



'''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


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

require_version("pytorch_lightning>=1.0.4")

_lowercase:				Union[str, 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
_lowercase:				Optional[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
}
_lowercase:				str       =	sorted(arg_to_scheduler.keys())
_lowercase:				List[str]       =	"{" + ", ".join(arg_to_scheduler_choices) + "}"



class 			_lowercase							(     pl.LightningModule			):





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	, lowerCamelCase_=None	, lowerCamelCase_="base"	, lowerCamelCase_=None	, lowerCamelCase_=None	, lowerCamelCase_=None	, **lowerCamelCase_	, ):
						"""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__	)
						a        =     0
						a        =     Path(self.hparams.output_dir	)
						a        =     self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
											a        =     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:
											a        =     config

						a        =     ("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:
											a        =     AutoTokenizer.from_pretrained(
											    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	, cache_dir=UpperCamelCase__	, )
						else:
											a        =     tokenizer
						a        =     MODEL_MODES[mode]
						if model is None:
											a        =     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:
											a        =     model




	def   UpperCamelCase_ (self	, *lowerCamelCase_	, **lowerCamelCase_	):
						"""simple docstring"""

						a        =     self.model_type.from_pretrained(*UpperCamelCase__	, **UpperCamelCase__	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     arg_to_scheduler[self.hparams.lr_scheduler]
						a        =     get_schedule_func(
						    self.opt	, num_warmup_steps=self.hparams.warmup_steps	, num_training_steps=self.total_steps()	)
						a        =     {"scheduler": scheduler, "interval": "step", "frequency": 1}
						return scheduler




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     self.model
						a        =     ["bias", "LayerNorm.weight"]
						a        =     [
						    {
						        "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        =     Adafactor(
											    UpperCamelCase__	, lr=self.hparams.learning_rate	, scale_parameter=UpperCamelCase__	, relative_step=UpperCamelCase__	)

						else:
											a        =     AdamW(
											    UpperCamelCase__	, lr=self.hparams.learning_rate	, eps=self.hparams.adam_epsilon	)
						a        =     optimizer

						a        =     self.get_lr_scheduler()

						return [optimizer], [scheduler]




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						return self.validation_step(UpperCamelCase__	, UpperCamelCase__	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						return self.validation_end(UpperCamelCase__	)




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						a        =     max(1	, self.hparams.gpus	)  # TODO: consider num_tpu_cores
						a        =     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	, lowerCamelCase_	):
						"""simple docstring"""

						if stage == "test":
											a        =     len(self.test_dataloader().dataset	)
						else:
											a        =     self.get_dataloader("train"	, self.hparams.train_batch_size	, shuffle=UpperCamelCase__	)
											a        =     len(self.train_dataloader().dataset	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_ = False	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						return self.train_loader




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	):
						"""simple docstring"""

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




	def   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""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   UpperCamelCase_ (self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     self.output_dir.joinpath("best_tfmr"	)
						a        =     self.step_count
						self.model.save_pretrained(UpperCamelCase__	)
						self.tokenizer.save_pretrained(UpperCamelCase__	)




	@staticmethod
	def   UpperCamelCase_ (lowerCamelCase_	, lowerCamelCase_	):
						"""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 			_lowercase							(     pl.Callback			):





	"""simple docstring"""




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""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 			_lowercase							(     pl.Callback			):





	"""simple docstring"""




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     trainer.lr_schedulers[0]["scheduler"]
						a        =     {F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
						pl_module.logger.log_metrics(UpperCamelCase__	)




	def   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						rank_zero_info("***** Validation results *****"	)
						a        =     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   UpperCamelCase_ (self	, lowerCamelCase_	, lowerCamelCase_	):
						"""simple docstring"""

						rank_zero_info("***** Test results *****"	)
						a        =     trainer.callback_metrics
						# Log and save results to file
						a        =     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 					a(   A :					List[Any]				,  A :					Tuple )	-> Dict:
					"""simple docstring"""





					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=42				,  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 					a(   A :					Optional[int]				,  A :					Dict				,  A :					List[str]=None				,  A :					Optional[Any]=True				,  A :					int=None				,  A :					List[str]=[]				,  A :					List[str]=None				,  A :					Optional[Any]=None				,  **A :					int				,  )	-> Union[str, Any]:
					"""simple docstring"""





					pl.seed_everything(args.seed )

					# init model
					a        =     Path(model.hparams.output_dir )
					odir.mkdir(exist_ok=A )

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

					a        =     {}

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

					if args.gpus > 1:
										a        =     "auto"  # "ddp"
										a        =     "ddp"

					a        =     args.accumulate_grad_batches
					a        =     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
					a        =     "auto"

					a        =     pl.Trainer.from_argparse_args(
					    A				,  weights_summary=A				,  callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]				,  logger=A				,  **A				,  )

					if args.do_train:
										trainer.fit(A )

					return trainer





def 					a(   A :					Optional[Any]				,  A :					List[str] )	-> List[Any]:
					"""simple docstring"""





					print("\nVertex Distance" )
					for i in range(A ):
										if dist[i] != float("inf" ):
															print(A				,  "\t"				,  int(dist[i] )				,  end="\t" )
										else:
															print(A				,  "\t"				,  "INF"				,  end="\t" )
										print()






def 					a(   A :					str				,  A :					int				,  A :					int )	-> Optional[int]:
					"""simple docstring"""





					a        =     float("inf" )
					a        =     -1
					for i in range(A ):
										if (not vset[i]) and mdist[i] < min_val:
															a        =     i
															a        =     mdist[i]
					return min_ind






def 					a(   A :					List[str]				,  A :					Union[str, Any]				,  A :					Dict )	-> Tuple:
					"""simple docstring"""





					a        =     [float("inf" ) for _ in range(A )]
					a        =     [False for _ in range(A )]
					a        =     0.0

					for _ in range(v - 1 ):
										a        =     min_dist(A				,  A				,  A )
										a        =     True

										for i in range(A ):
															if (
															    (not vset[i])
															    and graph[u][i] != float("inf" )
															    and mdist[u] + graph[u][i] < mdist[i]
															):
																				a        =     mdist[u] + graph[u][i]

					print_dist(A				,  A )


if __name__ == "__main__":
				_lowercase:				Any       =	int(input("Enter number of vertices: ").strip())
				_lowercase:				Optional[Any]       =	int(input("Enter number of edges: ").strip())

				_lowercase:				List[str]       =	[[float("inf") for i in range(V)] for j in range(V)]

				for i in range(V):
								_lowercase:				List[Any]       =	0.0

				for i in range(E):
								print("\nEdge ", i + 1)
								_lowercase:				Tuple       =	int(input("Enter source:").strip())
								_lowercase:				Tuple       =	int(input("Enter destination:").strip())
								_lowercase:				int       =	float(input("Enter weight:").strip())
								_lowercase:				str       =	weight

				_lowercase:				Union[str, Any]       =	int(input("\nEnter shortest path source:").strip())
				dijkstra(graph, V, gsrc)
