




"""simple docstring"""


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  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		List[Any]								=  """https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
  _snake_case							:		Optional[Any]								=  Image.open(requests.get(snake_case__		,       stream=snake_case__   ).raw   ).convert("""RGB"""   )

  return image


def  UpperCAmelCase__	(snake_case__							: str   ):
  """simple docstring"""

  _snake_case							:		List[str]								=  []
  # 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  UpperCAmelCase__	(snake_case__							: Dict		,       snake_case__							: Optional[int]		,       snake_case__							: Tuple   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  dct.pop(snake_case__   )
  _snake_case							:		Optional[int]								=  val


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]		,       snake_case__							: Tuple   ):
  """simple docstring"""

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

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


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Union[str, Any]   ):
  """simple docstring"""

  _snake_case							:		List[str]								=  3_64 if """coco""" in model_name else 2_24
  _snake_case							:		Tuple								=  BlipaVisionConfig(image_size=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:
    _snake_case							:		Optional[int]								=  OPTConfig.from_pretrained("""facebook/opt-2.7b"""		,       eos_token_id=snake_case__   ).to_dict()
  elif "opt-6.7b" in model_name:
    _snake_case							:		List[str]								=  OPTConfig.from_pretrained("""facebook/opt-6.7b"""		,       eos_token_id=snake_case__   ).to_dict()
  elif "t5-xl" in model_name:
    _snake_case							:		Union[str, Any]								=  TaConfig.from_pretrained("""google/flan-t5-xl"""		,       dense_act_fn="""gelu"""		,       bos_token_id=1   ).to_dict()
  elif "t5-xxl" in model_name:
    _snake_case							:		Optional[Any]								=  TaConfig.from_pretrained("""google/flan-t5-xxl"""		,       dense_act_fn="""gelu"""		,       bos_token_id=1   ).to_dict()

  _snake_case							:		Tuple								=  BlipaConfig(vision_config=snake_case__		,       text_config=snake_case__   )

  return config, image_size


@torch.no_grad()
def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: List[Any]=None		,       snake_case__							: List[Any]=False   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  (
      AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""   )
      if """opt""" in model_name
      else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""   )
  )
  _snake_case							:		Union[str, Any]								=  tokenizer("""\n"""		,       add_special_tokens=snake_case__   ).input_ids[0]
  _snake_case		, _snake_case							:		Union[str, Any]								=  get_blipa_config(snake_case__		,       eos_token_id=snake_case__   )

  _snake_case							:		Optional[int]								=  BlipaForConditionalGeneration(snake_case__   ).eval()

  _snake_case							:		Union[str, 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"""),
  }

  _snake_case		, _snake_case							:		Any								=  model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""   )
  _snake_case							:		str								=  """cuda""" if torch.cuda.is_available() else """cpu"""
  _snake_case		, _snake_case		, _snake_case							:		List[str]								=  load_model_and_preprocess(
      name=snake_case__		,       model_type=snake_case__		,       is_eval=snake_case__		,       device=snake_case__   )
  original_model.eval()
  print("""Done!"""   )

  # update state dict keys
  _snake_case							:		Dict								=  original_model.state_dict()
  _snake_case							:		Tuple								=  create_rename_keys(snake_case__   )
  for src, dest in rename_keys:
    rename_key(snake_case__		,       snake_case__		,       snake_case__   )

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

  # read in qv biases
  read_in_q_v_bias(snake_case__		,       snake_case__   )

  _snake_case		, _snake_case							:		Union[str, Any]								=  hf_model.load_state_dict(snake_case__		,       strict=snake_case__   )
  assert len(snake_case__   ) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  _snake_case							:		Union[str, Any]								=  load_demo_image()
  _snake_case							:		Union[str, Any]								=  vis_processors["""eval"""](snake_case__   ).unsqueeze(0   ).to(snake_case__   )
  _snake_case							:		str								=  tokenizer(["""\n"""]		,       return_tensors="""pt"""   ).input_ids.to(snake_case__   )

  # create processor
  _snake_case							:		Any								=  BlipImageProcessor(
      size={"""height""": image_size, """width""": image_size}		,       image_mean=snake_case__		,       image_std=snake_case__   )
  _snake_case							:		Any								=  BlipaProcessor(image_processor=snake_case__		,       tokenizer=snake_case__   )
  _snake_case							:		str								=  processor(images=snake_case__		,       return_tensors="""pt"""   ).pixel_values.to(snake_case__   )

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

  original_model.to(snake_case__   )
  hf_model.to(snake_case__   )
  with torch.no_grad():
    if "opt" in model_name:
      _snake_case							:		Union[str, Any]								=  original_model({"""image""": original_pixel_values, """text_input""": [""""""]}   ).logits
      _snake_case							:		int								=  hf_model(snake_case__		,       snake_case__   ).logits
    else:
      _snake_case							:		Optional[int]								=  original_model(
          {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}   ).logits
      _snake_case							:		Dict								=  input_ids.masked_fill(input_ids == tokenizer.pad_token_id		,       -1_00   )
      _snake_case							:		Dict								=  hf_model(snake_case__		,       snake_case__		,       labels=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":
    _snake_case							:		Any								=  torch.tensor(
        [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]		,       device=snake_case__   )
    assert torch.allclose(logits[0, :3, :3]		,       snake_case__		,       atol=1e-4   )
  elif model_name == "blip2-flan-t5-xl-coco":
    _snake_case							:		List[Any]								=  torch.tensor(
        [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]		,       device=snake_case__   )
  else:
    # cast to same type
    _snake_case							:		List[str]								=  logits.dtype
    assert torch.allclose(original_logits.to(snake_case__   )		,       snake_case__		,       atol=1e-2   )
  print("""Looks ok!"""   )

  print("""Generating a caption..."""   )
  _snake_case							:		List[Any]								=  """"""
  _snake_case							:		Union[str, Any]								=  tokenizer(snake_case__		,       return_tensors="""pt"""   ).input_ids.to(snake_case__   )

  _snake_case							:		List[Any]								=  original_model.generate({"""image""": original_pixel_values}   )
  _snake_case							:		Optional[int]								=  hf_model.generate(
      snake_case__		,       snake_case__		,       do_sample=snake_case__		,       num_beams=5		,       max_length=30		,       min_length=1		,       top_p=0.9		,       repetition_penalty=1.0		,       length_penalty=1.0		,       temperature=1		,       )
  print("""Original generation:"""		,       snake_case__   )
  _snake_case							:		Tuple								=  input_ids.shape[1]
  _snake_case							:		List[str]								=  processor.batch_decode(outputs[:, prompt_length:]		,       skip_special_tokens=snake_case__   )
  _snake_case							:		List[str]								=  [text.strip() for text in output_text]
  print("""HF generation:"""		,       snake_case__   )

  if pytorch_dump_folder_path is not None:
    processor.save_pretrained(snake_case__   )
    hf_model.save_pretrained(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_      =    argparse.ArgumentParser()
     A_      =    [
         '''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_      =    parser.parse_args()

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








"""simple docstring"""


import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A_      =    get_tests_dir('''fixtures/spiece.model''')






@require_sentencepiece
@require_tokenizers
class  lowercase(					__a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = AlbertTokenizer
 lowercase__			 = AlbertTokenizerFast
 lowercase__			 = True
 lowercase__			 = True
 lowercase__			 = True


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   _snake_case							:		Optional[int]								=  AlbertTokenizer(a_		)
   tokenizer.save_pretrained(self.tmpdirname		)


 def 			UpperCamelCase_       (    self:		Optional[int],				a_:		int		):




   '''simple docstring'''



   _snake_case							:		Dict								=  """this is a test"""
   _snake_case							:		Optional[int]								=  """this is a test"""
   return input_text, output_text


 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		str								=  """<pad>"""
   _snake_case							:		List[Any]								=  0

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


 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  list(self.get_tokenizer().get_vocab().keys()		)

   self.assertEqual(vocab_keys[0],				"""<pad>"""		)
   self.assertEqual(vocab_keys[1],				"""<unk>"""		)
   self.assertEqual(vocab_keys[-1],				"""▁eloquent"""		)
   self.assertEqual(len(a_		),				30_000		)


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size,				30_000		)


 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   if not self.test_rust_tokenizer:
     return

   _snake_case							:		Tuple								=  self.get_tokenizer()
   _snake_case							:		List[str]								=  self.get_rust_tokenizer()

   _snake_case							:		Optional[int]								=  """I was born in 92000, and this is falsé."""

   _snake_case							:		Optional[Any]								=  tokenizer.tokenize(a_		)
   _snake_case							:		List[str]								=  rust_tokenizer.tokenize(a_		)
   self.assertListEqual(a_,				a_		)

   _snake_case							:		Any								=  tokenizer.encode(a_,				add_special_tokens=a_		)
   _snake_case							:		Any								=  rust_tokenizer.encode(a_,				add_special_tokens=a_		)
   self.assertListEqual(a_,				a_		)

   _snake_case							:		int								=  self.get_rust_tokenizer()
   _snake_case							:		Dict								=  tokenizer.encode(a_		)
   _snake_case							:		Optional[int]								=  rust_tokenizer.encode(a_		)
   self.assertListEqual(a_,				a_		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  AlbertTokenizer(a_,				keep_accents=a_		)

   _snake_case							:		Any								=  tokenizer.tokenize("""This is a test"""		)
   self.assertListEqual(a_,				["""▁this""", """▁is""", """▁a""", """▁test"""]		)

   self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		),				[48, 25, 21, 1_289]		)

   _snake_case							:		Dict								=  tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
   self.assertListEqual(
       a_,				["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]		)
   _snake_case							:		str								=  tokenizer.convert_tokens_to_ids(a_		)
   self.assertListEqual(a_,				[31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]		)

   _snake_case							:		Tuple								=  tokenizer.convert_ids_to_tokens(a_		)
   self.assertListEqual(
       a_,				["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""],				)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  AlbertTokenizer(a_		)

   _snake_case							:		int								=  tokenizer.encode("""sequence builders"""		)
   _snake_case							:		Optional[int]								=  tokenizer.encode("""multi-sequence build"""		)

   _snake_case							:		Any								=  tokenizer.build_inputs_with_special_tokens(a_		)
   _snake_case							:		Union[str, Any]								=  tokenizer.build_inputs_with_special_tokens(a_,				a_		)

   assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
   assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
       tokenizer.sep_token_id
   ]


 @slow
 def 			UpperCamelCase_       (    self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Any								=  {"""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, 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], [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]], """input_ids""": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 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], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 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]], """token_type_ids""": [[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, 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, 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, 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=a_,				model_name="""albert-base-v2""",				revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e""",				)


