

import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowercase_			=  logging.getLogger(__name__)


@dataclass(frozen=_UpperCAmelCase			)
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							42
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None




@dataclass(frozen=_UpperCAmelCase			)
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None


if is_torch_available():
     import torch
     from torch.utils.data import Dataset


     class 			A						(							_UpperCAmelCase			):







         """simple docstring"""





         lowerCamelCase		     =							42


         def __init__(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	PreTrainedTokenizer,lowercase_			:	str,lowercase_			:	Optional[int] = None,lowercase_			:	Dict=False,lowercase_			:	bool = False,)->   Dict:





             '''simple docstring'''







             A__   				=       hans_processors[task]()

             A__   				=       os.path.join(
                 lowercase_,'cached_{}_{}_{}_{}'.format(
                     'dev' if evaluate else 'train',tokenizer.__class__.__name__,str(lowercase_		),lowercase_,),)
             A__   				=       processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                 # HACK(label indices are swapped in RoBERTa pretrained model)
                 A__				,						A__   				=       label_list[2], label_list[1]
             A__   				=       label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             A__   				=       cached_features_file + '.lock'
             with FileLock(lowercase_		):
                 if os.path.exists(lowercase_		) and not overwrite_cache:
                     logger.info(F'Loading features from cached file {cached_features_file}'		)
                     A__   				=       torch.load(lowercase_		)
                 else:
                     logger.info(F'Creating features from dataset file at {data_dir}'		)

                     A__   				=       (
                         processor.get_dev_examples(lowercase_		) if evaluate else processor.get_train_examples(lowercase_		)
                     )

                     logger.info('Training examples: %s',len(lowercase_		)		)
                     A__   				=       hans_convert_examples_to_features(lowercase_,lowercase_,lowercase_,lowercase_		)
                     logger.info('Saving features into cached file %s',lowercase_		)
                     torch.save(self.features,lowercase_		)


         def __len__(				self			:	Any		)->   List[Any]:





             '''simple docstring'''







             return len(self.features		)


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





             '''simple docstring'''







             return self.features[i]


         def 						snake_case__					(				self			:	List[Any]		)->   Optional[Any]:





             '''simple docstring'''







             return self.label_list


if is_tf_available():
     import tensorflow as tf


     class 			A						:







         """simple docstring"""





         lowerCamelCase		     =							42


         def __init__(				self			:	int,lowercase_			:	str,lowercase_			:	PreTrainedTokenizer,lowercase_			:	str,lowercase_			:	Optional[int] = 1_2_8,lowercase_			:	Tuple=False,lowercase_			:	bool = False,)->   str:





             '''simple docstring'''







             A__   				=       hans_processors[task]()
             A__   				=       processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                 # HACK(label indices are swapped in RoBERTa pretrained model)
                 A__				,						A__   				=       label_list[2], label_list[1]
             A__   				=       label_list

             A__   				=       processor.get_dev_examples(lowercase_		) if evaluate else processor.get_train_examples(lowercase_		)
             A__   				=       hans_convert_examples_to_features(lowercase_,lowercase_,lowercase_,lowercase_		)

             def gen():
                 for ex_index, ex in tqdm.tqdm(enumerate(self.features		),desc='convert examples to features'		):
                     if ex_index % 1_0_0_0_0 == 0:
                         logger.info('Writing example %d of %d' % (ex_index, len(lowercase_		))		)

                     yield (
                         {
                             "example_id": 0,
                             "input_ids": ex.input_ids,
                             "attention_mask": ex.attention_mask,
                             "token_type_ids": ex.token_type_ids,
                         },
                         ex.label,
                     )

             A__   				=       tf.data.Dataset.from_generator(
                 lowercase_,(
                     {
                         'example_id': tf.intaa,
                         'input_ids': tf.intaa,
                         'attention_mask': tf.intaa,
                         'token_type_ids': tf.intaa,
                     },
                     tf.intaa,
                 ),(
                     {
                         'example_id': tf.TensorShape([]		),
                         'input_ids': tf.TensorShape([None, None]		),
                         'attention_mask': tf.TensorShape([None, None]		),
                         'token_type_ids': tf.TensorShape([None, None]		),
                     },
                     tf.TensorShape([]		),
                 ),)


         def 						snake_case__					(				self			:	int		)->   Tuple:





             '''simple docstring'''







             return self.dataset


         def __len__(				self			:	Optional[int]		)->   List[str]:





             '''simple docstring'''







             return len(self.features		)


         def __getitem__(				self			:	Tuple,lowercase_			:	int		)->   InputFeatures:





             '''simple docstring'''







             return self.features[i]


         def 						snake_case__					(				self			:	List[Any]		)->   List[Any]:





             '''simple docstring'''







             return self.label_list


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Tuple		)->   Optional[Any]:





        '''simple docstring'''







        return self._create_examples(self._read_tsv(os.path.join(lowercase_,'heuristics_train_set.txt'		)		),'train'		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self._create_examples(self._read_tsv(os.path.join(lowercase_,'heuristics_evaluation_set.txt'		)		),'dev'		)


    def 						snake_case__					(				self			:	List[str]		)->   Any:





        '''simple docstring'''







        return ["contradiction", "entailment", "neutral"]


    def 						snake_case__					(				self			:	str,lowercase_			:	Any,lowercase_			:	List[str]		)->   str:





        '''simple docstring'''







        A__   				=       []
        for i, line in enumerate(lowercase_		):
            if i == 0:
                continue
            A__   				=       '%s-%s' % (set_type, line[0])
            A__   				=       line[5]
            A__   				=       line[6]
            A__   				=       line[7][2:] if line[7].startswith('ex'		) else line[7]
            A__   				=       line[0]
            examples.append(InputExample(guid=lowercase_,text_a=lowercase_,text_b=lowercase_,label=lowercase_,pairID=lowercase_		)		)
        return examples




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[InputExample]   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     PreTrainedTokenizer   ,      )							->				List[Any]:





    '''simple docstring'''






    A__   				=       {label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__				)}

    A__   				=       []
    for ex_index, example in tqdm.tqdm(enumerate(SCREAMING_SNAKE_CASE__				)   ,      desc='convert examples to features'				):
        if ex_index % 10000 == 0:
            logger.info('Writing example %d' % (ex_index)				)

        A__   				=       tokenizer(
            example.text_a   ,      example.text_b   ,      add_special_tokens=SCREAMING_SNAKE_CASE__   ,      max_length=SCREAMING_SNAKE_CASE__   ,      padding='max_length'   ,      truncation=SCREAMING_SNAKE_CASE__   ,      return_overflowing_tokens=SCREAMING_SNAKE_CASE__   ,      )

        A__   				=       label_map[example.label] if example.label in label_map else 0

        A__   				=       int(example.pairID				)

        features.append(InputFeatures(**SCREAMING_SNAKE_CASE__   ,      label=SCREAMING_SNAKE_CASE__   ,      pairID=SCREAMING_SNAKE_CASE__				)				)

    for i, example in enumerate(examples[:5]				):
        logger.info('*** Example ***'				)
        logger.info(f'guid: {example}'				)
        logger.info(f'features: {features[i]}'				)

    return features


lowercase_			=  {
    "hans": 3,
}

lowercase_			=  {
    "hans": HansProcessor,
}




from collections.abc import Sequence
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Sequence[int] | None = None				)							->				int:





    '''simple docstring'''






    if nums is None or not nums:
        raise ValueError('Input sequence should not be empty'				)

    A__   				=       nums[0]
    for i in range(1   ,      len(SCREAMING_SNAKE_CASE__				)				):
        A__   				=       nums[i]
        A__   				=       max(SCREAMING_SNAKE_CASE__   ,      ans + num   ,      SCREAMING_SNAKE_CASE__				)

    return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     lowercase_			=  int(input("Enter number of elements : ").strip())
     lowercase_			=  list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
     print(max_subsequence_sum(array))

