




import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase_		:					Union[str, Any]          =  logging.get_logger(__name__)




@dataclass
class 		__A   :
						UpperCamelCase      =       field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()      )}      )
						UpperCamelCase      =       field(
						    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}      )
						UpperCamelCase      =       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."""
						        )
						    }       ,    )
						UpperCamelCase      =       field(
						    default=UpperCamelCase__       ,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )

						def      A__    (		self :Optional[int]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=self.task_name.lower()




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """train"""
						UpperCamelCase      =       """dev"""
						UpperCamelCase      =       """test"""




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       42
						UpperCamelCase      =       42
						UpperCamelCase      =       42

						def __init__(		self :Optional[Any]     ,			__snake_case :GlueDataTrainingArguments     ,			__snake_case :PreTrainedTokenizerBase     ,			__snake_case :Optional[int] = None     ,			__snake_case :Union[str, Split] = Split.train     ,			__snake_case :Optional[str] = None     ,			):
										'''simple docstring'''

										warnings.warn(
										    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
										    """library. You can have a look at this example script for pointers: """
										    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""     ,			__snake_case     ,			)
										__magic_name__						:						Tuple										=args
										__magic_name__						:						List[str]										=glue_processors[args.task_name]()
										__magic_name__						:						Optional[int]										=glue_output_modes[args.task_name]
										if isinstance(__snake_case     ,			__snake_case			):
														try:
																		__magic_name__						:						List[Any]										=Split[mode]
														except KeyError:
																		raise KeyError("""mode is not a valid split name"""			)
        # Load data features from cache or dataset file
										__magic_name__						:						Optional[Any]										=os.path.join(
										    cache_dir if cache_dir is not None else args.data_dir     ,			f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"     ,			)
										__magic_name__						:						List[str]										=self.processor.get_labels()
										if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
										    "RobertaTokenizer",
										    "RobertaTokenizerFast",
										    "XLMRobertaTokenizer",
										    "BartTokenizer",
										    "BartTokenizerFast",
										):
														# HACK(label indices are swapped in RoBERTa pretrained model)
														__magic_name__					,			__magic_name__						:						int										=label_list[2], label_list[1]
										__magic_name__						:						str										=label_list

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										__magic_name__						:						Tuple										=cached_features_file + """.lock"""
										with FileLock(__snake_case			):
														if os.path.exists(__snake_case			) and not args.overwrite_cache:
																		__magic_name__						:						str										=time.time()
																		__magic_name__						:						Optional[int]										=torch.load(__snake_case			)
																		logger.info(
																		    f"Loading features from cached file {cached_features_file} [took %.3f s]"     ,			time.time() - start			)
														else:
																		logger.info(f"Creating features from dataset file at {args.data_dir}"			)

																		if mode == Split.dev:
																						__magic_name__						:						Optional[Any]										=self.processor.get_dev_examples(args.data_dir			)
																		elif mode == Split.test:
																						__magic_name__						:						Any										=self.processor.get_test_examples(args.data_dir			)
																		else:
																						__magic_name__						:						int										=self.processor.get_train_examples(args.data_dir			)
																		if limit_length is not None:
																						__magic_name__						:						Optional[int]										=examples[:limit_length]
																		__magic_name__						:						Any										=glue_convert_examples_to_features(
																		    __snake_case     ,			__snake_case     ,			max_length=args.max_seq_length     ,			label_list=__snake_case     ,			output_mode=self.output_mode     ,			)
																		__magic_name__						:						str										=time.time()
																		torch.save(self.features     ,			__snake_case			)
																		# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																		logger.info(
																		    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"			)

						def __len__(		self :Tuple			):
										'''simple docstring'''

										return len(self.features			)

						def __getitem__(		self :List[Any]     ,			__snake_case :Tuple			):
										'''simple docstring'''

										return self.features[i]


						def      A__    (		self :int			):
										'''simple docstring'''

										return self.label_list










import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						list[list]										=[]

				# 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
				__magic_name__						:						Tuple										=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
								__magic_name__						:						Tuple										=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]:
																__magic_name__						:						Tuple										=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()

							UpperCAmelCase_		:					Optional[int]          =  {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)}""")




