



'''simple docstring'''

import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


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



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Tuple:
		print('Loading config file...'  )

		def flatten_yaml_as_dict(lowerCAmelCase_						,			lowerCAmelCase_=""						,			lowerCAmelCase_="."  ):
				_a     :      Optional[Any]        =							[]
				for k, v in d.items():
						_a     :      str        =							parent_key + sep + k if parent_key else k
						if isinstance(lowerCAmelCase_						,			collections.abc.MutableMapping  ):
								items.extend(flatten_yaml_as_dict(lowerCAmelCase_						,			lowerCAmelCase_						,			sep=lowerCAmelCase_  ).items()  )
						else:
								items.append((new_key, v)  )
				return dict(lowerCAmelCase_  )

		_a     :      Optional[Any]        =							argparse.Namespace()
		with open(lowerCAmelCase_						,			'r'  ) as yaml_file:
				try:
						_a     :      List[str]        =							yaml.load(lowerCAmelCase_						,			Loader=yaml.FullLoader  )

						_a     :      str        =							flatten_yaml_as_dict(lowerCAmelCase_  )
						for k, v in flat_cfg.items():
								setattr(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
				except yaml.YAMLError as exc:
						logger.error('Error while loading config file: {}. Error message: {}'.format(lowerCAmelCase_						,			str(lowerCAmelCase_  )  )  )
		return config



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[str]:
		_a     :      int        =							MobileViTVaConfig()

		_a     :      Tuple        =							False

		# dataset
		if task_name.startswith('imagenet1k_'  ):
				_a     :      Any        =							1000
				if int(task_name.strip().split('_'  )[-1]  ) == 384:
						_a     :      Optional[Any]        =							384
				else:
						_a     :      List[Any]        =							256
				_a     :      Optional[Any]        =							'imagenet-1k-id2label.json'
		elif task_name.startswith('imagenet21k_to_1k_'  ):
				_a     :      str        =							21000
				if int(task_name.strip().split('_'  )[-1]  ) == 384:
						_a     :      Any        =							384
				else:
						_a     :      int        =							256
				_a     :      List[Any]        =							'imagenet-22k-id2label.json'
		elif task_name.startswith('ade20k_'  ):
				_a     :      Union[str, Any]        =							151
				_a     :      Any        =							512
				_a     :      str        =							'ade20k-id2label.json'
				_a     :      int        =							True
		elif task_name.startswith('voc_'  ):
				_a     :      Tuple        =							21
				_a     :      Optional[Any]        =							512
				_a     :      Optional[int]        =							'pascal-voc-id2label.json'
				_a     :      Tuple        =							True

		# orig_config
		_a     :      List[str]        =							load_orig_config_file(lowerCAmelCase_  )
		assert getattr(lowerCAmelCase_						,			'model.classification.name'						,			-1  ) == "mobilevit_v2", "Invalid model"
		_a     :      List[str]        =							getattr(lowerCAmelCase_						,			'model.classification.mitv2.width_multiplier'						,			1.0  )
		assert (
		    getattr(lowerCAmelCase_						,			'model.classification.mitv2.attn_norm_layer'						,			-1  ) == "layer_norm_2d"
		), "Norm layers other than layer_norm_2d is not supported"
		_a     :      Tuple        =							getattr(lowerCAmelCase_						,			'model.classification.activation.name'						,			'swish'  )
		# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

		if is_segmentation_model:
				_a     :      Tuple        =							getattr(lowerCAmelCase_						,			'model.segmentation.output_stride'						,			16  )
				if "_deeplabv3" in task_name:
						_a     :      str        =							getattr(lowerCAmelCase_						,			'model.segmentation.deeplabv3.aspp_rates'						,			[12, 24, 36]  )
						_a     :      str        =							getattr(lowerCAmelCase_						,			'model.segmentation.deeplabv3.aspp_out_channels'						,			512  )
						_a     :      int        =							getattr(lowerCAmelCase_						,			'model.segmentation.deeplabv3.aspp_dropout'						,			0.1  )

    # id2label
		_a     :      Optional[int]        =							'huggingface/label-files'
		_a     :      int        =							json.load(open(hf_hub_download(lowerCAmelCase_						,			lowerCAmelCase_						,			repo_type='dataset'  )						,			'r'  )  )
		_a     :      List[str]        =							{int(lowerCAmelCase_  ): v for k, v in idalabel.items()}
		_a     :      int        =							idalabel
		_a     :      List[Any]        =							{v: k for k, v in idalabel.items()}

		return config



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      List[Any]        =							dct.pop(lowerCAmelCase_  )
		_a     :      List[Any]        =							val



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=False  )			->      Union[str, Any]:
		if base_model:
				_a     :      str        =							''
		else:
				_a     :      Optional[int]        =							'mobilevitv2.'

		_a     :      Tuple        =							[]
		for k in state_dict.keys():
				if k[:8] == "encoder.":
						_a     :      Optional[Any]        =							k[8:]
				else:
						_a     :      Union[str, Any]        =							k

				if ".block." in k:
						_a     :      List[Any]        =							k_new.replace('.block.'						,			'.'  )
				if ".conv." in k:
						_a     :      Tuple        =							k_new.replace('.conv.'						,			'.convolution.'  )
				if ".norm." in k:
						_a     :      Optional[Any]        =							k_new.replace('.norm.'						,			'.normalization.'  )

				if "conv_1." in k:
						_a     :      List[str]        =							k_new.replace('conv_1.'						,			f"""{model_prefix}conv_stem."""  )
				for i in [1, 2]:
						if f"""layer_{i}.""" in k:
								_a     :      Optional[int]        =							k_new.replace(f"""layer_{i}."""						,			f"""{model_prefix}encoder.layer.{i-1}.layer."""  )
				if ".exp_1x1." in k:
						_a     :      int        =							k_new.replace('.exp_1x1.'						,			'.expand_1x1.'  )
				if ".red_1x1." in k:
						_a     :      Tuple        =							k_new.replace('.red_1x1.'						,			'.reduce_1x1.'  )

				for i in [3, 4, 5]:
						if f"""layer_{i}.0.""" in k:
								_a     :      Union[str, Any]        =							k_new.replace(f"""layer_{i}.0."""						,			f"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""  )
						if f"""layer_{i}.1.local_rep.0.""" in k:
								_a     :      Tuple        =							k_new.replace(f"""layer_{i}.1.local_rep.0."""						,			f"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""  )
						if f"""layer_{i}.1.local_rep.1.""" in k:
								_a     :      int        =							k_new.replace(f"""layer_{i}.1.local_rep.1."""						,			f"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""  )

				for i in [3, 4, 5]:
						if i == 3:
								_a     :      Dict        =							[0, 1]
						elif i == 4:
								_a     :      Union[str, Any]        =							[0, 1, 2, 3]
						elif i == 5:
								_a     :      Union[str, Any]        =							[0, 1, 2]

						for j in j_in:
								if f"""layer_{i}.1.global_rep.{j}.""" in k:
										_a     :      Optional[Any]        =							k_new.replace(
										    f"""layer_{i}.1.global_rep.{j}."""						,			f"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""  )
						if f"""layer_{i}.1.global_rep.{j+1}.""" in k:
								_a     :      Dict        =							k_new.replace(
								    f"""layer_{i}.1.global_rep.{j+1}."""						,			f"""{model_prefix}encoder.layer.{i-1}.layernorm."""  )

						if f"""layer_{i}.1.conv_proj.""" in k:
								_a     :      Any        =							k_new.replace(f"""layer_{i}.1.conv_proj."""						,			f"""{model_prefix}encoder.layer.{i-1}.conv_projection."""  )

				if "pre_norm_attn.0." in k:
						_a     :      List[str]        =							k_new.replace('pre_norm_attn.0.'						,			'layernorm_before.'  )
				if "pre_norm_attn.1." in k:
						_a     :      Tuple        =							k_new.replace('pre_norm_attn.1.'						,			'attention.'  )
				if "pre_norm_ffn.0." in k:
						_a     :      Optional[Any]        =							k_new.replace('pre_norm_ffn.0.'						,			'layernorm_after.'  )
				if "pre_norm_ffn.1." in k:
						_a     :      Union[str, Any]        =							k_new.replace('pre_norm_ffn.1.'						,			'ffn.conv1.'  )
				if "pre_norm_ffn.3." in k:
						_a     :      Optional[int]        =							k_new.replace('pre_norm_ffn.3.'						,			'ffn.conv2.'  )

				if "classifier.1." in k:
						_a     :      List[Any]        =							k_new.replace('classifier.1.'						,			'classifier.'  )

				if "seg_head." in k:
						_a     :      str        =							k_new.replace('seg_head.'						,			'segmentation_head.'  )
				if ".aspp_layer." in k:
						_a     :      Tuple        =							k_new.replace('.aspp_layer.'						,			'.'  )
				if ".aspp_pool." in k:
						_a     :      List[str]        =							k_new.replace('.aspp_pool.'						,			'.'  )

				rename_keys.append((k, k_new)  )
		return rename_keys



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Dict:
		_a     :      Any        =							[]
		for k in state_dict.keys():
				if k.startswith('seg_head.aux_head.'  ):
						keys_to_ignore.append(lowerCAmelCase_  )
		for k in keys_to_ignore:
				state_dict.pop(lowerCAmelCase_						,			lowerCAmelCase_  )



def      __lowerCamelCase     (      )			->      Any:
		_a     :      Optional[int]        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
		# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
		_a     :      Union[str, Any]        =							Image.open(requests.get(lowerCAmelCase_						,			stream=lowerCAmelCase_  ).raw  )
		return im



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      Union[str, Any]        =							get_mobilevitva_config(lowerCAmelCase_						,			lowerCAmelCase_  )

		# load original state_dict
		_a     :      Optional[int]        =							torch.load(lowerCAmelCase_						,			map_location='cpu'  )

		# load huggingface model
		if task_name.startswith('ade20k_'  ) or task_name.startswith('voc_'  ):
				_a     :      Optional[Any]        =							MobileViTVaForSemanticSegmentation(lowerCAmelCase_  ).eval()
				_a     :      Tuple        =							False
		else:
				_a     :      int        =							MobileViTVaForImageClassification(lowerCAmelCase_  ).eval()
				_a     :      Optional[Any]        =							False

		# remove and rename some keys of load the original model
		_a     :      str        =							checkpoint
		remove_unused_keys(lowerCAmelCase_  )
		_a     :      Dict        =							create_rename_keys(lowerCAmelCase_						,			base_model=lowerCAmelCase_  )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		# load modified state_dict
		model.load_state_dict(lowerCAmelCase_  )

		# Check outputs on an image, prepared by MobileViTImageProcessor
		_a     :      Optional[Any]        =							MobileViTImageProcessor(crop_size=config.image_size						,			size=config.image_size + 32  )
		_a     :      Dict        =							image_processor(images=prepare_img()						,			return_tensors='pt'  )
		_a     :      Dict        =							model(**lowerCAmelCase_  )

		# verify classification model
		if task_name.startswith('imagenet'  ):
				_a     :      Dict        =							outputs.logits
				_a     :      int        =							logits.argmax(-1  ).item()
				print('Predicted class:'						,			model.config.idalabel[predicted_class_idx]  )
				if task_name.startswith('imagenet1k_256'  ) and config.width_multiplier == 1.0:
						# expected_logits for base variant
						_a     :      List[str]        =							torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01]  )
						assert torch.allclose(logits[0, :3]						,			lowerCAmelCase_						,			atol=1E-4  )

		Path(lowerCAmelCase_  ).mkdir(exist_ok=lowerCAmelCase_  )
		print(f"""Saving model {task_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__":
							__lowerCAmelCase									=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--task''',
							    default='''imagenet1k_256''',
							    type=str,
							    help=(
							        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
							        '''
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            '''
							    ),
							    choices=[
							        '''imagenet1k_256''',
							        '''imagenet1k_384''',
							        '''imagenet21k_to_1k_256''',
							        '''imagenet21k_to_1k_384''',
							        '''ade20k_deeplabv3''',
							        '''voc_deeplabv3''',
							    ],
							)

							parser.add_argument(
							    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
							)
							parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
							)

							__lowerCAmelCase									=   parser.parse_args()
							convert_mobilevitva_checkpoint(
							    args.task, args.orig_checkpoint_path, args.orig_config_path, 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():
   A     ='pt'
elif is_tf_available():
   A     ='tf'
else:
   A     ='jax'


class 			_a  (	__a							,							unittest.TestCase ):
       __a				:     Optional[Any]   				=					PerceiverTokenizer
       __a				:     str   				=					False
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           super().setUp()
           UpperCAmelCase			       =		PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname				)
       @cached_property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''				)
       def     A      (	self						:	Union[str, Any]   ,				**lowercase						:	int				):



           '''simple docstring'''

           return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	str   ,				lowercase						:	List[str]=False   ,				lowercase						:	Union[str, Any]=20   ,				lowercase						:	Union[str, Any]=5				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		list(filter(lambda lowercase				: re.match(R'''^[ a-zA-Z]+$'''   ,				t[1]				)   ,				lowercase				)				)
           UpperCAmelCase			       =		list(filter(lambda lowercase				: [t[0]] == tokenizer.encode(t[1]   ,				add_special_tokens=lowercase				)   ,				lowercase				)				)
           if max_length is not None and len(lowercase				) > max_length:
               UpperCAmelCase			       =		toks[:max_length]
           if min_length is not None and len(lowercase				) < min_length and len(lowercase				) > 0:
               while len(lowercase				) < 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(lowercase   ,				clean_up_tokenization_spaces=lowercase				)
           if " " not in output_txt and len(lowercase				) > 1:
               UpperCAmelCase			       =		(
                   tokenizer.decode([toks_ids[0]]   ,				clean_up_tokenization_spaces=lowercase				)
                   + ''' '''
                   + tokenizer.decode(toks_ids[1:]   ,				clean_up_tokenization_spaces=lowercase				)
               )
           if with_prefix_space:
               UpperCAmelCase			       =		''' ''' + output_txt
           UpperCAmelCase			       =		tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
           return output_txt, output_ids
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		'''Unicode €.'''
           UpperCAmelCase			       =		tokenizer(lowercase				)
           UpperCAmelCase			       =		[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)

           # decoding
           UpperCAmelCase			       =		tokenizer.decode(lowercase				)
           self.assertEqual(lowercase   ,				'''[CLS]Unicode €.[SEP]'''				)

           UpperCAmelCase			       =		tokenizer('''e è é ê ë'''				)
           UpperCAmelCase			       =		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded['''input_ids''']   ,				lowercase				)
           # decoding
           UpperCAmelCase			       =		tokenizer.decode(lowercase				)
           self.assertEqual(lowercase   ,				'''[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     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           # fmt: off
           UpperCAmelCase			       =		[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
           # fmt: on
           UpperCAmelCase			       =		tokenizer(lowercase   ,				padding=lowercase   ,				return_tensors=lowercase				)
           self.assertIsInstance(lowercase   ,				lowercase				)

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

           self.assertListEqual(lowercase   ,				lowercase				)

           self.assertEqual((2, 38)   ,				batch.input_ids.shape				)
           self.assertEqual((2, 38)   ,				batch.attention_mask.shape				)
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
           UpperCAmelCase			       =		tokenizer(lowercase   ,				padding=lowercase   ,				return_tensors=lowercase				)
           # check if input_ids are returned and no decoder_input_ids
           self.assertIn('''input_ids'''   ,				lowercase				)
           self.assertIn('''attention_mask'''   ,				lowercase				)
           self.assertNotIn('''decoder_input_ids'''   ,				lowercase				)
           self.assertNotIn('''decoder_attention_mask'''   ,				lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           UpperCAmelCase			       =		[
               '''Summary of the text.''',
               '''Another summary.''',
           ]
           UpperCAmelCase			       =		tokenizer(
               text_target=lowercase   ,				max_length=32   ,				padding='''max_length'''   ,				truncation=lowercase   ,				return_tensors=lowercase				)
           self.assertEqual(32   ,				targets['''input_ids'''].shape[1]				)
       def     A      (	self						:	int				):



           '''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(lowercase   ,				add_special_tokens=lowercase				)
                   tokenizer.save_pretrained(lowercase				)

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase				)
                   UpperCAmelCase			       =		after_tokenizer.encode(lowercase   ,				add_special_tokens=lowercase				)
                   self.assertListEqual(lowercase   ,				lowercase				)

                   shutil.rmtree(lowercase				)

           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(lowercase   ,				add_special_tokens=lowercase				)
                   tokenizer.save_pretrained(lowercase				)

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

                   UpperCAmelCase			       =		tokenizer.__class__.from_pretrained(lowercase   ,				model_max_length=43				)
                   self.assertEqual(tokenizer.model_max_length   ,				43				)

                   shutil.rmtree(lowercase				)
       def     A      (	self						:	Optional[int]				):



           '''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(lowercase				)

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

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

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

                   # 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(
                       lowercase   ,				)
                   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			       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''   ,				lstrip=lowercase				)]
                   UpperCAmelCase			       =		tokenizer_class.from_pretrained(
                       lowercase   ,				additional_special_tokens=lowercase   ,				)

                   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     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178]				)   ,				'''�'''				)
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Any				):



           '''simple docstring'''

           pass
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           pass
       def     A      (	self						:	str				):



           '''simple docstring'''

           pass







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_tokenizers(fast=lowercase   ,				do_lower_case=lowercase				)
           for tokenizer in tokenizers:
               with self.subTest(f"{tokenizer.__class__.__name__}"				):
                   UpperCAmelCase			       =		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                   UpperCAmelCase			       =		tokenizer.convert_tokens_to_string(lowercase				)

                   self.assertIsInstance(lowercase   ,				lowercase				)




