

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						:  List[Any]									=			logging.get_logger(__name__)







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




				def        a				(   self			):
											snake_case_     							=       self.task_name.lower()







class 					lowercase							(			a_     ):
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     '''train'''
				__SCREAMING_SNAKE_CASE   :					Optional[int]			     =     '''dev'''
				__SCREAMING_SNAKE_CASE   :					Any			     =     '''test'''







class 					lowercase							(			a_     ):
				__SCREAMING_SNAKE_CASE   :					GlueDataTrainingArguments
				__SCREAMING_SNAKE_CASE   :					str
				__SCREAMING_SNAKE_CASE   :					List[InputFeatures]




				def __init__(   self ,  snake_case ,  snake_case ,  snake_case = None ,  snake_case = Split.train ,  snake_case = None ,  ):
											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' ,  lowerCAmelCase_ ,  )
											snake_case_     							=       args
											snake_case_     							=       glue_processors[args.task_name]()
											snake_case_     							=       glue_output_modes[args.task_name]
											if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_			):
																		try:
																									snake_case_     							=       Split[mode]
																		except KeyError:
																									raise KeyError('mode is not a valid split name'			)
        # Load data features from cache or dataset file
											snake_case_     							=       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}''' ,  )
											snake_case_     							=       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)
																		snake_case_							,       snake_case_     							=       label_list[2], label_list[1]
											snake_case_     							=       label_list

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											snake_case_     							=       cached_features_file + '.lock'
											with FileLock(lowerCAmelCase_			):
																		if os.path.exists(lowerCAmelCase_			) and not args.overwrite_cache:
																									snake_case_     							=       time.time()
																									snake_case_     							=       torch.load(lowerCAmelCase_			)
																									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:
																																snake_case_     							=       self.processor.get_dev_examples(args.data_dir			)
																									elif mode == Split.test:
																																snake_case_     							=       self.processor.get_test_examples(args.data_dir			)
																									else:
																																snake_case_     							=       self.processor.get_train_examples(args.data_dir			)
																									if limit_length is not None:
																																snake_case_     							=       examples[:limit_length]
																									snake_case_     							=       glue_convert_examples_to_features(
																									    lowerCAmelCase_ ,  lowerCAmelCase_ ,  max_length=args.max_seq_length ,  label_list=lowerCAmelCase_ ,  output_mode=self.output_mode ,  )
																									snake_case_     							=       time.time()
																									torch.save(self.features ,  lowerCAmelCase_			)
																									# ^ 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			):
											return len(self.features			)




				def __getitem__(   self ,  snake_case			):
											return self.features[i]




				def        a				(   self			):
											return self.label_list








'''simple docstring'''





class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Dict	)      ->		List[str]:
						__A=	name
						__A=	val
			def __str__(  self							:			int	)      ->		List[Any]:
						return F"""{self.__class__.__name__}({self.name}, {self.val})"""

			def __lt__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			str	)      ->		List[Any]:
						return self.val < other.val


class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			int	)      ->		Any:
						__A=	{}
						__A=	{}
						__A=	self.build_heap(lowerCAmelCase_	)
			def __getitem__(  self							:			int			,    lowerCAmelCase_							:			Dict	)      ->		Tuple:
						return self.get_value(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						return (idx - 1) // 2
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		List[str]:
						return idx * 2 + 1
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Any:
						return idx * 2 + 2
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Dict:
						return self.heap_dict[key]
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		Tuple:
						__A=	len(lowerCAmelCase_	) - 1
						__A=	self.get_parent_idx(lowerCAmelCase_	)

						for idx, i in enumerate(lowerCAmelCase_	):
									__A=	idx
									__A=	i.val

						for i in range(lowerCAmelCase_			,    -1			,    -1	):
									self.sift_down(lowerCAmelCase_			,    lowerCAmelCase_	)
						return array
			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			Tuple	)      ->		Dict:
						while True:
									__A=	self.get_left_child_idx(lowerCAmelCase_	)  # noqa: E741
									__A=	self.get_right_child_idx(lowerCAmelCase_	)

									__A=	idx
									if l < len(lowerCAmelCase_	) and array[l] < array[idx]:
												__A=	l
									if r < len(lowerCAmelCase_	) and array[r] < array[smallest]:
												__A=	r

									if smallest != idx:
												__A,	__A=	array[smallest], array[idx]
												(
												    (
												    __A
												),	(
												    __A
												),	
												)=	(
												    self.idx_of_element[array[smallest]],
												    self.idx_of_element[array[idx]],
												)
												__A=	smallest
									else:
												break
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			Dict	)      ->		str:
						__A=	self.get_parent_idx(lowerCAmelCase_	)
						while p >= 0 and self.heap[p] > self.heap[idx]:
									__A,	__A=	self.heap[idx], self.heap[p]
									__A,	__A=	(
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									__A=	p
									__A=	self.get_parent_idx(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						return self.heap[0]
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		int:
						__A,	__A=	self.heap[-1], self.heap[0]
						__A,	__A=	(
						    self.idx_of_element[self.heap[-1]],
						    self.idx_of_element[self.heap[0]],
						)

						__A=	self.heap.pop()
						del self.idx_of_element[x]
						self.sift_down(0			,    self.heap	)
						return x
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Optional[int]:
						self.heap.append(lowerCAmelCase_	)
						__A=	len(self.heap	) - 1
						__A=	node.val
						self.sift_up(len(self.heap	) - 1	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						return len(self.heap	) == 0

			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		Optional[Any]:
						assert (
						    self.heap[self.idx_of_element[node]].val > new_value
						), "newValue must be less that current value"
						__A=	new_value
						__A=	new_value
						self.sift_up(self.idx_of_element[node]	)


UpperCAmelCase__  			=				Node('''R''', -1)
UpperCAmelCase__  			=				Node('''B''', 6)
UpperCAmelCase__  			=				Node('''A''', 3)
UpperCAmelCase__  			=				Node('''X''', 1)
UpperCAmelCase__  			=				Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
UpperCAmelCase__  			=				MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
					print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()



