


'''simple docstring'''





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


__lowerCamelCase     :   int       				=	logging.getLogger(__name__)




@dataclass
class 							UpperCAmelCase :
 UpperCAmelCase				=				field(
     metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}	)
 UpperCAmelCase				=				field(
     default=_snake_case	,   metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
 UpperCAmelCase				=				field(
     default=_snake_case	,   metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}	)
 UpperCAmelCase				=				field(default=_snake_case	,   metadata={"help": "Set this flag to use fast tokenization."}	)
 # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
 # or just modify its tokenizer_config.json.
 UpperCAmelCase				=				field(
     default=_snake_case	,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,   )


def 					a__    (				):
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   UpperCAmelCase__  :Optional[int]            =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)

   if len(sys.argv	) == 2 and sys.argv[1].endswith('''.json'''	):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      UpperCAmelCase__  :Tuple            =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
   else:
      UpperCAmelCase__  :Any            =			parser.parse_args_into_dataclasses()

   if (
       os.path.exists(training_args.output_dir	)
       and os.listdir(training_args.output_dir	)
       and training_args.do_train
       and not training_args.overwrite_output_dir
   ):
      raise ValueError(
          F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
          ''' --overwrite_output_dir to overcome.'''	)

   # Setup logging
   logging.basicConfig(
       format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',      datefmt='''%m/%d/%Y %H:%M:%S''',      level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,      )
   logger.warning(
       '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',      training_args.local_rank,      training_args.device,      training_args.n_gpu,      bool(training_args.local_rank != -1	),      training_args.fpaa,      )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank	):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info('''Training/evaluation parameters %s''',      UpperCamelCase_	)

   # Prepare Question-Answering task
   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   UpperCAmelCase__  :List[Any]            =			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path,      cache_dir=model_args.cache_dir,      )
   UpperCAmelCase__  :Union[str, Any]            =			AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,      cache_dir=model_args.cache_dir,      use_fast=UpperCamelCase_,      )
   UpperCAmelCase__  :List[Any]            =			AutoModelForQuestionAnswering.from_pretrained(
       model_args.model_name_or_path,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path	),      config=UpperCamelCase_,      cache_dir=model_args.cache_dir,      )

   # Get datasets
   UpperCAmelCase__  :List[Any]            =			hasattr(model.config,      '''lang2id'''	)
   UpperCAmelCase__  :Any            =			(
       SquadDataset(
           UpperCamelCase_,      tokenizer=UpperCamelCase_,      is_language_sensitive=UpperCamelCase_,      cache_dir=model_args.cache_dir	)
       if training_args.do_train
       else None
   )
   UpperCAmelCase__  :List[str]            =			(
       SquadDataset(
           UpperCamelCase_,      tokenizer=UpperCamelCase_,      mode='''dev''',      is_language_sensitive=UpperCamelCase_,      cache_dir=model_args.cache_dir,      )
       if training_args.do_eval
       else None
   )

   # Data collator
   UpperCAmelCase__  :List[str]            =			DataCollatorWithPadding(UpperCamelCase_,      pad_to_multiple_of=8	) if training_args.fpaa else None

   # Initialize our Trainer
   UpperCAmelCase__  :str            =			Trainer(
       model=UpperCamelCase_,      args=UpperCamelCase_,      train_dataset=UpperCamelCase_,      eval_dataset=UpperCamelCase_,      data_collator=UpperCamelCase_,      )

   # Training
   if training_args.do_train:
      trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
      trainer.save_model()
      # For convenience, we also re-save the tokenizer to the same directory,
      # so that you can share your model easily on huggingface.co/models =)
      if trainer.is_world_master():
         tokenizer.save_pretrained(training_args.output_dir	)


def 					a__    (				UpperCamelCase_	:			Optional[int]	):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
    main()







'''simple docstring'''





import heapq


def 					a__    (				UpperCamelCase_	:			Dict,      UpperCamelCase_	:			Tuple,      UpperCamelCase_	:			Dict	):
   UpperCAmelCase__  :List[Any]            =			[(0, start)]  # cost from start node,end node
   UpperCAmelCase__  :Optional[int]            =			set()
   while heap:
      ((UpperCAmelCase__)      ,	(UpperCAmelCase__))  :Optional[Any]            =			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
         UpperCAmelCase__  :int            =			cost + c
         heapq.heappush(UpperCamelCase_,      (next_item, v)	)
   return -1


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

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

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

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

if __name__ == "__main__":
    import doctest

    doctest.testmod()



