





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()













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()






