





'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.getLogger(__name__)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any						,    lowerCamelCase_     :					List[str]			):
			return (preds == labels).mean()






@dataclass
class 							__lowercase    :







	'''simple docstring'''

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






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			str     =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()  )}  )
	a      :			str     =    field(metadata={"help": "Should contain the data files for the task."}  )
	a      :			int     =    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."
	        )
	    }	, )
	a      :			bool     =    field(
	    default=lowerCAmelCase__	, metadata={"help": "Overwrite the cached training and evaluation sets"}  )





def 				_lowerCAmelCase  (			):
			# 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'''						,    lowerCamelCase_			)

			# Set seed
			set_seed(training_args.seed			)

			try:
						__lowercase			  =							processors[data_args.task_name]()
						__lowercase			  =							processor.get_labels()
						__lowercase			  =							len(lowerCamelCase_			)
			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=lowerCamelCase_						,    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=lowerCamelCase_						,    cache_dir=model_args.cache_dir						,    )

			# Get datasets
			__lowercase			  =							(
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir						,    tokenizer=lowerCamelCase_						,    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=lowerCamelCase_						,    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_     :					EvalPrediction			) -> Dict:
						__lowercase			  =							np.argmax(p.predictions						,    axis=1			)
						return {"acc": simple_accuracy(lowerCamelCase_						,    p.label_ids			)}

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

			# Initialize our Trainer
			__lowercase			  =							Trainer(
			    model=lowerCamelCase_						,    args=lowerCamelCase_						,    train_dataset=lowerCamelCase_						,    eval_dataset=lowerCamelCase_						,    compute_metrics=lowerCamelCase_						,    data_collator=lowerCamelCase_						,    )

			# 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(lowerCamelCase_						,    '''w'''			) as writer:
												logger.info('''***** Eval results *****'''			)
												for key, value in result.items():
															logger.info('''  %s = %s'''						,    lowerCamelCase_						,    lowerCamelCase_			)
															writer.write('''%s = %s\n''' % (key, value)			)

												results.update(lowerCamelCase_			)

			return results




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Tuple			):
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
	main()












'''simple docstring'''



import heapq




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict			):
			__lowercase			  =							[]

			# for each node and his adjacency list add them and the rank of the node to queue
			# using heapq module the queue will be filled like a Priority Queue
			# heapq works with a min priority queue, so I used -1*len(v) to build it
			for key, value in graph.items():
						# O(log(n))
						heapq.heappush(lowerCamelCase_						,    [-1 * len(lowerCamelCase_			), (key, value)]			)

			# chosen_vertices = set of chosen vertices
			__lowercase			  =							set()

			# while queue isn't empty and there are still edges
			#   (queue[0][0] is the rank of the node with max rank)
			while queue and queue[0][0] != 0:
						# extract vertex with max rank from queue and add it to chosen_vertices
						__lowercase			  =							heapq.heappop(lowerCamelCase_			)[1][0]
						chosen_vertices.add(lowerCamelCase_			)

						# Remove all arcs adjacent to argmax
						for elem in queue:
									# if v haven't adjacent node, skip
									if elem[0] == 0:
												continue
									# if argmax is reachable from elem
									# remove argmax from elem's adjacent list and update his rank
									if argmax in elem[1][1]:
												__lowercase			  =							elem[1][1].index(lowerCamelCase_			)
												del elem[1][1][index]
												elem[0] += 1
        # re-order the queue
						heapq.heapify(lowerCamelCase_			)
			return chosen_vertices


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_SCREAMING_SNAKE_CASE   =      {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
	print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')





