





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


UpperCAmelCase						:       List[str]        =					logging.getLogger(__name__)

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

UpperCAmelCase						:       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
UpperCAmelCase						:       str        =					{
    '''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
}
UpperCAmelCase						:       List[Any]        =					sorted(arg_to_scheduler.keys())
UpperCAmelCase						:       str        =					'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class 						_A( pl.LightningModule       ):


				"""simple docstring"""


				def __init__(      self					, _A					, _A=None					, _A="base"					, _A=None					, _A=None					, _A=None					, **_A					, ):
									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					)
									__A     :						Optional[int]          =    0
									__A     :						Any          =    Path(self.hparams.output_dir					)
									__A     :						str          =    self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
														__A     :						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:
														__A     :						PretrainedConfig          =    config

									__A     :						Dict          =    ('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:
														__A     :						Optional[int]          =    AutoTokenizer.from_pretrained(
														    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					, cache_dir=_A					, )
									else:
														__A     :						PreTrainedTokenizer          =    tokenizer
									__A     :						int          =    MODEL_MODES[mode]
									if model is None:
														__A     :						int          =    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:
														__A     :						Optional[int]          =    model



				def  UpperCAmelCase_		(      self					, *_A					, **_A					):
									__A     :						Optional[int]          =    self.model_type.from_pretrained(*_A					, **_A					)



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



				def  UpperCAmelCase_		(      self					):
									__A     :						Tuple          =    self.model
									__A     :						Any          =    ['bias', 'LayerNorm.weight']
									__A     :						str          =    [
									    {
									        '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     :						Union[str, Any]          =    Adafactor(
														    _A					, lr=self.hparams.learning_rate					, scale_parameter=_A					, relative_step=_A					)

									else:
														__A     :						List[str]          =    AdamW(
														    _A					, lr=self.hparams.learning_rate					, eps=self.hparams.adam_epsilon					)
									__A     :						Tuple          =    optimizer

									__A     :						Tuple          =    self.get_lr_scheduler()

									return [optimizer], [scheduler]



				def  UpperCAmelCase_		(      self					, _A					, _A					):
									return self.validation_step(_A					, _A					)



				def  UpperCAmelCase_		(      self					, _A					):
									return self.validation_end(_A					)



				def  UpperCAmelCase_		(      self					):
									__A     :						List[Any]          =    max(1					, self.hparams.gpus					)  # TODO: consider num_tpu_cores
									__A     :						List[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  UpperCAmelCase_		(      self					, _A					):
									if mode == "test":
														__A     :						Any          =    len(self.test_dataloader().dataset					)
									else:
														__A     :						Any          =    self.get_dataloader('train'					, self.hparams.train_batch_size					, shuffle=_A					)
														__A     :						Optional[int]          =    len(self.train_dataloader().dataset					)



				def  UpperCAmelCase_		(      self					, _A					, _A					, _A = False					):
									raise NotImplementedError('You must implement this for your task'					)



				def  UpperCAmelCase_		(      self					):
									return self.train_loader



				def  UpperCAmelCase_		(      self					):
									return self.get_dataloader('dev'					, self.hparams.eval_batch_size					, shuffle=_A					)



				def  UpperCAmelCase_		(      self					):
									return self.get_dataloader('test'					, self.hparams.eval_batch_size					, shuffle=_A					)



				def  UpperCAmelCase_		(      self					, _A					):
									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  UpperCAmelCase_		(      self					, _A					):
									__A     :						List[Any]          =    self.output_dir.joinpath('best_tfmr'					)
									__A     :						Tuple          =    self.step_count
									self.model.save_pretrained(_A					)
									self.tokenizer.save_pretrained(_A					)





				@staticmethod
				def  UpperCAmelCase_		(      _A					, _A					):
									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 						_A( pl.Callback       ):


				"""simple docstring"""


				def  UpperCAmelCase_		(      self					, _A					, _A					):
									__A     :						Any          =    trainer.lr_schedulers[0]['scheduler']
									__A     :						str          =    {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
									pl_module.logger.log_metrics(_A					)



				def  UpperCAmelCase_		(      self					, _A					, _A					):
									rank_zero_info('***** Validation results *****'					)
									__A     :						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  UpperCAmelCase_		(      self					, _A					, _A					):
									rank_zero_info('***** Test results *****'					)
									__A     :						Union[str, Any]          =    trainer.callback_metrics
									# Log and save results to file
									__A     :						str          =    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 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				None:
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					parser.add_argument(
					    '--output_dir' ,						default=a ,						type=a ,						required=a ,						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=a ,						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=a  )
					parser.add_argument('--max_grad_norm' ,						dest='gradient_clip_val' ,						default=1.0 ,						type=a ,						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=a ,						default=1 ,						help='Number of updates steps to accumulate before performing a backward/update pass.' ,						)
					parser.add_argument('--seed' ,						type=a ,						default=42 ,						help='random seed for initialization'  )
					parser.add_argument(
					    '--data_dir' ,						default=a ,						type=a ,						required=a ,						help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.' ,						)







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a=None ,						a=True ,						a=[] ,						a=None ,						a=None ,						**a ,						)				->				str:
					pl.seed_everything(args.seed  )

					# init model
					__A     :						Tuple          =    Path(model.hparams.output_dir  )
					odir.mkdir(exist_ok=a  )

					# add custom checkpoints
					if checkpoint_callback is None:
										__A     :						Dict          =    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(a  )
					if logging_callback is None:
										__A     :						Any          =    LoggingCallback()

					__A     :						List[str]          =    {}

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

					if args.gpus > 1:
										__A     :						Optional[Any]          =    'ddp'

					__A     :						List[str]          =    args.accumulate_grad_batches
					__A     :						List[Any]          =    extra_train_kwargs.get('accelerator' ,						a  )
					__A     :						int          =    extra_train_kwargs.get('profiler' ,						a  )

					__A     :						List[str]          =    pl.Trainer.from_argparse_args(
					    a ,						weights_summary=a ,						callbacks=[logging_callback] + extra_callbacks ,						logger=a ,						checkpoint_callback=a ,						**a ,						)

					if args.do_train:
										trainer.fit(a  )

					return trainer











from __future__ import annotations

import math
import random







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list[int]:
					__A     :						int          =    d * e - 1
					__A     :						List[str]          =    0
					__A     :						Optional[Any]          =    0
					while p == 0:
										__A     :						List[Any]          =    random.randint(2 ,						n - 1  )
										__A     :						Optional[int]          =    k
										while True:
															if t % 2 == 0:
																				__A     :						Tuple          =    t // 2
																				__A     :						List[str]          =    (g**t) % n
																				__A     :						List[Any]          =    math.gcd(x - 1 ,						a  )
																				if x > 1 and y > 1:
																									__A     :						List[str]          =    y
																									__A     :						str          =    n // y
																									break  # find the correct factors
															else:
																				break  # t is not divisible by 2, break and choose another g
					return sorted([p, q]  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




