






import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

def     snake_case   ()			->							List[Any]:
  UpperCamelCase_:   List[Any]        =							'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
  UpperCamelCase_:   List[Any]        =							Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,       stream=SCREAMING_SNAKE_CASE__    ).raw    ).convert('RGB'    )

  return image

def     snake_case   (UpperCAmelCase__    )			->							Dict:
  UpperCamelCase_:   Union[str, Any]        =							[]
  # fmt: off

  # vision encoder
  rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')    )
  rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')    )
  rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')    )
  rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')    )
  rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')    )
  rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')    )

  for i in range(config.vision_config.num_hidden_layers    ):
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')    )
    rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')    )

  # QFormer
  rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight')    )
  rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias')    )

  # fmt: on
  return rename_keys

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							str:
  UpperCamelCase_:   Optional[int]        =							dct.pop(SCREAMING_SNAKE_CASE__    )
  UpperCamelCase_:   List[Any]        =							val

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Any:





  for i in range(config.vision_config.num_hidden_layers    ):
    # read in original q and v biases
    UpperCamelCase_:   List[Any]        =							state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''    )
    UpperCamelCase_:   Optional[Any]        =							state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''    )

    # next, set bias in the state dict
    UpperCamelCase_:   int        =							torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE__      ,       requires_grad=SCREAMING_SNAKE_CASE__    ), v_bias)    )
    UpperCamelCase_:   List[str]        =							qkv_bias

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Tuple:
  UpperCamelCase_:   str        =							3_6_4 if 'coco' in model_name else 2_2_4
  UpperCamelCase_:   Union[str, Any]        =							BlipaVisionConfig(image_size=SCREAMING_SNAKE_CASE__    ).to_dict()

  # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
  # seems like flan-T5 models don't have bos_token_id properly set?
  if "opt-2.7b" in model_name:
    UpperCamelCase_:   Optional[int]        =							OPTConfig.from_pretrained('facebook/opt-2.7b'      ,       eos_token_id=SCREAMING_SNAKE_CASE__    ).to_dict()
  elif "opt-6.7b" in model_name:
    UpperCamelCase_:   Any        =							OPTConfig.from_pretrained('facebook/opt-6.7b'      ,       eos_token_id=SCREAMING_SNAKE_CASE__    ).to_dict()
  elif "t5-xl" in model_name:
    UpperCamelCase_:   List[Any]        =							TaConfig.from_pretrained('google/flan-t5-xl'      ,       dense_act_fn='gelu'      ,       bos_token_id=1    ).to_dict()
  elif "t5-xxl" in model_name:
    UpperCamelCase_:   int        =							TaConfig.from_pretrained('google/flan-t5-xxl'      ,       dense_act_fn='gelu'      ,       bos_token_id=1    ).to_dict()

  UpperCamelCase_:   Dict        =							BlipaConfig(vision_config=SCREAMING_SNAKE_CASE__      ,       text_config=SCREAMING_SNAKE_CASE__    )

  return config, image_size

@torch.no_grad()
def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=None      ,       UpperCAmelCase__=False    )			->							Any:
  UpperCamelCase_:   str        =							(
      AutoTokenizer.from_pretrained('facebook/opt-2.7b'    )
      if 'opt' in model_name
      else AutoTokenizer.from_pretrained('google/flan-t5-xl'    )
  )
  UpperCamelCase_:   Any        =							tokenizer('\n'      ,       add_special_tokens=SCREAMING_SNAKE_CASE__    ).input_ids[0]
  UpperCamelCase_:   Tuple        =							get_blipa_config(SCREAMING_SNAKE_CASE__      ,       eos_token_id=SCREAMING_SNAKE_CASE__    )

  UpperCamelCase_:   Any        =							BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE__    ).eval()

  UpperCamelCase_:   List[Any]        =							{
      'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
      'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
      'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
      'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
      'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
      'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
      'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
  }

  UpperCamelCase_:   Any        =							model_name_to_original[model_name]

  # load original model
  print('Loading original model...'    )
  UpperCamelCase_:   Any        =							'cuda' if torch.cuda.is_available() else 'cpu'
  UpperCamelCase_:   List[Any]        =							load_model_and_preprocess(
      name=SCREAMING_SNAKE_CASE__      ,       model_type=SCREAMING_SNAKE_CASE__      ,       is_eval=SCREAMING_SNAKE_CASE__      ,       device=SCREAMING_SNAKE_CASE__    )
  original_model.eval()
  print('Done!'    )

  # update state dict keys
  UpperCamelCase_:   Tuple        =							original_model.state_dict()
  UpperCamelCase_:   Any        =							create_rename_keys(SCREAMING_SNAKE_CASE__    )
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__    )

  # some keys can be renamed efficiently
  for key, val in state_dict.copy().items():
    UpperCamelCase_:   str        =							state_dict.pop(SCREAMING_SNAKE_CASE__    )
    if key.startswith('Qformer.bert'    ):
      UpperCamelCase_:   Tuple        =							key.replace('Qformer.bert'      ,       'qformer'    )
    if "attention.self" in key:
      UpperCamelCase_:   str        =							key.replace('self'      ,       'attention'    )
    if "opt_proj" in key:
      UpperCamelCase_:   List[str]        =							key.replace('opt_proj'      ,       'language_projection'    )
    if "t5_proj" in key:
      UpperCamelCase_:   List[Any]        =							key.replace('t5_proj'      ,       'language_projection'    )
    if key.startswith('opt'    ):
      UpperCamelCase_:   Optional[int]        =							key.replace('opt'      ,       'language'    )
    if key.startswith('t5'    ):
      UpperCamelCase_:   List[str]        =							key.replace('t5'      ,       'language'    )
    UpperCamelCase_:   Union[str, Any]        =							val

  # read in qv biases
  read_in_q_v_bias(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__    )

  UpperCamelCase_:   Tuple        =							hf_model.load_state_dict(SCREAMING_SNAKE_CASE__      ,       strict=SCREAMING_SNAKE_CASE__    )
  assert len(SCREAMING_SNAKE_CASE__    ) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  UpperCamelCase_:   Any        =							load_demo_image()
  UpperCamelCase_:   Any        =							vis_processors['eval'](SCREAMING_SNAKE_CASE__    ).unsqueeze(0    ).to(SCREAMING_SNAKE_CASE__    )
  UpperCamelCase_:   str        =							tokenizer(['\n']      ,       return_tensors='pt'    ).input_ids.to(SCREAMING_SNAKE_CASE__    )

  # create processor
  UpperCamelCase_:   str        =							BlipImageProcessor(
      size={'height': image_size, 'width': image_size}      ,       image_mean=SCREAMING_SNAKE_CASE__      ,       image_std=SCREAMING_SNAKE_CASE__    )
  UpperCamelCase_:   Tuple        =							BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE__      ,       tokenizer=SCREAMING_SNAKE_CASE__    )
  UpperCamelCase_:   Optional[int]        =							processor(images=SCREAMING_SNAKE_CASE__      ,       return_tensors='pt'    ).pixel_values.to(SCREAMING_SNAKE_CASE__    )

  # make sure processor creates exact same pixel values
  assert torch.allclose(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__    )

  original_model.to(SCREAMING_SNAKE_CASE__    )
  hf_model.to(SCREAMING_SNAKE_CASE__    )
  with torch.no_grad():
    if "opt" in model_name:
      UpperCamelCase_:   int        =							original_model({'image': original_pixel_values, 'text_input': ['']}    ).logits
      UpperCamelCase_:   List[Any]        =							hf_model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__    ).logits
    else:
      UpperCamelCase_:   Tuple        =							original_model(
          {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}    ).logits
      UpperCamelCase_:   Dict        =							input_ids.masked_fill(input_ids == tokenizer.pad_token_id      ,       -1_0_0    )
      UpperCamelCase_:   int        =							hf_model(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       labels=SCREAMING_SNAKE_CASE__    ).logits

  assert original_logits.shape == logits.shape
  print('First values of original logits:'      ,       original_logits[0, :3, :3]    )
  print('First values of HF logits:'      ,       logits[0, :3, :3]    )

  # assert values
  if model_name == "blip2-flan-t5-xl":
    UpperCamelCase_:   Any        =							torch.tensor(
        [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]      ,       device=SCREAMING_SNAKE_CASE__    )
    assert torch.allclose(logits[0, :3, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1E-4    )
  elif model_name == "blip2-flan-t5-xl-coco":
    UpperCamelCase_:   int        =							torch.tensor(
        [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]      ,       device=SCREAMING_SNAKE_CASE__    )
  else:
    # cast to same type
    UpperCamelCase_:   Dict        =							logits.dtype
    assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE__    )      ,       SCREAMING_SNAKE_CASE__      ,       atol=1E-2    )
  print('Looks ok!'    )

  print('Generating a caption...'    )
  UpperCamelCase_:   Union[str, Any]        =							''
  UpperCamelCase_:   Optional[Any]        =							tokenizer(SCREAMING_SNAKE_CASE__      ,       return_tensors='pt'    ).input_ids.to(SCREAMING_SNAKE_CASE__    )

  UpperCamelCase_:   Optional[Any]        =							original_model.generate({'image': original_pixel_values}    )
  UpperCamelCase_:   List[str]        =							hf_model.generate(
      SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       do_sample=SCREAMING_SNAKE_CASE__      ,       num_beams=5      ,       max_length=3_0      ,       min_length=1      ,       top_p=0.9      ,       repetition_penalty=1.0      ,       length_penalty=1.0      ,       temperature=1      ,       )
  print('Original generation:'      ,       SCREAMING_SNAKE_CASE__    )
  UpperCamelCase_:   Dict        =							input_ids.shape[1]
  UpperCamelCase_:   List[str]        =							processor.batch_decode(outputs[:, prompt_length:]      ,       skip_special_tokens=SCREAMING_SNAKE_CASE__    )
  UpperCamelCase_:   List[Any]        =							[text.strip() for text in output_text]
  print('HF generation:'      ,       SCREAMING_SNAKE_CASE__    )

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(SCREAMING_SNAKE_CASE__    )
    hf_model.save_pretrained(SCREAMING_SNAKE_CASE__    )

  if push_to_hub:
    processor.push_to_hub(F'''nielsr/{model_name}'''    )
    hf_model.push_to_hub(F'''nielsr/{model_name}'''    )


if __name__ == "__main__":
   A_						:						Union[str, Any]						      =					argparse.ArgumentParser()
   A_						:						Any						      =					[
       'blip2-opt-2.7b',
       'blip2-opt-6.7b',
       'blip2-opt-2.7b-coco',
       'blip2-opt-6.7b-coco',
       'blip2-flan-t5-xl',
       'blip2-flan-t5-xl-coco',
       'blip2-flan-t5-xxl',
   ]
   parser.add_argument(
       '--model_name',
       default='blip2-opt-2.7b',
       choices=choices,
       type=str,
       help='Path to hf config.json of model to convert',
   )
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
       help='Whether to push the model and processor to the hub after converting',
   )

   A_						:						Tuple						      =					parser.parse_args()

   convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase		   =			get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    List[Any] =XLMRobertaTokenizer
      UpperCamelCase__				:    Union[str, Any] =XLMRobertaTokenizerFast
      UpperCamelCase__				:    int =True
      UpperCamelCase__				:    Optional[Any] =True






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Dict:
             """simple docstring"""




             super().setUp()

             # We have a SentencePiece fixture for testing
             _lowerCamelCase			:						Dict            =XLMRobertaTokenizer(lowercase_     ,  keep_accents=lowercase_					)
             tokenizer.save_pretrained(self.tmpdirname					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            ='<pad>'
             _lowerCamelCase			:						Optional[int]            =1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_					)     ,  lowercase_					)
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_					)     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Any					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						str            =list(self.get_tokenizer().get_vocab().keys()					)

             self.assertEqual(vocab_keys[0]     ,  '<s>'					)
             self.assertEqual(vocab_keys[1]     ,  '<pad>'					)
             self.assertEqual(vocab_keys[-1]     ,  '<mask>'					)
             self.assertEqual(len(lowercase_					)     ,  1002					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             self.assertEqual(self.get_tokenizer().vocab_size     ,  1002					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =XLMRobertaTokenizer(lowercase_     ,  keep_accents=lowercase_					)

             _lowerCamelCase			:						List[Any]            =tokenizer.tokenize('This is a test'					)
             self.assertListEqual(lowercase_     ,  ['▁This', '▁is', '▁a', '▁t', 'est']					)

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(lowercase_					)     ,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     ,  )

             _lowerCamelCase			:						int            =tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
             self.assertListEqual(
                 lowercase_     ,  [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]     ,  )
             _lowerCamelCase			:						Optional[Any]            =tokenizer.convert_tokens_to_ids(lowercase_					)
             self.assertListEqual(
                 lowercase_     ,  [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                     #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                 ]     ,  )

             _lowerCamelCase			:						Optional[Any]            =tokenizer.convert_ids_to_tokens(lowercase_					)
             self.assertListEqual(
                 lowercase_     ,  [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]     ,  )






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		Any:
             """simple docstring"""




             if not self.test_slow_tokenizer:
                    # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                    return

             _lowerCamelCase			:						List[str]            =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                           _lowerCamelCase			:						int            =self.rust_tokenizer_class.from_pretrained(lowercase_     ,  **lowercase_					)
                           _lowerCamelCase			:						List[Any]            =self.tokenizer_class.from_pretrained(lowercase_     ,  **lowercase_					)

                           _lowerCamelCase			:						int            =tempfile.mkdtemp()

                           _lowerCamelCase			:						List[str]            =tokenizer_r.save_pretrained(lowercase_					)
                           _lowerCamelCase			:						int            =tokenizer_p.save_pretrained(lowercase_					)

                           # Checks it save with the same files + the tokenizer.json file for the fast one
                           self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files					)					)
                           _lowerCamelCase			:						Optional[Any]            =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f					)
                           self.assertSequenceEqual(lowercase_     ,  lowercase_					)

                           # Checks everything loads correctly in the same way
                           _lowerCamelCase			:						int            =tokenizer_r.from_pretrained(lowercase_					)
                           _lowerCamelCase			:						Any            =tokenizer_p.from_pretrained(lowercase_					)

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(lowercase_     ,  lowercase_					)					)
                                  # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                                  # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                           shutil.rmtree(lowercase_					)

                           # Save tokenizer rust, legacy_format=True
                           _lowerCamelCase			:						Dict            =tempfile.mkdtemp()

                           _lowerCamelCase			:						int            =tokenizer_r.save_pretrained(lowercase_     ,  legacy_format=lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =tokenizer_p.save_pretrained(lowercase_					)

                           # Checks it save with the same files
                           self.assertSequenceEqual(lowercase_     ,  lowercase_					)

                           # Checks everything loads correctly in the same way
                           _lowerCamelCase			:						int            =tokenizer_r.from_pretrained(lowercase_					)
                           _lowerCamelCase			:						List[Any]            =tokenizer_p.from_pretrained(lowercase_					)

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(lowercase_     ,  lowercase_					)					)

                           shutil.rmtree(lowercase_					)

                           # Save tokenizer rust, legacy_format=False
                           _lowerCamelCase			:						str            =tempfile.mkdtemp()

                           _lowerCamelCase			:						Optional[Any]            =tokenizer_r.save_pretrained(lowercase_     ,  legacy_format=lowercase_					)
                           _lowerCamelCase			:						Any            =tokenizer_p.save_pretrained(lowercase_					)

                           # Checks it saved the tokenizer.json file
                           self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files					)					)

                           # Checks everything loads correctly in the same way
                           _lowerCamelCase			:						str            =tokenizer_r.from_pretrained(lowercase_					)
                           _lowerCamelCase			:						List[str]            =tokenizer_p.from_pretrained(lowercase_					)

                           # Check special tokens are set accordingly on Rust and Python
                           for key in tokenizer_pp.special_tokens_map:
                                  self.assertTrue(hasattr(lowercase_     ,  lowercase_					)					)

                           shutil.rmtree(lowercase_					)






      @cached_property
      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[Any]:
             """simple docstring"""




             return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Dict:
             """simple docstring"""




             with tempfile.NamedTemporaryFile() as f:
                    shutil.copyfile(lowercase_     ,  f.name					)
                    _lowerCamelCase			:						Union[str, Any]            =XLMRobertaTokenizer(f.name     ,  keep_accents=lowercase_					)
                    _lowerCamelCase			:						Dict            =pickle.dumps(lowercase_					)
             pickle.loads(lowercase_					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Union[str, Any]:
             """simple docstring"""




             if not self.test_rust_tokenizer:
                    return

             _lowerCamelCase			:						Any            =self.get_tokenizer()
             _lowerCamelCase			:						Optional[int]            =self.get_rust_tokenizer()

             _lowerCamelCase			:						Tuple            ='I was born in 92000, and this is falsé.'

             _lowerCamelCase			:						Any            =tokenizer.tokenize(lowercase_					)
             _lowerCamelCase			:						List[str]            =rust_tokenizer.tokenize(lowercase_					)
             self.assertListEqual(lowercase_     ,  lowercase_					)

             _lowerCamelCase			:						int            =tokenizer.encode(lowercase_     ,  add_special_tokens=lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =rust_tokenizer.encode(lowercase_     ,  add_special_tokens=lowercase_					)
             self.assertListEqual(lowercase_     ,  lowercase_					)

             _lowerCamelCase			:						Dict            =self.get_rust_tokenizer()
             _lowerCamelCase			:						Optional[int]            =tokenizer.encode(lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =rust_tokenizer.encode(lowercase_					)
             self.assertListEqual(lowercase_     ,  lowercase_					)






      @slow
      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            ='Hello World!'
             _lowerCamelCase			:						Union[str, Any]            =[0, 3_5378, 6661, 38, 2]
             # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
             # xlmr.eval()
             # xlmr.encode(symbols)

             self.assertListEqual(lowercase_     ,  self.big_tokenizer.encode(lowercase_					)					)






      @slow
      def 			lowerCamelCase			(   self		:	Any					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =(
                 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
                 ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
             )
             _lowerCamelCase			:						List[str]            =[
                 0,
                 3293,
                 83,
                 10,
                 4552,
                 4989,
                 7986,
                 678,
                 10,
                 5915,
                 111,
                 17_9459,
                 12_4850,
                 4,
                 6044,
                 237,
                 12,
                 6,
                 5,
                 6,
                 4,
                 6780,
                 705,
                 15,
                 1388,
                 44,
                 378,
                 1_0114,
                 711,
                 152,
                 20,
                 6,
                 5,
                 2_2376,
                 642,
                 1221,
                 1_5190,
                 3_4153,
                 450,
                 5608,
                 959,
                 1119,
                 5_7702,
                 136,
                 186,
                 47,
                 1098,
                 2_9367,
                 47,
                 # 4426, # What fairseq tokenizes from "<unk>": "_<"
                 # 3678, # What fairseq tokenizes from "<unk>": "unk"
                 # 2740, # What fairseq tokenizes from "<unk>": ">"
                 3,  # What we tokenize from "<unk>": "<unk>"
                 6,  # Residue from the tokenization: an extra sentencepiece underline
                 4,
                 6044,
                 237,
                 6284,
                 5_0901,
                 528,
                 31,
                 90,
                 34,
                 927,
                 2,
             ]
             # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
             # xlmr.eval()
             # xlmr.encode(symbols)

             self.assertListEqual(lowercase_     ,  self.big_tokenizer.encode(lowercase_					)					)






      @slow
      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            ={'input_ids': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=lowercase_     ,  model_name='xlm-roberta-base'     ,  revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'     ,  )


