

"""simple docstring"""




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_lowerCamelCase								=logging.get_logger(__name__)



@dataclass
class 		a_	:



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           return json.dumps(dataclasses.asdict(self    )      ,indent=2    ) + "\n"







@dataclass(frozen=_UpperCAmelCase					)
class 		a_	:



      """simple docstring"""







      __UpperCAmelCase      		=				42
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      __UpperCAmelCase      		=				None
      def 	_lowerCAmelCase					(				self					:							int    ):
           return json.dumps(dataclasses.asdict(self    )    ) + "\n"







class 		a_	:



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Optional[int]    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							List[Any]    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]      ,snake_case					:							str    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							Optional[Any]      ,snake_case					:							Tuple    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							Any    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							Union[str, Any]    ):


           if len(self.get_labels()    ) > 1:
                SCREAMING_SNAKE_CASE										=self.get_labels()[int(example.label    )]
           return example


      @classmethod
      def 	_lowerCAmelCase					(				cls					:							int      ,snake_case					:							Any      ,snake_case					:							Dict=None    ):


           with open(lowerCAmelCase__      ,'r'      ,encoding='utf-8-sig'    ) as f:
                return list(csv.reader(lowerCAmelCase__      ,delimiter='\t'      ,quotechar=lowerCAmelCase__    )    )







class 		a_	(							_UpperCAmelCase					):



      """simple docstring"""







      def __init__(				self					:							List[Any]      ,snake_case					:							List[str]=None      ,snake_case					:							Union[str, Any]=None      ,snake_case					:							Dict="classification"      ,snake_case					:							Optional[Any]=False    ):
           SCREAMING_SNAKE_CASE										=[] if labels is None else labels
           SCREAMING_SNAKE_CASE										=[] if examples is None else examples
           SCREAMING_SNAKE_CASE										=mode
           SCREAMING_SNAKE_CASE										=verbose
      def __len__(				self					:							Tuple    ):
           return len(self.examples    )
      def __getitem__(				self					:							Any      ,snake_case					:							List[str]    ):


           if isinstance(lowerCAmelCase__      ,lowerCAmelCase__    ):
                return SingleSentenceClassificationProcessor(labels=self.labels      ,examples=self.examples[idx]    )
           return self.examples[idx]
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							str      ,snake_case					:							Tuple      ,snake_case					:							List[Any]=""      ,snake_case					:							Tuple=0      ,snake_case					:							List[Any]=1      ,snake_case					:							Optional[int]=None      ,snake_case					:							Optional[Any]=False      ,**snake_case					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=cls(**lowerCAmelCase__    )
           processor.add_examples_from_csv(
               lowerCAmelCase__      ,split_name=lowerCAmelCase__      ,column_label=lowerCAmelCase__      ,column_text=lowerCAmelCase__      ,column_id=lowerCAmelCase__      ,skip_first_row=lowerCAmelCase__      ,overwrite_labels=lowerCAmelCase__      ,overwrite_examples=lowerCAmelCase__      ,)
           return processor
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							Optional[int]      ,snake_case					:							List[str]      ,snake_case					:							Optional[int]=None      ,**snake_case					:							int    ):
           SCREAMING_SNAKE_CASE										=cls(**lowerCAmelCase__    )
           processor.add_examples(lowerCAmelCase__      ,labels=lowerCAmelCase__    )
           return processor
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							Union[str, Any]      ,snake_case					:							Tuple=""      ,snake_case					:							Optional[Any]=0      ,snake_case					:							Any=1      ,snake_case					:							Optional[Any]=None      ,snake_case					:							List[str]=False      ,snake_case					:							List[Any]=False      ,snake_case					:							Union[str, Any]=False      ,):
           SCREAMING_SNAKE_CASE										=self._read_tsv(lowerCAmelCase__    )
           if skip_first_row:
                SCREAMING_SNAKE_CASE										=lines[1:]
           SCREAMING_SNAKE_CASE										=[]
           SCREAMING_SNAKE_CASE										=[]
           SCREAMING_SNAKE_CASE										=[]
           for i, line in enumerate(lowerCAmelCase__    ):
                texts.append(line[column_text]    )
                labels.append(line[column_label]    )
                if column_id is not None:
                     ids.append(line[column_id]    )
                else:
                     SCREAMING_SNAKE_CASE										=f'{split_name}-{i}' if split_name else str(lowerCAmelCase__    )
                     ids.append(lowerCAmelCase__    )

           return self.add_examples(
               lowerCAmelCase__      ,lowerCAmelCase__      ,lowerCAmelCase__      ,overwrite_labels=lowerCAmelCase__      ,overwrite_examples=lowerCAmelCase__    )
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							Union[str, Any]      ,snake_case					:							List[str]=None      ,snake_case					:							Union[str, Any]=None      ,snake_case					:							Dict=False      ,snake_case					:							Tuple=False    ):


           if labels is not None and len(lowerCAmelCase__    ) != len(lowerCAmelCase__    ):
                raise ValueError(
                    f'Text and labels have mismatched lengths {len(lowerCAmelCase__    )} and {len(lowerCAmelCase__    )}'    )
           if ids is not None and len(lowerCAmelCase__    ) != len(lowerCAmelCase__    ):
                raise ValueError(f'Text and ids have mismatched lengths {len(lowerCAmelCase__    )} and {len(lowerCAmelCase__    )}'    )
           if ids is None:
                SCREAMING_SNAKE_CASE										=[None] * len(lowerCAmelCase__    )
           if labels is None:
                SCREAMING_SNAKE_CASE										=[None] * len(lowerCAmelCase__    )
           SCREAMING_SNAKE_CASE										=[]
           SCREAMING_SNAKE_CASE										=set()
           for text_or_text_and_label, label, guid in zip(lowerCAmelCase__      ,lowerCAmelCase__      ,lowerCAmelCase__    ):
                if isinstance(lowerCAmelCase__      ,(tuple, list)    ) and label is None:
                     SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=text_or_text_and_label
                else:
                     SCREAMING_SNAKE_CASE										=text_or_text_and_label
                added_labels.add(lowerCAmelCase__    )
                examples.append(InputExample(guid=lowerCAmelCase__      ,text_a=lowerCAmelCase__      ,text_b=lowerCAmelCase__      ,label=lowerCAmelCase__    )    )

           # Update examples
           if overwrite_examples:
                SCREAMING_SNAKE_CASE										=examples
           else:
                self.examples.extend(lowerCAmelCase__    )

           # Update labels
           if overwrite_labels:
                SCREAMING_SNAKE_CASE										=list(lowerCAmelCase__    )
           else:
                SCREAMING_SNAKE_CASE										=list(set(self.labels    ).union(lowerCAmelCase__    )    )

           return self.examples


      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							Tuple      ,snake_case					:							List[Any]=None      ,snake_case					:							Union[str, Any]=False      ,snake_case					:							List[str]=0      ,snake_case					:							List[Any]=True      ,snake_case					:							str=None      ,):


           if max_length is None:
                SCREAMING_SNAKE_CASE										=tokenizer.max_len

           SCREAMING_SNAKE_CASE										={label: i for i, label in enumerate(self.labels    )}

           SCREAMING_SNAKE_CASE										=[]
           for ex_index, example in enumerate(self.examples    ):
                if ex_index % 10000 == 0:
                     logger.info(f'Tokenizing example {ex_index}'    )

                SCREAMING_SNAKE_CASE										=tokenizer.encode(
                    example.text_a      ,add_special_tokens=lowerCAmelCase__      ,max_length=min(lowerCAmelCase__      ,tokenizer.max_len    )      ,)
                all_input_ids.append(lowerCAmelCase__    )

           SCREAMING_SNAKE_CASE										=max(len(lowerCAmelCase__    ) for input_ids in all_input_ids    )

           SCREAMING_SNAKE_CASE										=[]
           for ex_index, (input_ids, example) in enumerate(zip(lowerCAmelCase__      ,self.examples    )    ):
                if ex_index % 10000 == 0:
                     logger.info(f'Writing example {ex_index}/{len(self.examples    )}'    )
                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                SCREAMING_SNAKE_CASE										=[1 if mask_padding_with_zero else 0] * len(lowerCAmelCase__    )

                # Zero-pad up to the sequence length.
                SCREAMING_SNAKE_CASE										=batch_length - len(lowerCAmelCase__    )
                if pad_on_left:
                     SCREAMING_SNAKE_CASE										=([pad_token] * padding_length) + input_ids
                     SCREAMING_SNAKE_CASE										=([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                else:
                     SCREAMING_SNAKE_CASE										=input_ids + ([pad_token] * padding_length)
                     SCREAMING_SNAKE_CASE										=attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

                if len(lowerCAmelCase__    ) != batch_length:
                     raise ValueError(f'Error with input length {len(lowerCAmelCase__    )} vs {batch_length}'    )
                if len(lowerCAmelCase__    ) != batch_length:
                     raise ValueError(f'Error with input length {len(lowerCAmelCase__    )} vs {batch_length}'    )

                if self.mode == "classification":
                     SCREAMING_SNAKE_CASE										=label_map[example.label]
                elif self.mode == "regression":
                     SCREAMING_SNAKE_CASE										=float(example.label    )
                else:
                     raise ValueError(self.mode    )

                if ex_index < 5 and self.verbose:
                     logger.info('*** Example ***'    )
                     logger.info(f'guid: {example.guid}'    )
                     logger.info(f'input_ids: {" ".join([str(lowerCAmelCase__    ) for x in input_ids]    )}'    )
                     logger.info(f'attention_mask: {" ".join([str(lowerCAmelCase__    ) for x in attention_mask]    )}'    )
                     logger.info(f'label: {example.label} (id = {label})'    )

                features.append(InputFeatures(input_ids=lowerCAmelCase__      ,attention_mask=lowerCAmelCase__      ,label=lowerCAmelCase__    )    )

           if return_tensors is None:
                return features
           elif return_tensors == "tf":
                if not is_tf_available():
                     raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'    )
                import tensorflow as tf

                def gen():
                     for ex in features:
                          yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

                SCREAMING_SNAKE_CASE										=tf.data.Dataset.from_generator(
                    lowerCAmelCase__      ,({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)      ,({'input_ids': tf.TensorShape([None]    ), 'attention_mask': tf.TensorShape([None]    )}, tf.TensorShape([]    ))      ,)
                return dataset
           elif return_tensors == "pt":
                if not is_torch_available():
                     raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'    )
                import torch
                from torch.utils.data import TensorDataset

                SCREAMING_SNAKE_CASE										=torch.tensor([f.input_ids for f in features]      ,dtype=torch.long    )
                SCREAMING_SNAKE_CASE										=torch.tensor([f.attention_mask for f in features]      ,dtype=torch.long    )
                if self.mode == "classification":
                     SCREAMING_SNAKE_CASE										=torch.tensor([f.label for f in features]      ,dtype=torch.long    )
                elif self.mode == "regression":
                     SCREAMING_SNAKE_CASE										=torch.tensor([f.label for f in features]      ,dtype=torch.float    )

                SCREAMING_SNAKE_CASE										=TensorDataset(lowerCAmelCase__      ,lowerCAmelCase__      ,lowerCAmelCase__    )
                return dataset
           else:
                raise ValueError('return_tensors should be one of \'tf\' or \'pt\''    )




def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=len(lowerCAmelCase_      )
     for _ in range(
         0,   int(((no_of_elements - 1) / 2) + 1      )      ):  # we don't need to traverse to end of list as
          for j in range(0,   no_of_elements - 1      ):
               if (
                   lst[j + 1] < lst[j]
               ):  # applying bubble sort algorithm from left to right (or forwards)
                    SCREAMING_SNAKE_CASE										=lst[j + 1]
                    SCREAMING_SNAKE_CASE										=lst[j]
                    SCREAMING_SNAKE_CASE										=temp
               if (
                   lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
               ):  # applying bubble sort algorithm from right to left (or backwards)
                    SCREAMING_SNAKE_CASE										=lst[no_of_elements - 1 - j]
                    SCREAMING_SNAKE_CASE										=lst[no_of_elements - 2 - j]
                    SCREAMING_SNAKE_CASE										=temp
     return lst


if __name__ == "__main__":
 print("enter the list to be sorted")
 _lowerCamelCase								=[int(x) for x in input().split()]  # inputing elements of the list in one line
 _lowerCamelCase								=double_sort(lst)
 print("the sorted list is")
 print(sorted_lst)

