





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__					:							Dict													=     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

snake_case__					:							str													=     {
    '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__					:							Tuple													=     {
    '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__					:							str													=     sorted(arg_to_scheduler.keys())
snake_case__					:							Union[str, Any]													=     '{' + ', '.join(arg_to_scheduler_choices) + '}'





class A_					(			pl.LightningModule   ):




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




   def _lowerCAmelCase      (self	:str )->					Tuple:
         __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 _lowerCAmelCase      (self	:int )->					List[Any]:
         __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 )
                 ],  # check this named paramters
                 '''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 _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					Optional[Any]:
         return self.validation_step(_UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Optional[int] )->					Any:
         return self.validation_end(_UpperCamelCase )




   def _lowerCAmelCase      (self	:Tuple )->					int:
         __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 _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:str )->					List[Any]:
         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 _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:bool = False )->					str:
         raise NotImplementedError('''You must implement this for your task''' )




   def _lowerCAmelCase      (self	:Any )->					List[Any]:
         return self.train_loader




   def _lowerCAmelCase      (self	:Tuple )->					List[Any]:
         return self.get_dataloader('''dev'''    ,   self.hparams.eval_batch_size    ,   shuffle=_UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict )->					Union[str, Any]:
         return self.get_dataloader('''test'''    ,   self.hparams.eval_batch_size    ,   shuffle=_UpperCamelCase )




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:Optional[int] )->					List[Any]:
         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 _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:Dict[str, Any] )->					None:
         __A          =				self.output_dir.joinpath('''best_tfmr''' )
         __A          =				self.step_count
         self.model.save_pretrained(_UpperCamelCase )
         self.tokenizer.save_pretrained(_UpperCamelCase )






   @staticmethod
   def _lowerCAmelCase      (_UpperCamelCase	:Optional[Any]    ,   _UpperCamelCase	:Any )->					Any:
         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=str(Path(_UpperCamelCase ).parent / '''test_run''' / '''cache''' )    ,   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 A_					(			pl.Callback   ):




   def _lowerCAmelCase      (self	:List[Any]    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:Union[str, Any] )->					Tuple:
         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 A_					(			pl.Callback   ):




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # print(pl_module.model.rag)
         for name, param in pl_module.model.rag.named_parameters():
               if param.grad is None:
                     print(_UpperCamelCase )





class A_					(			pl.Callback   ):




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[Any] )->					List[Any]:
         __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 _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:pl.Trainer    ,   _UpperCamelCase	:pl.LightningModule )->					Dict:
         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 _lowerCAmelCase      (self	:Tuple    ,   _UpperCamelCase	:pl.Trainer    ,   _UpperCamelCase	:pl.LightningModule )->					Optional[Any]:
         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				(	lowerCamelCase:   Any					,				lowerCamelCase:   List[Any] )	->						None:







      '''simple docstring'''






      parser.add_argument(
          '''--output_dir'''					,				default=str(Path(lowerCamelCase ).parent / '''test_run''' / '''model_checkpoints''' )					,				type=lowerCamelCase					,				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=lowerCamelCase					,				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=lowerCamelCase )
      parser.add_argument('''--max_grad_norm'''					,				dest='''gradient_clip_val'''					,				default=1.0					,				type=lowerCamelCase					,				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=lowerCamelCase					,				default=1					,				help='''Number of updates steps to accumulate before performing a backward/update pass.'''					,				)
      parser.add_argument('''--seed'''					,				type=lowerCamelCase					,				default=42					,				help='''random seed for initialization''' )
      parser.add_argument(
          '''--data_dir'''					,				default=str(Path(lowerCamelCase ).parent / '''test_run''' / '''dummy-train-data''' )					,				type=lowerCamelCase					,				help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''					,				)







def _a				(	lowerCamelCase:   BaseTransformer					,				lowerCamelCase:   argparse.Namespace					,				lowerCamelCase:   int=None					,				lowerCamelCase:   Any=True					,				lowerCamelCase:   Tuple=[]					,				lowerCamelCase:   str=None					,				lowerCamelCase:   List[str]=None					,				**lowerCamelCase:   Any					,				)	->						Optional[Any]:







      '''simple docstring'''






      pl.seed_everything(args.seed )

      # init model
      __A          =				Path(model.hparams.output_dir )
      odir.mkdir(exist_ok=lowerCamelCase )

      # 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(lowerCamelCase )
      if logging_callback is None:
            __A          =				LoggingCallback()

      __A          =				{}

      if args.fpaa:
            __A          =				16

      if args.gpus > 1:
            __A          =				'''auto'''
            __A          =				'''ddp'''

      __A          =				args.accumulate_grad_batches
      __A          =				None
      __A          =				'''auto'''

      __A          =				pl.Trainer.from_argparse_args(
          lowerCamelCase					,				weights_summary=lowerCamelCase					,				callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]					,				logger=lowerCamelCase					,				val_check_interval=1					,				num_sanity_val_steps=2					,				**lowerCamelCase					,				)

      if args.do_train:
            trainer.fit(lowerCamelCase )

      else:
            print('''RAG modeling tests with new set functions successfuly executed!''' )
      return trainer












from __future__ import annotations



def _a				(	lowerCamelCase:   list[float]					,				lowerCamelCase:   Tuple )	->						List[str]:







      '''simple docstring'''






      print(F"""Vertex\tShortest Distance from vertex {src}""" )
      for i, d in enumerate(lowerCamelCase ):
            print(F"""{i}\t\t{d}""" )



def _a				(	lowerCamelCase:   list[dict[str, int]]					,				lowerCamelCase:   list[float]					,				lowerCamelCase:   int )	->						Union[str, Any]:







      '''simple docstring'''






      for j in range(lowerCamelCase ):
            __A   ,						__A   ,						__A          =				(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
            if distance[u] != float('''inf''' ) and distance[u] + w < distance[v]:
                  return True
      return False



def _a				(	lowerCamelCase:   list[dict[str, int]]					,				lowerCamelCase:   int					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						list[float]:







      '''simple docstring'''






      __A          =				[float('''inf''' )] * vertex_count
      __A          =				0.0

      for _ in range(vertex_count - 1 ):
            for j in range(lowerCamelCase ):
                  __A   ,						__A   ,						__A          =				(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

                  if distance[u] != float('''inf''' ) and distance[u] + w < distance[v]:
                        __A          =				distance[u] + w

      __A          =				check_negative_cycle(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      if negative_cycle_exists:
            raise Exception('''Negative cycle found''' )

      return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       snake_case__					:							Dict													=     int(input('Enter number of vertices: ').strip())
       snake_case__					:							Optional[int]													=     int(input('Enter number of edges: ').strip())

       snake_case__					:							list[dict[str, int]]													=     [{} for _ in range(E)]

       for i in range(E):
              print('Edge ', i + 1)
              snake_case__						,    snake_case__						,    snake_case__					:							Dict													=     (
                  int(x)
                  for x in input('Enter source, destination, weight: ').strip().split(' ')
              )
              snake_case__					:							List[Any]													=     {'src': src, 'dst': dest, 'weight': weight}

       snake_case__					:							Union[str, Any]													=     int(input('\nEnter shortest path source:').strip())
       snake_case__					:							List[Any]													=     bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)





