






'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase							=logging.get_logger(__name__)



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: Any=False	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =					[]
      for i in range(config.num_hidden_layers	):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f"blocks.{i}.norm1.weight", f"deit.encoder.layer.{i}.layernorm_before.weight")	)
            rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")	)
            rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")	)
            rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")	)
            rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")	)
            rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")	)
            rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")	)
            rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")	)
            rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")	)
            rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")	)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """deit.embeddings.cls_token"""),
              ("""dist_token""", """deit.embeddings.distillation_token"""),
              ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """deit.embeddings.position_embeddings"""),
          ]	)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ("""norm.weight""", """layernorm.weight"""),
                    ("""norm.bias""", """layernorm.bias"""),
                    ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                    ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
                ]	)

            # if just the base model, we should remove "deit" from all keys that start with "deit"
            UpperCamelCase_				:   List[Any]					    =					[(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""	) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("""norm.weight""", """deit.layernorm.weight"""),
                    ("""norm.bias""", """deit.layernorm.bias"""),
                    ("""head.weight""", """cls_classifier.weight"""),
                    ("""head.bias""", """cls_classifier.bias"""),
                    ("""head_dist.weight""", """distillation_classifier.weight"""),
                    ("""head_dist.bias""", """distillation_classifier.bias"""),
                ]	)

      return rename_keys



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: str    ,   a_							: Optional[int]=False	)       ->				Any:





      """simple docstring"""

      for i in range(config.num_hidden_layers	):
            if base_model:
                  UpperCamelCase_				:   int					    =					''
            else:
                  UpperCamelCase_				:   int					    =					'deit.'
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            UpperCamelCase_				:   Optional[int]					    =					state_dict.pop(f"blocks.{i}.attn.qkv.weight"	)
            UpperCamelCase_				:   Tuple					    =					state_dict.pop(f"blocks.{i}.attn.qkv.bias"	)
            # next, add query, keys and values (in that order) to the state dict
            UpperCamelCase_				:   str					    =					in_proj_weight[
                : config.hidden_size, :
            ]
            UpperCamelCase_				:   List[str]					    =					in_proj_bias[: config.hidden_size]
            UpperCamelCase_				:   Tuple					    =					in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            UpperCamelCase_				:   Tuple					    =					in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            UpperCamelCase_				:   Optional[Any]					    =					in_proj_weight[
                -config.hidden_size :, :
            ]
            UpperCamelCase_				:   Any					    =					in_proj_bias[-config.hidden_size :]



def 							snake_case					(     a_							: str    ,   a_							: int    ,   a_							: List[Any]	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   Union[str, Any]					    =					dct.pop(lowerCamelCase_	)
      UpperCamelCase_				:   Optional[Any]					    =					val



def 							snake_case					(     )       ->				str:





      """simple docstring"""

      UpperCamelCase_				:   Union[str, Any]					    =					'http://images.cocodataset.org/val2017/000000039769.jpg'
      UpperCamelCase_				:   List[str]					    =					Image.open(requests.get(lowerCamelCase_    ,   stream=lowerCamelCase_	).raw	)
      return im



@torch.no_grad()
def 							snake_case					(     a_							: int    ,   a_							: Optional[Any]	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =					DeiTConfig()
      # all deit models have fine-tuned heads
      UpperCamelCase_				:   Tuple					    =					False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      UpperCamelCase_				:   Tuple					    =					1_000
      UpperCamelCase_				:   List[Any]					    =					'huggingface/label-files'
      UpperCamelCase_				:   Any					    =					'imagenet-1k-id2label.json'
      UpperCamelCase_				:   int					    =					json.load(open(hf_hub_download(lowerCamelCase_    ,   lowerCamelCase_    ,   repo_type="""dataset"""	)    ,   """r"""	)	)
      UpperCamelCase_				:   str					    =					{int(lowerCamelCase_	): v for k, v in idalabel.items()}
      UpperCamelCase_				:   Tuple					    =					idalabel
      UpperCamelCase_				:   int					    =					{v: k for k, v in idalabel.items()}
      UpperCamelCase_				:   int					    =					int(deit_name[-6:-4]	)
      UpperCamelCase_				:   int					    =					int(deit_name[-3:]	)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""	):
            UpperCamelCase_				:   Union[str, Any]					    =					192
            UpperCamelCase_				:   int					    =					768
            UpperCamelCase_				:   List[str]					    =					12
            UpperCamelCase_				:   Union[str, Any]					    =					3
      elif deit_name[9:].startswith("""small"""	):
            UpperCamelCase_				:   str					    =					384
            UpperCamelCase_				:   Union[str, Any]					    =					1_536
            UpperCamelCase_				:   List[str]					    =					12
            UpperCamelCase_				:   List[Any]					    =					6
      if deit_name[9:].startswith("""base"""	):
            pass
      elif deit_name[4:].startswith("""large"""	):
            UpperCamelCase_				:   List[Any]					    =					1_024
            UpperCamelCase_				:   Union[str, Any]					    =					4_096
            UpperCamelCase_				:   List[Any]					    =					24
            UpperCamelCase_				:   str					    =					16

      # load original model from timm
      UpperCamelCase_				:   Optional[Any]					    =					timm.create_model(lowerCamelCase_    ,   pretrained=lowerCamelCase_	)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      UpperCamelCase_				:   List[Any]					    =					timm_model.state_dict()
      UpperCamelCase_				:   List[Any]					    =					create_rename_keys(lowerCamelCase_    ,   lowerCamelCase_	)
      for src, dest in rename_keys:
            rename_key(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_	)
      read_in_q_k_v(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_	)

      # load HuggingFace model
      UpperCamelCase_				:   List[str]					    =					DeiTForImageClassificationWithTeacher(lowerCamelCase_	).eval()
      model.load_state_dict(lowerCamelCase_	)

      # Check outputs on an image, prepared by DeiTImageProcessor
      UpperCamelCase_				:   Dict					    =					int(
          (256 / 224) * config.image_size	)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      UpperCamelCase_				:   int					    =					DeiTImageProcessor(size=lowerCamelCase_    ,   crop_size=config.image_size	)
      UpperCamelCase_				:   List[Any]					    =					image_processor(images=prepare_img()    ,   return_tensors="""pt"""	)
      UpperCamelCase_				:   Optional[Any]					    =					encoding['pixel_values']
      UpperCamelCase_				:   str					    =					model(lowerCamelCase_	)

      UpperCamelCase_				:   List[str]					    =					timm_model(lowerCamelCase_	)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(lowerCamelCase_    ,   outputs.logits    ,   atol=1e-3	)

      Path(lowerCamelCase_	).mkdir(exist_ok=lowerCamelCase_	)
      print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"	)
      model.save_pretrained(lowerCamelCase_	)
      print(f"Saving image processor to {pytorch_dump_folder_path}"	)
      image_processor.save_pretrained(lowerCamelCase_	)


if __name__ == "__main__":
      UpperCamelCase							=argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--deit_name",
          default="vit_deit_base_distilled_patch16_224",
          type=str,
          help="Name of the DeiT timm model you\'d like to convert.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
      )

      UpperCamelCase							=parser.parse_args()
      convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)














'''simple docstring'''

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      UpperCamelCase							="pt"
elif is_tf_available():
      UpperCamelCase							="tf"
else:
      UpperCamelCase							="jax"




class     A    (      SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						int      =						PerceiverTokenizer
 __a			:						List[str]      =						False
 def   _UpperCAmelCase       (							self ):
       super().setUp()
       UpperCamelCase_				:   Optional[int]					    =							PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def   _UpperCAmelCase       (							self ):
       return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver""" )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return self.tokenizer_class.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=False			,     __lowerCAmelCase=20			,     __lowerCAmelCase=5 ):
       # XXX The default common tokenizer tests assume that every ID is decodable on its own.
       # This assumption is invalid for Perceiver because single bytes might not be
       # valid utf-8 (byte 128 for instance).
       # Here we're overriding the smallest possible method to provide
       # a clean sequence without making the same assumption.

       UpperCamelCase_				:   str					    =							[]
       for i in range(len(__lowerCAmelCase ) ):
             try:
                   UpperCamelCase_				:   Tuple					    =							tokenizer.decode([i]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
             except UnicodeDecodeError:
                   pass
             toks.append((i, tok) )

       UpperCamelCase_				:   Optional[Any]					    =							list(filter(lambda __lowerCAmelCase : re.match(R"""^[ a-zA-Z]+$"""			,     t[1] )			,     __lowerCAmelCase ) )
       UpperCamelCase_				:   int					    =							list(filter(lambda __lowerCAmelCase : [t[0]] == tokenizer.encode(t[1]			,     add_special_tokens=__lowerCAmelCase )			,     __lowerCAmelCase ) )
       if max_length is not None and len(__lowerCAmelCase ) > max_length:
             UpperCamelCase_				:   List[Any]					    =							toks[:max_length]
       if min_length is not None and len(__lowerCAmelCase ) < min_length and len(__lowerCAmelCase ) > 0:
             while len(__lowerCAmelCase ) < min_length:
                   UpperCamelCase_				:   Tuple					    =							toks + toks
        # toks_str = [t[1] for t in toks]
       UpperCamelCase_				:   List[Any]					    =							[t[0] for t in toks]

       # Ensure consistency
       UpperCamelCase_				:   str					    =							tokenizer.decode(__lowerCAmelCase			,     clean_up_tokenization_spaces=__lowerCAmelCase )
       if " " not in output_txt and len(__lowerCAmelCase ) > 1:
             UpperCamelCase_				:   Tuple					    =							(
                 tokenizer.decode([toks_ids[0]]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
                 + """ """
                 + tokenizer.decode(toks_ids[1:]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
             )
       if with_prefix_space:
             UpperCamelCase_				:   Tuple					    =							""" """ + output_txt
       UpperCamelCase_				:   List[str]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
       return output_txt, output_ids
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Optional[int]					    =							"""Unicode €."""
       UpperCamelCase_				:   Dict					    =							tokenizer(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
       self.assertEqual(encoded["""input_ids"""]			,     __lowerCAmelCase )

       # decoding
       UpperCamelCase_				:   str					    =							tokenizer.decode(__lowerCAmelCase )
       self.assertEqual(__lowerCAmelCase			,     """[CLS]Unicode €.[SEP]""" )

       UpperCamelCase_				:   str					    =							tokenizer("""e è é ê ë""" )
       UpperCamelCase_				:   List[str]					    =							[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
       self.assertEqual(encoded["""input_ids"""]			,     __lowerCAmelCase )
       # decoding
       UpperCamelCase_				:   List[str]					    =							tokenizer.decode(__lowerCAmelCase )
       self.assertEqual(__lowerCAmelCase			,     """[CLS]e è é ê ë[SEP]""" )

       # encode/decode, but with `encode` instead of `__call__`
       self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë""" ) )			,     """[CLS]e è é ê ë[SEP]""" )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Optional[Any]					    =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       # fmt: off
       UpperCamelCase_				:   int					    =							[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
       # fmt: on
       UpperCamelCase_				:   Any					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )

       if FRAMEWORK != "jax":
             UpperCamelCase_				:   Optional[Any]					    =							list(batch.input_ids.numpy()[0] )
       else:
             UpperCamelCase_				:   Optional[int]					    =							list(batch.input_ids.tolist()[0] )

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

       self.assertEqual((2, 38)			,     batch.input_ids.shape )
       self.assertEqual((2, 38)			,     batch.attention_mask.shape )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Union[str, Any]					    =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       UpperCamelCase_				:   int					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn("""input_ids"""			,     __lowerCAmelCase )
       self.assertIn("""attention_mask"""			,     __lowerCAmelCase )
       self.assertNotIn("""decoder_input_ids"""			,     __lowerCAmelCase )
       self.assertNotIn("""decoder_attention_mask"""			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   List[str]					    =							[
           """Summary of the text.""",
           """Another summary.""",
       ]
       UpperCamelCase_				:   Dict					    =							tokenizer(
           text_target=__lowerCAmelCase			,     max_length=32			,     padding="""max_length"""			,     truncation=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.assertEqual(32			,     targets["""input_ids"""].shape[1] )
 def   _UpperCAmelCase       (							self ):
       # safety check on max_len default value so we are sure the test works
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   self.assertNotEqual(tokenizer.model_max_length			,     42 )

        # Now let's start the test
       UpperCamelCase_				:   Tuple					    =							self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCamelCase_				:   Tuple					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   Optional[int]					    =							""" He is very happy, UNwant\u00E9d,running"""
                   UpperCamelCase_				:   Dict					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   Union[str, Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

                   shutil.rmtree(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							self.get_tokenizers(model_max_length=42 )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   # Isolate this from the other tests because we save additional tokens/etc
                   UpperCamelCase_				:   Tuple					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   List[str]					    =							""" He is very happy, UNwant\u00E9d,running"""
                   tokenizer.add_tokens(["""bim""", """bambam"""] )
                   UpperCamelCase_				:   Any					    =							tokenizer.additional_special_tokens
                   additional_special_tokens.append("""new_additional_special_token""" )
                   tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
                   UpperCamelCase_				:   Union[str, Any]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   List[Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
                   self.assertIn("""new_additional_special_token"""			,     after_tokenizer.additional_special_tokens )
                   self.assertEqual(after_tokenizer.model_max_length			,     42 )

                   UpperCamelCase_				:   int					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase			,     model_max_length=43 )
                   self.assertEqual(tokenizer.model_max_length			,     43 )

                   shutil.rmtree(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							[]
       if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

       if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                   tokenizer_utils.save_pretrained(__lowerCAmelCase )

                   with open(os.path.join(__lowerCAmelCase			,     """special_tokens_map.json""" )			,     encoding="""utf-8""" ) as json_file:
                         UpperCamelCase_				:   Union[str, Any]					    =							json.load(__lowerCAmelCase )

                   with open(os.path.join(__lowerCAmelCase			,     """tokenizer_config.json""" )			,     encoding="""utf-8""" ) as json_file:
                         UpperCamelCase_				:   List[str]					    =							json.load(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							[F"<extra_id_{i}>" for i in range(1_25 )]

                   UpperCamelCase_				:   Union[str, Any]					    =							added_tokens_extra_ids + [
                       """an_additional_special_token"""
                   ]
                   UpperCamelCase_				:   List[Any]					    =							added_tokens_extra_ids + [
                       """an_additional_special_token"""
                   ]

                   with open(os.path.join(__lowerCAmelCase			,     """special_tokens_map.json""" )			,     """w"""			,     encoding="""utf-8""" ) as outfile:
                         json.dump(__lowerCAmelCase			,     __lowerCAmelCase )
                   with open(os.path.join(__lowerCAmelCase			,     """tokenizer_config.json""" )			,     """w"""			,     encoding="""utf-8""" ) as outfile:
                         json.dump(__lowerCAmelCase			,     __lowerCAmelCase )

                   # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                   # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                   # "special_tokens_map.json" files
                   UpperCamelCase_				:   Optional[int]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     )
                   self.assertIn(
                       """an_additional_special_token"""			,     tokenizer_without_change_in_init.additional_special_tokens )
                   self.assertEqual(
                       ["""an_additional_special_token"""]			,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                           tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""] ) )			,     )

                   # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                   UpperCamelCase_				:   Optional[Any]					    =							added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""			,     lstrip=__lowerCAmelCase )]
                   UpperCamelCase_				:   Union[str, Any]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     additional_special_tokens=__lowerCAmelCase			,     )

                   self.assertIn("""a_new_additional_special_token"""			,     tokenizer.additional_special_tokens )
                   self.assertEqual(
                       ["""a_new_additional_special_token"""]			,     tokenizer.convert_ids_to_tokens(
                           tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""] ) )			,     )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([1_78] )			,     """�""" )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass





 def   _UpperCAmelCase       (							self ):
       # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
       # strings and special added tokens as tokens
       UpperCamelCase_				:   Dict					    =							self.get_tokenizers(fast=__lowerCAmelCase			,     do_lower_case=__lowerCAmelCase )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   UpperCamelCase_				:   str					    =							["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                   UpperCamelCase_				:   Optional[Any]					    =							tokenizer.convert_tokens_to_string(__lowerCAmelCase )

                   self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )






