import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


a_							=      {
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}




def       __lowerCAmelCase      (						A_   : Optional[int]					)    ->  Union[str, Any]:
  assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
  assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
  if args.mlm:
    assert os.path.isfile(args.token_counts					)
    assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
  else:
    assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

  assert args.teacher_type == args.student_type or (
      args.student_type == "distilbert" and args.teacher_type == "bert"
  )
  assert os.path.isfile(args.student_config					)
  if args.student_pretrained_weights is not None:
    assert os.path.isfile(args.student_pretrained_weights					)

  if args.freeze_token_type_embds:
    assert args.student_type in ["roberta"]

  assert args.alpha_ce >= 0.0
  assert args.alpha_mlm >= 0.0
  assert args.alpha_clm >= 0.0
  assert args.alpha_mse >= 0.0
  assert args.alpha_cos >= 0.0
  assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0




def       __lowerCAmelCase      (						A_   : str						,	A_   : Tuple					)    ->  Union[str, Any]:
  if args.student_type == "roberta":
    __UpperCAmelCase						    =       False
  elif args.student_type == "gpt2":
    __UpperCAmelCase						    =       False




def       __lowerCAmelCase      (						A_   : Any						,	A_   : List[str]					)    ->  Tuple:
  if args.student_type == "roberta":
    __UpperCAmelCase						    =       False




def       __lowerCAmelCase      (						)    ->  Optional[Any]:
  __UpperCAmelCase						    =       argparse.ArgumentParser(description="Training"					)
  parser.add_argument("--force"						,	action="store_true"						,	help="Overwrite dump_path if it already exists."					)

  parser.add_argument(
      "--dump_path"						,	type=A_						,	required=A_						,	help="The output directory (log, checkpoints, parameters, etc.)"					)
  parser.add_argument(
      "--data_file"						,	type=A_						,	required=A_						,	help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence."						,	)

  parser.add_argument(
      "--student_type"						,	type=A_						,	choices=["distilbert", "roberta", "gpt2"]						,	required=A_						,	help="The student type (DistilBERT, RoBERTa)."						,	)
  parser.add_argument("--student_config"						,	type=A_						,	required=A_						,	help="Path to the student configuration."					)
  parser.add_argument(
      "--student_pretrained_weights"						,	default=A_						,	type=A_						,	help="Load student initialization checkpoint."					)

  parser.add_argument(
      "--teacher_type"						,	choices=["bert", "roberta", "gpt2"]						,	required=A_						,	help="Teacher type (BERT, RoBERTa)."					)
  parser.add_argument("--teacher_name"						,	type=A_						,	required=A_						,	help="The teacher model."					)

  parser.add_argument("--temperature"						,	default=2.0						,	type=A_						,	help="Temperature for the softmax temperature."					)
  parser.add_argument(
      "--alpha_ce"						,	default=0.5						,	type=A_						,	help="Linear weight for the distillation loss. Must be >=0."					)
  parser.add_argument(
      "--alpha_mlm"						,	default=0.0						,	type=A_						,	help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."						,	)
  parser.add_argument("--alpha_clm"						,	default=0.5						,	type=A_						,	help="Linear weight for the CLM loss. Must be >=0."					)
  parser.add_argument("--alpha_mse"						,	default=0.0						,	type=A_						,	help="Linear weight of the MSE loss. Must be >=0."					)
  parser.add_argument(
      "--alpha_cos"						,	default=0.0						,	type=A_						,	help="Linear weight of the cosine embedding loss. Must be >=0."					)

  parser.add_argument(
      "--mlm"						,	action="store_true"						,	help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."					)
  parser.add_argument(
      "--mlm_mask_prop"						,	default=0.15						,	type=A_						,	help="Proportion of tokens for which we need to make a prediction."						,	)
  parser.add_argument("--word_mask"						,	default=0.8						,	type=A_						,	help="Proportion of tokens to mask out."					)
  parser.add_argument("--word_keep"						,	default=0.1						,	type=A_						,	help="Proportion of tokens to keep."					)
  parser.add_argument("--word_rand"						,	default=0.1						,	type=A_						,	help="Proportion of tokens to randomly replace."					)
  parser.add_argument(
      "--mlm_smoothing"						,	default=0.7						,	type=A_						,	help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."						,	)
  parser.add_argument("--token_counts"						,	type=A_						,	help="The token counts in the data_file for MLM."					)

  parser.add_argument(
      "--restrict_ce_to_mask"						,	action="store_true"						,	help="If true, compute the distillation loss only the [MLM] prediction distribution."						,	)
  parser.add_argument(
      "--freeze_pos_embs"						,	action="store_true"						,	help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."						,	)
  parser.add_argument(
      "--freeze_token_type_embds"						,	action="store_true"						,	help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."						,	)

  parser.add_argument("--n_epoch"						,	type=A_						,	default=3						,	help="Number of pass on the whole dataset."					)
  parser.add_argument("--batch_size"						,	type=A_						,	default=5						,	help="Batch size (for each process)."					)
  parser.add_argument(
      "--group_by_size"						,	action="store_false"						,	help="If true, group sequences that have similar length into the same batch. Default is true."						,	)

  parser.add_argument(
      "--gradient_accumulation_steps"						,	type=A_						,	default=50						,	help="Gradient accumulation for larger training batches."						,	)
  parser.add_argument("--warmup_prop"						,	default=0.05						,	type=A_						,	help="Linear warmup proportion."					)
  parser.add_argument("--weight_decay"						,	default=0.0						,	type=A_						,	help="Weight decay if we apply some."					)
  parser.add_argument("--learning_rate"						,	default=5e-4						,	type=A_						,	help="The initial learning rate for Adam."					)
  parser.add_argument("--adam_epsilon"						,	default=1e-6						,	type=A_						,	help="Epsilon for Adam optimizer."					)
  parser.add_argument("--max_grad_norm"						,	default=5.0						,	type=A_						,	help="Max gradient norm."					)
  parser.add_argument("--initializer_range"						,	default=0.02						,	type=A_						,	help="Random initialization range."					)

  parser.add_argument(
      "--fp16"						,	action="store_true"						,	help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"						,	)
  parser.add_argument(
      "--fp16_opt_level"						,	type=A_						,	default="O1"						,	help=(
          "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
          "See details at https://nvidia.github.io/apex/amp.html"
      )						,	)
  parser.add_argument("--n_gpu"						,	type=A_						,	default=1						,	help="Number of GPUs in the node."					)
  parser.add_argument("--local_rank"						,	type=A_						,	default=-1						,	help="Distributed training - Local rank"					)
  parser.add_argument("--seed"						,	type=A_						,	default=56						,	help="Random seed"					)

  parser.add_argument("--log_interval"						,	type=A_						,	default=5_00						,	help="Tensorboard logging interval."					)
  parser.add_argument("--checkpoint_interval"						,	type=A_						,	default=40_00						,	help="Checkpoint interval."					)
  __UpperCAmelCase						    =       parser.parse_args()
  sanity_checks(A_					)

  # ARGS #
  init_gpu_params(A_					)
  set_seed(A_					)
  if args.is_master:
    if os.path.exists(args.dump_path					):
      if not args.force:
        raise ValueError(
            F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
            " itUse `--force` if you want to overwrite it"					)
      else:
        shutil.rmtree(args.dump_path					)

    if not os.path.exists(args.dump_path					):
      os.makedirs(args.dump_path					)
    logger.info(F'''Experiment will be dumped and logged in {args.dump_path}'''					)

    # SAVE PARAMS #
    logger.info(F'''Param: {args}'''					)
    with open(os.path.join(args.dump_path						,	"parameters.json"					)						,	"w"					) as f:
      json.dump(vars(A_					)						,	A_						,	indent=4					)
    git_log(args.dump_path					)

  __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       MODEL_CLASSES[args.student_type]
  __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       MODEL_CLASSES[args.teacher_type]

  # TOKENIZER #
  __UpperCAmelCase						    =       teacher_tokenizer_class.from_pretrained(args.teacher_name					)
  __UpperCAmelCase						    =       {}
  for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
    __UpperCAmelCase						    =       tokenizer.all_special_tokens.index(A_					)
    __UpperCAmelCase						    =       tokenizer.all_special_ids[idx]
  logger.info(F'''Special tokens {special_tok_ids}'''					)
  __UpperCAmelCase						    =       special_tok_ids
  __UpperCAmelCase						    =       tokenizer.max_model_input_sizes[args.teacher_name]

  # DATA LOADER #
  logger.info(F'''Loading data from {args.data_file}'''					)
  with open(args.data_file						,	"rb"					) as fp:
    __UpperCAmelCase						    =       pickle.load(A_					)

  if args.mlm:
    logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)'''					)
    with open(args.token_counts						,	"rb"					) as fp:
      __UpperCAmelCase						    =       pickle.load(A_					)

    __UpperCAmelCase						    =       np.maximum(A_						,	1					) ** -args.mlm_smoothing
    for idx in special_tok_ids.values():
      __UpperCAmelCase						    =       0.0  # do not predict special tokens
    __UpperCAmelCase						    =       torch.from_numpy(A_					)
  else:
    __UpperCAmelCase						    =       None

  __UpperCAmelCase						    =       LmSeqsDataset(params=A_						,	data=A_					)
  logger.info("Data loader created."					)

  # STUDENT #
  logger.info(F'''Loading student config from {args.student_config}'''					)
  __UpperCAmelCase						    =       student_config_class.from_pretrained(args.student_config					)
  __UpperCAmelCase						    =       True

  if args.student_pretrained_weights is not None:
    logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''					)
    __UpperCAmelCase						    =       student_model_class.from_pretrained(args.student_pretrained_weights						,	config=A_					)
  else:
    __UpperCAmelCase						    =       student_model_class(A_					)

  if args.n_gpu > 0:
    student.to(F'''cuda:{args.local_rank}'''					)
  logger.info("Student loaded."					)

  # TEACHER #
  __UpperCAmelCase						    =       teacher_model_class.from_pretrained(args.teacher_name						,	output_hidden_states=A_					)
  if args.n_gpu > 0:
    teacher.to(F'''cuda:{args.local_rank}'''					)
  logger.info(F'''Teacher loaded from {args.teacher_name}.'''					)

  # FREEZING #
  if args.freeze_pos_embs:
    freeze_pos_embeddings(A_						,	A_					)
  if args.freeze_token_type_embds:
    freeze_token_type_embeddings(A_						,	A_					)

  # SANITY CHECKS #
  assert student.config.vocab_size == teacher.config.vocab_size
  assert student.config.hidden_size == teacher.config.hidden_size
  assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
  if args.mlm:
    assert token_probs.size(0					) == stu_architecture_config.vocab_size

  # DISTILLER #
  torch.cuda.empty_cache()
  __UpperCAmelCase						    =       Distiller(
      params=A_						,	dataset=A_						,	token_probs=A_						,	student=A_						,	teacher=A_					)
  distiller.train()
  logger.info("Let's go get some drinks."					)


if __name__ == "__main__":
       main()





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 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	List[Any]    		= AlbertTokenizer
  lowerCAmelCase__		:	int    		= AlbertTokenizerFast
  lowerCAmelCase__		:	str    		= True
  lowerCAmelCase__		:	Union[str, Any]    		= True
  lowerCAmelCase__		:	int    		= True






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> str:




    '''simple docstring'''




    super().setUp()

    # We have a SentencePiece fixture for testing
    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase   )
    tokenizer.save_pretrained(self.tmpdirname   )






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple   ) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "this is a test"
    __UpperCAmelCase						    =       "this is a test"
    return input_text, output_text






  def 	_UpperCAmelCase				(				self:			Dict   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       "<pad>"
    __UpperCAmelCase						    =       0

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






  def 	_UpperCAmelCase				(				self:			str   ) -> Optional[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       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(__lowerCAmelCase   )				,				30_000   )






  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> List[str]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			str   ) -> int:




    '''simple docstring'''




    if not self.test_rust_tokenizer:
      return

    __UpperCAmelCase						    =       self.get_tokenizer()
    __UpperCAmelCase						    =       self.get_rust_tokenizer()

    __UpperCAmelCase						    =       "I was born in 92000, and this is falsé."

    __UpperCAmelCase						    =       tokenizer.tokenize(__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.tokenize(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.encode(__lowerCAmelCase				,				add_special_tokens=__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )

    __UpperCAmelCase						    =       self.get_rust_tokenizer()
    __UpperCAmelCase						    =       tokenizer.encode(__lowerCAmelCase   )
    __UpperCAmelCase						    =       rust_tokenizer.encode(__lowerCAmelCase   )
    self.assertListEqual(__lowerCAmelCase				,				__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase				,				keep_accents=__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.tokenize("This is a test"   )
    self.assertListEqual(__lowerCAmelCase				,				["▁this", "▁is", "▁a", "▁test"]   )

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

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

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






  def 	_UpperCAmelCase				(				self:			str   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       AlbertTokenizer(__lowerCAmelCase   )

    __UpperCAmelCase						    =       tokenizer.encode("sequence builders"   )
    __UpperCAmelCase						    =       tokenizer.encode("multi-sequence build"   )

    __UpperCAmelCase						    =       tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase   )
    __UpperCAmelCase						    =       tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase				,				__lowerCAmelCase   )

    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:			Optional[int]   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       {"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=__lowerCAmelCase				,				model_name="albert-base-v2"				,				revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"				,				)



