

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


a_         =      logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

a_         =      {
    '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
a_         =      {
    '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
}
a_         =      sorted(arg_to_scheduler.keys())
a_         =      '{' + ', '.join(arg_to_scheduler_choices) + '}'






class _UpperCamelCase							(							pl.LightningModule				):
			'''simple docstring'''







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

				"""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(a		)
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    0
				SCREAMING_SNAKE_CASE				: List[str]			       =    Path(self.hparams.output_dir		)
				SCREAMING_SNAKE_CASE				: str			       =    self.hparams.cache_dir if self.hparams.cache_dir else None
				if config is None:
					SCREAMING_SNAKE_CASE				: 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:
					SCREAMING_SNAKE_CASE				: PretrainedConfig			       =    config

				SCREAMING_SNAKE_CASE				: int			       =    ("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:
					SCREAMING_SNAKE_CASE				: str			       =    AutoTokenizer.from_pretrained(
					    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=a , )
				else:
					SCREAMING_SNAKE_CASE				: PreTrainedTokenizer			       =    tokenizer
				SCREAMING_SNAKE_CASE				: Dict			       =    MODEL_MODES[mode]
				if model is None:
					SCREAMING_SNAKE_CASE				: 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=a , )
				else:
					SCREAMING_SNAKE_CASE				: str			       =    model
			def 							__UpperCamelCase	(     self							:	str , *a							:	Dict , **a							:	str		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    self.model_type.from_pretrained(*a , **a		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    arg_to_scheduler[self.hparams.lr_scheduler]
				SCREAMING_SNAKE_CASE				: List[str]			       =    get_schedule_func(
				    self.opt , num_warmup_steps=self.hparams.warmup_steps , num_training_steps=self.total_steps()		)
				SCREAMING_SNAKE_CASE				: int			       =    {"scheduler": scheduler, "interval": "step", "frequency": 1}
				return scheduler
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.model
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    ["bias", "LayerNorm.weight"]
				SCREAMING_SNAKE_CASE				: List[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:
					SCREAMING_SNAKE_CASE				: int			       =    Adafactor(
					    a , lr=self.hparams.learning_rate , scale_parameter=a , relative_step=a		)

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

				SCREAMING_SNAKE_CASE				: str			       =    self.get_lr_scheduler()

				return [optimizer], [scheduler]
			def 							__UpperCamelCase	(     self							:	List[str] , a							:	Any , a							:	List[str]		)							->    List[str]:

				"""simple docstring"""

				return self.validation_step(a , a		)
			def 							__UpperCamelCase	(     self							:	Dict , a							:	int		)							->    Tuple:

				"""simple docstring"""

				return self.validation_end(a		)
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    max(1 , self.hparams.gpus		)  # TODO: consider num_tpu_cores
				SCREAMING_SNAKE_CASE				: str			       =    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							:	Union[str, Any] , a							:	Dict		)							->    Optional[Any]:

				"""simple docstring"""

				if mode == "test":
					SCREAMING_SNAKE_CASE				: List[Any]			       =    len(self.test_dataloader().dataset		)
				else:
					SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.get_dataloader("train" , self.hparams.train_batch_size , shuffle=a		)
					SCREAMING_SNAKE_CASE				: Tuple			       =    len(self.train_dataloader().dataset		)
			def 							__UpperCamelCase	(     self							:	List[str] , a							:	str , a							:	int , a							:	bool = False		)							->    List[str]:

				"""simple docstring"""

				raise NotImplementedError("You must implement this for your task"		)
			def 							__UpperCamelCase	(     self							:	Dict		)							->    List[Any]:

				"""simple docstring"""

				return self.train_loader
			def 							__UpperCamelCase	(     self							:	int		)							->    Any:

				"""simple docstring"""

				return self.get_dataloader("dev" , self.hparams.eval_batch_size , shuffle=a		)
			def 							__UpperCamelCase	(     self							:	Any		)							->    Optional[Any]:

				"""simple docstring"""

				return self.get_dataloader("test" , self.hparams.eval_batch_size , shuffle=a		)
			def 							__UpperCamelCase	(     self							:	List[Any] , a							:	Union[str, Any]		)							->    str:

				"""simple docstring"""

				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 							__UpperCamelCase	(     self							:	Union[str, Any] , a							:	Dict[str, Any]		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.output_dir.joinpath("best_tfmr"		)
				SCREAMING_SNAKE_CASE				: int			       =    self.step_count
				self.model.save_pretrained(a		)
				self.tokenizer.save_pretrained(a		)






			@staticmethod
			def 							__UpperCamelCase	(     a							:	int , a							:	List[Any]		)							->    Optional[Any]:

				"""simple docstring"""

				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 _UpperCamelCase							(							pl.Callback				):
			'''simple docstring'''







			def 							__UpperCamelCase	(     self							:	Dict , a							:	List[str] , a							:	Union[str, Any]		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    trainer.lr_schedulers[0]["scheduler"]
				SCREAMING_SNAKE_CASE				: List[Any]			       =    {F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
				pl_module.logger.log_metrics(a		)
			def 							__UpperCamelCase	(     self							:	Any , a							:	pl.Trainer , a							:	pl.LightningModule		)							->    str:

				"""simple docstring"""

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

				"""simple docstring"""

				rank_zero_info("***** Test results *****"		)
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    trainer.callback_metrics
				# Log and save results to file
				SCREAMING_SNAKE_CASE				: int			       =    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 	lowerCamelCase__	(   _a							,     _a):
	#  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=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 	lowerCamelCase__	(   _a							,     _a							,     _a=None							,     _a=True							,     _a=[]							,     _a=None							,     _a=None							,     **_a							,     ):
	pl.seed_everything(args.seed)

	# init model
	SCREAMING_SNAKE_CASE				: Any			       =    Path(model.hparams.output_dir)
	odir.mkdir(exist_ok=_a)

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

	SCREAMING_SNAKE_CASE				: Any			       =    {}

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

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

	SCREAMING_SNAKE_CASE				: List[str]			       =    args.accumulate_grad_batches
	SCREAMING_SNAKE_CASE				: Dict			       =    extra_train_kwargs.get("accelerator"							,     _a)
	SCREAMING_SNAKE_CASE				: List[str]			       =    extra_train_kwargs.get("profiler"							,     _a)

	SCREAMING_SNAKE_CASE				: List[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


from __future__ import annotations






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	List[Any] , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vertices
				SCREAMING_SNAKE_CASE				: Tuple			       =    [[0] * vertices for _ in range(a		)]
			def 							__UpperCamelCase	(     self							:	Dict , a							:	list[int]		)							->    None:

				"""simple docstring"""

				print("Vertex \t Distance from Source"		)
				for vertex in range(self.vertices		):
					print(a , "\t\t" , distances_from_source[vertex]		)
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	list[int] , a							:	list[bool]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    1e7
				SCREAMING_SNAKE_CASE				: int			       =    0

				# Search not nearest vertex not in the shortest path tree
				for vertex in range(self.vertices		):
					if distances_from_source[vertex] < minimum and visited[vertex] is False:
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    distances_from_source[vertex]
						SCREAMING_SNAKE_CASE				: Optional[Any]			       =    vertex
				return min_index






			def 							__UpperCamelCase	(     self							:	Any , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    [int(1e7		)] * self.vertices  # distances from the source
				SCREAMING_SNAKE_CASE				: Tuple			       =    0
				SCREAMING_SNAKE_CASE				: List[Any]			       =    [False] * self.vertices

				for _ in range(self.vertices		):
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.minimum_distance(a , a		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    True

					# Update dist value of the adjacent vertices
					# of the picked vertex only if the current
					# distance is greater than new distance and
					# the vertex in not in the shortest path tree
					for v in range(self.vertices		):
						if (
						    self.graph[u][v] > 0
						    and visited[v] is False
						    and distances[v] > distances[u] + self.graph[u][v]
						):
							SCREAMING_SNAKE_CASE				: int			       =    distances[u] + self.graph[u][v]

				self.print_solution(a		)


if __name__ == "__main__":
			a_         =      Graph(9)
			a_         =      [
			    [0, 4, 0, 0, 0, 0, 0, 8, 0],
			    [4, 0, 8, 0, 0, 0, 0, 11, 0],
			    [0, 8, 0, 7, 0, 4, 0, 0, 2],
			    [0, 0, 7, 0, 9, 14, 0, 0, 0],
			    [0, 0, 0, 9, 0, 10, 0, 0, 0],
			    [0, 0, 4, 14, 10, 0, 2, 0, 0],
			    [0, 0, 0, 0, 0, 2, 0, 1, 6],
			    [8, 11, 0, 0, 0, 0, 1, 0, 7],
			    [0, 0, 2, 0, 0, 0, 6, 7, 0],
			]
			graph.dijkstra(0)