




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__					    =    logging.get_logger(__name__)
@dataclass
class     a__					:
						_a							:					str      =					field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()   )}   )
						_a							:					str      =					field(
						    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
						_a							:					int      =					field(
						    default=1_2_8				,       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=snake_case__				,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.task_name.lower()






class     a__					(      snake_case__   ):
						_a							:					List[Any]      =					"""train"""
						_a							:					Optional[Any]      =					"""dev"""
						_a							:					Optional[int]      =					"""test"""






class     a__					(      snake_case__   ):
						_a							:					GlueDataTrainingArguments
						_a							:					str
						_a							:					List[InputFeatures]






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

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




											"""simple docstring"""



											return len(self.features    )






						def __getitem__(     self	,    _A    ):




											"""simple docstring"""



											return self.features[i]






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return self.label_list










from __future__ import annotations

from collections.abc import Iterator
from typing import Any
class     a__					:






						def __init__(     self	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = data
											__lowerCAmelCase            = None






class     a__					:






						def __init__(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = None
											__lowerCAmelCase            = None






						def __iter__(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.head
											while self.head:
																yield node.data
																__lowerCAmelCase            = node.next
																if node == self.head:
																					break






						def __len__(     self    ):




											"""simple docstring"""



											return sum(1 for _ in self    )






						def __repr__(     self    ):




											"""simple docstring"""



											return "->".join(str(_A    ) for item in iter(self    )    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											self.insert_nth(len(self    )	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



											self.insert_nth(0	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											if index < 0 or index > len(self    ):
																raise IndexError("list index out of range."    )
											__lowerCAmelCase            = Node(_A    )
											if self.head is None:
																__lowerCAmelCase            = new_node  # first node points itself
																__lowerCAmelCase            = __lowerCAmelCase            = new_node
											elif index == 0:  # insert at head
																__lowerCAmelCase            = self.head
																__lowerCAmelCase            = __lowerCAmelCase            = new_node
											else:
																__lowerCAmelCase            = self.head
																for _ in range(index - 1    ):
																					__lowerCAmelCase            = temp.next
																__lowerCAmelCase            = temp.next
																__lowerCAmelCase            = new_node
																if index == len(self    ) - 1:  # insert at tail
																					__lowerCAmelCase            = new_node






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return self.delete_nth(0    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return self.delete_nth(len(self    ) - 1    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A = 0    ):




											"""simple docstring"""



											if not 0 <= index < len(self    ):
																raise IndexError("list index out of range."    )
											__lowerCAmelCase            = self.head
											if self.head == self.tail:  # just one node
																__lowerCAmelCase            = __lowerCAmelCase            = None
											elif index == 0:  # delete head node
																__lowerCAmelCase            = self.tail.next.next
																__lowerCAmelCase            = self.head.next
											else:
																__lowerCAmelCase            = self.head
																for _ in range(index - 1    ):
																					__lowerCAmelCase            = temp.next
																__lowerCAmelCase            = temp.next
																__lowerCAmelCase            = temp.next.next
																if index == len(self    ) - 1:  # delete at tail
																					__lowerCAmelCase            = temp
											return delete_node.data






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return len(self    ) == 0







def 			_a   (		):
					__lowerCAmelCase            = CircularLinkedList()
					assert len(SCREAMING_SNAKE_CASE_  ) == 0
					assert circular_linked_list.is_empty() is True
					assert str(SCREAMING_SNAKE_CASE_  ) == ""

					try:
										circular_linked_list.delete_front()
										raise AssertionError  # This should not happen
					except IndexError:
										assert True  # This should happen

					try:
										circular_linked_list.delete_tail()
										raise AssertionError  # This should not happen
					except IndexError:
										assert True  # This should happen

					try:
										circular_linked_list.delete_nth(-1  )
										raise AssertionError
					except IndexError:
										assert True

					try:
										circular_linked_list.delete_nth(0  )
										raise AssertionError
					except IndexError:
										assert True

					assert circular_linked_list.is_empty() is True
					for i in range(5  ):
										assert len(SCREAMING_SNAKE_CASE_  ) == i
										circular_linked_list.insert_nth(SCREAMING_SNAKE_CASE_   ,   i + 1  )
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1   ,   6  )  )

					circular_linked_list.insert_tail(6  )
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1   ,   7  )  )
					circular_linked_list.insert_head(0  )
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(0   ,   7  )  )

					assert circular_linked_list.delete_front() == 0
					assert circular_linked_list.delete_tail() == 6
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1   ,   6  )  )
					assert circular_linked_list.delete_nth(2  ) == 3

					circular_linked_list.insert_nth(2   ,   3  )
					assert str(SCREAMING_SNAKE_CASE_  ) == "->".join(str(SCREAMING_SNAKE_CASE_  ) for i in range(1   ,   6  )  )

					assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
					import doctest

					doctest.testmod()




