

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase     : List[Any]       =						logging.get_logger(__name__)







def       lowercase					(	__A :					Any		)				->			Dict:





    '''simple docstring'''




    snake_case							:		int        =		DPTConfig()

    if "large" in checkpoint_url:
        snake_case							:		Optional[Any]        =		1024
        snake_case							:		List[str]        =		4096
        snake_case							:		Optional[int]        =		24
        snake_case							:		Dict        =		16
        snake_case							:		Optional[Any]        =		[5, 11, 17, 23]
        snake_case							:		int        =		[256, 512, 1024, 1024]
        snake_case							:		int        =		(1, 384, 384)

    if "ade" in checkpoint_url:
        snake_case							:		str        =		True

        snake_case							:		Optional[int]        =		150
        snake_case							:		List[str]        =		"""huggingface/label-files"""
        snake_case							:		Optional[int]        =		"""ade20k-id2label.json"""
        snake_case							:		Optional[int]        =		json.load(open(cached_download(hf_hub_url(__A ,    __A ,    repo_type="""dataset"""		)		) ,    """r"""		)		)
        snake_case							:		Tuple        =		{int(__A		): v for k, v in idalabel.items()}
        snake_case							:		Any        =		idalabel
        snake_case							:		Any        =		{v: k for k, v in idalabel.items()}
        snake_case							:		Dict        =		[1, 150, 480, 480]

    return config, expected_shape







def       lowercase					(	__A :					int		)				->			int:





    '''simple docstring'''




    snake_case							:		List[Any]        =		["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
    for k in ignore_keys:
        state_dict.pop(__A ,    __A		)







def       lowercase					(	__A :					Tuple		)				->			str:





    '''simple docstring'''




    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        snake_case							:		List[str]        =		name.replace("""pretrained.model""" ,    """dpt.encoder"""		)
    if "pretrained.model" in name:
        snake_case							:		int        =		name.replace("""pretrained.model""" ,    """dpt.embeddings"""		)
    if "patch_embed" in name:
        snake_case							:		Tuple        =		name.replace("""patch_embed""" ,    """patch_embeddings"""		)
    if "pos_embed" in name:
        snake_case							:		List[str]        =		name.replace("""pos_embed""" ,    """position_embeddings"""		)
    if "attn.proj" in name:
        snake_case							:		int        =		name.replace("""attn.proj""" ,    """attention.output.dense"""		)
    if "proj" in name and "project" not in name:
        snake_case							:		int        =		name.replace("""proj""" ,    """projection"""		)
    if "blocks" in name:
        snake_case							:		Union[str, Any]        =		name.replace("""blocks""" ,    """layer"""		)
    if "mlp.fc1" in name:
        snake_case							:		Optional[int]        =		name.replace("""mlp.fc1""" ,    """intermediate.dense"""		)
    if "mlp.fc2" in name:
        snake_case							:		List[Any]        =		name.replace("""mlp.fc2""" ,    """output.dense"""		)
    if "norm1" in name:
        snake_case							:		List[Any]        =		name.replace("""norm1""" ,    """layernorm_before"""		)
    if "norm2" in name:
        snake_case							:		List[str]        =		name.replace("""norm2""" ,    """layernorm_after"""		)
    if "scratch.output_conv" in name:
        snake_case							:		str        =		name.replace("""scratch.output_conv""" ,    """head"""		)
    if "scratch" in name:
        snake_case							:		Dict        =		name.replace("""scratch""" ,    """neck"""		)
    if "layer1_rn" in name:
        snake_case							:		Optional[int]        =		name.replace("""layer1_rn""" ,    """convs.0"""		)
    if "layer2_rn" in name:
        snake_case							:		Tuple        =		name.replace("""layer2_rn""" ,    """convs.1"""		)
    if "layer3_rn" in name:
        snake_case							:		Any        =		name.replace("""layer3_rn""" ,    """convs.2"""		)
    if "layer4_rn" in name:
        snake_case							:		Optional[Any]        =		name.replace("""layer4_rn""" ,    """convs.3"""		)
    if "refinenet" in name:
        snake_case							:		int        =		int(name[len("""neck.refinenet"""		) : len("""neck.refinenet"""		) + 1]		)
        # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
        snake_case							:		Tuple        =		name.replace(f"""refinenet{layer_idx}""" ,    f"""fusion_stage.layers.{abs(layer_idx-4		)}"""		)
    if "out_conv" in name:
        snake_case							:		Tuple        =		name.replace("""out_conv""" ,    """projection"""		)
    if "resConfUnit1" in name:
        snake_case							:		Optional[Any]        =		name.replace("""resConfUnit1""" ,    """residual_layer1"""		)
    if "resConfUnit2" in name:
        snake_case							:		Optional[Any]        =		name.replace("""resConfUnit2""" ,    """residual_layer2"""		)
    if "conv1" in name:
        snake_case							:		Dict        =		name.replace("""conv1""" ,    """convolution1"""		)
    if "conv2" in name:
        snake_case							:		List[Any]        =		name.replace("""conv2""" ,    """convolution2"""		)
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        snake_case							:		List[Any]        =		name.replace("""pretrained.act_postprocess1.0.project.0""" ,    """neck.reassemble_stage.readout_projects.0.0"""		)
    if "pretrained.act_postprocess2.0.project.0" in name:
        snake_case							:		List[str]        =		name.replace("""pretrained.act_postprocess2.0.project.0""" ,    """neck.reassemble_stage.readout_projects.1.0"""		)
    if "pretrained.act_postprocess3.0.project.0" in name:
        snake_case							:		Optional[Any]        =		name.replace("""pretrained.act_postprocess3.0.project.0""" ,    """neck.reassemble_stage.readout_projects.2.0"""		)
    if "pretrained.act_postprocess4.0.project.0" in name:
        snake_case							:		List[str]        =		name.replace("""pretrained.act_postprocess4.0.project.0""" ,    """neck.reassemble_stage.readout_projects.3.0"""		)
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        snake_case							:		Any        =		name.replace("""pretrained.act_postprocess1.3""" ,    """neck.reassemble_stage.layers.0.projection"""		)
    if "pretrained.act_postprocess1.4" in name:
        snake_case							:		Tuple        =		name.replace("""pretrained.act_postprocess1.4""" ,    """neck.reassemble_stage.layers.0.resize"""		)
    if "pretrained.act_postprocess2.3" in name:
        snake_case							:		Optional[int]        =		name.replace("""pretrained.act_postprocess2.3""" ,    """neck.reassemble_stage.layers.1.projection"""		)
    if "pretrained.act_postprocess2.4" in name:
        snake_case							:		Any        =		name.replace("""pretrained.act_postprocess2.4""" ,    """neck.reassemble_stage.layers.1.resize"""		)
    if "pretrained.act_postprocess3.3" in name:
        snake_case							:		List[Any]        =		name.replace("""pretrained.act_postprocess3.3""" ,    """neck.reassemble_stage.layers.2.projection"""		)
    if "pretrained.act_postprocess4.3" in name:
        snake_case							:		List[str]        =		name.replace("""pretrained.act_postprocess4.3""" ,    """neck.reassemble_stage.layers.3.projection"""		)
    if "pretrained.act_postprocess4.4" in name:
        snake_case							:		str        =		name.replace("""pretrained.act_postprocess4.4""" ,    """neck.reassemble_stage.layers.3.resize"""		)
    if "pretrained" in name:
        snake_case							:		Any        =		name.replace("""pretrained""" ,    """dpt"""		)
    if "bn" in name:
        snake_case							:		Optional[Any]        =		name.replace("""bn""" ,    """batch_norm"""		)
    if "head" in name:
        snake_case							:		Optional[int]        =		name.replace("""head""" ,    """head.head"""		)
    if "encoder.norm" in name:
        snake_case							:		Optional[int]        =		name.replace("""encoder.norm""" ,    """layernorm"""		)
    if "auxlayer" in name:
        snake_case							:		Union[str, Any]        =		name.replace("""auxlayer""" ,    """auxiliary_head.head"""		)

    return name







def       lowercase					(	__A :					Dict ,    __A :					Tuple		)				->			Dict:





    '''simple docstring'''




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







def       lowercase					(	)				->			Any:





    '''simple docstring'''




    snake_case							:		Optional[int]        =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    snake_case							:		List[Any]        =		Image.open(requests.get(__A ,    stream=__A		).raw		)
    return im







@torch.no_grad()
def       lowercase					(	__A :					Tuple ,    __A :					Optional[int] ,    __A :					Optional[Any] ,    __A :					Optional[Any]		)				->			Union[str, Any]:





    '''simple docstring'''




    snake_case      ,		snake_case							:		Union[str, Any]        =		get_dpt_config(__A		)
    # load original state_dict from URL
    snake_case							:		Union[str, Any]        =		torch.hub.load_state_dict_from_url(__A ,    map_location="""cpu"""		)
    # remove certain keys
    remove_ignore_keys_(__A		)
    # rename keys
    for key in state_dict.copy().keys():
        snake_case							:		Optional[Any]        =		state_dict.pop(__A		)
        snake_case							:		List[str]        =		val
    # read in qkv matrices
    read_in_q_k_v(__A ,    __A		)

    # load HuggingFace model
    snake_case							:		int        =		DPTForSemanticSegmentation(__A		) if """ade""" in checkpoint_url else DPTForDepthEstimation(__A		)
    model.load_state_dict(__A		)
    model.eval()

    # Check outputs on an image
    snake_case							:		int        =		480 if """ade""" in checkpoint_url else 384
    snake_case							:		Optional[int]        =		DPTImageProcessor(size=__A		)

    snake_case							:		List[Any]        =		prepare_img()
    snake_case							:		Optional[Any]        =		image_processor(__A ,    return_tensors="""pt"""		)

    # forward pass
    snake_case							:		Any        =		model(**__A		).logits if """ade""" in checkpoint_url else model(**__A		).predicted_depth

    # Assert logits
    snake_case							:		int        =		torch.tensor([[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]		)
    if "ade" in checkpoint_url:
        snake_case							:		List[str]        =		torch.tensor([[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]		)
    assert outputs.shape == torch.Size(__A		)
    assert (
        torch.allclose(outputs[0, 0, :3, :3] ,    __A ,    atol=1E-4		)
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3] ,    __A		)
    )

    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:
        print("""Pushing model to hub..."""		)
        model.push_to_hub(
            repo_path_or_name=Path(__A ,    __A		) ,    organization="""nielsr""" ,    commit_message="""Add model""" ,    use_temp_dir=__A ,    )
        image_processor.push_to_hub(
            repo_path_or_name=Path(__A ,    __A		) ,    organization="""nielsr""" ,    commit_message="""Add image processor""" ,    use_temp_dir=__A ,    )


if __name__ == "__main__":
     __lowercase     : Tuple       =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
         type=str,
         help='''URL of the original DPT checkpoint you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to the output PyTorch model directory.''',
     )
     parser.add_argument(
         '''--push_to_hub''',
         action='''store_true''',
     )
     parser.add_argument(
         '''--model_name''',
         default='''dpt-large''',
         type=str,
         help='''Name of the model, in case you\'re pushing to the hub.''',
     )

     __lowercase     : List[Any]       =						parser.parse_args()
     convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)








"""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():
     snake_case   					=				'''pt'''
elif is_tf_available():
     snake_case   					=				'''tf'''
else:
     snake_case   					=				'''jax'''



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Union[str, Any]           =  PerceiverTokenizer
   A__		:	Optional[int]           =  False



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     super().setUp()
     _snake_case   	=      PerceiverTokenizer()
     tokenizer.save_pretrained(self.tmpdirname				)



   @cached_property
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       **__lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     return self.tokenizer_class.from_pretrained(self.tmpdirname							,       **__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	List[str]=False							,       __lowerCamelCase    :	int=2_0							,       __lowerCamelCase    :	List[Any]=5				):



     """simple docstring"""
     # 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.

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

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

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



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     _snake_case   	=      '''Unicode €.'''
     _snake_case   	=      tokenizer(__lowerCamelCase				)
     _snake_case   	=      [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
     self.assertEqual(encoded['''input_ids''']							,       __lowerCamelCase				)

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

     _snake_case   	=      tokenizer('''e è é ê ë'''				)
     _snake_case   	=      [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
     self.assertEqual(encoded['''input_ids''']							,       __lowerCamelCase				)
     # decoding
     _snake_case   	=      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    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     _snake_case   	=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     # fmt: off
     _snake_case   	=      [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
     # fmt: on
     _snake_case   	=      tokenizer(__lowerCamelCase							,       padding=__lowerCamelCase							,       return_tensors=__lowerCamelCase				)
     self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)

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

     self.assertListEqual(__lowerCamelCase							,       __lowerCamelCase				)

     self.assertEqual((2, 3_8)							,       batch.input_ids.shape				)
     self.assertEqual((2, 3_8)							,       batch.attention_mask.shape				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     _snake_case   	=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     _snake_case   	=      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    :	int				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     _snake_case   	=      [
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     _snake_case   	=      tokenizer(
         text_target=__lowerCamelCase							,       max_length=3_2							,       padding='''max_length'''							,       truncation=__lowerCamelCase							,       return_tensors=__lowerCamelCase				)
     self.assertEqual(3_2							,       targets['''input_ids'''].shape[1]				)



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     # safety check on max_len default value so we are sure the test works
     _snake_case   	=      self.get_tokenizers()
     for tokenizer in tokenizers:
       with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
         self.assertNotEqual(tokenizer.model_max_length							,       4_2				)

        # Now let's start the test
     _snake_case   	=      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
         _snake_case   	=      tempfile.mkdtemp()

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

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

         shutil.rmtree(__lowerCamelCase				)

     _snake_case   	=      self.get_tokenizers(model_max_length=4_2				)
     for tokenizer in tokenizers:
       with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
         # Isolate this from the other tests because we save additional tokens/etc
         _snake_case   	=      tempfile.mkdtemp()

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

         _snake_case   	=      tokenizer.__class__.from_pretrained(__lowerCamelCase				)
         _snake_case   	=      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							,       4_2				)

         _snake_case   	=      tokenizer.__class__.from_pretrained(__lowerCamelCase							,       model_max_length=4_3				)
         self.assertEqual(tokenizer.model_max_length							,       4_3				)

         shutil.rmtree(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      []
     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:
           _snake_case   	=      json.load(__lowerCamelCase				)

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

         _snake_case   	=      [f"""<extra_id_{i}>""" for i in range(1_2_5				)]

         _snake_case   	=      added_tokens_extra_ids + [
             '''an_additional_special_token'''
         ]
         _snake_case   	=      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
         _snake_case   	=      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
         _snake_case   	=      added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''							,       lstrip=__lowerCamelCase				)]
         _snake_case   	=      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    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      self.perceiver_tokenizer
     self.assertEqual(tokenizer.decode([1_7_8]				)							,       '''�'''				)



   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     pass





   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
     # strings and special added tokens as tokens
     _snake_case   	=      self.get_tokenizers(fast=__lowerCamelCase							,       do_lower_case=__lowerCamelCase				)
     for tokenizer in tokenizers:
       with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
         _snake_case   	=      ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
         _snake_case   	=      tokenizer.convert_tokens_to_string(__lowerCamelCase				)

         self.assertIsInstance(__lowerCamelCase							,       __lowerCamelCase				)





