




import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_ : List[str]    		=       logging.get_logger(__name__)


def    A						(__A   :    Dict   ,				__A   :    List[Any]=False   ,				__A   :    List[str]=False       )   ->      Any:







     """simple docstring"""


     UpperCAmelCase_          =							'''backbone.''' if is_semantic else ''''''

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

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (F"""{prefix}cls_token""", '''beit.embeddings.cls_token'''),
             (F"""{prefix}patch_embed.proj.weight""", '''beit.embeddings.patch_embeddings.projection.weight'''),
             (F"""{prefix}patch_embed.proj.bias""", '''beit.embeddings.patch_embeddings.projection.bias'''),
             (F"""{prefix}pos_embed""", '''beit.embeddings.position_embeddings'''),
         ]       )

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ('''mask_token''', '''beit.embeddings.mask_token'''),
                  ('''norm.weight''', '''layernorm.weight'''),
                  ('''norm.bias''', '''layernorm.bias'''),
              ]       )
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                  ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                  ('''head.weight''', '''classifier.weight'''),
                  ('''head.bias''', '''classifier.bias'''),
              ]       )

     return rename_keys


def    A						(__A   :    List[str]   ,				__A   :    Any   ,				__A   :    str=False   ,				__A   :    Union[str, Any]=False       )   ->      str:







     """simple docstring"""


     for i in range(config.num_hidden_layers       ):
          UpperCAmelCase_          =							'''backbone.''' if is_semantic else ''''''
          # queries, keys and values
          UpperCAmelCase_          =							state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""       )
          UpperCAmelCase_          =							state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""       )
          UpperCAmelCase_          =							state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""       )

          UpperCAmelCase_          =							in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCAmelCase_          =							q_bias
          UpperCAmelCase_          =							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCAmelCase_          =							in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCAmelCase_          =							v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          UpperCAmelCase_          =							state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""       )
          UpperCAmelCase_          =							state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""       )

          UpperCAmelCase_          =							gamma_a
          UpperCAmelCase_          =							gamma_a


def    A						(__A   :    List[Any]   ,				__A   :    Tuple   ,				__A   :    Optional[Any]       )   ->      Dict:







     """simple docstring"""


     UpperCAmelCase_          =							dct.pop(__A       )
     UpperCAmelCase_          =							val


def    A						()   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     UpperCAmelCase_          =							Image.open(requests.get(__A   ,				stream=__A       ).raw       )
     return im


@torch.no_grad()
def    A						(__A   :    int   ,				__A   :    int   ,				__A   :    Optional[int]=False       )   ->      Optional[int]:







     """simple docstring"""


     UpperCAmelCase_          =							False if '''rvlcdip''' in checkpoint_url else True
     UpperCAmelCase_          =							BeitConfig(use_absolute_position_embeddings=__A   ,				use_mask_token=__A       )

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          UpperCAmelCase_          =							1024
          UpperCAmelCase_          =							4096
          UpperCAmelCase_          =							24
          UpperCAmelCase_          =							16

     # labels
     if "rvlcdip" in checkpoint_url:
          UpperCAmelCase_          =							16
          UpperCAmelCase_          =							'''huggingface/label-files'''
          UpperCAmelCase_          =							'''rvlcdip-id2label.json'''
          UpperCAmelCase_          =							json.load(open(hf_hub_download(__A   ,				__A   ,				repo_type='''dataset'''       )   ,				'''r'''       )       )
          UpperCAmelCase_          =							{int(__A       ): v for k, v in idalabel.items()}
          UpperCAmelCase_          =							idalabel
          UpperCAmelCase_          =							{v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase_          =							torch.hub.load_state_dict_from_url(__A   ,				map_location='''cpu'''       )['''model''']

     UpperCAmelCase_          =							create_rename_keys(__A   ,				has_lm_head=__A       )
     for src, dest in rename_keys:
          rename_key(__A   ,				__A   ,				__A       )
     read_in_q_k_v(__A   ,				__A   ,				has_lm_head=__A       )

     # load HuggingFace model
     UpperCAmelCase_          =							BeitForMaskedImageModeling(__A       ) if has_lm_head else BeitForImageClassification(__A       )
     model.eval()
     model.load_state_dict(__A       )

     # Check outputs on an image
     UpperCAmelCase_          =							BeitImageProcessor(
         size=config.image_size   ,				resample=PILImageResampling.BILINEAR   ,				do_center_crop=__A       )
     UpperCAmelCase_          =							prepare_img()

     UpperCAmelCase_          =							image_processor(images=__A   ,				return_tensors='''pt'''       )
     UpperCAmelCase_          =							encoding['''pixel_values''']

     UpperCAmelCase_          =							model(__A       )
     UpperCAmelCase_          =							outputs.logits

     # verify logits
     UpperCAmelCase_          =							[1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(__A       ), "Shape of logits not as expected"

     Path(__A       ).mkdir(exist_ok=__A       )
     print(F"""Saving model to {pytorch_dump_folder_path}"""       )
     model.save_pretrained(__A       )
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""       )
     image_processor.save_pretrained(__A       )

     if push_to_hub:
          if has_lm_head:
               UpperCAmelCase_          =							'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
          else:
               UpperCAmelCase_          =							'''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
          image_processor.push_to_hub(
              repo_path_or_name=Path(__A   ,				__A       )   ,				organization='''nielsr'''   ,				commit_message='''Add image processor'''   ,				use_temp_dir=__A   ,				)
          model.push_to_hub(
              repo_path_or_name=Path(__A   ,				__A       )   ,				organization='''nielsr'''   ,				commit_message='''Add model'''   ,				use_temp_dir=__A   ,				)


if __name__ == "__main__":
    snake_case_ : Tuple    		=       argparse.ArgumentParser()

    parser.add_argument(
        "--checkpoint_url",
        default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
        type=str,
        help="URL to the original PyTorch checkpoint (.pth file).",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
    )
    parser.add_argument(
        "--push_to_hub",
        action="store_true",
    )
    snake_case_ : Dict    		=       parser.parse_args()
    convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












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

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
    snake_case_ : Optional[Any]    		=       "pt"
elif is_tf_available():
    snake_case_ : Union[str, Any]    		=       "tf"
else:
    snake_case_ : str    		=       "jax"







class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  ByTaTokenizer
   UpperCAmelCase__  :    int          =  False







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        super().setUp()
        UpperCAmelCase_          =							ByTaTokenizer()
        tokenizer.save_pretrained(self.tmpdirname)







   @cached_property
   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        return ByTaTokenizer.from_pretrained('''google/byt5-small''')







   def lowerCamelCase						(      self		:						List[str]					,						**_snake_case		:						Union[str, Any]):

        """simple docstring"""


        return self.tokenizer_class.from_pretrained(self.tmpdirname					,						**_snake_case)







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						int					,						_snake_case		:						Tuple=False					,						_snake_case		:						Dict=20					,						_snake_case		:						Optional[Any]=5):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        for i in range(len(_snake_case)):
             try:
                  UpperCAmelCase_          =							tokenizer.decode([i]					,						clean_up_tokenization_spaces=_snake_case)
             except UnicodeDecodeError:
                  pass
             toks.append((i, tok))

        UpperCAmelCase_          =							list(filter(lambda _snake_case: re.match(r'''^[ a-zA-Z]+$'''					,						t[1])					,						_snake_case))
        UpperCAmelCase_          =							list(filter(lambda _snake_case: [t[0]] == tokenizer.encode(t[1]					,						add_special_tokens=_snake_case)					,						_snake_case))
        if max_length is not None and len(_snake_case) > max_length:
             UpperCAmelCase_          =							toks[:max_length]
        if min_length is not None and len(_snake_case) < min_length and len(_snake_case) > 0:
             while len(_snake_case) < min_length:
                  UpperCAmelCase_          =							toks + toks
        # toks_str = [t[1] for t in toks]
        UpperCAmelCase_          =							[t[0] for t in toks]

        # Ensure consistency
        UpperCAmelCase_          =							tokenizer.decode(_snake_case					,						clean_up_tokenization_spaces=_snake_case)
        if " " not in output_txt and len(_snake_case) > 1:
             UpperCAmelCase_          =							(
                 tokenizer.decode([toks_ids[0]]					,						clean_up_tokenization_spaces=_snake_case)
                 + ''' '''
                 + tokenizer.decode(toks_ids[1:]					,						clean_up_tokenization_spaces=_snake_case)
             )
        if with_prefix_space:
             UpperCAmelCase_          =							''' ''' + output_txt
        UpperCAmelCase_          =							tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
        return output_txt, output_ids







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

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							tokenizer(['''hi</s>''', '''I went to the gym</s>''', '''</s>'''])
        UpperCAmelCase_          =							tokenizer(['''hi''', '''I went to the gym''', ''''''])
        self.assertListEqual(batch_with_eos_added['''input_ids''']					,						batch_without_eos_added['''input_ids'''])







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							'''Unicode €.'''
        UpperCAmelCase_          =							tokenizer(_snake_case)
        UpperCAmelCase_          =							[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
        self.assertEqual(encoded['''input_ids''']					,						_snake_case)

        # decoding
        UpperCAmelCase_          =							tokenizer.decode(_snake_case)
        self.assertEqual(_snake_case					,						'''Unicode €.</s>''')

        UpperCAmelCase_          =							tokenizer('''e è é ê ë''')
        UpperCAmelCase_          =							[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
        self.assertEqual(encoded['''input_ids''']					,						_snake_case)
        # decoding
        UpperCAmelCase_          =							tokenizer.decode(_snake_case)
        self.assertEqual(_snake_case					,						'''e è é ê ë</s>''')

        # encode/decode, but with `encode` instead of `__call__`
        self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''))					,						'''e è é ê ë</s>''')







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        # fmt: off
        UpperCAmelCase_          =							[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
        # fmt: on
        UpperCAmelCase_          =							tokenizer(_snake_case					,						padding=_snake_case					,						return_tensors=_snake_case)
        self.assertIsInstance(_snake_case					,						_snake_case)

        if FRAMEWORK != "jax":
             UpperCAmelCase_          =							list(batch.input_ids.numpy()[0])
        else:
             UpperCAmelCase_          =							list(batch.input_ids.tolist()[0])

        self.assertListEqual(_snake_case					,						_snake_case)

        self.assertEqual((2, 37)					,						batch.input_ids.shape)
        self.assertEqual((2, 37)					,						batch.attention_mask.shape)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        UpperCAmelCase_          =							tokenizer(_snake_case					,						padding=_snake_case					,						return_tensors=_snake_case)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn('''input_ids'''					,						_snake_case)
        self.assertIn('''attention_mask'''					,						_snake_case)
        self.assertNotIn('''decoder_input_ids'''					,						_snake_case)
        self.assertNotIn('''decoder_attention_mask'''					,						_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        UpperCAmelCase_          =							tokenizer(
            text_target=_snake_case					,						max_length=32					,						padding='''max_length'''					,						truncation=_snake_case					,						return_tensors=_snake_case)
        self.assertEqual(32					,						targets['''input_ids'''].shape[1])







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							self.ta_base_tokenizer
        UpperCAmelCase_          =							['''A long paragraph for summarization. </s>''']
        UpperCAmelCase_          =							['''Summary of the text. </s>''']
        # fmt: off
        UpperCAmelCase_          =							[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
        UpperCAmelCase_          =							[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
        # fmt: on

        UpperCAmelCase_          =							tokenizer(_snake_case					,						text_target=_snake_case)

        self.assertEqual(_snake_case					,						batch['''input_ids'''][0])
        self.assertEqual(_snake_case					,						batch['''labels'''][0])







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							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_          =							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_          =							tempfile.mkdtemp()

                  UpperCAmelCase_          =							''' He is very happy, UNwant\u00E9d,running'''
                  UpperCAmelCase_          =							tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
                  tokenizer.save_pretrained(_snake_case)

                  UpperCAmelCase_          =							tokenizer.__class__.from_pretrained(_snake_case)
                  UpperCAmelCase_          =							after_tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
                  self.assertListEqual(_snake_case					,						_snake_case)

                  shutil.rmtree(_snake_case)

        UpperCAmelCase_          =							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_          =							tempfile.mkdtemp()

                  UpperCAmelCase_          =							''' He is very happy, UNwant\u00E9d,running'''
                  tokenizer.add_tokens(['''bim''', '''bambam'''])
                  UpperCAmelCase_          =							tokenizer.additional_special_tokens
                  additional_special_tokens.append('''new_additional_special_token''')
                  tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens})
                  UpperCAmelCase_          =							tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
                  tokenizer.save_pretrained(_snake_case)

                  UpperCAmelCase_          =							tokenizer.__class__.from_pretrained(_snake_case)
                  UpperCAmelCase_          =							after_tokenizer.encode(_snake_case					,						add_special_tokens=_snake_case)
                  self.assertListEqual(_snake_case					,						_snake_case)
                  self.assertIn('''new_additional_special_token'''					,						after_tokenizer.additional_special_tokens)
                  self.assertEqual(after_tokenizer.model_max_length					,						42)

                  UpperCAmelCase_          =							tokenizer.__class__.from_pretrained(_snake_case					,						model_max_length=43)
                  self.assertEqual(tokenizer.model_max_length					,						43)

                  shutil.rmtree(_snake_case)







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        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(_snake_case)

                  with open(os.path.join(_snake_case					,						'''special_tokens_map.json''')					,						encoding='''utf-8''') as json_file:
                       UpperCAmelCase_          =							json.load(_snake_case)

                  with open(os.path.join(_snake_case					,						'''tokenizer_config.json''')					,						encoding='''utf-8''') as json_file:
                       UpperCAmelCase_          =							json.load(_snake_case)

                  UpperCAmelCase_          =							[F"""<extra_id_{i}>""" for i in range(125)]

                  UpperCAmelCase_          =							added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]
                  UpperCAmelCase_          =							added_tokens_extra_ids + [
                      '''an_additional_special_token'''
                  ]

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

                  # 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_          =							tokenizer_class.from_pretrained(
                      _snake_case					,						)
                  self.assertIn(
                      '''an_additional_special_token'''					,						tokenizer_without_change_in_init.additional_special_tokens)
                  # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                  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_          =							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''					,						lstrip=_snake_case)]
                  UpperCAmelCase_          =							tokenizer_class.from_pretrained(
                      _snake_case					,						additional_special_tokens=_snake_case					,						)

                  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 lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        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(_snake_case)

                  UpperCAmelCase_          =							tokenizer_class.from_pretrained(_snake_case)

                  self.assertTrue(tokenizer.decode([255]) == '''''')







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_tokenizers(fast=_snake_case					,						do_lower_case=_snake_case)
        for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""):
                  UpperCAmelCase_          =							['''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''x''', '''t''', '''</s>''']
                  UpperCAmelCase_          =							tokenizer.convert_tokens_to_string(_snake_case)

                  self.assertIsInstance(_snake_case					,						_snake_case)







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

        """simple docstring"""


        UpperCAmelCase_          =							self.get_tokenizers()
        for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""):
                  UpperCAmelCase_          =							[
                      '''bos_token''',
                      '''eos_token''',
                      '''unk_token''',
                      '''sep_token''',
                      '''pad_token''',
                      '''cls_token''',
                      '''mask_token''',
                  ]

                  UpperCAmelCase_          =							0
                  UpperCAmelCase_          =							tokenizer.convert_ids_to_tokens(
                      _snake_case					,						skip_special_tokens=_snake_case)

                  for attr in attributes_list:
                       setattr(_snake_case					,						attr + '''_id'''					,						_snake_case)
                       self.assertEqual(getattr(_snake_case					,						_snake_case)					,						_snake_case)
                       self.assertEqual(getattr(_snake_case					,						attr + '''_id''')					,						_snake_case)

                       setattr(_snake_case					,						attr + '''_id'''					,						_snake_case)
                       self.assertEqual(getattr(_snake_case					,						_snake_case)					,						_snake_case)
                       self.assertEqual(getattr(_snake_case					,						attr + '''_id''')					,						_snake_case)

                  setattr(_snake_case					,						'''additional_special_tokens_ids'''					,						[])
                  self.assertListEqual(getattr(_snake_case					,						'''additional_special_tokens''')					,						[])
                  self.assertListEqual(getattr(_snake_case					,						'''additional_special_tokens_ids''')					,						[])

                  setattr(_snake_case					,						'''additional_special_tokens_ids'''					,						[token_id_to_test_setters])
                  self.assertListEqual(getattr(_snake_case					,						'''additional_special_tokens''')					,						[token_to_test_setters])
                  self.assertListEqual(getattr(_snake_case					,						'''additional_special_tokens_ids''')					,						[token_id_to_test_setters])






