

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							:						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
lowerCAmelCase							:						List[str]     			=		{
    """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							:						List[str]     			=		sorted(arg_to_scheduler.keys())
lowerCAmelCase							:						Any     			=		"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""



class 						__lowercase       (				pl.LightningModule			):




			"""simple docstring"""







			def __init__(				self						:  List[str]     ,		lowerCAmelCase__						:  argparse.Namespace     ,		lowerCAmelCase__						:  List[str]=None     ,		lowerCAmelCase__						:  Any="base"     ,		lowerCAmelCase__						:  Any=None     ,		lowerCAmelCase__						:  Union[str, Any]=None     ,		lowerCAmelCase__						:  Any=None     ,		**lowerCAmelCase__						:  Tuple     ,		):
									super().__init__()
									# TODO: move to self.save_hyperparameters()
									# self.save_hyperparameters()
									# can also expand arguments into trainer signature for easier reading

									self.save_hyperparameters(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= 0
									SCREAMING_SNAKE_CASE_:				Tuple							= Path(self.hparams.output_dir)
									SCREAMING_SNAKE_CASE_:				Dict							= self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
															SCREAMING_SNAKE_CASE_:				Union[str, 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=lowerCAmelCase__     ,		**lowerCAmelCase__     ,		)
									else:
															SCREAMING_SNAKE_CASE_:				PretrainedConfig							= config

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
									for p in extra_model_params:
															if getattr(self.hparams     ,		lowerCAmelCase__     ,		lowerCAmelCase__):
																					assert hasattr(self.config     ,		lowerCAmelCase__), F"model config doesn't have a `{p}` attribute"
																					setattr(self.config     ,		lowerCAmelCase__     ,		getattr(self.hparams     ,		lowerCAmelCase__))

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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		*lowerCAmelCase__						:  List[Any]     ,		**lowerCAmelCase__						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.model_type.from_pretrained(*lowerCAmelCase__     ,		**lowerCAmelCase__)



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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict):
									SCREAMING_SNAKE_CASE_:				List[str]							= self.model
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= ["bias", "LayerNorm.weight"]
									SCREAMING_SNAKE_CASE_:				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:
															SCREAMING_SNAKE_CASE_:				List[str]							= Adafactor(
															    lowerCAmelCase__     ,		lr=self.hparams.learning_rate     ,		scale_parameter=lowerCAmelCase__     ,		relative_step=lowerCAmelCase__)

									else:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= AdamW(
															    lowerCAmelCase__     ,		lr=self.hparams.learning_rate     ,		eps=self.hparams.adam_epsilon)
									SCREAMING_SNAKE_CASE_:				Dict							= optimizer

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

									return [optimizer], [scheduler]



			def  _SCREAMING_SNAKE_CASE    (				self						:  str     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  List[str]):
									return self.validation_step(lowerCAmelCase__     ,		lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple     ,		lowerCAmelCase__						:  str):
									return self.validation_end(lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]):
									SCREAMING_SNAKE_CASE_:				str							= max(1     ,		self.hparams.gpus)  # TODO: consider num_tpu_cores
									SCREAMING_SNAKE_CASE_:				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  _SCREAMING_SNAKE_CASE    (				self						:  Tuple     ,		lowerCAmelCase__						:  str):
									if mode == "test":
															SCREAMING_SNAKE_CASE_:				Dict							= len(self.test_dataloader().dataset)
									else:
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.get_dataloader("train"     ,		self.hparams.train_batch_size     ,		shuffle=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= len(self.train_dataloader().dataset)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  bool = False):
									raise NotImplementedError("You must implement this for your task")



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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any):
									return self.get_dataloader("dev"     ,		self.hparams.eval_batch_size     ,		shuffle=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]):
									return self.get_dataloader("test"     ,		self.hparams.eval_batch_size     ,		shuffle=lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		lowerCAmelCase__						:  Dict):
									return os.path.join(
									    self.hparams.data_dir     ,		"cached_{}_{}_{}".format(
									        lowerCAmelCase__     ,		list(filter(lowerCAmelCase__     ,		self.hparams.model_name_or_path.split("/"))).pop()     ,		str(self.hparams.max_seq_length)     ,		)     ,		)



			@pl.utilities.rank_zero_only
			def  _SCREAMING_SNAKE_CASE    (				self						:  str     ,		lowerCAmelCase__						:  Dict[str, Any]):
									SCREAMING_SNAKE_CASE_:				int							= self.output_dir.joinpath("best_tfmr")
									SCREAMING_SNAKE_CASE_:				Tuple							= self.step_count
									self.model.save_pretrained(lowerCAmelCase__)
									self.tokenizer.save_pretrained(lowerCAmelCase__)




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







class 						__lowercase       (				pl.Callback			):




			"""simple docstring"""







			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Tuple):
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= trainer.lr_schedulers[0]["scheduler"]
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
									pl_module.logger.log_metrics(lowerCAmelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  pl.Trainer     ,		lowerCAmelCase__						:  pl.LightningModule):
									rank_zero_info("***** Validation results *****")
									SCREAMING_SNAKE_CASE_:				List[str]							= trainer.callback_metrics
									# Log results
									for key in sorted(lowerCAmelCase__):
															if key not in ["log", "progress_bar"]:
																					rank_zero_info("{} = {}\n".format(lowerCAmelCase__     ,		str(metrics[key])))




			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  pl.Trainer     ,		lowerCAmelCase__						:  pl.LightningModule):
									rank_zero_info("***** Test results *****")
									SCREAMING_SNAKE_CASE_:				Optional[int]							= trainer.callback_metrics
									# Log and save results to file
									SCREAMING_SNAKE_CASE_:				Optional[int]							= os.path.join(pl_module.hparams.output_dir     ,		"test_results.txt")
									with open(lowerCAmelCase__     ,		"w") as writer:
															for key in sorted(lowerCAmelCase__):
																					if key not in ["log", "progress_bar"]:
																											rank_zero_info("{} = {}\n".format(lowerCAmelCase__     ,		str(metrics[key])))
																											writer.write("{} = {}\n".format(lowerCAmelCase__     ,		str(metrics[key])))







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):

						'''simple docstring'''





						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     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=True       ,			_UpperCAmelCase=[]       ,			_UpperCAmelCase=None       ,			_UpperCAmelCase=None       ,			**_UpperCAmelCase       ,			):

						'''simple docstring'''





						pl.seed_everything(args.seed					)

						# init model
						SCREAMING_SNAKE_CASE_:				str							= Path(model.hparams.output_dir					)
						odir.mkdir(exist_ok=_UpperCAmelCase					)

						# add custom checkpoints
						if checkpoint_callback is None:
												SCREAMING_SNAKE_CASE_:				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:
												SCREAMING_SNAKE_CASE_:				List[str]							= LoggingCallback()

						SCREAMING_SNAKE_CASE_:				Optional[int]							= {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= 16
												SCREAMING_SNAKE_CASE_:				Any							= args.fpaa_opt_level

						if args.gpus > 1:
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= "ddp"

						SCREAMING_SNAKE_CASE_:				List[Any]							= args.accumulate_grad_batches
						SCREAMING_SNAKE_CASE_:				Any							= extra_train_kwargs.get("accelerator"       ,			_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				List[Any]							= extra_train_kwargs.get("profiler"       ,			_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_:				Optional[int]							= 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









import heapq







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				List[Any]							= [(0, start)]  # cost from start node,end node
						SCREAMING_SNAKE_CASE_:				Any							= set()
						while heap:
												((SCREAMING_SNAKE_CASE_)	,					(SCREAMING_SNAKE_CASE_)):				Tuple							= heapq.heappop(_UpperCAmelCase					)
												if u in visited:
																		continue
												visited.add(_UpperCAmelCase					)
												if u == end:
																		return cost
												for v, c in graph[u]:
																		if v in visited:
																								continue
																		SCREAMING_SNAKE_CASE_:				List[str]							= cost + c
																		heapq.heappush(_UpperCAmelCase       ,			(next_item, v)					)
						return -1


lowerCAmelCase							:						Optional[Any]     			=		{
    """A""": [["""B""", 2], ["""C""", 5]],
    """B""": [["""A""", 2], ["""D""", 3], ["""E""", 1], ["""F""", 1]],
    """C""": [["""A""", 5], ["""F""", 3]],
    """D""": [["""B""", 3]],
    """E""": [["""B""", 4], ["""F""", 3]],
    """F""": [["""C""", 3], ["""E""", 3]],
}
lowerCAmelCase							:						Tuple     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
lowerCAmelCase							:						str     			=		{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowerCAmelCase							:						Tuple     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

lowerCAmelCase							:						Union[str, Any]     			=		dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()






