'''simple docstring'''







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 _SCREAMING_SNAKE_CASE:
						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=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=_SCREAMING_SNAKE_CASE						,						metadata={'help': 'Overwrite the cached training and evaluation sets'}					)




						def      __lowerCamelCase       (    self :       List[str]      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :int						    = self.task_name.lower()




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= 'train'
						A_			:							Any     		= 'dev'
						A_			:							Any     		= 'test'




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							GlueDataTrainingArguments
						A_			:							str
						A_			:							List[InputFeatures]




						def __init__(    self :       Optional[Any]							,							UpperCamelCase_ :       GlueDataTrainingArguments							,							UpperCamelCase_ :       PreTrainedTokenizerBase							,							UpperCamelCase_ :       Optional[int] = None							,							UpperCamelCase_ :       Union[str, Split] = Split.train							,							UpperCamelCase_ :       Optional[str] = None							,							)     ->				Union[str, Any]:
													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'							,							UpperCamelCase_							,							)
													SCREAMING_SNAKE_CASE__     :int						    = args
													SCREAMING_SNAKE_CASE__     :Any						    = glue_processors[args.task_name]()
													SCREAMING_SNAKE_CASE__     :str						    = glue_output_modes[args.task_name]
													if isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																				try:
																											SCREAMING_SNAKE_CASE__     :List[Any]						    = Split[mode]
																				except KeyError:
																											raise KeyError('mode is not a valid split name'      )
        # Load data features from cache or dataset file
													SCREAMING_SNAKE_CASE__     :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}'''							,							)
													SCREAMING_SNAKE_CASE__     :Any						    = 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)
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = label_list[2], label_list[1]
													SCREAMING_SNAKE_CASE__     :Tuple						    = label_list

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




						def __getitem__(    self :       Optional[Any]							,							UpperCamelCase_ :       List[Any]      )     ->				InputFeatures:
													return self.features[i]




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Union[str, Any]:
													return self.label_list







'''simple docstring'''







def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :list[list[str]]						    = [[] for _ in range(UpperCAmelCase__		)]
							SCREAMING_SNAKE_CASE__     :Any						    = key - 1

							if key <= 0:
														raise ValueError('Height of grid can\'t be 0 or negative'		)
							if key == 1 or len(UpperCAmelCase__		) <= key:
														return input_string

							for position, character in enumerate(UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :Dict						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Tuple						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														temp_grid[num].append(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :int						    = [''.join(UpperCAmelCase__		) for row in temp_grid]
							SCREAMING_SNAKE_CASE__     :str						    = ''.join(UpperCAmelCase__		)

							return output_string


def    lowerCamelCase       (			UpperCAmelCase__				:  str				,						UpperCAmelCase__				:  int		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = []
							SCREAMING_SNAKE_CASE__     :str						    = key - 1

							if key <= 0:
														raise ValueError('Height of grid can\'t be 0 or negative'		)
							if key == 1:
														return input_string

							SCREAMING_SNAKE_CASE__     :list[list[str]]						    = [[] for _ in range(UpperCAmelCase__		)]  # generates template
							for position in range(len(UpperCAmelCase__		)		):
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Dict						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														temp_grid[num].append('*'		)

							SCREAMING_SNAKE_CASE__     :Any						    = 0
							for row in temp_grid:  # fills in the characters
														SCREAMING_SNAKE_CASE__     :int						    = input_string[counter : counter + len(UpperCAmelCase__		)]
														grid.append(list(UpperCAmelCase__		)		)
														counter += len(UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :Tuple						    = ''  # reads as zigzag
							for position in range(len(UpperCAmelCase__		)		):
														SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = position % (lowest * 2)  # puts it in bounds
														SCREAMING_SNAKE_CASE__     :Any						    = min(UpperCAmelCase__				,						lowest * 2 - num		)  # creates zigzag pattern
														output_string += grid[num][0]
														grid[num].pop(0		)
							return output_string


def    lowerCamelCase       (			UpperCAmelCase__				:  str		)			->	dict[int, str]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :int						    = {}
							for key_guess in range(1				,						len(UpperCAmelCase__		)		):  # tries every key
														SCREAMING_SNAKE_CASE__     :List[str]						    = decrypt(UpperCAmelCase__				,						UpperCAmelCase__		)
							return results


if __name__ == "__main__":
		import doctest

		doctest.testmod()






