




'''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


lowercase	:      List[Any]				=       logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowercase	:      Optional[int]				=       {
    '''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
lowercase	:      Any				=       {
    '''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
}
lowercase	:      Any				=       sorted(arg_to_scheduler.keys())
lowercase	:      Tuple				=       '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''

class   SCREAMING_SNAKE_CASE_		(       pl.LightningModule				):



 def __init__(  self   ,		A   ,		A=None   ,		A="base"   ,		A=None   ,		A=None   ,		A=None   ,		**A   ,		):






        '''simple docstring'''




        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.save_hyperparameters(A )
        lowercase  :		Optional[Any]						=      0
        lowercase  :		Tuple						=      Path(self.hparams.output_dir )
        lowercase  :		Tuple						=      self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
               lowercase  :		List[str]						=      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=A   ,		**A   ,		)
        else:
               lowercase  :		PretrainedConfig						=      config

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

        if tokenizer is None:
               lowercase  :		List[str]						=      AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path   ,		cache_dir=A   ,		)
        else:
               lowercase  :		PreTrainedTokenizer						=      tokenizer
        lowercase  :		Optional[Any]						=      MODEL_MODES[mode]
        if model is None:
               lowercase  :		List[Any]						=      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=A   ,		)
        else:
               lowercase  :		List[str]						=      model



 def    SCREAMING_SNAKE_CASE(  self   ,		*A   ,		**A ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      self.model_type.from_pretrained(*A   ,		**A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      arg_to_scheduler[self.hparams.lr_scheduler]
        lowercase  :		List[Any]						=      get_schedule_func(
            self.opt   ,		num_warmup_steps=self.hparams.warmup_steps   ,		num_training_steps=self.total_steps() )
        lowercase  :		Tuple						=      {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
        return scheduler



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      self.model
        lowercase  :		Any						=      ['''bias''', '''LayerNorm.weight''']
        lowercase  :		Union[str, Any]						=      [
            {
                '''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:
               lowercase  :		str						=      Adafactor(
                   A   ,		lr=self.hparams.learning_rate   ,		scale_parameter=A   ,		relative_step=A )

        else:
               lowercase  :		str						=      AdamW(
                   A   ,		lr=self.hparams.learning_rate   ,		eps=self.hparams.adam_epsilon )
        lowercase  :		int						=      optimizer

        lowercase  :		Optional[Any]						=      self.get_lr_scheduler()

        return [optimizer], [scheduler]



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




        return self.validation_step(A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        return self.validation_end(A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Tuple						=      max(1   ,		self.hparams.gpus )  # TODO: consider num_tpu_cores
        lowercase  :		Optional[Any]						=      self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
        return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        if stage == "test":
               lowercase  :		Tuple						=      len(self.test_dataloader().dataset )
        else:
               lowercase  :		Tuple						=      self.get_dataloader('''train'''   ,		self.hparams.train_batch_size   ,		shuffle=A )
               lowercase  :		List[str]						=      len(self.train_dataloader().dataset )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A = False ):






        '''simple docstring'''




        raise NotImplementedError('''You must implement this for your task''' )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return self.train_loader



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return self.get_dataloader('''dev'''   ,		self.hparams.eval_batch_size   ,		shuffle=A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return self.get_dataloader('''test'''   ,		self.hparams.eval_batch_size   ,		shuffle=A )



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        return os.path.join(
            self.hparams.data_dir   ,		'''cached_{}_{}_{}'''.format(
                A   ,		list(filter(A   ,		self.hparams.model_name_or_path.split('''/''' ) ) ).pop()   ,		str(self.hparams.max_seq_length )   ,		)   ,		)



 @pl.utilities.rank_zero_only
 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self.output_dir.joinpath('''best_tfmr''' )
        lowercase  :		Optional[int]						=      self.step_count
        self.model.save_pretrained(A )
        self.tokenizer.save_pretrained(A )





 @staticmethod
 def    SCREAMING_SNAKE_CASE(  A   ,		A ):






        '''simple docstring'''




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






class   SCREAMING_SNAKE_CASE_		(       pl.Callback				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




        if (
            trainer.is_global_zero and trainer.global_rank == 0
        ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
               pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.






class   SCREAMING_SNAKE_CASE_		(       pl.Callback				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		Any						=      trainer.lr_schedulers[0]['''scheduler''']
        lowercase  :		Tuple						=      {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr() )}
        pl_module.logger.log_metrics(A )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




        rank_zero_info('''***** Validation results *****''' )
        lowercase  :		List[Any]						=      trainer.callback_metrics
        # Log results
        for key in sorted(A ):
               if key not in ["log", "progress_bar"]:
                      rank_zero_info('''{} = {}\n'''.format(A   ,		str(metrics[key] ) ) )





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




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






def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->None:






       '''simple docstring'''




       parser.add_argument(
           '''--output_dir'''   ,				default=lowerCamelCase   ,				type=lowerCamelCase   ,				required=lowerCamelCase   ,				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=lowerCamelCase   ,				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=lowerCamelCase      )
       parser.add_argument('''--max_grad_norm'''   ,				dest='''gradient_clip_val'''   ,				default=1.0   ,				type=lowerCamelCase   ,				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=lowerCamelCase   ,				default=1   ,				help='''Number of updates steps to accumulate before performing a backward/update pass.'''   ,				)
       parser.add_argument('''--seed'''   ,				type=lowerCamelCase   ,				default=42   ,				help='''random seed for initialization'''      )
       parser.add_argument(
           '''--data_dir'''   ,				default=lowerCamelCase   ,				type=lowerCamelCase   ,				required=lowerCamelCase   ,				help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''   ,				)





def 		a_( lowerCamelCase   ,				lowerCamelCase   ,				lowerCamelCase=None   ,				lowerCamelCase=True   ,				lowerCamelCase=None   ,				lowerCamelCase=[]   ,				lowerCamelCase=None   ,				lowerCamelCase=None   ,				**lowerCamelCase   ,				)				->Dict:






       '''simple docstring'''




       pl.seed_everything(args.seed      )

       # init model
       lowercase  :		List[Any]						=      Path(model.hparams.output_dir      )
       odir.mkdir(exist_ok=lowerCamelCase      )

       # add custom checkpoints
       if checkpoint_callback is None:
              lowercase  :		Any						=      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(lowerCamelCase      )
       if logging_callback is None:
              lowercase  :		List[str]						=      LoggingCallback()

       lowercase  :		int						=      {}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              lowercase  :		Optional[Any]						=      16
              # train_params["amp_level"] = args.fp16_opt_level

       if args.gpus > 1:
              lowercase  :		Tuple						=      '''auto'''  # "ddp"
              lowercase  :		List[Any]						=      '''ddp'''

       lowercase  :		str						=      args.accumulate_grad_batches
       lowercase  :		Optional[Any]						=      None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
       lowercase  :		Tuple						=      '''auto'''

       lowercase  :		Optional[Any]						=      pl.Trainer.from_argparse_args(
           lowerCamelCase   ,				weights_summary=lowerCamelCase   ,				callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]   ,				logger=lowerCamelCase   ,				**lowerCamelCase   ,				)

       if args.do_train:
              trainer.fit(lowerCamelCase      )

       return trainer











'''simple docstring'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




