





"""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	     =					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 			__lowercase     (     pl.LightningModule							):







					'''simple docstring'''




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

									__a	:  List[str]         =					('''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	:  Any         =					AutoTokenizer.from_pretrained(
													    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,     cache_dir=_UpperCAmelCase		,     )
									else:
													__a	:  PreTrainedTokenizer         =					tokenizer
									__a	:  int         =					MODEL_MODES[mode]
									if model is None:
													__a	:  Tuple         =					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	:  Dict         =					model







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									__a	:  Optional[Any]         =					self.model_type.from_pretrained(*_UpperCAmelCase		,     **_UpperCAmelCase       )







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					self.model
									__a	:  List[Any]         =					['''bias''', '''LayerNorm.weight''']
									__a	:  List[Any]         =					[
									    {
									        '''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	:  str         =					Adafactor(
													    _UpperCAmelCase		,     lr=self.hparams.learning_rate		,     scale_parameter=_UpperCAmelCase		,     relative_step=_UpperCAmelCase       )

									else:
													__a	:  List[str]         =					AdamW(
													    _UpperCAmelCase		,     lr=self.hparams.learning_rate		,     eps=self.hparams.adam_epsilon       )
									__a	:  Dict         =					optimizer

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

									return [optimizer], [scheduler]







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									return self.validation_step(_UpperCAmelCase		,     _UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									return self.validation_end(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					max(1		,     self.hparams.gpus       )  # TODO: consider num_tpu_cores
									__a	:  Optional[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 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									if stage == "test":
													__a	:  Optional[Any]         =					len(self.test_dataloader().dataset       )
									else:
													__a	:  str         =					self.get_dataloader('''train'''		,     self.hparams.train_batch_size		,     shuffle=_UpperCAmelCase       )
													__a	:  Optional[Any]         =					len(self.train_dataloader().dataset       )







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







					def 	_lowerCamelCase						(  self       ):
									return self.train_loader







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







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







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									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 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					self.output_dir.joinpath('''best_tfmr'''       )
									__a	:  str         =					self.step_count
									self.model.save_pretrained(_UpperCAmelCase       )
									self.tokenizer.save_pretrained(_UpperCAmelCase       )







					@staticmethod
					def 	_lowerCamelCase						(  _UpperCAmelCase		,     _UpperCAmelCase       ):
									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=str(Path(_UpperCAmelCase       ).parent / '''test_run''' / '''cache'''       )		,     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 			__lowercase     (     pl.Callback							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									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 			__lowercase     (     pl.Callback							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									# print(pl_module.model.rag)
									for name, param in pl_module.model.rag.named_parameters():
													if param.grad is None:
																	print(_UpperCAmelCase       )






class 			__lowercase     (     pl.Callback							):







					'''simple docstring'''




					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									__a	:  str         =					trainer.lr_schedulers[0]['''scheduler''']
									__a	:  List[str]         =					{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
									pl_module.logger.log_metrics(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									rank_zero_info('''***** Validation results *****'''       )
									__a	:  Any         =					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 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									rank_zero_info('''***** Test results *****'''       )
									__a	:  int         =					trainer.callback_metrics
									# Log and save results to file
									__a	:  Optional[int]         =					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 (     a_  :Dict				,    a_  :Optional[Any])						->						None:
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '''--output_dir'''				,    default=str(Path(a_).parent / '''test_run''' / '''model_checkpoints''')				,    type=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=str(Path(a_).parent / '''test_run''' / '''dummy-train-data''')				,    type=a_				,    help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''				,    )






def       __A (     a_  :BaseTransformer				,    a_  :argparse.Namespace				,    a_  :int=None				,    a_  :Optional[Any]=True				,    a_  :List[str]=[]				,    a_  :List[Any]=None				,    a_  :Union[str, Any]=None				,    **a_  :List[Any]				,    )						->						List[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	:  Optional[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(a_)
				if logging_callback is None:
								__a	:  Union[str, Any]         =					LoggingCallback()

				__a	:  int         =					{}

				if args.fpaa:
								__a	:  int         =					16

				if args.gpus > 1:
								__a	:  Any         =					'''auto'''
								__a	:  Any         =					'''ddp'''

				__a	:  Union[str, Any]         =					args.accumulate_grad_batches
				__a	:  str         =					None
				__a	:  Tuple         =					'''auto'''

				__a	:  Tuple         =					pl.Trainer.from_argparse_args(
				    a_				,    weights_summary=a_				,    callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]				,    logger=a_				,    val_check_interval=1				,    num_sanity_val_steps=2				,    **a_				,    )

				if args.do_train:
								trainer.fit(a_)

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






"""simple docstring"""






def       __A (     a_  :int)						->						Union[str, Any]:
				__a	:  int         =					[]
				__a	:  Dict         =					[]
				__a	:  str         =					{
				    '''^''': 3,
				    '''*''': 2,
				    '''/''': 2,
				    '''%''': 2,
				    '''+''': 1,
				    '''-''': 1,
				}  # Priority of each operator
				__a	:  Tuple         =					len(a_) if (len(a_) > 7) else 7

				# Print table header for output
				print(
				    '''Symbol'''.center(8)				,    '''Stack'''.center(a_)				,    '''Postfix'''.center(a_)				,    sep=''' | '''				,    )
				print('''-''' * (print_width * 3 + 7))

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(a_)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(a_)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(a_) == 0:
																stack.append(a_)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(a_) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop())  # pop stack & add to Postfix
																stack.append(a_)  # push x to stack

								print(
								    x.center(8)				,    (''''''.join(a_)).ljust(a_)				,    (''''''.join(a_)).ljust(a_)				,    sep=''' | '''				,    )  # Output in tabular format

				while len(a_) > 0:  # while stack is not empty
								post_fix.append(stack.pop())  # pop stack & add to Postfix
								print(
								    ''' '''.center(8)				,    (''''''.join(a_)).ljust(a_)				,    (''''''.join(a_)).ljust(a_)				,    sep=''' | '''				,    )  # Output in tabular format

				return "".join(a_)  # return Postfix as str

def       __A (     a_  :int)						->						List[Any]:
				__a	:  Dict         =					list(infix[::-1])  # reverse the infix equation

				for i in range(len(a_)):
								if infix[i] == "(":
												__a	:  Union[str, Any]         =					''')'''  # change "(" to ")"
								elif infix[i] == ")":
												__a	:  List[str]         =					'''('''  # change ")" to "("

				return (infix_2_postfix(''''''.join(a_)))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		A	     =					input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
		A	     =					''''''.join(Infix.split())  # Remove spaces from the input
		print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')