



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__						:						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__						:						Union[str, 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[Any]          =      sorted(arg_to_scheduler.keys())
A__						:						int          =      '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class        __snake_case		(  pl.LightningModule		):




							def __init__( self  : Any       ,       A_  : argparse.Namespace       ,       A_  : str=None       ,       A_  : int="base"       ,       A_  : Optional[int]=None       ,       A_  : Dict=None       ,       A_  : Any=None       ,       **A_  : 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_)
									lowerCAmelCase_    :  List[str]             =				0
									lowerCAmelCase_    :  Any             =				Path(self.hparams.output_dir)
									lowerCAmelCase_    :  str             =				self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
											lowerCAmelCase_    :  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=A_       ,       **A_       ,       )
									else:
											lowerCAmelCase_    :  PretrainedConfig             =				config

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




							def      UpperCAmelCase__    ( self  : str       ,       *A_  : Union[str, Any]       ,       **A_  : Tuple):
									lowerCAmelCase_    :  Any             =				self.model_type.from_pretrained(*A_       ,       **A_)




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




							def      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_    :  List[str]             =				self.model
									lowerCAmelCase_    :  Tuple             =				['''bias''', '''LayerNorm.weight''']
									lowerCAmelCase_    :  Optional[int]             =				[
									    {
									        '''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:
											lowerCAmelCase_    :  Optional[int]             =				Adafactor(
											    A_       ,       lr=self.hparams.learning_rate       ,       scale_parameter=A_       ,       relative_step=A_)

									else:
											lowerCAmelCase_    :  List[str]             =				AdamW(
											    A_       ,       lr=self.hparams.learning_rate       ,       eps=self.hparams.adam_epsilon)
									lowerCAmelCase_    :  Union[str, Any]             =				optimizer

									lowerCAmelCase_    :  str             =				self.get_lr_scheduler()

									return [optimizer], [scheduler]




							def      UpperCAmelCase__    ( self  : List[Any]       ,       A_  : int       ,       A_  : Dict):
									return self.validation_step(A_       ,       A_)




							def      UpperCAmelCase__    ( self  : Optional[Any]       ,       A_  : Dict):
									return self.validation_end(A_)




							def      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_    :  Optional[int]             =				max(1       ,       self.hparams.gpus)  # TODO: consider num_tpu_cores
									lowerCAmelCase_    :  Optional[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      UpperCAmelCase__    ( self  : List[Any]       ,       A_  : Union[str, Any]):
									if mode == "test":
											lowerCAmelCase_    :  Optional[int]             =				len(self.test_dataloader().dataset)
									else:
											lowerCAmelCase_    :  Dict             =				self.get_dataloader('''train'''       ,       self.hparams.train_batch_size       ,       shuffle=A_)
											lowerCAmelCase_    :  Tuple             =				len(self.train_dataloader().dataset)




							def      UpperCAmelCase__    ( self  : Dict       ,       A_  : str       ,       A_  : int       ,       A_  : bool = False):
									raise NotImplementedError('''You must implement this for your task''')




							def      UpperCAmelCase__    ( self  : Any):
									return self.train_loader




							def      UpperCAmelCase__    ( self  : Tuple):
									return self.get_dataloader('''dev'''       ,       self.hparams.eval_batch_size       ,       shuffle=A_)




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




							def      UpperCAmelCase__    ( self  : int       ,       A_  : List[Any]):
									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  : List[str]       ,       A_  : Dict[str, Any]):
									lowerCAmelCase_    :  Any             =				self.output_dir.joinpath('''best_tfmr''')
									lowerCAmelCase_    :  Optional[Any]             =				self.step_count
									self.model.save_pretrained(A_)
									self.tokenizer.save_pretrained(A_)







							@staticmethod
							def      UpperCAmelCase__    ( A_  : Dict       ,       A_  : int):
									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=3_2       ,       type=A_)
									parser.add_argument('''--eval_batch_size'''       ,       default=3_2       ,       type=A_)
									parser.add_argument('''--adafactor'''       ,       action='''store_true''')





class        __snake_case		(  pl.Callback		):




							def      UpperCAmelCase__    ( self  : int       ,       A_  : str       ,       A_  : List[str]):
									lowerCAmelCase_    :  int             =				trainer.lr_schedulers[0]['''scheduler''']
									lowerCAmelCase_    :  Optional[int]             =				{F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr())}
									pl_module.logger.log_metrics(A_)




							def      UpperCAmelCase__    ( self  : Tuple       ,       A_  : pl.Trainer       ,       A_  : pl.LightningModule):
									rank_zero_info('''***** Validation results *****''')
									lowerCAmelCase_    :  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  : Dict       ,       A_  : pl.Trainer       ,       A_  : pl.LightningModule):
									rank_zero_info('''***** Test results *****''')
									lowerCAmelCase_    :  Tuple             =				trainer.callback_metrics
									# Log and save results to file
									lowerCAmelCase_    :  Union[str, Any]             =				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     UpperCamelCase(						__UpperCamelCase							:	List[Any]  ,__UpperCamelCase							:	Optional[Any]				):
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    '''--output_dir'''  ,default=__UpperCamelCase  ,type=__UpperCamelCase  ,required=__UpperCamelCase  ,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=__UpperCamelCase  ,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=__UpperCamelCase				)
		parser.add_argument('''--max_grad_norm'''  ,dest='''gradient_clip_val'''  ,default=1.0  ,type=__UpperCamelCase  ,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=__UpperCamelCase  ,default=1  ,help='''Number of updates steps to accumulate before performing a backward/update pass.'''  ,)
		parser.add_argument('''--seed'''  ,type=__UpperCamelCase  ,default=42  ,help='''random seed for initialization'''				)
		parser.add_argument(
		    '''--data_dir'''  ,default=__UpperCamelCase  ,type=__UpperCamelCase  ,required=__UpperCamelCase  ,help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''  ,)





def     UpperCamelCase(						__UpperCamelCase							:	BaseTransformer  ,__UpperCamelCase							:	argparse.Namespace  ,__UpperCamelCase							:	Tuple=None  ,__UpperCamelCase							:	Optional[int]=True  ,__UpperCamelCase							:	Tuple=[]  ,__UpperCamelCase							:	Tuple=None  ,__UpperCamelCase							:	Union[str, Any]=None  ,**__UpperCamelCase							:	int  ,):
		pl.seed_everything(args.seed				)

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

		# add custom checkpoints
		if checkpoint_callback is None:
				lowerCAmelCase_    :  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(__UpperCamelCase				)
		if logging_callback is None:
				lowerCAmelCase_    :  Dict             =				LoggingCallback()

		lowerCAmelCase_    :  Union[str, Any]             =				{}

		# TODO: remove with PyTorch 1.6 since pl uses native amp
		if args.fpaa:
				lowerCAmelCase_    :  Dict             =				16
				lowerCAmelCase_    :  List[str]             =				args.fpaa_opt_level

		if args.gpus > 1:
				lowerCAmelCase_    :  Union[str, Any]             =				'''ddp'''

		lowerCAmelCase_    :  List[str]             =				args.accumulate_grad_batches
		lowerCAmelCase_    :  str             =				extra_train_kwargs.get('''accelerator'''  ,__UpperCamelCase				)
		lowerCAmelCase_    :  Any             =				extra_train_kwargs.get('''profiler'''  ,__UpperCamelCase				)

		lowerCAmelCase_    :  Union[str, Any]             =				pl.Trainer.from_argparse_args(
		    __UpperCamelCase  ,weights_summary=__UpperCamelCase  ,callbacks=[logging_callback] + extra_callbacks  ,logger=__UpperCamelCase  ,checkpoint_callback=__UpperCamelCase  ,**__UpperCamelCase  ,)

		if args.do_train:
				trainer.fit(__UpperCamelCase				)

		return trainer










from __future__ import annotations

from typing import Generic, TypeVar

A__						:						Dict          =      TypeVar('''T''')



class        __snake_case		(  Generic[T]		):




							def __init__( self  : List[Any]       ,       A_  : T):
									lowerCAmelCase_    :  Optional[Any]             =				data  # Assign data
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize next as null
									lowerCAmelCase_    :  Node[T] | None             =				None  # Initialize prev as null





class        __snake_case		(  Generic[T]		):




							def __init__( self  : Union[str, Any]):
									lowerCAmelCase_    :  Node[T] | None             =				None




							def      UpperCAmelCase__    ( self  : List[str]       ,       A_  : T):
									if self.head is None:
											lowerCAmelCase_    :  List[Any]             =				Node(A_)
									else:
											lowerCAmelCase_    :  Tuple             =				Node(A_)
											lowerCAmelCase_    :  Optional[int]             =				new_node
											lowerCAmelCase_    :  Optional[int]             =				self.head
											lowerCAmelCase_    :  Optional[Any]             =				None
											lowerCAmelCase_    :  Dict             =				new_node




							def      UpperCAmelCase__    ( self  : List[str]):
									if self.head is None:
											return None
									else:
											assert self.head is not None
											lowerCAmelCase_    :  Tuple             =				self.head.data
											lowerCAmelCase_    :  Any             =				self.head.next
											if self.head is not None:
													lowerCAmelCase_    :  Optional[Any]             =				None
											return temp




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									return self.head.data if self.head is not None else None




							def __len__( self  : Dict):
									lowerCAmelCase_    :  List[str]             =				self.head
									lowerCAmelCase_    :  str             =				0
									while temp is not None:
											count += 1
											lowerCAmelCase_    :  Union[str, Any]             =				temp.next
									return count




							def      UpperCAmelCase__    ( self  : Dict):
									return self.head is None







							def      UpperCAmelCase__    ( self  : Dict):
									print('''stack elements are:''')
									lowerCAmelCase_    :  Optional[Any]             =				self.head
									while temp is not None:
											print(temp.data       ,       end='''->''')
											lowerCAmelCase_    :  List[str]             =				temp.next


# Code execution starts here
if __name__ == "__main__":
						# Start with the empty stack
						A__						:						Stack[int]          =      Stack()

						# Insert 4 at the beginning. So stack becomes 4->None
						print('''Stack operations using Doubly LinkedList''')
						stack.push(4)

						# Insert 5 at the beginning. So stack becomes 4->5->None
						stack.push(5)

						# Insert 6 at the beginning. So stack becomes 4->5->6->None
						stack.push(6)

						# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
						stack.push(7)

						# Print the stack
						stack.print_stack()

						# Print the top element
						print('''\nTop element is ''', stack.top())

						# Print the stack size
						print('''Size of the stack is ''', len(stack))

						# pop the top element
						stack.pop()

						# pop the top element
						stack.pop()

						# two elements have now been popped off
						stack.print_stack()

						# Print True if the stack is empty else False
						print('''\nstack is empty:''', stack.is_empty())





