






'''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


_UpperCamelCase		:		Union[str, Any]								=			logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

_UpperCamelCase		:		Dict								=			{
    '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		:		int								=			{
    '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		:		Optional[int]								=			sorted(arg_to_scheduler.keys())
_UpperCamelCase		:		Tuple								=			'{' + ', '.join(arg_to_scheduler_choices) + '}'


class 				snake_case__     (			pl.LightningModule):




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

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




					def 						A     (       self       :						str							,				*_A       :						Dict							,				**_A       :						int     )  ->    List[str]:
								UpperCAmelCase_     :   Union[str, Any]       =   self.model_type.from_pretrained(*_A							,				**_A     )




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




					def 						A     (       self       :						List[str]     )  ->    List[str]:
								UpperCAmelCase_     :   str       =   self.model
								UpperCAmelCase_     :   Union[str, Any]       =   ['''bias''', '''LayerNorm.weight''']
								UpperCAmelCase_     :   Union[str, 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:
											UpperCAmelCase_     :   str       =   Adafactor(
											    _A							,				lr=self.hparams.learning_rate							,				scale_parameter=_A							,				relative_step=_A     )

								else:
											UpperCAmelCase_     :   Optional[Any]       =   AdamW(
											    _A							,				lr=self.hparams.learning_rate							,				eps=self.hparams.adam_epsilon     )
								UpperCAmelCase_     :   Any       =   optimizer

								UpperCAmelCase_     :   List[Any]       =   self.get_lr_scheduler()

								return [optimizer], [scheduler]




					def 						A     (       self       :						Any							,				_A       :						Union[str, Any]							,				_A       :						Dict     )  ->    Optional[int]:
								return self.validation_step(_A							,				_A     )




					def 						A     (       self       :						Optional[Any]							,				_A       :						str     )  ->    Tuple:
								return self.validation_end(_A     )




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




					def 						A     (       self       :						List[Any]							,				_A       :						str							,				_A       :						int							,				_A       :						bool = False     )  ->    List[str]:
								raise NotImplementedError('''You must implement this for your task'''     )




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




					def 						A     (       self       :						Dict     )  ->    Union[str, Any]:
								return self.get_dataloader('''dev'''							,				self.hparams.eval_batch_size							,				shuffle=_A     )




					def 						A     (       self       :						List[str]     )  ->    Dict:
								return self.get_dataloader('''test'''							,				self.hparams.eval_batch_size							,				shuffle=_A     )




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





					@staticmethod
					def 						A     (       _A       :						int							,				_A       :						Tuple     )  ->    str:
								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 				snake_case__     (			pl.Callback):




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




					def 						A     (       self       :						Optional[int]							,				_A       :						pl.Trainer							,				_A       :						pl.LightningModule     )  ->    List[Any]:
								rank_zero_info('''***** Validation results *****'''     )
								UpperCAmelCase_     :   int       =   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       :						str							,				_A       :						pl.Trainer							,				_A       :						pl.LightningModule     )  ->    Tuple:
								rank_zero_info('''***** Test results *****'''     )
								UpperCAmelCase_     :   Optional[int]       =   trainer.callback_metrics
								# Log and save results to file
								UpperCAmelCase_     :   Optional[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 (			A			:	str	,   A			:	Dict						)  -> 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=4_2	,   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 __UpperCAmelCase (			A			:	BaseTransformer	,   A			:	argparse.Namespace	,   A			:	Union[str, Any]=None	,   A			:	Optional[int]=True	,   A			:	int=[]	,   A			:	List[Any]=None	,   A			:	str=None	,   **A			:	Any	,   )  -> Dict:
			pl.seed_everything(args.seed						)

			# init model
			UpperCAmelCase_     :   Optional[int]       =   Path(model.hparams.output_dir						)
			odir.mkdir(exist_ok=A						)

			# add custom checkpoints
			if checkpoint_callback is None:
						UpperCAmelCase_     :   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:
						UpperCAmelCase_     :   Tuple       =   LoggingCallback()

			UpperCAmelCase_     :   List[Any]       =   {}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						UpperCAmelCase_     :   int       =   1_6
						UpperCAmelCase_     :   int       =   args.fpaa_opt_level

			if args.gpus > 1:
						UpperCAmelCase_     :   Optional[int]       =   '''ddp'''

			UpperCAmelCase_     :   str       =   args.accumulate_grad_batches
			UpperCAmelCase_     :   Optional[int]       =   extra_train_kwargs.get('''accelerator'''	,   A						)
			UpperCAmelCase_     :   str       =   extra_train_kwargs.get('''profiler'''	,   A						)

			UpperCAmelCase_     :   Optional[Any]       =   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










'''simple docstring'''





def __UpperCAmelCase (			A			:	list[list[int]]						)  -> bool:
			# must be
			UpperCAmelCase_     :   int       =   [list(A						) for row in matrix]
			if matrix and isinstance(A	,   A						):
						if isinstance(matrix[0]	,   A						):
									UpperCAmelCase_     :   Optional[Any]       =   0
									for row in matrix:
												if prev_len == 0:
															UpperCAmelCase_     :   List[str]       =   len(A						)
															UpperCAmelCase_     :   Any       =   True
												else:
															UpperCAmelCase_     :   Tuple       =   prev_len == len(A						)
						else:
									UpperCAmelCase_     :   Any       =   True
			else:
						UpperCAmelCase_     :   Dict       =   False

			return result
def __UpperCAmelCase (			A			:	list[list[int]]						)  -> None:
			if check_matrix(A						) and len(A						) > 0:
						UpperCAmelCase_     :   Dict       =   [list(A						) for row in a]
						UpperCAmelCase_     :   List[Any]       =   len(A						)
						if isinstance(a[0]	,   A						):
									UpperCAmelCase_     :   Any       =   len(a[0]						)
						else:
									for dat in a:
												print(A						)
									return

						# horizotal printing increasing
						for i in range(0	,   A						):
									print(a[0][i]						)
						# vertical printing down
						for i in range(1	,   A						):
									print(a[i][mat_col - 1]						)
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2	,   -1	,   -1						):
												print(a[mat_row - 1][i]						)
        # vertical printing up
						for i in range(mat_row - 2	,   0	,   -1						):
									print(a[i][0]						)
						UpperCAmelCase_     :   Union[str, Any]       =   [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(A						) > 0:
									spiral_print_clockwise(A						)
						else:
									return
			else:
						print('''Not a valid matrix'''						)
						return
def __UpperCAmelCase (			A			:	list[list]						)  -> list[int]:
			if matrix:
						return list(matrix.pop(0						)						) + spiral_traversal(list(zip(*A						)						)[::-1]						)
			else:
						return []


# driver code
if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_UpperCamelCase		:		List[Any]								=			[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
						spiral_print_clockwise(a)


