

"""simple docstring"""






import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__lowerCamelCase					    =		logging.getLogger(__name__)
__lowerCamelCase					    =		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__lowerCamelCase					    =		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 							_lowercase       :
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={
             '''help''': (
                 '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
             )
         } ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A__  )} ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={
             '''help''': (
                 '''Override some existing default config settings when a model is trained from scratch. Example: '''
                 '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
             )
         } ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}  )
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}  )
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} ,			)
     _lowerCamelCase	 =							field(
         default='''main''' ,			metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={
             '''help''': (
                 '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                 '''with private models).'''
             )
         } ,			)







     def 						lowerCAmelCase__     (   self							):
      if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
       raise ValueError(
           '''--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'''							)


@dataclass
class 							_lowercase       :
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}  )
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}  )
     _lowerCamelCase	 =							field(default=A__ ,			metadata={'''help''': '''The input training data file (a text file).'''}  )
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''} ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''} ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}  )
     _lowerCamelCase	 =							field(
         default=5 ,			metadata={
             '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
         } ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={
             '''help''': (
                 '''The maximum total input sequence length after tokenization. Sequences longer '''
                 '''than this will be truncated. Default to the max input length of the model.'''
             )
         } ,			)
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={'''help''': '''The number of processes to use for the preprocessing.'''} ,			)
     _lowerCamelCase	 =							field(
         default=0.1_5 ,			metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}  )
     _lowerCamelCase	 =							field(
         default=A__ ,			metadata={
             '''help''': (
                 '''Whether to pad all samples to `max_seq_length`. '''
                 '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
             )
         } ,			)







     def 						lowerCAmelCase__     (   self							):
      if self.train_file is not None:
       __magic_name__	 =   self.train_file.split('''.'''							)[-1]
       assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
      if self.validation_file is not None:
       __magic_name__	 =   self.validation_file.split('''.'''							)[-1]
       assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 						lowercase   ( __UpperCamelCase   , __UpperCamelCase    )			->   Optional[Any]:
 with open(__lowerCAmelCase   , '''r'''   , encoding='''utf-8'''    ) as f:
  __magic_name__	 =   [json.loads(__lowerCAmelCase    ) for line in f.read().splitlines() if (len(__lowerCAmelCase    ) > 0 and not line.isspace())]
 assert len(__lowerCAmelCase    ) == len(__lowerCAmelCase    )

 __magic_name__	 =   {c: dataset[c] for c in dataset.column_names}
 __magic_name__	 =   refs
 return Dataset.from_dict(__lowerCAmelCase    )





def 						lowercase   ( )			->   List[Any]:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 __magic_name__	 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
 if len(sys.argv    ) == 2 and sys.argv[1].endswith('''.json'''    ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  __magic_name__ ,    __magic_name__ ,    __magic_name__	 =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
 else:
  __magic_name__ ,    __magic_name__ ,    __magic_name__	 =   parser.parse_args_into_dataclasses()

 # Detecting last checkpoint.
 __magic_name__	 =   None
 if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
  __magic_name__	 =   get_last_checkpoint(training_args.output_dir    )
  if last_checkpoint is None and len(os.listdir(training_args.output_dir    )    ) > 0:
   raise ValueError(
       f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
       '''Use --overwrite_output_dir to overcome.'''    )
  elif last_checkpoint is not None:
   logger.info(
       f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
       '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''    )

    # Setup logging
 logging.basicConfig(
     format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   , datefmt='''%m/%d/%Y %H:%M:%S'''   , handlers=[logging.StreamHandler(sys.stdout    )]   , )
 logger.setLevel(logging.INFO if is_main_process(training_args.local_rank    ) else logging.WARN    )

 # Log on each process the small summary:
 logger.warning(
     f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
     + f'''distributed training: {bool(training_args.local_rank != -1    )}, 16-bits training: {training_args.fpaa}'''    )
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank    ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info('''Training/evaluation parameters %s'''   , __lowerCAmelCase    )

 # Set seed before initializing model.
 set_seed(training_args.seed    )

 # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
 # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
 # (the dataset will be downloaded automatically from the datasets Hub).
 #
 # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
 # 'text' is found. You can easily tweak this behavior (see below).
 #
 # In distributed training, the load_dataset function guarantee that only one local process can concurrently
 # download the dataset.
 if data_args.dataset_name is not None:
  # Downloading and loading a dataset from the hub.
  __magic_name__	 =   load_dataset(data_args.dataset_name   , data_args.dataset_config_name    )
  if "validation" not in datasets.keys():
   __magic_name__	 =   load_dataset(
       data_args.dataset_name   , data_args.dataset_config_name   , split=f'''train[:{data_args.validation_split_percentage}%]'''   , )
   __magic_name__	 =   load_dataset(
       data_args.dataset_name   , data_args.dataset_config_name   , split=f'''train[{data_args.validation_split_percentage}%:]'''   , )
 else:
  __magic_name__	 =   {}
  if data_args.train_file is not None:
   __magic_name__	 =   data_args.train_file
  if data_args.validation_file is not None:
   __magic_name__	 =   data_args.validation_file
  __magic_name__	 =   data_args.train_file.split('''.'''    )[-1]
  if extension == "txt":
   __magic_name__	 =   '''text'''
  __magic_name__	 =   load_dataset(__lowerCAmelCase   , data_files=__lowerCAmelCase    )
 # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
 # https://huggingface.co/docs/datasets/loading_datasets.html.

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 __magic_name__	 =   {
     '''cache_dir''': model_args.cache_dir,
     '''revision''': model_args.model_revision,
     '''use_auth_token''': True if model_args.use_auth_token else None,
 }
 if model_args.config_name:
  __magic_name__	 =   AutoConfig.from_pretrained(model_args.config_name   , **__lowerCAmelCase    )
 elif model_args.model_name_or_path:
  __magic_name__	 =   AutoConfig.from_pretrained(model_args.model_name_or_path   , **__lowerCAmelCase    )
 else:
  __magic_name__	 =   CONFIG_MAPPING[model_args.model_type]()
  logger.warning('''You are instantiating a new config instance from scratch.'''    )
  if model_args.config_overrides is not None:
   logger.info(f'''Overriding config: {model_args.config_overrides}'''    )
   config.update_from_string(model_args.config_overrides    )
   logger.info(f'''New config: {config}'''    )

 __magic_name__	 =   {
     '''cache_dir''': model_args.cache_dir,
     '''use_fast''': model_args.use_fast_tokenizer,
     '''revision''': model_args.model_revision,
     '''use_auth_token''': True if model_args.use_auth_token else None,
 }
 if model_args.tokenizer_name:
  __magic_name__	 =   AutoTokenizer.from_pretrained(model_args.tokenizer_name   , **__lowerCAmelCase    )
 elif model_args.model_name_or_path:
  __magic_name__	 =   AutoTokenizer.from_pretrained(model_args.model_name_or_path   , **__lowerCAmelCase    )
 else:
  raise ValueError(
      '''You are instantiating a new tokenizer from scratch. This is not supported by this script.'''
      '''You can do it from another script, save it, and load it from here, using --tokenizer_name.'''    )

 if model_args.model_name_or_path:
  __magic_name__	 =   AutoModelForMaskedLM.from_pretrained(
      model_args.model_name_or_path   , from_tf=bool('''.ckpt''' in model_args.model_name_or_path    )   , config=__lowerCAmelCase   , cache_dir=model_args.cache_dir   , revision=model_args.model_revision   , use_auth_token=True if model_args.use_auth_token else None   , )
 else:
  logger.info('''Training new model from scratch'''    )
  __magic_name__	 =   AutoModelForMaskedLM.from_config(__lowerCAmelCase    )

 model.resize_token_embeddings(len(__lowerCAmelCase    )    )

 # Preprocessing the datasets.
 # First we tokenize all the texts.
 if training_args.do_train:
  __magic_name__	 =   datasets['''train'''].column_names
 else:
  __magic_name__	 =   datasets['''validation'''].column_names
 __magic_name__	 =   '''text''' if '''text''' in column_names else column_names[0]

 __magic_name__	 =   '''max_length''' if data_args.pad_to_max_length else False

 def tokenize_function(__UpperCamelCase    ):
  # Remove empty lines
  __magic_name__	 =   [line for line in examples['''text'''] if len(__lowerCAmelCase    ) > 0 and not line.isspace()]
  return tokenizer(examples['''text''']   , padding=__lowerCAmelCase   , truncation=__lowerCAmelCase   , max_length=data_args.max_seq_length    )

 __magic_name__	 =   datasets.map(
     __lowerCAmelCase   , batched=__lowerCAmelCase   , num_proc=data_args.preprocessing_num_workers   , remove_columns=[text_column_name]   , load_from_cache_file=not data_args.overwrite_cache   , )

 # Add the chinese references if provided
 if data_args.train_ref_file is not None:
  __magic_name__	 =   add_chinese_references(tokenized_datasets['''train''']   , data_args.train_ref_file    )
 if data_args.validation_ref_file is not None:
  __magic_name__	 =   add_chinese_references(
      tokenized_datasets['''validation''']   , data_args.validation_ref_file    )
 # If we have ref files, need to avoid it removed by trainer
 __magic_name__	 =   data_args.train_ref_file or data_args.validation_ref_file
 if has_ref:
  __magic_name__	 =   False

 # Data collator
 # This one will take care of randomly masking the tokens.
 __magic_name__	 =   DataCollatorForWholeWordMask(tokenizer=__lowerCAmelCase   , mlm_probability=data_args.mlm_probability    )

 # Initialize our Trainer
 __magic_name__	 =   Trainer(
     model=__lowerCAmelCase   , args=__lowerCAmelCase   , train_dataset=tokenized_datasets['''train'''] if training_args.do_train else None   , eval_dataset=tokenized_datasets['''validation'''] if training_args.do_eval else None   , tokenizer=__lowerCAmelCase   , data_collator=__lowerCAmelCase   , )

 # Training
 if training_args.do_train:
  if last_checkpoint is not None:
   __magic_name__	 =   last_checkpoint
  elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path    ):
   __magic_name__	 =   model_args.model_name_or_path
  else:
   __magic_name__	 =   None
  __magic_name__	 =   trainer.train(resume_from_checkpoint=__lowerCAmelCase    )
  trainer.save_model()  # Saves the tokenizer too for easy upload

  __magic_name__	 =   os.path.join(training_args.output_dir   , '''train_results.txt'''    )
  if trainer.is_world_process_zero():
   with open(__lowerCAmelCase   , '''w'''    ) as writer:
    logger.info('''***** Train results *****'''    )
    for key, value in sorted(train_result.metrics.items()    ):
     logger.info(f'''  {key} = {value}'''    )
     writer.write(f'''{key} = {value}\n'''    )

    # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
   trainer.state.save_to_json(os.path.join(training_args.output_dir   , '''trainer_state.json'''    )    )

    # Evaluation
 __magic_name__	 =   {}
 if training_args.do_eval:
  logger.info('''*** Evaluate ***'''    )

  __magic_name__	 =   trainer.evaluate()

  __magic_name__	 =   math.exp(eval_output['''eval_loss''']    )
  __magic_name__	 =   perplexity

  __magic_name__	 =   os.path.join(training_args.output_dir   , '''eval_results_mlm_wwm.txt'''    )
  if trainer.is_world_process_zero():
   with open(__lowerCAmelCase   , '''w'''    ) as writer:
    logger.info('''***** Eval results *****'''    )
    for key, value in sorted(results.items()    ):
     logger.info(f'''  {key} = {value}'''    )
     writer.write(f'''{key} = {value}\n'''    )

 return results





def 						lowercase   ( __UpperCamelCase    )			->   int:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
 main()








'''simple docstring'''




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 			_lowerCAmelCase		(			A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   DebertaTokenizer
     snake_case_						      =   True
     snake_case_						      =   DebertaTokenizerFast



     def      lowerCAmelCase			(   self							:			Any						)->	Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        snake_case 						=   [
            """l""",
            """o""",
            """w""",
            """e""",
            """r""",
            """s""",
            """t""",
            """i""",
            """d""",
            """n""",
            """\u0120""",
            """\u0120l""",
            """\u0120n""",
            """\u0120lo""",
            """\u0120low""",
            """er""",
            """\u0120lowest""",
            """\u0120newer""",
            """\u0120wider""",
            """[UNK]""",
        ]
        snake_case 						=   dict(zip(__snake_case   ,       range(len(__snake_case						)						)						)						)
        snake_case 						=   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
        snake_case 						=   {"""unk_token""": """[UNK]"""}

        snake_case 						=   os.path.join(self.tmpdirname   ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
        snake_case 						=   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(__snake_case						) + """\n"""						)
        with open(self.merges_file   ,       """w"""   ,       encoding="""utf-8"""						) as fp:
           fp.write("""\n""".join(__snake_case						)						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       **__snake_case							:			Union[str, Any]						)->	int:
        kwargs.update(self.special_tokens_map						)
        return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			int						)->	Any:
        snake_case 						=   """lower newer"""
        snake_case 						=   """lower newer"""
        return input_text, output_text



     def      lowerCAmelCase			(   self							:			str						)->	Any:
        snake_case 						=   self.get_tokenizer()
        snake_case 						=   """lower newer"""
        snake_case 						=   ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
        snake_case 						=   tokenizer.tokenize(__snake_case						)
        self.assertListEqual(__snake_case   ,       __snake_case						)

        snake_case 						=   tokens + [tokenizer.unk_token]
        snake_case 						=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case						)   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			Any						)->	List[str]:
        snake_case 						=   self.get_tokenizer()
        snake_case 						=   tokenizer("""Hello"""   ,       """World"""						)
        snake_case 						=   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd["""token_type_ids"""]   ,       __snake_case						)



     @slow
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Union[str, Any]:
        snake_case 						=   self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

        snake_case 						=   tokenizer.encode("""sequence builders"""   ,       add_special_tokens=__snake_case						)
        snake_case 						=   tokenizer.encode("""multi-sequence build"""   ,       add_special_tokens=__snake_case						)

        snake_case 						=   tokenizer.encode(
            """sequence builders"""   ,       add_special_tokens=__snake_case   ,       add_prefix_space=__snake_case						)
        snake_case 						=   tokenizer.encode(
            """sequence builders"""   ,       """multi-sequence build"""   ,       add_special_tokens=__snake_case   ,       add_prefix_space=__snake_case						)

        snake_case 						=   tokenizer.build_inputs_with_special_tokens(__snake_case						)
        snake_case 						=   tokenizer.build_inputs_with_special_tokens(__snake_case   ,       __snake_case						)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



     @slow
     def      lowerCAmelCase			(   self							:			Any						)->	Union[str, Any]:
        snake_case 						=   [self.tokenizer_class]
        if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

        for tokenizer_class in tokenizer_classes:
           snake_case 						=   tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

           snake_case 						=   [
               """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
               """ALBERT incorporates two parameter reduction techniques""",
               """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
               """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
               """ vocabulary embedding.""",
           ]

           snake_case 						=   tokenizer(__snake_case   ,       padding=__snake_case						)
           snake_case 						=   [tokenizer.decode(__snake_case   ,       skip_special_tokens=__snake_case						) for seq in encoding["""input_ids"""]]

           # fmt: off
           snake_case 						=   {
               """input_ids""": [
                   [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
               ],
               """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]
               ],
               """attention_mask""": [
                   [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],
                   [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],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           snake_case 						=   [
               """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
               """ALBERT incorporates two parameter reduction techniques""",
               """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
               """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
               """ vocabulary embedding.""",
           ]

           self.assertDictEqual(encoding.data   ,       __snake_case						)

           for expected, decoded in zip(__snake_case   ,       __snake_case						):
              self.assertEqual(__snake_case   ,       __snake_case						)

