




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


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






  def  __a      (  self		:     int				)		->   List[Any]:






       """simple docstring"""







       lowercase :       Dict       =			self.task_name.lower()


class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      int			=      '''train'''
  _UpperCamelCase       :      List[str]			=      '''dev'''
  _UpperCamelCase       :      Tuple			=      '''test'''


class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      GlueDataTrainingArguments
  _UpperCamelCase       :      str
  _UpperCamelCase       :      List[InputFeatures]






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






       """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				,						)
       lowercase :       Tuple       =			args
       lowercase :       Union[str, Any]       =			glue_processors[args.task_name]()
       lowercase :       Any       =			glue_output_modes[args.task_name]
       if isinstance(_A				,						_A				):
            try:
                 lowercase :       Tuple       =			Split[mode]
            except KeyError:
                 raise KeyError('''mode is not a valid split name'''				)
        # Load data features from cache or dataset file
       lowercase :       str       =			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}"""				,						)
       lowercase :       Union[str, 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)
            lowercase      ,	lowercase :       Tuple       =			label_list[2], label_list[1]
       lowercase :       Optional[Any]       =			label_list

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






       """simple docstring"""







       return len(self.features				)






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






       """simple docstring"""







       return self.features[i]






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       return self.label_list





def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__   )			->							int:





     '''simple docstring'''


     if len(__magic_name__   ) != len(__magic_name__   ):
          raise ValueError('''The length of profit and weight must be same.'''   )
     if max_weight <= 0:
          raise ValueError('''max_weight must greater than zero.'''   )
     if any(p < 0 for p in profit   ):
          raise ValueError('''Profit can not be negative.'''   )
     if any(w < 0 for w in weight   ):
          raise ValueError('''Weight can not be negative.'''   )

     # List created to store profit gained for the 1kg in case of each weight
     # respectively.  Calculate and append profit/weight for each element.
     lowercase :       str       =			[p / w for p, w in zip(__magic_name__			,		__magic_name__   )]

     # Creating a copy of the list and sorting profit/weight in ascending order
     lowercase :       str       =			sorted(__magic_name__   )

     # declaring useful variables
     lowercase :       Union[str, Any]       =			len(__magic_name__   )
     lowercase :       Optional[int]       =			0
     lowercase :       Optional[int]       =			0
     lowercase :       int       =			0

     # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
     while limit <= max_weight and i < length:
          # flag value for encountered greatest element in sorted_profit_by_weight
          lowercase :       Optional[int]       =			sorted_profit_by_weight[length - i - 1]
          lowercase :       Union[str, Any]       =			profit_by_weight.index(__magic_name__   )
          lowercase :       Any       =			-1

          # check if the weight encountered is less than the total weight
          # encountered before.
          if max_weight - limit >= weight[index]:
               limit += weight[index]
               # Adding profit gained for the given weight 1 ===
               # weight[index]/weight[index]
               gain += 1 * profit[index]
          else:
               # Since the weight encountered is greater than limit, therefore take the
               # required number of remaining kgs and calculate profit for it.
               # weight remaining / weight[index]
               gain += (max_weight - limit) / weight[index] * profit[index]
               break
          i += 1
     return gain


if __name__ == "__main__":
 print(
     'Input profits, weights, and then max_weight (all positive ints) separated by '
     'spaces.'
 )

 lowerCAmelCase_						=			[int(x) for x in input('Input profits separated by spaces: ').split()]
 lowerCAmelCase_						=			[int(x) for x in input('Input weights separated by spaces: ').split()]
 lowerCAmelCase_						=			int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)