


"""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


__snake_case												=							logging.getLogger(__name__)

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

__snake_case												=							{
    '''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
__snake_case												=							{
    '''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
}
__snake_case												=							sorted(arg_to_scheduler.keys())
__snake_case												=							'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class      __lowerCamelCase	(       pl.LightningModule					):

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase="base"     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					)							->				List[Any]:
          super().__init__()
          # TODO: move to self.save_hyperparameters()
          # self.save_hyperparameters()
          # can also expand arguments into trainer signature for easier reading

          self.save_hyperparameters(__UpperCAmelCase				)
          _a     						=					0
          _a     						=					Path(self.hparams.output_dir				)
          _a     						=					self.hparams.cache_dir if self.hparams.cache_dir else None
          if config is None:
               _a     						=					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=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
          else:
               _a     						=					config

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

          if tokenizer is None:
               _a     						=					AutoTokenizer.from_pretrained(
                   self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path     ,					cache_dir=__UpperCAmelCase     ,					)
          else:
               _a     						=					tokenizer
          _a     						=					MODEL_MODES[mode]
          if model is None:
               _a     						=					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=__UpperCAmelCase     ,					)
          else:
               _a     						=					model


     def 				_UpperCAmelCase   (   self     ,					*__UpperCAmelCase     ,					**__UpperCAmelCase				)							->				Optional[Any]:
          _a     						=					self.model_type.from_pretrained(*__UpperCAmelCase     ,					**__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Union[str, Any]:
          _a     						=					arg_to_scheduler[self.hparams.lr_scheduler]
          _a     						=					get_schedule_func(
              self.opt     ,					num_warmup_steps=self.hparams.warmup_steps     ,					num_training_steps=self.total_steps()				)
          _a     						=					{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
          return scheduler


     def 				_UpperCAmelCase   (   self				)							->				List[Any]:
          _a     						=					self.model
          _a     						=					['''bias''', '''LayerNorm.weight''']
          _a     						=					[
              {
                  '''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:
               _a     						=					Adafactor(
                   __UpperCAmelCase     ,					lr=self.hparams.learning_rate     ,					scale_parameter=__UpperCAmelCase     ,					relative_step=__UpperCAmelCase				)

          else:
               _a     						=					AdamW(
                   __UpperCAmelCase     ,					lr=self.hparams.learning_rate     ,					eps=self.hparams.adam_epsilon				)
          _a     						=					optimizer

          _a     						=					self.get_lr_scheduler()

          return [optimizer], [scheduler]


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Tuple:
          return self.validation_step(__UpperCAmelCase     ,					__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				List[str]:
          return self.validation_end(__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					max(1     ,					self.hparams.gpus				)  # TODO: consider num_tpu_cores
          _a     						=					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
          return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				Dict:
          if mode == "test":
               _a     						=					len(self.test_dataloader().dataset				)
          else:
               _a     						=					self.get_dataloader('''train'''     ,					self.hparams.train_batch_size     ,					shuffle=__UpperCAmelCase				)
               _a     						=					len(self.train_dataloader().dataset				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False				)							->				Dict:
          raise NotImplementedError('''You must implement this for your task'''				)


     def 				_UpperCAmelCase   (   self				)							->				Any:
          return self.train_loader


     def 				_UpperCAmelCase   (   self				)							->				Any:
          return self.get_dataloader('''dev'''     ,					self.hparams.eval_batch_size     ,					shuffle=__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          return self.get_dataloader('''test'''     ,					self.hparams.eval_batch_size     ,					shuffle=__UpperCAmelCase				)


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


     @pl.utilities.rank_zero_only
     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				None:
          _a     						=					self.output_dir.joinpath('''best_tfmr'''				)
          _a     						=					self.step_count
          self.model.save_pretrained(__UpperCAmelCase				)
          self.tokenizer.save_pretrained(__UpperCAmelCase				)





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







class      __lowerCamelCase	(       pl.Callback					):

     '''simple docstring'''


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Dict:
          _a     						=					trainer.lr_schedulers[0]['''scheduler''']
          _a     						=					{F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()				)}
          pl_module.logger.log_metrics(__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				int:
          rank_zero_info('''***** Validation results *****'''				)
          _a     						=					trainer.callback_metrics
          # Log results
          for key in sorted(__UpperCAmelCase				):
               if key not in ["log", "progress_bar"]:
                    rank_zero_info('''{} = {}\n'''.format(__UpperCAmelCase     ,					str(metrics[key]				)				)				)





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







def    A_ ( _lowerCAmelCase  :       List[str],  _lowerCAmelCase  :       List[str]							):






     """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  :       BaseTransformer,  _lowerCAmelCase  :       argparse.Namespace,  _lowerCAmelCase  :       List[Any]=None,  _lowerCAmelCase  :       str=True,  _lowerCAmelCase  :       Union[str, Any]=[],  _lowerCAmelCase  :       Optional[int]=None,  _lowerCAmelCase  :       int=None,  **_lowerCAmelCase  :       Any,  ):






     """simple docstring"""

     pl.seed_everything(args.seed							)

     # init model
     _a     						=					Path(model.hparams.output_dir							)
     odir.mkdir(exist_ok=_lowerCAmelCase							)

     # add custom checkpoints
     if checkpoint_callback is None:
          _a     						=					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:
          _a     						=					LoggingCallback()

     _a     						=					{}

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

     if args.gpus > 1:
          _a     						=					'''ddp'''

     _a     						=					args.accumulate_grad_batches
     _a     						=					extra_train_kwargs.get('''accelerator''',  _lowerCAmelCase							)
     _a     						=					extra_train_kwargs.get('''profiler''',  _lowerCAmelCase							)

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

     if args.do_train:
          trainer.fit(_lowerCAmelCase							)

     return trainer



"""simple docstring"""






import math
import os

import cva
import numpy as np

__snake_case												=							255.0
def    A_ ( _lowerCAmelCase  :       float,  _lowerCAmelCase  :       float							):






     """simple docstring"""

     _a     						=					np.mean((original - contrast) ** 2							)
     if mse == 0:
          return 1_00

     return 20 * math.logaa(PIXEL_MAX / math.sqrt(_lowerCAmelCase							)							)
def    A_ ( ):






     """simple docstring"""

     _a     						=					os.path.dirname(os.path.realpath(_lowerCAmelCase							)							)
     # Loading images (original image and compressed image)
     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/original_image.png'''							)							)
     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/compressed_image.png'''							),  1							)

     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/PSNR-example-base.png'''							)							)
     _a     						=					cva.imread(
         os.path.join(_lowerCAmelCase,  '''image_data/PSNR-example-comp-10.jpg'''							),  1							)

     # Value expected: 29.73dB
     print('''-- First Test --'''							)
     print(f'PSNR value is {peak_signal_to_noise_ratio(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)

     # # Value expected: 31.53dB (Wikipedia Example)
     print('''\n-- Second Test --'''							)
     print(f'PSNR value is {peak_signal_to_noise_ratio(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)


if __name__ == "__main__":
   main()