






"""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__       :			Union[str, Any]=						logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

A__       :			Union[str, Any]=						{
    """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__       :			Optional[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
}
A__       :			Optional[int]=						sorted(arg_to_scheduler.keys())
A__       :			Any=						"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""
class  __lowerCamelCase     (	pl.LightningModule	):

   def __init__(       self			,					snake_case_			,					snake_case_=None			,					snake_case_="base"			,					snake_case_=None			,					snake_case_=None			,					snake_case_=None			,					**snake_case_			,					)		->							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(snake_case_   )
       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=snake_case_			,					**snake_case_			,					)
       else:
           UpperCamelCase__								=     config

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

       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=snake_case_			,					)
       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=snake_case_			,					)
       else:
           UpperCamelCase__								=     model

   def     SCREAMING_SNAKE_CASE__			(       self			,					*snake_case_			,					**snake_case_   )		->							Optional[int]:
       UpperCamelCase__								=     self.model_type.from_pretrained(*snake_case_			,					**snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       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(
               snake_case_			,					lr=self.hparams.learning_rate			,					scale_parameter=snake_case_			,					relative_step=snake_case_   )

       else:
           UpperCamelCase__								=     AdamW(
               snake_case_			,					lr=self.hparams.learning_rate			,					eps=self.hparams.adam_epsilon   )
       UpperCamelCase__								=     optimizer

       UpperCamelCase__								=     self.get_lr_scheduler()

       return [optimizer], [scheduler]

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							Dict:
       return self.validation_step(snake_case_			,					snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Dict:
       return self.validation_end(snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       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     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							Union[str, Any]:
       if stage == "test":
           UpperCamelCase__								=     len(self.test_dataloader().dataset   )
       else:
           UpperCamelCase__								=     self.get_dataloader('train'			,					self.hparams.train_batch_size			,					shuffle=snake_case_   )
           UpperCamelCase__								=     len(self.train_dataloader().dataset   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_ = False   )		->							Optional[Any]:
       raise NotImplementedError('You must implement this for your task'   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       return self.train_loader

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       return self.get_dataloader('dev'			,					self.hparams.eval_batch_size			,					shuffle=snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       return self.get_dataloader('test'			,					self.hparams.eval_batch_size			,					shuffle=snake_case_   )

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

   @pl.utilities.rank_zero_only
   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_   )		->							None:
       UpperCamelCase__								=     self.output_dir.joinpath('best_tfmr'   )
       UpperCamelCase__								=     self.step_count
       self.model.save_pretrained(snake_case_   )
       self.tokenizer.save_pretrained(snake_case_   )







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







class  __lowerCamelCase     (	pl.Callback	):

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							Union[str, Any]:
       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  __lowerCamelCase     (	pl.Callback	):

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							List[Any]:
       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(snake_case_   )

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







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_   )		->							Optional[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(snake_case_			,					'w'   ) as writer:
           for key in sorted(snake_case_   ):
               if key not in ["log", "progress_bar"]:
                   rank_zero_info('{} = {}\n'.format(snake_case_			,					str(metrics[key]   )   )   )
                   writer.write('{} = {}\n'.format(snake_case_			,					str(metrics[key]   )   )   )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						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 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=None			,						SCREAMING_SNAKE_CASE=True			,						SCREAMING_SNAKE_CASE=None			,						SCREAMING_SNAKE_CASE=[]			,						SCREAMING_SNAKE_CASE=None			,						SCREAMING_SNAKE_CASE=None			,						**SCREAMING_SNAKE_CASE			,						)       ->						Union[str, Any]:
    """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
        # train_params["amp_level"] = args.fp16_opt_level

    if args.gpus > 1:
        UpperCamelCase__								=     'auto'  # "ddp"
        UpperCamelCase__								=     'ddp'

    UpperCamelCase__								=     args.accumulate_grad_batches
    UpperCamelCase__								=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
    UpperCamelCase__								=     'auto'

    UpperCamelCase__								=     pl.Trainer.from_argparse_args(
        SCREAMING_SNAKE_CASE			,						weights_summary=SCREAMING_SNAKE_CASE			,						callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]			,						logger=SCREAMING_SNAKE_CASE			,						**SCREAMING_SNAKE_CASE			,						)

    if args.do_train:
        trainer.fit(SCREAMING_SNAKE_CASE       )

    return trainer












"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__       :			str=						[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    return [int(SCREAMING_SNAKE_CASE       ) for c in F'{ruleset:08}'[:8]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

    UpperCamelCase__								=     len(cells[0]       )  # 31
    UpperCamelCase__								=     []
    for i in range(SCREAMING_SNAKE_CASE       ):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        UpperCamelCase__								=     0 if i == 0 else cells[time][i - 1]
        UpperCamelCase__								=     0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        UpperCamelCase__								=     7 - int(F'{left_neighbor}{cells[time][i]}{right_neighbor}'			,						2       )
        next_generation.append(rule[situation]       )
    return next_generation







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Image.Image:
    """simple docstring"""

    UpperCamelCase__								=     Image.new('RGB'			,						(len(cells[0]       ), len(SCREAMING_SNAKE_CASE       ))       )
    UpperCamelCase__								=     img.load()
    # Generates image
    for w in range(img.width       ):
        for h in range(img.height       ):
            UpperCamelCase__								=     2_55 - int(2_55 * cells[h][w]       )
            UpperCamelCase__								=     (color, color, color)
    return img


if __name__ == "__main__":
  A__       :			Optional[int]=						bin(int(input("""Rule:\n""").strip()))[2:]
  A__       :			str=						format_ruleset(int(rule_num))
  for time in range(16):
    CELLS.append(new_generation(CELLS, rule, time))
  A__       :			Tuple=						generate_image(CELLS)
  # Uncomment to save the image
  # img.save(f"rule_{rule_num}.png")
  img.show()




