






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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__	:			Any			   = logging.getLogger(__name__)






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				return (preds == labels).mean()
@dataclass
class       _UpperCAmelCase					:

				"""simple docstring"""




				lowercase__											=       field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
				lowercase__											=       field(
				    default=snake_case__		,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
				lowercase__											=       field(
				    default=snake_case__		,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
				lowercase__											=       field(
				    default=snake_case__		,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}		,)



@dataclass
class       _UpperCAmelCase					:

				"""simple docstring"""




				lowercase__											=       field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()	)}	)
				lowercase__											=       field(metadata={"""help""": """Should contain the data files for the task."""}	)
				lowercase__											=       field(
				    default=128		,metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }		,)
				lowercase__											=       field(
				    default=snake_case__		,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}	)






def   a			(						):






				'''simple docstring'''
				# 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.

				lowercase__    =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
				lowercase__ ,						lowercase__ ,						lowercase__    =			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'''		,   SCREAMING_SNAKE_CASE_				)

				# Set seed
				set_seed(training_args.seed				)

				try:
								lowercase__    =			processors[data_args.task_name]()
								lowercase__    =			processor.get_labels()
								lowercase__    =			len(SCREAMING_SNAKE_CASE_				)
				except KeyError:
								raise ValueError('''Task not found: %s''' % (data_args.task_name)				)

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

				lowercase__    =			AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,   num_labels=SCREAMING_SNAKE_CASE_		,   finetuning_task=data_args.task_name		,   cache_dir=model_args.cache_dir		,   )
				lowercase__    =			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		,   )
				lowercase__    =			AutoModelForMultipleChoice.from_pretrained(
				    model_args.model_name_or_path		,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)		,   config=SCREAMING_SNAKE_CASE_		,   cache_dir=model_args.cache_dir		,   )

				# Get datasets
				lowercase__    =			(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir		,   tokenizer=SCREAMING_SNAKE_CASE_		,   task=data_args.task_name		,   max_seq_length=data_args.max_seq_length		,   overwrite_cache=data_args.overwrite_cache		,   mode=Split.train		,   )
				    if training_args.do_train
				    else None
				)
				lowercase__    =			(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir		,   tokenizer=SCREAMING_SNAKE_CASE_		,   task=data_args.task_name		,   max_seq_length=data_args.max_seq_length		,   overwrite_cache=data_args.overwrite_cache		,   mode=Split.dev		,   )
				    if training_args.do_eval
				    else None
				)

				def compute_metrics(lowerCamelCase_				) -> Dict:
								lowercase__    =			np.argmax(p.predictions		,   axis=1				)
								return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE_		,   p.label_ids				)}

				# Data collator
				lowercase__    =			DataCollatorWithPadding(SCREAMING_SNAKE_CASE_		,   pad_to_multiple_of=8				) if training_args.fpaa else None

				# Initialize our Trainer
				lowercase__    =			Trainer(
				    model=SCREAMING_SNAKE_CASE_		,   args=SCREAMING_SNAKE_CASE_		,   train_dataset=SCREAMING_SNAKE_CASE_		,   eval_dataset=SCREAMING_SNAKE_CASE_		,   compute_metrics=SCREAMING_SNAKE_CASE_		,   data_collator=SCREAMING_SNAKE_CASE_		,   )

				# 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				)

    # Evaluation
				lowercase__    =			{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''				)

								lowercase__    =			trainer.evaluate()

								lowercase__    =			os.path.join(training_args.output_dir		,   '''eval_results.txt'''				)
								if trainer.is_world_master():
												with open(SCREAMING_SNAKE_CASE_		,   '''w'''				) as writer:
																logger.info('''***** Eval results *****'''				)
																for key, value in result.items():
																				logger.info('''  %s = %s'''		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_				)
																				writer.write('''%s = %s\n''' % (key, value)				)

																results.update(SCREAMING_SNAKE_CASE_				)

				return results






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()










from queue import PriorityQueue
from typing import Any

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				PriorityQueue      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				float | int      ,      ):
					for nxt, d in graph[v]:
										if nxt in visited_forward:
															continue
										__lowerCAmelCase				   =       cst_fwd.get(SCREAMING_SNAKE_CASE_      ,      np.inf     )
										__lowerCAmelCase				   =       cst_fwd[v] + d
										if new_cost_f < old_cost_f:
															queue.put((new_cost_f, nxt)     )
															__lowerCAmelCase				   =       new_cost_f
															__lowerCAmelCase				   =       v
										if nxt in visited_backward:
															if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																				__lowerCAmelCase				   =       cst_fwd[v] + d + cst_bwd[nxt]
					return shortest_distance







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict     ):
					__lowerCAmelCase				   =       -1

					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       {source: 0}
					__lowerCAmelCase				   =       {destination: 0}
					__lowerCAmelCase				   =       {source: None}
					__lowerCAmelCase				   =       {destination: None}
					__lowerCAmelCase				   =       PriorityQueue()
					__lowerCAmelCase				   =       PriorityQueue()

					__lowerCAmelCase				   =       np.inf

					queue_forward.put((0, source)     )
					queue_backward.put((0, destination)     )

					if source == destination:
										return 0

					while not queue_forward.empty() and not queue_backward.empty():
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_forward.get()
										visited_forward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_backward.get()
										visited_backward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
															break

					if shortest_distance != np.inf:
										__lowerCAmelCase				   =       shortest_distance
					return shortest_path_distance


UpperCamelCase__    			=			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
UpperCamelCase__    			=			{
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()




