

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					:							Any    ):
           return json.dumps(dataclasses.asdict(self    )      ,indent=2    ) + "\n"







@dataclass(frozen=lowerCamelCase_					)
class 		a_	:



      """simple docstring"""







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







class 		a_	:



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Optional[Any]    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							Optional[Any]    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							List[str]      ,snake_case					:							Any    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							Any    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							int    ):
           raise NotImplementedError()
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]      ,snake_case					:							int    ):
           if len(self.get_labels()    ) > 1:
                SCREAMING_SNAKE_CASE										=self.get_labels()[int(example.label    )]
           return example


      @classmethod
      def 	_lowerCAmelCase					(				cls					:							List[Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							Any=None    ):
           with open(snake_case      ,'r'      ,encoding='utf-8-sig'    ) as f:
                return list(csv.reader(snake_case      ,delimiter='\t'      ,quotechar=snake_case    )    )







class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      def __init__(				self					:							Any      ,snake_case					:							Union[str, Any]=None      ,snake_case					:							Tuple=None      ,snake_case					:							int="classification"      ,snake_case					:							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					:							Any    ):
           return len(self.examples    )
      def __getitem__(				self					:							int      ,snake_case					:							Dict    ):
           if isinstance(snake_case      ,snake_case    ):
                return SingleSentenceClassificationProcessor(labels=self.labels      ,examples=self.examples[idx]    )
           return self.examples[idx]
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							Optional[Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							List[Any]=""      ,snake_case					:							List[Any]=0      ,snake_case					:							Dict=1      ,snake_case					:							Optional[int]=None      ,snake_case					:							Optional[int]=False      ,**snake_case					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=cls(**snake_case    )
           processor.add_examples_from_csv(
               snake_case      ,split_name=snake_case      ,column_label=snake_case      ,column_text=snake_case      ,column_id=snake_case      ,skip_first_row=snake_case      ,overwrite_labels=snake_case      ,overwrite_examples=snake_case      ,)
           return processor
      @classmethod
      def 	_lowerCAmelCase					(				cls					:							Tuple      ,snake_case					:							List[str]      ,snake_case					:							Union[str, Any]=None      ,**snake_case					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=cls(**snake_case    )
           processor.add_examples(snake_case      ,labels=snake_case    )
           return processor
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							List[Any]      ,snake_case					:							Optional[Any]=""      ,snake_case					:							List[str]=0      ,snake_case					:							Optional[int]=1      ,snake_case					:							Optional[int]=None      ,snake_case					:							List[str]=False      ,snake_case					:							Union[str, Any]=False      ,snake_case					:							Optional[Any]=False      ,):
           SCREAMING_SNAKE_CASE										=self._read_tsv(snake_case    )
           if skip_first_row:
                SCREAMING_SNAKE_CASE										=lines[1:]
           SCREAMING_SNAKE_CASE										=[]
           SCREAMING_SNAKE_CASE										=[]
           SCREAMING_SNAKE_CASE										=[]
           for i, line in enumerate(snake_case    ):
                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(snake_case    )
                     ids.append(snake_case    )

           return self.add_examples(
               snake_case      ,snake_case      ,snake_case      ,overwrite_labels=snake_case      ,overwrite_examples=snake_case    )
      def 	_lowerCAmelCase					(				self					:							int      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[int]=None      ,snake_case					:							int=None      ,snake_case					:							Any=False      ,snake_case					:							Any=False    ):
           if labels is not None and len(snake_case    ) != len(snake_case    ):
                raise ValueError(
                    f'Text and labels have mismatched lengths {len(snake_case    )} and {len(snake_case    )}'    )
           if ids is not None and len(snake_case    ) != len(snake_case    ):
                raise ValueError(f'Text and ids have mismatched lengths {len(snake_case    )} and {len(snake_case    )}'    )
           if ids is None:
                SCREAMING_SNAKE_CASE										=[None] * len(snake_case    )
           if labels is None:
                SCREAMING_SNAKE_CASE										=[None] * len(snake_case    )
           SCREAMING_SNAKE_CASE										=[]
           SCREAMING_SNAKE_CASE										=set()
           for text_or_text_and_label, label, guid in zip(snake_case      ,snake_case      ,snake_case    ):
                if isinstance(snake_case      ,(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(snake_case    )
                examples.append(InputExample(guid=snake_case      ,text_a=snake_case      ,text_b=snake_case      ,label=snake_case    )    )

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

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

           return self.examples


      def 	_lowerCAmelCase					(				self					:							Dict      ,snake_case					:							str      ,snake_case					:							Union[str, Any]=None      ,snake_case					:							Tuple=False      ,snake_case					:							Tuple=0      ,snake_case					:							str=True      ,snake_case					:							Tuple=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=snake_case      ,max_length=min(snake_case      ,tokenizer.max_len    )      ,)
                all_input_ids.append(snake_case    )

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

           SCREAMING_SNAKE_CASE										=[]
           for ex_index, (input_ids, example) in enumerate(zip(snake_case      ,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(snake_case    )

                # Zero-pad up to the sequence length.
                SCREAMING_SNAKE_CASE										=batch_length - len(snake_case    )
                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(snake_case    ) != batch_length:
                     raise ValueError(f'Error with input length {len(snake_case    )} vs {batch_length}'    )
                if len(snake_case    ) != batch_length:
                     raise ValueError(f'Error with input length {len(snake_case    )} 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(snake_case    ) for x in input_ids]    )}'    )
                     logger.info(f'attention_mask: {" ".join([str(snake_case    ) for x in attention_mask]    )}'    )
                     logger.info(f'label: {example.label} (id = {label})'    )

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

           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(
                    snake_case      ,({'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(snake_case      ,snake_case      ,snake_case    )
                return dataset
           else:
                raise ValueError('return_tensors should be one of \'tf\' or \'pt\''    )




import random
import sys

_lowerCamelCase								="ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=input('Enter message: '      )
     SCREAMING_SNAKE_CASE										='LFWOAYUISVKMNXPBDCRJTQEGHZ'
     SCREAMING_SNAKE_CASE										=input('Encrypt/Decrypt [e/d]: '      )

     check_valid_key(lowerCAmelCase_      )

     if resp.lower().startswith('e'      ):
          SCREAMING_SNAKE_CASE										='encrypt'
          SCREAMING_SNAKE_CASE										=encrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )
     elif resp.lower().startswith('d'      ):
          SCREAMING_SNAKE_CASE										='decrypt'
          SCREAMING_SNAKE_CASE										=decrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )

     print(F'\n{mode.title()}ion: \n{translated}'      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     key_list.sort()
     letters_list.sort()

     if key_list != letters_list:
          sys.exit('Error in the key or symbol set.'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return translate_message(lowerCAmelCase_,   lowerCAmelCase_,   'encrypt'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return translate_message(lowerCAmelCase_,   lowerCAmelCase_,   'decrypt'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=''
     SCREAMING_SNAKE_CASE										=LETTERS
     SCREAMING_SNAKE_CASE										=key

     if mode == "decrypt":
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=chars_b, chars_a

     for symbol in message:
          if symbol.upper() in chars_a:
               SCREAMING_SNAKE_CASE										=chars_a.find(symbol.upper()      )
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               translated += symbol

     return translated







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     random.shuffle(lowerCAmelCase_      )
     return "".join(lowerCAmelCase_      )


if __name__ == "__main__":
 main()

