




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


_lowerCamelCase					:     List[Any]					  =  logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

_lowerCamelCase					:     Optional[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
_lowerCamelCase					:     Optional[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
}
_lowerCamelCase					:     Union[str, Any]					  =  sorted(arg_to_scheduler.keys())
_lowerCamelCase					:     List[str]					  =  "{" + ", ".join(arg_to_scheduler_choices) + "}"
class 				__snake_case     (pl.LightningModule						):



				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						argparse.Namespace						,		_UpperCAmelCase					:						Dict=None						,		_UpperCAmelCase					:						Any="base"						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Any=None						,		_UpperCAmelCase					:						List[str]=None						,		**_UpperCAmelCase					:						Dict						,		)     ->			Dict:





								'''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(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					0
								_lowerCAmelCase			:			Optional[Any]        =					Path(self.hparams.output_dir					)
								_lowerCAmelCase			:			int        =					self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
												_lowerCAmelCase			:			Dict        =					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:
												_lowerCAmelCase			:			PretrainedConfig        =					config

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



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		*_UpperCAmelCase					:						Union[str, Any]						,		**_UpperCAmelCase					:						str					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					self.model_type.from_pretrained(*_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			str:





								'''simple docstring'''




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



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					self.model
								_lowerCAmelCase			:			List[Any]        =					["""bias""", """LayerNorm.weight"""]
								_lowerCAmelCase			:			List[str]        =					[
								    {
								        """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			:			Union[str, Any]        =					Adafactor(
												    _UpperCAmelCase						,		lr=self.hparams.learning_rate						,		scale_parameter=_UpperCAmelCase						,		relative_step=_UpperCAmelCase					)

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

								_lowerCAmelCase			:			Optional[int]        =					self.get_lr_scheduler()

								return [optimizer], [scheduler]



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						Optional[Any]						,		_UpperCAmelCase					:						Optional[Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								return self.validation_step(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple						,		_UpperCAmelCase					:						List[str]					)     ->			Union[str, Any]:





								'''simple docstring'''




								return self.validation_end(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			int:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					max(1						,		self.hparams.gpus					)  # TODO: consider num_tpu_cores
								_lowerCAmelCase			:			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 					SCREAMING_SNAKE_CASE				(					self					:						str						,		_UpperCAmelCase					:						Optional[int]					)     ->			Optional[int]:





								'''simple docstring'''




								if stage == "test":
												_lowerCAmelCase			:			int        =					len(self.test_dataloader().dataset					)
								else:
												_lowerCAmelCase			:			List[str]        =					self.get_dataloader("""train"""						,		self.hparams.train_batch_size						,		shuffle=_UpperCAmelCase					)
												_lowerCAmelCase			:			Optional[Any]        =					len(self.train_dataloader().dataset					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						bool = False					)     ->			Tuple:





								'''simple docstring'''




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



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			Tuple:





								'''simple docstring'''




								return self.train_loader



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			List[str]:





								'''simple docstring'''




								return self.get_dataloader("""dev"""						,		self.hparams.eval_batch_size						,		shuffle=_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			List[str]:





								'''simple docstring'''




								return self.get_dataloader("""test"""						,		self.hparams.eval_batch_size						,		shuffle=_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						int					)     ->			Dict:





								'''simple docstring'''




								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 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						Dict[str, Any]					)     ->			None:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					self.output_dir.joinpath("""best_tfmr"""					)
								_lowerCAmelCase			:			List[str]        =					self.step_count
								self.model.save_pretrained(_UpperCAmelCase					)
								self.tokenizer.save_pretrained(_UpperCAmelCase					)



				@staticmethod
				def 					SCREAMING_SNAKE_CASE				(					_UpperCAmelCase					:						List[Any]						,		_UpperCAmelCase					:						List[Any]					)     ->			Tuple:





								'''simple docstring'''




								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=""""""						,		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 				__snake_case     (pl.Callback						):



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						Optional[int]					)     ->			List[Any]:





								'''simple docstring'''




								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 				__snake_case     (pl.Callback						):



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		_UpperCAmelCase					:						Tuple						,		_UpperCAmelCase					:						str					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					trainer.lr_schedulers[0]["""scheduler"""]
								_lowerCAmelCase			:			List[Any]        =					{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()					)}
								pl_module.logger.log_metrics(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]						,		_UpperCAmelCase					:						pl.Trainer						,		_UpperCAmelCase					:						pl.LightningModule					)     ->			Any:





								'''simple docstring'''




								rank_zero_info("""***** Validation results *****"""					)
								_lowerCAmelCase			:			int        =					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 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]						,		_UpperCAmelCase					:						pl.Trainer						,		_UpperCAmelCase					:						pl.LightningModule					)     ->			Optional[int]:





								'''simple docstring'''




								rank_zero_info("""***** Test results *****"""					)
								_lowerCAmelCase			:			List[Any]        =					trainer.callback_metrics
								# Log and save results to file
								_lowerCAmelCase			:			Dict        =					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 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				#  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_       :       str=None					,			UpperCamelCase_       :       List[Any]=True					,			UpperCamelCase_       :       List[str]=None					,			UpperCamelCase_       :       Union[str, Any]=[]					,			UpperCamelCase_       :       int=None					,			UpperCamelCase_       :       str=None					,			**UpperCamelCase_       :       str					,			):
				'''simple docstring'''
				pl.seed_everything(args.seed				)

				# init model
				_lowerCAmelCase			:			str        =					Path(model.hparams.output_dir				)
				odir.mkdir(exist_ok=UpperCamelCase_				)

				# add custom checkpoints
				if checkpoint_callback is None:
								_lowerCAmelCase			:			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(UpperCamelCase_				)
				if logging_callback is None:
								_lowerCAmelCase			:			List[Any]        =					LoggingCallback()

				_lowerCAmelCase			:			int        =					{}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								_lowerCAmelCase			:			Optional[int]        =					16
								# train_params["amp_level"] = args.fp16_opt_level

				if args.gpus > 1:
								_lowerCAmelCase			:			Tuple        =					"""auto"""  # "ddp"
								_lowerCAmelCase			:			Any        =					"""ddp"""

				_lowerCAmelCase			:			str        =					args.accumulate_grad_batches
				_lowerCAmelCase			:			Optional[int]        =					None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
				_lowerCAmelCase			:			List[str]        =					"""auto"""

				_lowerCAmelCase			:			Any        =					pl.Trainer.from_argparse_args(
				    UpperCamelCase_					,			weights_summary=UpperCamelCase_					,			callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]					,			logger=UpperCamelCase_					,			**UpperCamelCase_					,			)

				if args.do_train:
								trainer.fit(UpperCamelCase_				)

				return trainer







def 							_UpperCAmelCase     (UpperCamelCase_       :       int					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Dict        =					len(UpperCamelCase_				)
				# Create a stack and push index of fist element to it
				_lowerCAmelCase			:			Tuple        =					[]
				st.append(0				)

				# Span value of first element is always 1
				_lowerCAmelCase			:			List[Any]        =					1

				# Calculate span values for rest of the elements
				for i in range(1					,			UpperCamelCase_				):
								# Pop elements from stack while stack is not
								# empty and top of stack is smaller than price[i]
								while len(UpperCamelCase_				) > 0 and price[st[0]] <= price[i]:
												st.pop()

								# If stack becomes empty, then price[i] is greater
								# than all elements on left of it, i.e. price[0],
								# price[1], ..price[i-1]. Else the price[i]  is
								# greater than elements after top of stack
								_lowerCAmelCase			:			Union[str, Any]        =					i + 1 if len(UpperCamelCase_				) <= 0 else (i - st[0])

								# Push this element to stack
								st.append(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				for i in range(0					,			UpperCamelCase_				):
								print(arr[i]					,			end=""" """				)


# Driver program to test above function
_lowerCamelCase					:     Union[str, Any]					  =  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
_lowerCamelCase					:     List[Any]					  =  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))

