperCamelCase           =			BlenderbotSmallTokenizer
  __UpperCamelCase           =			False







  def  a_ (					self      :       Tuple		)       ->						int:





      '''simple docstring'''







      super().setUp()

      _A					  =			["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
      _A					  =			dict(zip(__SCREAMING_SNAKE_CASE    ,   range(len(__SCREAMING_SNAKE_CASE		)		)		)		)

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

      _A					  =			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES["vocab_file"]		)
      _A					  =			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(__SCREAMING_SNAKE_CASE		) + "\n"		)
      with open(self.merges_file    ,   "w"    ,   encoding="utf-8"		) as fp:
          fp.write("\n".join(__SCREAMING_SNAKE_CASE		)		)







  def  a_ (					self      :       Union[str, Any]    ,   **a__      :       Any		)       ->						List[str]:





      '''simple docstring'''







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







  def  a_ (					self      :       Dict    ,   a__      :       Union[str, Any]		)       ->						str:





      '''simple docstring'''







      _A					  =			"adapt act apte"
      _A					  =			"adapt act apte"
      return input_text, output_text







  def  a_ (					self      :       Optional[int]		)       ->						Dict:





      '''simple docstring'''







      _A					  =			BlenderbotSmallTokenizer(self.vocab_file    ,   self.merges_file    ,   **self.special_tokens_map		)
      _A					  =			"adapt act apte"
      _A					  =			["adapt", "act", "ap@@", "te"]
      _A					  =			tokenizer.tokenize(__SCREAMING_SNAKE_CASE		)
      self.assertListEqual(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE		)

      _A					  =			[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

      _A					  =			[0, 1, 2, 3, 4, 5]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)    ,   __SCREAMING_SNAKE_CASE		)







  def  a_ (					self      :       Union[str, Any]		)       ->						int:





      '''simple docstring'''







      _A					  =			BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"		)
      assert tok("sam"		).input_ids == [13_84]
      _A					  =			"I am a small frog."
      _A					  =			tok([src_text]    ,   padding=__SCREAMING_SNAKE_CASE    ,   truncation=__SCREAMING_SNAKE_CASE		)["input_ids"]
      _A					  =			tok.batch_decode(__SCREAMING_SNAKE_CASE    ,   skip_special_tokens=__SCREAMING_SNAKE_CASE    ,   clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE		)[0]
      assert src_text != decoded  # I wish it did!
      assert decoded == "i am a small frog ."







  def  a_ (					self      :       Union[str, Any]		)       ->						Union[str, Any]:





      '''simple docstring'''







      _A					  =			BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"		)
      _A					  =			"I am a small frog ."
      _A					  =			"."
      _A					  =			tok(__SCREAMING_SNAKE_CASE		)["input_ids"]
      _A					  =			tok(__SCREAMING_SNAKE_CASE		)["input_ids"]

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



"""simple docstring"""


import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a_       							=    logging.get_logger(__name__)
class snake_case      (  _UpperCamelCase):







		def __init__(					self      :       str    ,   *a__      :       Dict    ,   **a__      :       Optional[int]		)       ->						None:





						'''simple docstring'''







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