




"""simple docstring"""




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a_										=       logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

a_										=       {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a_										=       {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a_										=       sorted(arg_to_scheduler.keys())
a_										=       "{" + ", ".join(arg_to_scheduler_choices) + "}"

class 	_lowerCamelCase  (     pl.LightningModule				):

      """simple docstring"""

      def __init__(       self   :	Optional[Any]	,    snake_case   :	Optional[Any]	,    snake_case   :	Dict=None	,    snake_case   :	Tuple="base"	,    snake_case   :	Tuple=None	,    snake_case   :	Optional[int]=None	,    snake_case   :	Optional[int]=None	,    **snake_case   :	Optional[int]	,    ):
          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(lowercase__			)
          __UpperCamelCase											=  0
          __UpperCamelCase											=  Path(self.hparams.output_dir			)
          __UpperCamelCase											=  self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
              __UpperCamelCase											=  AutoConfig.from_pretrained(
                  self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path	,    **({'''num_labels''': num_labels} if num_labels is not None else {})	,    cache_dir=lowercase__	,    **lowercase__	,    )
          else:
              __UpperCamelCase											=  config

          __UpperCamelCase											=  ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
          for p in extra_model_params:
              if getattr(self.hparams	,    lowercase__	,    lowercase__			):
                  assert hasattr(self.config	,    lowercase__			), F"model config doesn't have a `{p}` attribute"
                  setattr(self.config	,    lowercase__	,    getattr(self.hparams	,    lowercase__			)			)

          if tokenizer is None:
              __UpperCamelCase											=  AutoTokenizer.from_pretrained(
                  self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,    cache_dir=lowercase__	,    )
          else:
              __UpperCamelCase											=  tokenizer
          __UpperCamelCase											=  MODEL_MODES[mode]
          if model is None:
              __UpperCamelCase											=  self.model_type.from_pretrained(
                  self.hparams.model_name_or_path	,    from_tf=bool('''.ckpt''' in self.hparams.model_name_or_path			)	,    config=self.config	,    cache_dir=lowercase__	,    )
          else:
              __UpperCamelCase											=  model







      def  snake_case		(       self   :	List[Any]	,    *snake_case   :	Optional[int]	,    **snake_case   :	Optional[Any]			):
          __UpperCamelCase											=  self.model_type.from_pretrained(*lowercase__	,    **lowercase__			)







      def  snake_case		(       self   :	Optional[int]			):
          __UpperCamelCase											=  arg_to_scheduler[self.hparams.lr_scheduler]
          __UpperCamelCase											=  get_schedule_func(
              self.opt	,    num_warmup_steps=self.hparams.warmup_steps	,    num_training_steps=self.total_steps()			)
          __UpperCamelCase											=  {"scheduler": scheduler, "interval": "step", "frequency": 1}
          return scheduler







      def  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  self.model
          __UpperCamelCase											=  ["bias", "LayerNorm.weight"]
          __UpperCamelCase											=  [
              {
                  "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)],
                  "weight_decay": self.hparams.weight_decay,
              },
              {
                  "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay			)],
                  "weight_decay": 0.0,
              },
          ]
          if self.hparams.adafactor:
              __UpperCamelCase											=  Adafactor(
                  lowercase__	,    lr=self.hparams.learning_rate	,    scale_parameter=lowercase__	,    relative_step=lowercase__			)

          else:
              __UpperCamelCase											=  AdamW(
                  lowercase__	,    lr=self.hparams.learning_rate	,    eps=self.hparams.adam_epsilon			)
          __UpperCamelCase											=  optimizer

          __UpperCamelCase											=  self.get_lr_scheduler()

          return [optimizer], [scheduler]







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	Dict	,    snake_case   :	str			):
          return self.validation_step(lowercase__	,    lowercase__			)







      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	List[Any]			):
          return self.validation_end(lowercase__			)







      def  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  max(1	,    self.hparams.gpus			)  # TODO: consider num_tpu_cores
          __UpperCamelCase											=  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs







      def  snake_case		(       self   :	Optional[Any]	,    snake_case   :	Union[str, Any]			):






          if mode == "test":
              __UpperCamelCase											=  len(self.test_dataloader().dataset			)
          else:
              __UpperCamelCase											=  self.get_dataloader('''train'''	,    self.hparams.train_batch_size	,    shuffle=lowercase__			)
              __UpperCamelCase											=  len(self.train_dataloader().dataset			)







      def  snake_case		(       self   :	Any	,    snake_case   :	Union[str, Any]	,    snake_case   :	Optional[Any]	,    snake_case   :	Optional[int] = False			):
          raise NotImplementedError('''You must implement this for your task'''			)







      def  snake_case		(       self   :	Tuple			):
          return self.train_loader







      def  snake_case		(       self   :	Tuple			):
          return self.get_dataloader('''dev'''	,    self.hparams.eval_batch_size	,    shuffle=lowercase__			)







      def  snake_case		(       self   :	Tuple			):
          return self.get_dataloader('''test'''	,    self.hparams.eval_batch_size	,    shuffle=lowercase__			)







      def  snake_case		(       self   :	int	,    snake_case   :	Tuple			):
          return os.path.join(
              self.hparams.data_dir	,    '''cached_{}_{}_{}'''.format(
                  lowercase__	,    list(filter(lowercase__	,    self.hparams.model_name_or_path.split('''/'''			)			)			).pop()	,    str(self.hparams.max_seq_length			)	,    )	,    )







      @pl.utilities.rank_zero_only
      def  snake_case		(       self   :	List[str]	,    snake_case   :	str			):
          __UpperCamelCase											=  self.output_dir.joinpath('''best_tfmr'''			)
          __UpperCamelCase											=  self.step_count
          self.model.save_pretrained(lowercase__			)
          self.tokenizer.save_pretrained(lowercase__			)







      @staticmethod
      def  snake_case		(       snake_case   :	Dict	,    snake_case   :	str			):
          parser.add_argument(
              '''--model_name_or_path'''	,    default=lowercase__	,    type=lowercase__	,    required=lowercase__	,    help='''Path to pretrained model or model identifier from huggingface.co/models'''	,    )
          parser.add_argument(
              '''--config_name'''	,    default=''''''	,    type=lowercase__	,    help='''Pretrained config name or path if not the same as model_name'''			)
          parser.add_argument(
              '''--tokenizer_name'''	,    default=lowercase__	,    type=lowercase__	,    help='''Pretrained tokenizer name or path if not the same as model_name'''	,    )
          parser.add_argument(
              '''--cache_dir'''	,    default=''''''	,    type=lowercase__	,    help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''	,    )
          parser.add_argument(
              '''--encoder_layerdrop'''	,    type=lowercase__	,    help='''Encoder layer dropout probability (Optional). Goes into model.config'''	,    )
          parser.add_argument(
              '''--decoder_layerdrop'''	,    type=lowercase__	,    help='''Decoder layer dropout probability (Optional). Goes into model.config'''	,    )
          parser.add_argument(
              '''--dropout'''	,    type=lowercase__	,    help='''Dropout probability (Optional). Goes into model.config'''	,    )
          parser.add_argument(
              '''--attention_dropout'''	,    type=lowercase__	,    help='''Attention dropout probability (Optional). Goes into model.config'''	,    )
          parser.add_argument('''--learning_rate'''	,    default=5E-5	,    type=lowercase__	,    help='''The initial learning rate for Adam.'''			)
          parser.add_argument(
              '''--lr_scheduler'''	,    default='''linear'''	,    choices=lowercase__	,    metavar=lowercase__	,    type=lowercase__	,    help='''Learning rate scheduler'''	,    )
          parser.add_argument('''--weight_decay'''	,    default=0.0	,    type=lowercase__	,    help='''Weight decay if we apply some.'''			)
          parser.add_argument('''--adam_epsilon'''	,    default=1E-8	,    type=lowercase__	,    help='''Epsilon for Adam optimizer.'''			)
          parser.add_argument('''--warmup_steps'''	,    default=0	,    type=lowercase__	,    help='''Linear warmup over warmup_steps.'''			)
          parser.add_argument('''--num_workers'''	,    default=4	,    type=lowercase__	,    help='''kwarg passed to DataLoader'''			)
          parser.add_argument('''--num_train_epochs'''	,    dest='''max_epochs'''	,    default=3	,    type=lowercase__			)
          parser.add_argument('''--train_batch_size'''	,    default=32	,    type=lowercase__			)
          parser.add_argument('''--eval_batch_size'''	,    default=32	,    type=lowercase__			)
          parser.add_argument('''--adafactor'''	,    action='''store_true'''			)







class 	_lowerCamelCase  (     pl.Callback				):

      """simple docstring"""

      def  snake_case		(       self   :	Union[str, Any]	,    snake_case   :	Optional[int]	,    snake_case   :	Optional[int]			):
          __UpperCamelCase											=  trainer.lr_schedulers[0]["scheduler"]
          __UpperCamelCase											=  {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
          pl_module.logger.log_metrics(lowercase__			)







      def  snake_case		(       self   :	Optional[Any]	,    snake_case   :	str	,    snake_case   :	Optional[int]			):
          rank_zero_info('''***** Validation results *****'''			)
          __UpperCamelCase											=  trainer.callback_metrics
          # Log results
          for key in sorted(lowercase__			):
              if key not in ["log", "progress_bar"]:
                  rank_zero_info('''{} = {}\n'''.format(lowercase__	,    str(metrics[key]			)			)			)







      def  snake_case		(       self   :	List[str]	,    snake_case   :	Dict	,    snake_case   :	List[Any]			):
          rank_zero_info('''***** Test results *****'''			)
          __UpperCamelCase											=  trainer.callback_metrics
          # Log and save results to file
          __UpperCamelCase											=  os.path.join(pl_module.hparams.output_dir	,    '''test_results.txt'''			)
          with open(lowercase__	,    '''w'''			) as writer:
              for key in sorted(lowercase__			):
                  if key not in ["log", "progress_bar"]:
                      rank_zero_info('''{} = {}\n'''.format(lowercase__	,    str(metrics[key]			)			)			)
                      writer.write('''{} = {}\n'''.format(lowercase__	,    str(metrics[key]			)			)			)







def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		None:

    """simple docstring"""


    parser.add_argument(
        '''--output_dir'''   ,       default=SCREAMING_SNAKE_CASE_   ,       type=SCREAMING_SNAKE_CASE_   ,       required=SCREAMING_SNAKE_CASE_   ,       help='''The output directory where the model predictions and checkpoints will be written.'''   ,       )
    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=SCREAMING_SNAKE_CASE_   ,       default='''O2'''   ,       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_tpu_cores'''   ,       dest='''tpu_cores'''   ,       type=SCREAMING_SNAKE_CASE_ )
    parser.add_argument('''--max_grad_norm'''   ,       dest='''gradient_clip_val'''   ,       default=1.0   ,       type=SCREAMING_SNAKE_CASE_   ,       help='''Max gradient norm''' )
    parser.add_argument('''--do_train'''   ,       action='''store_true'''   ,       help='''Whether to run training.''' )
    parser.add_argument('''--do_predict'''   ,       action='''store_true'''   ,       help='''Whether to run predictions on the test set.''' )
    parser.add_argument(
        '''--gradient_accumulation_steps'''   ,       dest='''accumulate_grad_batches'''   ,       type=SCREAMING_SNAKE_CASE_   ,       default=1   ,       help='''Number of updates steps to accumulate before performing a backward/update pass.'''   ,       )
    parser.add_argument('''--seed'''   ,       type=SCREAMING_SNAKE_CASE_   ,       default=42   ,       help='''random seed for initialization''' )
    parser.add_argument(
        '''--data_dir'''   ,       default=SCREAMING_SNAKE_CASE_   ,       type=SCREAMING_SNAKE_CASE_   ,       required=SCREAMING_SNAKE_CASE_   ,       help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''   ,       )




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_=None   ,       lowercase_=True   ,       lowercase_=[]   ,       lowercase_=None   ,       lowercase_=None   ,       **lowercase_   ,       )    ->		Optional[int]:

    """simple docstring"""


    pl.seed_everything(args.seed )

    # init model
    __UpperCamelCase											=  Path(model.hparams.output_dir )
    odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )

    # add custom checkpoints
    if checkpoint_callback is None:
        __UpperCamelCase											=  pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir   ,       prefix='''checkpoint'''   ,       monitor='''val_loss'''   ,       mode='''min'''   ,       save_top_k=1 )
    if early_stopping_callback:
        extra_callbacks.append(SCREAMING_SNAKE_CASE_ )
    if logging_callback is None:
        __UpperCamelCase											=  LoggingCallback()

    __UpperCamelCase											=  {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fpaa:
        __UpperCamelCase											=  16
        __UpperCamelCase											=  args.fpaa_opt_level

    if args.gpus > 1:
        __UpperCamelCase											=  "ddp"

    __UpperCamelCase											=  args.accumulate_grad_batches
    __UpperCamelCase											=  extra_train_kwargs.get('''accelerator'''   ,       SCREAMING_SNAKE_CASE_ )
    __UpperCamelCase											=  extra_train_kwargs.get('''profiler'''   ,       SCREAMING_SNAKE_CASE_ )

    __UpperCamelCase											=  pl.Trainer.from_argparse_args(
        SCREAMING_SNAKE_CASE_   ,       weights_summary=SCREAMING_SNAKE_CASE_   ,       callbacks=[logging_callback] + extra_callbacks   ,       logger=SCREAMING_SNAKE_CASE_   ,       checkpoint_callback=SCREAMING_SNAKE_CASE_   ,       **SCREAMING_SNAKE_CASE_   ,       )

    if args.do_train:
        trainer.fit(SCREAMING_SNAKE_CASE_ )

    return trainer












import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center )
    except (IndexError, TypeError):
        return 0




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [1, 2, 4, 8, 16, 32, 64, 1_28]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    __UpperCamelCase											=  [
        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.
 a_										=       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
 a_										=       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]):
   a_										=       local_binary_value(image, i, j)

 cva.imshow("local binary pattern", lbp_image)
 cva.waitKey(0)
 cva.destroyAllWindows()






