




"""simple docstring"""





_lowercase	      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]

class       __lowercase       :


				'''simple docstring'''


				def __init__(   self   :     Dict      ,SCREAMING_SNAKE_CASE_   :     Tuple      ):
										snake_case__										=					name
										snake_case__										=					[]
										snake_case__										=					[]

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ):
										self.inbound.append(SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ):
										self.outbound.append(SCREAMING_SNAKE_CASE_      )





				def __repr__(   self   :     Dict      ):
										return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''







def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase=3      ,			lowerCamelCase=0.85		):
						'''simple docstring'''


						snake_case__										=					{}
						for node in nodes:
												snake_case__										=					1

						snake_case__										=					{}
						for node in nodes:
												snake_case__										=					len(node.outbound		)

						for i in range(lowerCamelCase		):
												print(f'''======= Iteration {i + 1} ======='''		)
												for _, node in enumerate(lowerCamelCase		):
																		snake_case__										=					(1 - d) + d * sum(
																		    ranks[ib] / outbounds[ib] for ib in node.inbound		)
												print(lowerCamelCase		)

def 		lowerCAmelCase							( ):
						'''simple docstring'''


						snake_case__										=					list(input('''Enter Names of the Nodes: '''		).split()		)

						snake_case__										=					[Node(lowerCamelCase		) for name in names]

						for ri, row in enumerate(lowerCamelCase		):
												for ci, col in enumerate(lowerCamelCase		):
																		if col == 1:
																								nodes[ci].add_inbound(names[ri]		)
																								nodes[ri].add_outbound(names[ci]		)

						print('''======= Nodes ======='''		)
						for node in nodes:
												print(lowerCamelCase		)

						page_rank(lowerCamelCase		)


if __name__ == "__main__":
							main()








"""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	      =    logging.getLogger(__name__)

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

_lowercase	      =    {
    """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	      =    {
    """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	      =    sorted(arg_to_scheduler.keys())
_lowercase	      =    """{""" + """, """.join(arg_to_scheduler_choices) + """}"""

class       __lowercase       (			pl.LightningModule	):


				'''simple docstring'''


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

										self.save_hyperparameters(SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Any										=					0
										snake_case__       :					int										=					Path(self.hparams.output_dir      )
										snake_case__       :					Any										=					self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																snake_case__       :					Tuple										=					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=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
										else:
																snake_case__       :					PretrainedConfig										=					config

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

										if tokenizer is None:
																snake_case__       :					Optional[int]										=					AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,cache_dir=SCREAMING_SNAKE_CASE_      ,)
										else:
																snake_case__       :					PreTrainedTokenizer										=					tokenizer
										snake_case__       :					Union[str, Any]										=					MODEL_MODES[mode]
										if model is None:
																snake_case__       :					str										=					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=SCREAMING_SNAKE_CASE_      ,)
										else:
																snake_case__       :					Union[str, Any]										=					model

				def       _lowerCamelCase					(   self   :     Any      ,*SCREAMING_SNAKE_CASE_   :     int      ,**SCREAMING_SNAKE_CASE_   :     str      ):
										snake_case__       :					Any										=					self.model_type.from_pretrained(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      )

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

				def       _lowerCamelCase					(   self   :     Any      ):
										snake_case__       :					Tuple										=					self.model
										snake_case__       :					Optional[int]										=					['''bias''', '''LayerNorm.weight''']
										snake_case__       :					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:
																snake_case__       :					str										=					Adafactor(
																    SCREAMING_SNAKE_CASE_      ,lr=self.hparams.learning_rate      ,scale_parameter=SCREAMING_SNAKE_CASE_      ,relative_step=SCREAMING_SNAKE_CASE_      )

										else:
																snake_case__       :					List[str]										=					AdamW(
																    SCREAMING_SNAKE_CASE_      ,lr=self.hparams.learning_rate      ,eps=self.hparams.adam_epsilon      )
										snake_case__       :					str										=					optimizer

										snake_case__       :					List[str]										=					self.get_lr_scheduler()

										return [optimizer], [scheduler]

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     Any      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
										return self.validation_step(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[int]      ):
										return self.validation_end(SCREAMING_SNAKE_CASE_      )

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

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     List[Any]      ):
										if mode == "test":
																snake_case__       :					Dict										=					len(self.test_dataloader().dataset      )
										else:
																snake_case__       :					Optional[Any]										=					self.get_dataloader('''train'''      ,self.hparams.train_batch_size      ,shuffle=SCREAMING_SNAKE_CASE_      )
																snake_case__       :					Tuple										=					len(self.train_dataloader().dataset      )

				def       _lowerCamelCase					(   self   :     Optional[int]      ,SCREAMING_SNAKE_CASE_   :     str      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     bool = False      ):
										raise NotImplementedError('''You must implement this for your task'''      )

				def       _lowerCamelCase					(   self   :     str      ):
										return self.train_loader

				def       _lowerCamelCase					(   self   :     Dict      ):
										return self.get_dataloader('''dev'''      ,self.hparams.eval_batch_size      ,shuffle=SCREAMING_SNAKE_CASE_      )

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

				def       _lowerCamelCase					(   self   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]      ):
										return os.path.join(
										    self.hparams.data_dir      ,'''cached_{}_{}_{}'''.format(
										        SCREAMING_SNAKE_CASE_      ,list(filter(SCREAMING_SNAKE_CASE_      ,self.hparams.model_name_or_path.split('''/'''      )      )      ).pop()      ,str(self.hparams.max_seq_length      )      ,)      ,)

				@pl.utilities.rank_zero_only
				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Dict[str, Any]      ):
										snake_case__       :					Optional[int]										=					self.output_dir.joinpath('''best_tfmr'''      )
										snake_case__       :					List[Any]										=					self.step_count
										self.model.save_pretrained(SCREAMING_SNAKE_CASE_      )
										self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_      )





				@staticmethod
				def       _lowerCamelCase					(   SCREAMING_SNAKE_CASE_   :     List[str]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ):
										parser.add_argument(
										    '''--model_name_or_path'''      ,default=SCREAMING_SNAKE_CASE_      ,type=SCREAMING_SNAKE_CASE_      ,required=SCREAMING_SNAKE_CASE_      ,help='''Path to pretrained model or model identifier from huggingface.co/models'''      ,)
										parser.add_argument(
										    '''--config_name'''      ,default=''''''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Pretrained config name or path if not the same as model_name'''      )
										parser.add_argument(
										    '''--tokenizer_name'''      ,default=SCREAMING_SNAKE_CASE_      ,type=SCREAMING_SNAKE_CASE_      ,help='''Pretrained tokenizer name or path if not the same as model_name'''      ,)
										parser.add_argument(
										    '''--cache_dir'''      ,default=''''''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''      ,)
										parser.add_argument(
										    '''--encoder_layerdrop'''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Encoder layer dropout probability (Optional). Goes into model.config'''      ,)
										parser.add_argument(
										    '''--decoder_layerdrop'''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Decoder layer dropout probability (Optional). Goes into model.config'''      ,)
										parser.add_argument(
										    '''--dropout'''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Dropout probability (Optional). Goes into model.config'''      ,)
										parser.add_argument(
										    '''--attention_dropout'''      ,type=SCREAMING_SNAKE_CASE_      ,help='''Attention dropout probability (Optional). Goes into model.config'''      ,)
										parser.add_argument('''--learning_rate'''      ,default=5E-5      ,type=SCREAMING_SNAKE_CASE_      ,help='''The initial learning rate for Adam.'''      )
										parser.add_argument(
										    '''--lr_scheduler'''      ,default='''linear'''      ,choices=SCREAMING_SNAKE_CASE_      ,metavar=SCREAMING_SNAKE_CASE_      ,type=SCREAMING_SNAKE_CASE_      ,help='''Learning rate scheduler'''      ,)
										parser.add_argument('''--weight_decay'''      ,default=0.0      ,type=SCREAMING_SNAKE_CASE_      ,help='''Weight decay if we apply some.'''      )
										parser.add_argument('''--adam_epsilon'''      ,default=1E-8      ,type=SCREAMING_SNAKE_CASE_      ,help='''Epsilon for Adam optimizer.'''      )
										parser.add_argument('''--warmup_steps'''      ,default=0      ,type=SCREAMING_SNAKE_CASE_      ,help='''Linear warmup over warmup_steps.'''      )
										parser.add_argument('''--num_workers'''      ,default=4      ,type=SCREAMING_SNAKE_CASE_      ,help='''kwarg passed to DataLoader'''      )
										parser.add_argument('''--num_train_epochs'''      ,dest='''max_epochs'''      ,default=3      ,type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--train_batch_size'''      ,default=3_2      ,type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--eval_batch_size'''      ,default=3_2      ,type=SCREAMING_SNAKE_CASE_      )
										parser.add_argument('''--adafactor'''      ,action='''store_true'''      )







class       __lowercase       (			pl.Callback	):


				'''simple docstring'''


				def       _lowerCamelCase					(   self   :     List[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Any      ):
										snake_case__       :					int										=					trainer.lr_schedulers[0]['''scheduler''']
										snake_case__       :					Union[str, Any]										=					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
										pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE_      )

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





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







def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''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 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase=None      ,			lowerCamelCase=True      ,			lowerCamelCase=[]      ,			lowerCamelCase=None      ,			lowerCamelCase=None      ,			**lowerCamelCase      ,			):
						'''simple docstring'''


						pl.seed_everything(args.seed		)

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

						# add custom checkpoints
						if checkpoint_callback is None:
												snake_case__       :					List[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:
												snake_case__       :					Dict										=					LoggingCallback()

						snake_case__       :					List[Any]										=					{}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												snake_case__       :					str										=					16
												snake_case__       :					Optional[Any]										=					args.fpaa_opt_level

						if args.gpus > 1:
												snake_case__       :					Tuple										=					'''ddp'''

						snake_case__       :					Tuple										=					args.accumulate_grad_batches
						snake_case__       :					Union[str, Any]										=					extra_train_kwargs.get('''accelerator'''      ,			lowerCamelCase		)
						snake_case__       :					int										=					extra_train_kwargs.get('''profiler'''      ,			lowerCamelCase		)

						snake_case__       :					List[str]										=					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


