
"""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


_snake_case          =						logging.get_logger(__name__)
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				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=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."
				        )
				    }      ,				)
				SCREAMING_SNAKE_CASE_:							bool							=					field(
				    default=UpperCAmelCase      ,				metadata={"help": "Overwrite the cached training and evaluation sets"}							)


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.task_name.lower()






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"train"
				SCREAMING_SNAKE_CASE_:							Any							=					"dev"
				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"test"






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							GlueDataTrainingArguments
				SCREAMING_SNAKE_CASE_:							str
				SCREAMING_SNAKE_CASE_:							List[InputFeatures]


				def __init__(					self	:			str				,     UpperCAmelCase_	:			GlueDataTrainingArguments				,     UpperCAmelCase_	:			PreTrainedTokenizerBase				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			Union[str, Split] = Split.train				,     UpperCAmelCase_	:			Optional[str] = None				,     )		->  Tuple:







											"""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'				,     UpperCAmelCase_				,     )
											_lowerCAmelCase  	=			args
											_lowerCAmelCase  	=			glue_processors[args.task_name]()
											_lowerCAmelCase  	=			glue_output_modes[args.task_name]
											if isinstance(UpperCAmelCase_				,     UpperCAmelCase_  ):
																		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(UpperCAmelCase_  ):
																		if os.path.exists(UpperCAmelCase_  ) and not args.overwrite_cache:
																									_lowerCAmelCase  	=			time.time()
																									_lowerCAmelCase  	=			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:
																																_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(
																									    UpperCAmelCase_				,     UpperCAmelCase_				,     max_length=args.max_seq_length				,     label_list=UpperCAmelCase_				,     output_mode=self.output_mode				,     )
																									_lowerCAmelCase  	=			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	:			Optional[int]  )		->  Dict:







											"""simple docstring"""




											return len(self.features  )


				def __getitem__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Optional[Any]  )		->  InputFeatures:







											"""simple docstring"""




											return self.features[i]







				def 				__lowerCamelCase	(					self	:			Dict  )		->  Tuple:







											"""simple docstring"""




											return self.label_list








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[0 for i in range(len(SCREAMING_SNAKE_CASE						)						)]

							# initialize interval's left pointer and right pointer
							_lowerCAmelCase	,		_lowerCAmelCase  	=			0, 0

							for i in range(1     ,  len(SCREAMING_SNAKE_CASE						)						):
														# case when current index is inside the interval
														if i <= right_pointer:
																					_lowerCAmelCase  	=			min(right_pointer - i + 1     ,  z_result[i - left_pointer]						)
																					_lowerCAmelCase  	=			min_edge

														while go_next(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
																					z_result[i] += 1

														# if new index's result gives us more right interval,
														# we've to update left_pointer and right_pointer
														if i + z_result[i] - 1 > right_pointer:
																					_lowerCAmelCase	,		_lowerCAmelCase  	=			i, i + z_result[i] - 1

							return z_result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					list[int]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							return i + z_result[i] < len(SCREAMING_SNAKE_CASE						) and s[z_result[i]] == s[i + z_result[i]]






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0
							# concatenate 'pattern' and 'input_str' and call z_function
							# with concatenated string
							_lowerCAmelCase  	=			z_function(pattern + input_str						)

							for val in z_result:
														# if value is greater then length of the pattern string
														# that means this index is starting position of substring
														# which is equal to pattern string
														if val >= len(SCREAMING_SNAKE_CASE						):
																					answer += 1

							return answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()






