






"""simple docstring"""




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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_snake_case          =   logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')

_snake_case          =   list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_snake_case          =   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class        UpperCamelCase			:
 UpperCamelCase					:       Optional[str]			=					field(
     default='''cifar10'''		,     metadata={'''help''': '''Name of a dataset from the datasets package'''}      )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}      )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}		,     )
 UpperCamelCase					:       Optional[str]			=					field(default=snake_case_		,     metadata={'''help''': '''A folder containing the training data.'''}      )
 UpperCamelCase					:       Optional[str]			=					field(default=snake_case_		,     metadata={'''help''': '''A folder containing the validation data.'''}      )
 UpperCamelCase					:       Optional[float]			=					field(
     default=0.1_5		,     metadata={'''help''': '''Percent to split off of train for validation.'''}      )
 UpperCamelCase					:       int			=					field(default=32		,     metadata={'''help''': '''The size of the square patches to use for masking.'''}      )
 UpperCamelCase					:       float			=					field(
     default=0.6		,     metadata={'''help''': '''Percentage of patches to mask.'''}		,     )
 UpperCamelCase					:       Optional[int]			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }		,     )
 UpperCamelCase					:       Optional[int]			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     }		,     )


 def _lowercase	(     self		:						Union[str, Any]							)					->       int:
   _a  :       Any             =	{}
   if self.train_dir is not None:
     _a  :       List[Any]             =	self.train_dir
   if self.validation_dir is not None:
     _a  :       Tuple             =	self.validation_dir
   _a  :       Any             =	data_files if data_files else None







@dataclass
class        UpperCamelCase			:
 UpperCamelCase					:       str			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
             '''checkpoint identifier on the hub. '''
             '''Don\'t set if you want to train a model from scratch.'''
         )
     }		,     )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(snake_case_      )}		,     )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}      )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     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'''
         )
     }		,     )
 UpperCamelCase					:       Optional[str]			=					field(
     default=snake_case_		,     metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}		,     )
 UpperCamelCase					:       str			=					field(
     default='''main'''		,     metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}		,     )
 UpperCamelCase					:       str			=					field(default=snake_case_		,     metadata={'''help''': '''Name or path of preprocessor config.'''}      )
 UpperCamelCase					:       bool			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     }		,     )
 UpperCamelCase					:       Optional[int]			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
         )
     }		,     )
 UpperCamelCase					:       Optional[int]			=					field(
     default=snake_case_		,     metadata={
         '''help''': (
             '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
         )
     }		,     )
 UpperCamelCase					:       Optional[int]			=					field(
     default=snake_case_		,     metadata={'''help''': '''Stride to use for the encoder.'''}		,     )







class        UpperCamelCase			:


 def __init__(     self		:						Any							,						UpperCAmelCase__		:						str=192							,						UpperCAmelCase__		:						List[Any]=32							,						UpperCAmelCase__		:						List[str]=4							,						UpperCAmelCase__		:						List[Any]=0.6							)					->       List[Any]:
   _a  :       int             =	input_size
   _a  :       List[Any]             =	mask_patch_size
   _a  :       Tuple             =	model_patch_size
   _a  :       str             =	mask_ratio

   if self.input_size % self.mask_patch_size != 0:
     raise ValueError("""Input size must be divisible by mask patch size"""							)
   if self.mask_patch_size % self.model_patch_size != 0:
     raise ValueError("""Mask patch size must be divisible by model patch size"""							)

   _a  :       Any             =	self.input_size // self.mask_patch_size
   _a  :       Optional[Any]             =	self.mask_patch_size // self.model_patch_size

   _a  :       Optional[int]             =	self.rand_size**2
   _a  :       List[str]             =	int(np.ceil(self.token_count * self.mask_ratio							)							)







 def __call__(     self		:						List[Any]							)					->       int:
   _a  :       Any             =	np.random.permutation(self.token_count							)[: self.mask_count]
   _a  :       Optional[int]             =	np.zeros(self.token_count							,						dtype=UpperCAmelCase__							)
   _a  :       str             =	1

   _a  :       Optional[Any]             =	mask.reshape((self.rand_size, self.rand_size)							)
   _a  :       List[str]             =	mask.repeat(self.scale							,						axis=0							).repeat(self.scale							,						axis=1							)

   return torch.tensor(mask.flatten()							)







def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Optional[int]             =	torch.stack([example["""pixel_values"""] for example in examples]				)
  _a  :       List[str]             =	torch.stack([example["""mask"""] for example in examples]				)
  return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  # 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.

  _a  :       Tuple             =	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.
    _a      ,  _a      ,  _a  :       Optional[int]             =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
  else:
    _a      ,  _a      ,  _a  :       List[Any]             =	parser.parse_args_into_dataclasses()

  # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
  # information sent is the one passed as arguments along with your Python/PyTorch versions.
  send_example_telemetry("""run_mim"""     ,   UpperCamelCase__     ,   UpperCamelCase__				)

  # 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				)]     ,   )

  if training_args.should_log:
    # The default of training_args.log_level is passive, so we set log level at info here to have that default.
    transformers.utils.logging.set_verbosity_info()

  _a  :       Optional[int]             =	training_args.get_process_log_level()
  logger.setLevel(UpperCamelCase__				)
  transformers.utils.logging.set_verbosity(UpperCamelCase__				)
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()

  # 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}"""				)
  logger.info(F"""Training/evaluation parameters {training_args}"""				)

  # Detecting last checkpoint.
  _a  :       Tuple             =	None
  if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
    _a  :       Optional[int]             =	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 and training_args.resume_from_checkpoint is 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."""				)

    # Initialize our dataset.
  _a  :       Tuple             =	load_dataset(
      data_args.dataset_name     ,   data_args.dataset_config_name     ,   data_files=data_args.data_files     ,   cache_dir=model_args.cache_dir     ,   use_auth_token=True if model_args.use_auth_token else None     ,   )

  # If we don't have a validation split, split off a percentage of train as validation.
  _a  :       Optional[Any]             =	None if """validation""" in ds.keys() else data_args.train_val_split
  if isinstance(data_args.train_val_split     ,   UpperCamelCase__				) and data_args.train_val_split > 0.0:
    _a  :       Tuple             =	ds["""train"""].train_test_split(data_args.train_val_split				)
    _a  :       Dict             =	split["""train"""]
    _a  :       Any             =	split["""test"""]

  # Create config
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.
  _a  :       Union[str, Any]             =	{
      """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_or_path:
    _a  :       str             =	AutoConfig.from_pretrained(model_args.config_name_or_path     ,   **UpperCamelCase__				)
  elif model_args.model_name_or_path:
    _a  :       List[Any]             =	AutoConfig.from_pretrained(model_args.model_name_or_path     ,   **UpperCamelCase__				)
  else:
    _a  :       int             =	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}"""				)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
  if hasattr(UpperCamelCase__     ,   """decoder_type"""				):
    _a  :       str             =	"""simmim"""

  # adapt config
  _a  :       Tuple             =	model_args.image_size if model_args.image_size is not None else config.image_size
  _a  :       Dict             =	model_args.patch_size if model_args.patch_size is not None else config.patch_size
  _a  :       Optional[Any]             =	(
      model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
  )

  config.update(
      {
          """image_size""": model_args.image_size,
          """patch_size""": model_args.patch_size,
          """encoder_stride""": model_args.encoder_stride,
      }				)

  # create image processor
  if model_args.image_processor_name:
    _a  :       Tuple             =	AutoImageProcessor.from_pretrained(model_args.image_processor_name     ,   **UpperCamelCase__				)
  elif model_args.model_name_or_path:
    _a  :       List[Any]             =	AutoImageProcessor.from_pretrained(model_args.model_name_or_path     ,   **UpperCamelCase__				)
  else:
    _a  :       Optional[int]             =	{
        conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
    }
    _a  :       Any             =	IMAGE_PROCESSOR_TYPES[model_args.model_type]()

  # create model
  if model_args.model_name_or_path:
    _a  :       Any             =	AutoModelForMaskedImageModeling.from_pretrained(
        model_args.model_name_or_path     ,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path				)     ,   config=UpperCamelCase__     ,   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"""				)
    _a  :       Union[str, Any]             =	AutoModelForMaskedImageModeling.from_config(UpperCamelCase__				)

  if training_args.do_train:
    _a  :       List[str]             =	ds["""train"""].column_names
  else:
    _a  :       Union[str, Any]             =	ds["""validation"""].column_names

  if data_args.image_column_name is not None:
    _a  :       List[Any]             =	data_args.image_column_name
  elif "image" in column_names:
    _a  :       Optional[Any]             =	"""image"""
  elif "img" in column_names:
    _a  :       Union[str, Any]             =	"""img"""
  else:
    _a  :       int             =	column_names[0]

  # transformations as done in original SimMIM paper
  # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
  _a  :       List[Any]             =	Compose(
      [
          Lambda(lambda UpperCamelCase__				: img.convert("""RGB"""				) if img.mode != "RGB" else img				),
          RandomResizedCrop(model_args.image_size     ,   scale=(0.67, 1.0)     ,   ratio=(3.0 / 4.0, 4.0 / 3.0)				),
          RandomHorizontalFlip(),
          ToTensor(),
          Normalize(mean=image_processor.image_mean     ,   std=image_processor.image_std				),
      ]				)

  # create mask generator
  _a  :       Dict             =	MaskGenerator(
      input_size=model_args.image_size     ,   mask_patch_size=data_args.mask_patch_size     ,   model_patch_size=model_args.patch_size     ,   mask_ratio=data_args.mask_ratio     ,   )

  def preprocess_images(UpperCamelCase__				):
    _a  :       Any             =	[transforms(UpperCamelCase__				) for image in examples[image_column_name]]
    _a  :       int             =	[mask_generator() for i in range(len(examples[image_column_name]				)				)]

    return examples

  if training_args.do_train:
    if "train" not in ds:
      raise ValueError("""--do_train requires a train dataset"""				)
    if data_args.max_train_samples is not None:
      _a  :       str             =	ds["""train"""].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
    # Set the training transforms
    ds["train"].set_transform(UpperCamelCase__				)

  if training_args.do_eval:
    if "validation" not in ds:
      raise ValueError("""--do_eval requires a validation dataset"""				)
    if data_args.max_eval_samples is not None:
      _a  :       List[Any]             =	(
          ds["""validation"""].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
      )
    # Set the validation transforms
    ds["validation"].set_transform(UpperCamelCase__				)

  # Initialize our trainer
  _a  :       List[str]             =	Trainer(
      model=UpperCamelCase__     ,   args=UpperCamelCase__     ,   train_dataset=ds["""train"""] if training_args.do_train else None     ,   eval_dataset=ds["""validation"""] if training_args.do_eval else None     ,   tokenizer=UpperCamelCase__     ,   data_collator=UpperCamelCase__     ,   )

  # Training
  if training_args.do_train:
    _a  :       Tuple             =	None
    if training_args.resume_from_checkpoint is not None:
      _a  :       int             =	training_args.resume_from_checkpoint
    elif last_checkpoint is not None:
      _a  :       Optional[int]             =	last_checkpoint
    _a  :       Dict             =	trainer.train(resume_from_checkpoint=UpperCamelCase__				)
    trainer.save_model()
    trainer.log_metrics("""train"""     ,   train_result.metrics				)
    trainer.save_metrics("""train"""     ,   train_result.metrics				)
    trainer.save_state()

  # Evaluation
  if training_args.do_eval:
    _a  :       int             =	trainer.evaluate()
    trainer.log_metrics("""eval"""     ,   UpperCamelCase__				)
    trainer.save_metrics("""eval"""     ,   UpperCamelCase__				)

  # Write model card and (optionally) push to hub
  _a  :       int             =	{
      """finetuned_from""": model_args.model_name_or_path,
      """tasks""": """masked-image-modeling""",
      """dataset""": data_args.dataset_name,
      """tags""": ["""masked-image-modeling"""],
  }
  if training_args.push_to_hub:
    trainer.push_to_hub(**UpperCamelCase__				)
  else:
    trainer.create_model_card(**UpperCamelCase__				)


if __name__ == "__main__":
 main()









"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_snake_case          =   logging.getLogger(__name__)






class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Optional[Any]							,						UpperCAmelCase__		:						Optional[int]=-1							)					->       Tuple:
   # in NER datasets, the last column is usually reserved for NER label
   _a  :       Optional[int]             =	label_idx


 def _lowercase	(     self		:						Any							,						UpperCAmelCase__		:						Dict							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       Any             =	mode.value
   _a  :       Optional[int]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       int             =	1
   _a  :       int             =	[]
   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     _a  :       str             =	[]
     _a  :       str             =	[]
     for line in f:
       if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
         if words:
           examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
           guid_index += 1
           _a  :       List[str]             =	[]
           _a  :       str             =	[]
       else:
         _a  :       List[Any]             =	line.split(""" """							)
         words.append(splits[0]							)
         if len(UpperCAmelCase__							) > 1:
           labels.append(splits[self.label_idx].replace("""\n"""							,						""""""							)							)
         else:
           # Examples could have no label for mode = "test"
           labels.append("""O"""							)
     if words:
       examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
   return examples


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Union[str, Any]:
   _a  :       List[str]             =	0
   for line in test_input_reader:
     if line.startswith("""-DOCSTART-"""							) or line == "" or line == "\n":
       writer.write(UpperCAmelCase__							)
       if not preds_list[example_id]:
         example_id += 1
     elif preds_list[example_id]:
       _a  :       int             =	line.split()[0] + """ """ + preds_list[example_id].pop(0							) + """\n"""
       writer.write(UpperCAmelCase__							)
     else:
       logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""							,						line.split()[0]							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       List[Any]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Union[str, Any]             =	["""O"""] + labels
     return labels
   else:
     return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]







class        UpperCamelCase			(	snake_case_      ):


 def __init__(     self		:						Union[str, Any]							)					->       List[str]:
   # in CONLL2003 dataset chunk column is second-to-last
   super().__init__(label_idx=-2							)







 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       _a  :       Optional[int]             =	f.read().splitlines()
     if "O" not in labels:
       _a  :       Optional[Any]             =	["""O"""] + labels
     return labels
   else:
     return [
         "O",
         "B-ADVP",
         "B-INTJ",
         "B-LST",
         "B-PRT",
         "B-NP",
         "B-SBAR",
         "B-VP",
         "B-ADJP",
         "B-CONJP",
         "B-PP",
         "I-ADVP",
         "I-INTJ",
         "I-LST",
         "I-PRT",
         "I-NP",
         "I-SBAR",
         "I-VP",
         "I-ADJP",
         "I-CONJP",
         "I-PP",
     ]







class        UpperCamelCase			(	snake_case_      ):


 def _lowercase	(     self		:						Optional[Any]							,						UpperCAmelCase__		:						List[Any]							,						UpperCAmelCase__		:						Union[Split, str]							)					->       List[InputExample]:
   if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       List[Any]             =	mode.value
   _a  :       Union[str, Any]             =	os.path.join(UpperCAmelCase__							,						f"""{mode}.txt"""							)
   _a  :       List[str]             =	1
   _a  :       Optional[Any]             =	[]

   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as f:
     for sentence in parse_incr(UpperCAmelCase__							):
       _a  :       List[Any]             =	[]
       _a  :       Any             =	[]
       for token in sentence:
         words.append(token["""form"""]							)
         labels.append(token["""upos"""]							)
       assert len(UpperCAmelCase__							) == len(UpperCAmelCase__							)
       if words:
         examples.append(InputExample(guid=f"""{mode}-{guid_index}"""							,						words=UpperCAmelCase__							,						labels=UpperCAmelCase__							)							)
         guid_index += 1
   return examples


 def _lowercase	(     self		:						Tuple							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						TextIO							,						UpperCAmelCase__		:						List							)					->       Dict:
   _a  :       Optional[Any]             =	0
   for sentence in parse_incr(UpperCAmelCase__							):
     _a  :       List[str]             =	preds_list[example_id]
     _a  :       str             =	""""""
     for token in sentence:
       out += f"""{token['form']} ({token['upos']}|{s_p.pop(0							)}) """
     out += "\n"
     writer.write(UpperCAmelCase__							)
     example_id += 1







 def _lowercase	(     self		:						List[str]							,						UpperCAmelCase__		:						str							)					->       List[str]:
   if path:
     with open(UpperCAmelCase__							,						"""r"""							) as f:
       return f.read().splitlines()
   else:
     return [
         "ADJ",
         "ADP",
         "ADV",
         "AUX",
         "CCONJ",
         "DET",
         "INTJ",
         "NOUN",
         "NUM",
         "PART",
         "PRON",
         "PROPN",
         "PUNCT",
         "SCONJ",
         "SYM",
         "VERB",
         "X",
     ]

