


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()
__lowerCAmelCase			=							logging.get_logger(__name__)




def        snake_case_					(  snake_case					)  ->    Any:
       lowercase__:       str        =       DPTConfig(embedding_type='hybrid'					)

       if "large" in checkpoint_url:
              lowercase__:       Tuple        =       10_24
              lowercase__:       int        =       40_96
              lowercase__:       List[str]        =       24
              lowercase__:       Tuple        =       16
              lowercase__:       Union[str, Any]        =       [5, 11, 17, 23]
              lowercase__:       str        =       [2_56, 5_12, 10_24, 10_24]
              lowercase__:       str        =       (1, 3_84, 3_84)

       if "nyu" or "midas" in checkpoint_url:
              lowercase__:       Tuple        =       7_68
              lowercase__:       Optional[int]        =       [1, 1, 1, 0.5]
              lowercase__:       List[Any]        =       [2_56, 5_12, 7_68, 7_68]
              lowercase__:       Union[str, Any]        =       1_50
              lowercase__:       int        =       16
              lowercase__:       Optional[Any]        =       (1, 3_84, 3_84)
              lowercase__:       Optional[int]        =       False
              lowercase__:       Optional[int]        =       'project'

       if "ade" in checkpoint_url:
              lowercase__:       List[str]        =       True
              lowercase__:       Dict        =       7_68
              lowercase__:       Optional[int]        =       [1, 1, 1, 0.5]
              lowercase__:       Tuple        =       1_50
              lowercase__:       str        =       16
              lowercase__:       Dict        =       'huggingface/label-files'
              lowercase__:       int        =       'ade20k-id2label.json'
              lowercase__:       Dict        =       json.load(open(cached_download(hf_hub_url(lowercase__      ,							lowercase__      ,							repo_type='dataset'					)					)      ,							'r'					)					)
              lowercase__:       Dict        =       {int(lowercase__					): v for k, v in idalabel.items()}
              lowercase__:       Tuple        =       idalabel
              lowercase__:       List[str]        =       {v: k for k, v in idalabel.items()}
              lowercase__:       Any        =       [1, 1_50, 4_80, 4_80]

       return config, expected_shape




def        snake_case_					(  snake_case					)  ->    int:
       lowercase__:       int        =       ['pretrained.model.head.weight', 'pretrained.model.head.bias']
       for k in ignore_keys:
              state_dict.pop(lowercase__      ,							lowercase__					)




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

       # resize blocks
       if "pretrained.act_postprocess1.3" in name:
              lowercase__:       List[Any]        =       name.replace('pretrained.act_postprocess1.3'      ,							'neck.reassemble_stage.layers.0.projection'					)
       if "pretrained.act_postprocess1.4" in name:
              lowercase__:       List[Any]        =       name.replace('pretrained.act_postprocess1.4'      ,							'neck.reassemble_stage.layers.0.resize'					)
       if "pretrained.act_postprocess2.3" in name:
              lowercase__:       Union[str, Any]        =       name.replace('pretrained.act_postprocess2.3'      ,							'neck.reassemble_stage.layers.1.projection'					)
       if "pretrained.act_postprocess2.4" in name:
              lowercase__:       Any        =       name.replace('pretrained.act_postprocess2.4'      ,							'neck.reassemble_stage.layers.1.resize'					)
       if "pretrained.act_postprocess3.3" in name:
              lowercase__:       Optional[Any]        =       name.replace('pretrained.act_postprocess3.3'      ,							'neck.reassemble_stage.layers.2.projection'					)
       if "pretrained.act_postprocess4.3" in name:
              lowercase__:       List[str]        =       name.replace('pretrained.act_postprocess4.3'      ,							'neck.reassemble_stage.layers.3.projection'					)
       if "pretrained.act_postprocess4.4" in name:
              lowercase__:       Any        =       name.replace('pretrained.act_postprocess4.4'      ,							'neck.reassemble_stage.layers.3.resize'					)
       if "pretrained" in name:
              lowercase__:       List[Any]        =       name.replace('pretrained'      ,							'dpt'					)
       if "bn" in name:
              lowercase__:       Dict        =       name.replace('bn'      ,							'batch_norm'					)
       if "head" in name:
              lowercase__:       Any        =       name.replace('head'      ,							'head.head'					)
       if "encoder.norm" in name:
              lowercase__:       List[Any]        =       name.replace('encoder.norm'      ,							'layernorm'					)
       if "auxlayer" in name:
              lowercase__:       int        =       name.replace('auxlayer'      ,							'auxiliary_head.head'					)
       if "backbone" in name:
              lowercase__:       str        =       name.replace('backbone'      ,							'backbone.bit.encoder'					)

       if ".." in name:
              lowercase__:       Dict        =       name.replace('..'      ,							'.'					)

       if "stem.conv" in name:
              lowercase__:       Union[str, Any]        =       name.replace('stem.conv'      ,							'bit.embedder.convolution'					)
       if "blocks" in name:
              lowercase__:       Union[str, Any]        =       name.replace('blocks'      ,							'layers'					)
       if "convolution" in name and "backbone" in name:
              lowercase__:       Tuple        =       name.replace('convolution'      ,							'conv'					)
       if "layer" in name and "backbone" in name:
              lowercase__:       List[Any]        =       name.replace('layer'      ,							'layers'					)
       if "backbone.bit.encoder.bit" in name:
              lowercase__:       List[Any]        =       name.replace('backbone.bit.encoder.bit'      ,							'backbone.bit'					)
       if "embedder.conv" in name:
              lowercase__:       Optional[Any]        =       name.replace('embedder.conv'      ,							'embedder.convolution'					)
       if "backbone.bit.encoder.stem.norm" in name:
              lowercase__:       Any        =       name.replace('backbone.bit.encoder.stem.norm'      ,							'backbone.bit.embedder.norm'					)
       return name




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




def        snake_case_					(  )  ->    Optional[int]:
       lowercase__:       int        =       'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:       str        =       Image.open(requests.get(lowercase__      ,							stream=lowercase__					).raw					)
       return im




@torch.no_grad()
def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    Optional[int]:
       lowercase__:       Any        =       get_dpt_config(lowercase__					)
       # load original state_dict from URL
       # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
       lowercase__:       str        =       torch.load(lowercase__      ,							map_location='cpu'					)
       # remove certain keys
       remove_ignore_keys_(lowercase__					)
       # rename keys
       for key in state_dict.copy().keys():
              lowercase__:       Any        =       state_dict.pop(lowercase__					)
              lowercase__:       str        =       val
       # read in qkv matrices
       read_in_q_k_v(lowercase__      ,							lowercase__					)

       # load HuggingFace model
       lowercase__:       List[Any]        =       DPTForSemanticSegmentation(lowercase__					) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase__					)
       model.load_state_dict(lowercase__					)
       model.eval()

       # Check outputs on an image
       lowercase__:       Optional[int]        =       4_80 if 'ade' in checkpoint_url else 3_84
       lowercase__:       Union[str, Any]        =       DPTImageProcessor(size=lowercase__					)

       lowercase__:       Optional[int]        =       prepare_img()
       lowercase__:       str        =       image_processor(lowercase__      ,							return_tensors='pt'					)

       # forward pass
       lowercase__:       Tuple        =       model(**lowercase__					).logits if 'ade' in checkpoint_url else model(**lowercase__					).predicted_depth

       if show_prediction:
              lowercase__:       Optional[int]        =       (
                  torch.nn.functional.interpolate(
                      outputs.unsqueeze(1					)      ,							size=(image.size[1], image.size[0])      ,							mode='bicubic'      ,							align_corners=lowercase__      ,							)
                  .squeeze()
                  .cpu()
                  .numpy()
              )

              Image.fromarray((prediction / prediction.max()) * 2_55					).show()

       if pytorch_dump_folder_path is not None:
              Path(lowercase__					).mkdir(exist_ok=lowercase__					)
              print(f'Saving model to {pytorch_dump_folder_path}'					)
              model.save_pretrained(lowercase__					)
              print(f'Saving image processor to {pytorch_dump_folder_path}'					)
              image_processor.save_pretrained(lowercase__					)

       if push_to_hub:
              model.push_to_hub('ybelkada/dpt-hybrid-midas'					)
              image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'					)


if __name__ == "__main__":
  __lowerCAmelCase			=							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=False,
      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.''',
  )
  parser.add_argument(
      '''--show_prediction''',
      action='''store_true''',
  )

  __lowerCAmelCase			=							parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )






import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Optional[int]       					=			CLIPTokenizer
 __lowercase			:							str       					=			CLIPTokenizerFast
 __lowercase			:							Tuple       					=			True
 __lowercase			:							str       					=			{}
 __lowercase			:							Dict       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        super().setUp()

        # fmt: off
        lowercase__:       str        =       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
        # fmt: on
        lowercase__:       List[str]        =       dict(zip(lowerCAmelCase__    ,			range(len(lowerCAmelCase__			)			)			)			)
        lowercase__:       int        =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
        lowercase__:       Optional[int]        =       {'unk_token': '<unk>'}

        lowercase__:       int        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       Union[str, Any]        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['merges_file']			)
        with open(self.vocab_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write(json.dumps(lowerCAmelCase__			) + '\n'			)
        with open(self.merges_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write('\n'.join(lowerCAmelCase__			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        kwargs.update(self.special_tokens_map			)
        return CLIPTokenizer.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        kwargs.update(self.special_tokens_map			)
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Dict        =       'lower newer'
        lowercase__:       Dict        =       'lower newer'
        return input_text, output_text
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       CLIPTokenizer(self.vocab_file    ,			self.merges_file    ,			**self.special_tokens_map			)
        lowercase__:       Dict        =       'lower newer'
        lowercase__:       Union[str, Any]        =       ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
        lowercase__:       Any        =       tokenizer.tokenize(lowerCAmelCase__			)
        self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

        lowercase__:       Tuple        =       tokens + [tokenizer.unk_token]
        lowercase__:       Tuple        =       [10, 2, 16, 9, 3, 2, 16, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__			)    ,			lowerCAmelCase__			)
 @require_ftfy
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       List[str]        =       self.tokenizer_class.from_pretrained(lowerCAmelCase__    ,			**lowerCAmelCase__			)
                      lowercase__:       Optional[int]        =       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__    ,			**lowerCAmelCase__			)

                      lowercase__:       Optional[int]        =       'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
                      lowercase__:       Optional[Any]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                      lowercase__:       Dict        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                      self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                      # with Tilde) encoded in 2 different ways
                      lowercase__:       Dict        =       'xa\u0303y' + ' ' + 'x\xe3y'
                      lowercase__:       Tuple        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                      lowercase__:       int        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                      self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on unicode of space type
                      lowercase__:       str        =       [
                          '\u0009',  # (horizontal tab, '\t')
                          '\u000B',  # (vertical tab)
                          '\u000C',  # (form feed)
                          '\u0020',  # (space, ' ')
                          '\u200E',  # (left-to-right mark):w
                          '\u200F',  # (right-to-left mark)
                      ]
                      for unicode_seq in spaces_unicodes:
                             lowercase__:       Optional[Any]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                             lowercase__:       Tuple        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                             self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on unicode of line break type
                      lowercase__:       str        =       [
                          '\u000A',  # (line feed, '\n')
                          '\r\n',  # (carriage return and line feed, '\r\n')
                          '\u000D',  # (carriage return, '\r')
                          '\r',  # (carriage return, '\r')
                          '\u000D',  # (carriage return, '\r')
                          '\u2028',  # (line separator)
                          '\u2029',  # (paragraph separator)
                          # "\u0085", # (next line)
                      ]

                      # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                      # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                      # space (and thus into an empty list).

                      for unicode_seq in line_break_unicodes:
                             lowercase__:       Optional[int]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                             lowercase__:       Optional[int]        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                             self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       Optional[int]        =       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                      lowercase__:       Optional[int]        =       F'{text_of_1_token} {text_of_1_token}'

                      lowercase__:       int        =       self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase__    ,			use_fast=lowerCAmelCase__    ,			)
                      lowercase__:       Dict        =       tokenizer_r(lowerCAmelCase__    ,			return_offsets_mapping=lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			)
                      self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(lowerCAmelCase__			))			)
                      self.assertEqual(
                          encoding.offset_mapping[1]    ,			(len(lowerCAmelCase__			) + 1, len(lowerCAmelCase__			) + 1 + len(lowerCAmelCase__			))    ,			)

                      lowercase__:       Any        =       F' {text}'

                      lowercase__:       Union[str, Any]        =       self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase__    ,			use_fast=lowerCAmelCase__    ,			)
                      lowercase__:       int        =       tokenizer_r(lowerCAmelCase__    ,			return_offsets_mapping=lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			)
                      self.assertEqual(encoding.offset_mapping[0]    ,			(1, 1 + len(lowerCAmelCase__			))			)
                      self.assertEqual(
                          encoding.offset_mapping[1]    ,			(1 + len(lowerCAmelCase__			) + 1, 1 + len(lowerCAmelCase__			) + 1 + len(lowerCAmelCase__			))    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        # Test related to the breaking change introduced in transformers v4.17.0
        # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
        with self.assertRaises(lowerCAmelCase__			) as context:
               self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'			)

        self.assertTrue(
            context.exception.args[0].startswith(
                'The `backend_tokenizer` provided does not match the expected format.'			)			)
 @require_ftfy
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        super().test_tokenization_python_rust_equals()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # CLIP always lower cases letters
        pass


