

'''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      SCREAMING_SNAKE_CASE__      (    pl.LightningModule				):




    def __init__( self    ,					lowercase__    ,					lowercase__=None    ,					lowercase__="base"    ,					lowercase__=None    ,					lowercase__=None    ,					lowercase__=None    ,					**lowercase__    ,					):






          """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__      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	0
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	Path(self.hparams.output_dir      )
          SCREAMING_SNAKE_CASE_				:      int					=	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=lowercase__    ,					**lowercase__    ,					)
          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    ,					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:
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	AutoTokenizer.from_pretrained(
                    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,					cache_dir=lowercase__    ,					)
          else:
                SCREAMING_SNAKE_CASE_				:      PreTrainedTokenizer					=	tokenizer
          SCREAMING_SNAKE_CASE_				:      List[str]					=	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=lowercase__    ,					)
          else:
                SCREAMING_SNAKE_CASE_				:      Tuple					=	model




    def   __lowerCamelCase				( self    ,					*lowercase__    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.model_type.from_pretrained(*lowercase__    ,					**lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	arg_to_scheduler[self.hparams.lr_scheduler]
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	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   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.model
          SCREAMING_SNAKE_CASE_				:      str					=	["bias", "LayerNorm.weight"]
          SCREAMING_SNAKE_CASE_				:      Tuple					=	[
              {
                  "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(
                    lowercase__    ,					lr=self.hparams.learning_rate    ,					scale_parameter=lowercase__    ,					relative_step=lowercase__      )

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

          SCREAMING_SNAKE_CASE_				:      Dict					=	self.get_lr_scheduler()

          return [optimizer], [scheduler]




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          return self.validation_step(lowercase__    ,					lowercase__      )




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          return self.validation_end(lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	max(1    ,					self.hparams.gpus      )  # TODO: consider num_tpu_cores
          SCREAMING_SNAKE_CASE_				:      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   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__ = False      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return self.train_loader




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """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   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.output_dir.joinpath("best_tfmr"      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	self.step_count
          self.model.save_pretrained(lowercase__      )
          self.tokenizer.save_pretrained(lowercase__      )







    @staticmethod
    def   __lowerCamelCase				( lowercase__    ,					lowercase__      ):






          """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      SCREAMING_SNAKE_CASE__      (    pl.Callback				):




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	trainer.lr_schedulers[0]["scheduler"]
          SCREAMING_SNAKE_CASE_				:      List[str]					=	{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()      )}
          pl_module.logger.log_metrics(lowercase__      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          rank_zero_info("***** Validation results *****"      )
          SCREAMING_SNAKE_CASE_				:      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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          rank_zero_info("***** Test results *****"      )
          SCREAMING_SNAKE_CASE_				:      List[str]					=	trainer.callback_metrics
          # Log and save results to file
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	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  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     int  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       None:
      """simple docstring"""

      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  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     BaseTransformer  ,  SCREAMING_SNAKE_CASE_					:     argparse.Namespace  ,  SCREAMING_SNAKE_CASE_					:     Tuple=None  ,  SCREAMING_SNAKE_CASE_					:     Union[str, Any]=True  ,  SCREAMING_SNAKE_CASE_					:     int=[]  ,  SCREAMING_SNAKE_CASE_					:     str=None  ,  SCREAMING_SNAKE_CASE_					:     Any=None  ,  **SCREAMING_SNAKE_CASE_					:     Optional[int]  ,  )							->       Optional[int]:
      """simple docstring"""

      pl.seed_everything(args.seed   )

      # init model
      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	Path(model.hparams.output_dir   )
      odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_   )

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

      SCREAMING_SNAKE_CASE_				:      Optional[int]					=	{}

      # TODO: remove with PyTorch 1.6 since pl uses native amp
      if args.fpaa:
            SCREAMING_SNAKE_CASE_				:      Tuple					=	1_6
            SCREAMING_SNAKE_CASE_				:      Tuple					=	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_				:      Optional[int]					=	extra_train_kwargs.get("accelerator"  ,  SCREAMING_SNAKE_CASE_   )
      SCREAMING_SNAKE_CASE_				:      int					=	extra_train_kwargs.get("profiler"  ,  SCREAMING_SNAKE_CASE_   )

      SCREAMING_SNAKE_CASE_				:      Dict					=	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'''






import heapq


def  __lowerCamelCase      (				SCREAMING_SNAKE_CASE_					:     List[str]  ,  SCREAMING_SNAKE_CASE_					:     Any  ,  SCREAMING_SNAKE_CASE_					:     Tuple   )							->       Union[str, Any]:
      """simple docstring"""

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


snake_case_					  =	{
    '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]],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
snake_case_					  =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

snake_case_					  =	dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

snake_case_					  =	dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()

