




'''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


snake_case__      				=  logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

snake_case__      				=  {
    """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
snake_case__      				=  {
    """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
}
snake_case__      				=  sorted(arg_to_scheduler.keys())
snake_case__      				=  """{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class 			UpperCamelCase_   (pl.LightningModule   ):







						"""simple docstring"""


						def __init__(   self					:				Dict							,       _lowerCamelCase					:				Optional[int]							,       _lowerCamelCase					:				List[Any]=None							,       _lowerCamelCase					:				Tuple="base"							,       _lowerCamelCase					:				List[Any]=None							,       _lowerCamelCase					:				List[Any]=None							,       _lowerCamelCase					:				List[str]=None							,       **_lowerCamelCase					:				List[Any]							,       ):





											"""simple docstring"""







											super().__init__()
											# TODO: move to self.save_hyperparameters()
											# self.save_hyperparameters()
											# can also expand arguments into trainer signature for easier reading

											self.save_hyperparameters(lowercase__			)
											A_       :   List[Any]       =							0
											A_       :   List[Any]       =							Path(self.hparams.output_dir			)
											A_       :   Dict       =							self.hparams.cache_dir if self.hparams.cache_dir else None
											if config is None:
																A_       :   Optional[int]       =							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=lowercase__							,       **lowercase__							,       )
											else:
																A_       :   Optional[Any]       =							config

											A_       :   Tuple       =							('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
											for p in extra_model_params:
																if getattr(self.hparams							,       lowercase__							,       lowercase__			):
																					assert hasattr(self.config							,       lowercase__			), f'model config doesn\'t have a `{p}` attribute'
																					setattr(self.config							,       lowercase__							,       getattr(self.hparams							,       lowercase__			)			)

											if tokenizer is None:
																A_       :   int       =							AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,       cache_dir=lowercase__							,       )
											else:
																A_       :   List[Any]       =							tokenizer
											A_       :   str       =							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=lowercase__							,       )
											else:
																A_       :   List[Any]       =							model







						def 					_a			(   self					:				str							,       *_lowerCamelCase					:				Any							,       **_lowerCamelCase					:				Union[str, Any]			):





											"""simple docstring"""







											A_       :   str       =							self.model_type.from_pretrained(*lowercase__							,       **lowercase__			)







						def 					_a			(   self					:				str			):





											"""simple docstring"""







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







						def 					_a			(   self					:				int			):





											"""simple docstring"""







											A_       :   int       =							self.model
											A_       :   Any       =							['''bias''', '''LayerNorm.weight''']
											A_       :   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:
																A_       :   int       =							Adafactor(
																    lowercase__							,       lr=self.hparams.learning_rate							,       scale_parameter=lowercase__							,       relative_step=lowercase__			)

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

											A_       :   Union[str, Any]       =							self.get_lr_scheduler()

											return [optimizer], [scheduler]







						def 					_a			(   self					:				Union[str, Any]							,       _lowerCamelCase					:				Union[str, Any]							,       _lowerCamelCase					:				Tuple			):





											"""simple docstring"""







											return self.validation_step(lowercase__							,       lowercase__			)







						def 					_a			(   self					:				Tuple							,       _lowerCamelCase					:				List[Any]			):





											"""simple docstring"""







											return self.validation_end(lowercase__			)







						def 					_a			(   self					:				Union[str, Any]			):





											"""simple docstring"""







											A_       :   int       =							max(1							,       self.hparams.gpus			)  # TODO: consider num_tpu_cores
											A_       :   Dict       =							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]							,       _lowerCamelCase					:				List[str]			):





											"""simple docstring"""







											if mode == "test":
																A_       :   Tuple       =							len(self.test_dataloader().dataset			)
											else:
																A_       :   Optional[Any]       =							self.get_dataloader('''train'''							,       self.hparams.train_batch_size							,       shuffle=lowercase__			)
																A_       :   str       =							len(self.train_dataloader().dataset			)







						def 					_a			(   self					:				Tuple							,       _lowerCamelCase					:				Dict							,       _lowerCamelCase					:				Union[str, Any]							,       _lowerCamelCase					:				Optional[Any] = False			):





											"""simple docstring"""







											raise NotImplementedError('''You must implement this for your task'''			)







						def 					_a			(   self					:				Tuple			):





											"""simple docstring"""







											return self.train_loader







						def 					_a			(   self					:				Union[str, Any]			):





											"""simple docstring"""







											return self.get_dataloader('''dev'''							,       self.hparams.eval_batch_size							,       shuffle=lowercase__			)







						def 					_a			(   self					:				str			):





											"""simple docstring"""







											return self.get_dataloader('''test'''							,       self.hparams.eval_batch_size							,       shuffle=lowercase__			)







						def 					_a			(   self					:				Union[str, Any]							,       _lowerCamelCase					:				Optional[Any]			):





											"""simple docstring"""







											return os.path.join(
											    self.hparams.data_dir							,       '''cached_{}_{}_{}'''.format(
											        lowercase__							,       list(filter(lowercase__							,       self.hparams.model_name_or_path.split('''/'''			)			)			).pop()							,       str(self.hparams.max_seq_length			)							,       )							,       )







						@pl.utilities.rank_zero_only
						def 					_a			(   self					:				List[str]							,       _lowerCamelCase					:				Any			):





											"""simple docstring"""







											A_       :   int       =							self.output_dir.joinpath('''best_tfmr'''			)
											A_       :   List[str]       =							self.step_count
											self.model.save_pretrained(lowercase__			)
											self.tokenizer.save_pretrained(lowercase__			)







						@staticmethod
						def 					_a			(   _lowerCamelCase					:				Tuple							,       _lowerCamelCase					:				Union[str, Any]			):





											"""simple docstring"""







											parser.add_argument(
											    '''--model_name_or_path'''							,       default=lowercase__							,       type=lowercase__							,       required=lowercase__							,       help='''Path to pretrained model or model identifier from huggingface.co/models'''							,       )
											parser.add_argument(
											    '''--config_name'''							,       default=''''''							,       type=lowercase__							,       help='''Pretrained config name or path if not the same as model_name'''			)
											parser.add_argument(
											    '''--tokenizer_name'''							,       default=lowercase__							,       type=lowercase__							,       help='''Pretrained tokenizer name or path if not the same as model_name'''							,       )
											parser.add_argument(
											    '''--cache_dir'''							,       default=''''''							,       type=lowercase__							,       help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''							,       )
											parser.add_argument(
											    '''--encoder_layerdrop'''							,       type=lowercase__							,       help='''Encoder layer dropout probability (Optional). Goes into model.config'''							,       )
											parser.add_argument(
											    '''--decoder_layerdrop'''							,       type=lowercase__							,       help='''Decoder layer dropout probability (Optional). Goes into model.config'''							,       )
											parser.add_argument(
											    '''--dropout'''							,       type=lowercase__							,       help='''Dropout probability (Optional). Goes into model.config'''							,       )
											parser.add_argument(
											    '''--attention_dropout'''							,       type=lowercase__							,       help='''Attention dropout probability (Optional). Goes into model.config'''							,       )
											parser.add_argument('''--learning_rate'''							,       default=5E-5							,       type=lowercase__							,       help='''The initial learning rate for Adam.'''			)
											parser.add_argument(
											    '''--lr_scheduler'''							,       default='''linear'''							,       choices=lowercase__							,       metavar=lowercase__							,       type=lowercase__							,       help='''Learning rate scheduler'''							,       )
											parser.add_argument('''--weight_decay'''							,       default=0.0							,       type=lowercase__							,       help='''Weight decay if we apply some.'''			)
											parser.add_argument('''--adam_epsilon'''							,       default=1E-8							,       type=lowercase__							,       help='''Epsilon for Adam optimizer.'''			)
											parser.add_argument('''--warmup_steps'''							,       default=0							,       type=lowercase__							,       help='''Linear warmup over warmup_steps.'''			)
											parser.add_argument('''--num_workers'''							,       default=4							,       type=lowercase__							,       help='''kwarg passed to DataLoader'''			)
											parser.add_argument('''--num_train_epochs'''							,       dest='''max_epochs'''							,       default=3							,       type=lowercase__			)
											parser.add_argument('''--train_batch_size'''							,       default=32							,       type=lowercase__			)
											parser.add_argument('''--eval_batch_size'''							,       default=32							,       type=lowercase__			)
											parser.add_argument('''--adafactor'''							,       action='''store_true'''			)






class 			UpperCamelCase_   (pl.Callback   ):







						"""simple docstring"""


						def 					_a			(   self					:				str							,       _lowerCamelCase					:				List[str]							,       _lowerCamelCase					:				int			):





											"""simple docstring"""







											A_       :   Union[str, Any]       =							trainer.lr_schedulers[0]['''scheduler''']
											A_       :   Any       =							{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
											pl_module.logger.log_metrics(lowercase__			)







						def 					_a			(   self					:				List[str]							,       _lowerCamelCase					:				List[Any]							,       _lowerCamelCase					:				Dict			):





											"""simple docstring"""







											rank_zero_info('''***** Validation results *****'''			)
											A_       :   Dict       =							trainer.callback_metrics
											# Log results
											for key in sorted(lowercase__			):
																if key not in ["log", "progress_bar"]:
																					rank_zero_info('''{} = {}\n'''.format(lowercase__							,       str(metrics[key]			)			)			)







						def 					_a			(   self					:				Tuple							,       _lowerCamelCase					:				Any							,       _lowerCamelCase					:				Optional[Any]			):





											"""simple docstring"""







											rank_zero_info('''***** Test results *****'''			)
											A_       :   Tuple       =							trainer.callback_metrics
											# Log and save results to file
											A_       :   Tuple       =							os.path.join(pl_module.hparams.output_dir							,       '''test_results.txt'''			)
											with open(lowercase__							,       '''w'''			) as writer:
																for key in sorted(lowercase__			):
																					if key not in ["log", "progress_bar"]:
																										rank_zero_info('''{} = {}\n'''.format(lowercase__							,       str(metrics[key]			)			)			)
																										writer.write('''{} = {}\n'''.format(lowercase__							,       str(metrics[key]			)			)			)






def   snake_case__			(       lowerCamelCase__ :      Dict	,						lowerCamelCase__ :      Optional[Any]	)      ->      Tuple:
					#  To allow all pl args uncomment the following line
					#  parser = pl.Trainer.add_argparse_args(parser)
					parser.add_argument(
					    '''--output_dir'''	,						default=SCREAMING_SNAKE_CASE_	,						type=SCREAMING_SNAKE_CASE_	,						required=SCREAMING_SNAKE_CASE_	,						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=SCREAMING_SNAKE_CASE_	,						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=SCREAMING_SNAKE_CASE_	)
					parser.add_argument('''--max_grad_norm'''	,						dest='''gradient_clip_val'''	,						default=1.0	,						type=SCREAMING_SNAKE_CASE_	,						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=SCREAMING_SNAKE_CASE_	,						default=1	,						help='''Number of updates steps to accumulate before performing a backward/update pass.'''	,						)
					parser.add_argument('''--seed'''	,						type=SCREAMING_SNAKE_CASE_	,						default=4_2	,						help='''random seed for initialization'''	)
					parser.add_argument(
					    '''--data_dir'''	,						default=SCREAMING_SNAKE_CASE_	,						type=SCREAMING_SNAKE_CASE_	,						required=SCREAMING_SNAKE_CASE_	,						help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''	,						)





def   snake_case__			(       lowerCamelCase__ :      str	,						lowerCamelCase__ :      List[str]	,						lowerCamelCase__ :      int=None	,						lowerCamelCase__ :      int=True	,						lowerCamelCase__ :      Union[str, Any]=[]	,						lowerCamelCase__ :      List[str]=None	,						lowerCamelCase__ :      Optional[int]=None	,						**lowerCamelCase__ :      List[str]	,						)      ->      str:
					pl.seed_everything(args.seed	)

					# init model
					A_       :   Any       =							Path(model.hparams.output_dir	)
					odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)

					# add custom checkpoints
					if checkpoint_callback is None:
										A_       :   Union[str, 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(SCREAMING_SNAKE_CASE_	)
					if logging_callback is None:
										A_       :   Optional[int]       =							LoggingCallback()

					A_       :   str       =							{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										A_       :   Tuple       =							1_6
										A_       :   Optional[Any]       =							args.fpaa_opt_level

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

					A_       :   Union[str, Any]       =							args.accumulate_grad_batches
					A_       :   Dict       =							extra_train_kwargs.get('''accelerator'''	,						SCREAMING_SNAKE_CASE_	)
					A_       :   Union[str, Any]       =							extra_train_kwargs.get('''profiler'''	,						SCREAMING_SNAKE_CASE_	)

					A_       :   int       =							pl.Trainer.from_argparse_args(
					    SCREAMING_SNAKE_CASE_	,						weights_summary=SCREAMING_SNAKE_CASE_	,						callbacks=[logging_callback] + extra_callbacks	,						logger=SCREAMING_SNAKE_CASE_	,						checkpoint_callback=SCREAMING_SNAKE_CASE_	,						**SCREAMING_SNAKE_CASE_	,						)

					if args.do_train:
										trainer.fit(SCREAMING_SNAKE_CASE_	)

					return trainer





"""simple docstring"""




from functools import lru_cache

@lru_cache
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('k must be an integer.'			)
					if k < 0:
										raise ValueError('k integer must be greater or equal to zero.'			)
					if k == 0:
										return 0
					elif k == 1:
										return 1
					else:
										return fibonacci(k - 1			) + fibonacci(k - 2			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	len(SCREAMING_SNAKE_CASE_			)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					SCREAMING_SNAKE_CASE				=	0
					while True:
										if fibonacci(SCREAMING_SNAKE_CASE_			) >= len_list:
															SCREAMING_SNAKE_CASE				=	i
															break
										i += 1
					SCREAMING_SNAKE_CASE				=	0
					while fibb_k > 0:
										SCREAMING_SNAKE_CASE				=	min(
										    offset + fibonacci(fibb_k - 1			),     len_list - 1			)  # Prevent out of range
										SCREAMING_SNAKE_CASE				=	arr[index_k]
										if item_k_1 == val:
															return index_k
										elif val < item_k_1:
															fibb_k -= 1
										elif val > item_k_1:
															offset += fibonacci(fibb_k - 1			)
															fibb_k -= 2
					else:
										return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()
