code: 
  
  
  
  
  
  import logging
  import os
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  
  import numpy as np
  import torch
  from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      default_data_collator,
      set_seed,
  )
  from transformers.trainer_utils import is_main_process
  
  
  lowerCAmelCase_   =   logging.getLogger(__name__)
  
  @dataclass
  class 				A    :
        _SCREAMING_SNAKE_CASE					=							field(
            metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}				)
        _SCREAMING_SNAKE_CASE					=							field(
            default=SCREAMING_SNAKE_CASE__       ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}				)
        _SCREAMING_SNAKE_CASE					=							field(
            default=SCREAMING_SNAKE_CASE__       ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}				)
        _SCREAMING_SNAKE_CASE					=							field(
            default=SCREAMING_SNAKE_CASE__       ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,)
  
  
  @dataclass
  class 				A    :
        _SCREAMING_SNAKE_CASE					=							field(
            metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()				)}				)
        _SCREAMING_SNAKE_CASE					=							field(
            metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
        _SCREAMING_SNAKE_CASE					=							field(
            default=128       ,metadata={
                """help""": (
                    """The maximum total input sequence length after tokenization. Sequences longer """
                    """than this will be truncated, sequences shorter will be padded."""
                )
            }       ,)
        _SCREAMING_SNAKE_CASE					=							field(
            default=SCREAMING_SNAKE_CASE__       ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)
  
  
  
  
  def 						A_			(  lowercase_  )			->						Dict[str, torch.Tensor]:
         _snake_case      :							int     				=						default_data_collator(lowercase_  )
         _snake_case      :							int     				=						batch.pop('''pairID'''      ,		lowercase_  )
         return batch
  
  
  
  
  def 						A_			(  )			->						Union[str, Any]:
         _snake_case      :							int     				=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
         _snake_case				,    _snake_case				,    _snake_case      :							Dict     				=						parser.parse_args_into_dataclasses()
  
         if (
             os.path.exists(training_args.output_dir  )
             and os.listdir(training_args.output_dir  )
             and training_args.do_train
             and not training_args.overwrite_output_dir
         ):
                raise ValueError(
                    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
                    ''' --overwrite_output_dir to overcome.'''  )
  
         # Setup logging
         logging.basicConfig(
             format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''      ,		datefmt='''%m/%d/%Y %H:%M:%S'''      ,		level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,		)
         logger.warning(
             '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''      ,		training_args.local_rank      ,		training_args.device      ,		training_args.n_gpu      ,		bool(training_args.local_rank != -1  )      ,		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'''      ,		lowercase_  )
  
         # Set seed
         set_seed(training_args.seed  )
  
         try:
                _snake_case      :							int     				=						hans_tasks_num_labels[data_args.task_name]
         except KeyError:
                raise ValueError('''Task not found: %s''' % (data_args.task_name)  )
  
         # Load pretrained model and tokenizer
         #
         # Distributed training:
         # The .from_pretrained methods guarantee that only one local process can concurrently
         # download model & vocab.
  
         _snake_case      :							Optional[int]     				=						AutoConfig.from_pretrained(
             model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,		num_labels=lowercase_      ,		finetuning_task=data_args.task_name      ,		cache_dir=model_args.cache_dir      ,		)
         _snake_case      :							Dict     				=						AutoTokenizer.from_pretrained(
             model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,		cache_dir=model_args.cache_dir      ,		)
         _snake_case      :							str     				=						AutoModelForSequenceClassification.from_pretrained(
             model_args.model_name_or_path      ,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path  )      ,		config=lowercase_      ,		cache_dir=model_args.cache_dir      ,		)
  
         # Get datasets
         _snake_case      :							List[Any]     				=						(
             HansDataset(
                 data_dir=data_args.data_dir      ,		tokenizer=lowercase_      ,		task=data_args.task_name      ,		max_seq_length=data_args.max_seq_length      ,		overwrite_cache=data_args.overwrite_cache      ,		)
             if training_args.do_train
             else None
         )
         _snake_case      :							Optional[Any]     				=						(
             HansDataset(
                 data_dir=data_args.data_dir      ,		tokenizer=lowercase_      ,		task=data_args.task_name      ,		max_seq_length=data_args.max_seq_length      ,		overwrite_cache=data_args.overwrite_cache      ,		evaluate=lowercase_      ,		)
             if training_args.do_eval
             else None
         )
  
         # Initialize our Trainer
         _snake_case      :							Dict     				=						Trainer(
             model=lowercase_      ,		args=lowercase_      ,		train_dataset=lowercase_      ,		eval_dataset=lowercase_      ,		data_collator=lowercase_      ,		)
  
         # Training
         if training_args.do_train:
                trainer.train(
                    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
                trainer.save_model()
                # For convenience, we also re-save the tokenizer to the same directory,
                # so that you can share your model easily on huggingface.co/models =)
                if trainer.is_world_master():
                       tokenizer.save_pretrained(training_args.output_dir  )
  
      # Evaluation
         if training_args.do_eval:
                logger.info('''*** Evaluate ***'''  )
  
                _snake_case      :							Tuple     				=						trainer.predict(lowercase_  )
                _snake_case      :							Tuple     				=						output.predictions
                _snake_case      :							Optional[int]     				=						np.argmax(lowercase_      ,		axis=1  )
  
                _snake_case      :							List[Any]     				=						[ex.pairID for ex in eval_dataset]
                _snake_case      :							Tuple     				=						os.path.join(training_args.output_dir      ,		'''hans_predictions.txt'''  )
                _snake_case      :							Union[str, Any]     				=						eval_dataset.get_labels()
                if trainer.is_world_master():
                       with open(lowercase_      ,		'''w'''  ) as writer:
                              writer.write('''pairID,gold_label\n'''  )
                              for pid, pred in zip(lowercase_      ,		lowercase_  ):
                                     writer.write('''ex''' + str(lowercase_  ) + ''',''' + label_list[int(lowercase_  )] + '''\n'''  )
  
                trainer._log(output.metrics  )
  
  
  
  
  def 						A_			(  lowercase_  )			->						List[Any]:
         main()
  
  
  if __name__ == "__main__":
     main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import cva
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
  
         try:
                return int(image[x_coordinate][y_coordinate] >= center  )
         except (IndexError, TypeError):
                return 0
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
         _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
         _snake_case      :							List[str]     				=						[1, 2, 4, 8, 16, 32, 64, 128]
  
         # skip get_neighbors_pixel if center is null
         if center is None:
                return 0
  
         # Starting from the top right, assigning value to pixels clockwise
         _snake_case      :							Any     				=						[
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
         ]
  
         # Converting the binary value to decimal.
         return sum(
             binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )
  
  
  if __name__ == "__main__":
     # Reading the image and converting it to grayscale.
     lowerCAmelCase_   =   cva.imread(
         "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
     )
  
     # Create a numpy array as the same height and width of read image
     lowerCAmelCase_   =   np.zeros((image.shape[0], image.shape[1]))
  
     # Iterating through the image and calculating the
     # local binary pattern value for each pixel.
     for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
           lowerCAmelCase_   =   local_binary_value(image, i, j)
  
     cva.imshow("local binary pattern", lbp_image)
     cva.waitKey(0)
     cva.destroyAllWindows()
  
  
  
  
  
  
  