






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      =				logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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





class 						SCREAMING_SNAKE_CASE				( pl.LightningModule					):





							"""simple docstring"""

							def __init__(  self:   Union[str, Any]		,     __A:   argparse.Namespace		,     __A:   Optional[int]=None		,     __A:   List[Any]="base"		,     __A:   int=None		,     __A:   str=None		,     __A:   List[Any]=None		,     **__A:   int		,     )    ->				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(__A   )
												_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=__A		,     **__A		,     )
												else:
																	_A								= config

												_A								= ('''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								= AutoTokenizer.from_pretrained(
																	    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,     cache_dir=__A		,     )
												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=__A		,     )
												else:
																	_A								= model

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

							def       __A (  self:   Optional[int]   )    ->				Optional[int]:
												_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       __A (  self:   str   )    ->				Tuple:
												_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   )
												        ],  # check this named paramters
												        '''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(
																	    __A		,     lr=self.hparams.learning_rate		,     scale_parameter=__A		,     relative_step=__A   )

												else:
																	_A								= AdamW(
																	    __A		,     lr=self.hparams.learning_rate		,     eps=self.hparams.adam_epsilon   )
												_A								= optimizer

												_A								= self.get_lr_scheduler()

												return [optimizer], [scheduler]

							def       __A (  self:   List[Any]		,     __A:   Any		,     __A:   Optional[Any]   )    ->				int:
												return self.validation_step(__A		,     __A   )

							def       __A (  self:   int		,     __A:   Any   )    ->				Optional[int]:
												return self.validation_end(__A   )

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

							def       __A (  self:   int		,     __A:   str		,     __A:   int		,     __A:   bool = False   )    ->				Tuple:
												raise NotImplementedError('''You must implement this for your task'''   )

							def       __A (  self:   Union[str, Any]   )    ->				str:
												return self.train_loader

							def       __A (  self:   Dict   )    ->				int:
												return self.get_dataloader('''dev'''		,     self.hparams.eval_batch_size		,     shuffle=__A   )

							def       __A (  self:   Optional[Any]   )    ->				Optional[Any]:
												return self.get_dataloader('''test'''		,     self.hparams.eval_batch_size		,     shuffle=__A   )

							def       __A (  self:   Tuple		,     __A:   List[Any]   )    ->				List[str]:
												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       __A (  self:   Optional[Any]		,     __A:   Dict[str, Any]   )    ->				None:
												_A								= self.output_dir.joinpath('''best_tfmr'''   )
												_A								= self.step_count
												self.model.save_pretrained(__A   )
												self.tokenizer.save_pretrained(__A   )



							@staticmethod
							def       __A (  __A:   Union[str, Any]		,     __A:   Optional[Any]   )    ->				Dict:
												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=str(Path(__A   ).parent / '''test_run''' / '''cache'''   )		,     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					):





							"""simple docstring"""

							def       __A (  self:   Optional[Any]		,     __A:   str		,     __A:   List[str]   )    ->				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 						SCREAMING_SNAKE_CASE				( pl.Callback					):





							"""simple docstring"""

							def       __A (  self:   str		,     __A:   int		,     __A:   List[str]   )    ->				List[Any]:
												# print(pl_module.model.rag)
												for name, param in pl_module.model.rag.named_parameters():
																	if param.grad is None:
																						print(__A   )





class 						SCREAMING_SNAKE_CASE				( pl.Callback					):





							"""simple docstring"""

							def       __A (  self:   int		,     __A:   Union[str, Any]		,     __A:   Any   )    ->				Optional[Any]:
												_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(__A   )

							def       __A (  self:   str		,     __A:   pl.Trainer		,     __A:   pl.LightningModule   )    ->				Optional[Any]:
												rank_zero_info('''***** Validation results *****'''   )
												_A								= 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       __A (  self:   Tuple		,     __A:   pl.Trainer		,     __A:   pl.LightningModule   )    ->				Optional[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(__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					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase=None					,			_lowercase=True					,			_lowercase=[]					,			_lowercase=None					,			_lowercase=None					,			**_lowercase					,			):

					'''simple docstring'''







					pl.seed_everything(args.seed )

					# init model
					_A								= Path(model.hparams.output_dir )
					odir.mkdir(exist_ok=_lowercase )

					# 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(_lowercase )
					if logging_callback is None:
										_A								= LoggingCallback()

					_A								= {}

					if args.fpaa:
										_A								= 16

					if args.gpus > 1:
										_A								= '''auto'''
										_A								= '''ddp'''

					_A								= args.accumulate_grad_batches
					_A								= None
					_A								= '''auto'''

					_A								= pl.Trainer.from_argparse_args(
					    _lowercase					,			weights_summary=_lowercase					,			callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					,			logger=_lowercase					,			val_check_interval=1					,			num_sanity_val_steps=2					,			**_lowercase					,			)

					if args.do_train:
										trainer.fit(_lowercase )

					else:
										print('''RAG modeling tests with new set functions successfuly executed!''' )
					return trainer











import math




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= []
					_A								= 2
					_A								= int(math.sqrt(_lowercase ) )  # Size of every segment
					_A								= [True] * (end + 1)
					_A								= []

					while start <= end:
										if temp[start] is True:
															in_prime.append(_lowercase )
															for i in range(start * start					,			end + 1					,			_lowercase ):
																				_A								= False
										start += 1
					prime += in_prime

					_A								= end + 1
					_A								= min(2 * end					,			_lowercase )

					while low <= n:
										_A								= [True] * (high - low + 1)
										for each in in_prime:
															_A								= math.floor(low / each ) * each
															if t < low:
																				t += each

															for j in range(_lowercase					,			high + 1					,			_lowercase ):
																				_A								= False

										for j in range(len(_lowercase ) ):
															if temp[j] is True:
																				prime.append(j + low )

										_A								= high + 1
										_A								= min(high + end					,			_lowercase )

					return prime


print(sieve(10**6))



