


'''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


__magic_name__   :   List[str]  							=logging.get_logger(__name__)






@dataclass
class 		UpperCamelCase_		:





						"""simple docstring"""




						UpperCAmelCase__	:       str         =     42
						UpperCAmelCase__	:       List[str]         =     42
						UpperCAmelCase__	:       Union[str, Any]         =     None
						UpperCAmelCase__	:       List[Any]         =     None
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										return json.dumps(dataclasses.asdict(self  )	,				indent=2  ) + "\n"






@dataclass(frozen=UpperCAmelCase_   )
class 		UpperCamelCase_		:





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     42
						UpperCAmelCase__	:       Union[str, Any]         =     None
						UpperCAmelCase__	:       int         =     None
						UpperCAmelCase__	:       Optional[Any]         =     None
						def 					__A					( self				:					Union[str, Any]  )							->					Tuple:
										return json.dumps(dataclasses.asdict(self  )  ) + "\n"






class 		UpperCamelCase_		:





						"""simple docstring"""




						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					Union[str, Any]  )							->					str:
										raise NotImplementedError()
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					List[Any]  )							->					str:
										raise NotImplementedError()
						def 					__A					( self				:					Any	,				_lowerCamelCase				:					List[str]  )							->					Dict:
										raise NotImplementedError()
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					Dict  )							->					Optional[int]:
										raise NotImplementedError()
						def 					__A					( self				:					Dict  )							->					int:
										raise NotImplementedError()
						def 					__A					( self				:					Union[str, Any]	,				_lowerCamelCase				:					Dict  )							->					int:
										if len(self.get_labels()  ) > 1:
														__magic_name__	  = self.get_labels()[int(example.label  )]
										return example

						@classmethod
						def 					__A					( cls				:					str	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Any=None  )							->					int:
										with open(_lowerCamelCase	,				"r"	,				encoding="utf-8-sig"  ) as f:
														return list(csv.reader(_lowerCamelCase	,				delimiter="\t"	,				quotechar=_lowerCamelCase  )  )






class 		UpperCamelCase_		(					UpperCAmelCase_   ):





						"""simple docstring"""




						def __init__( self				:					Dict	,				_lowerCamelCase				:					int=None	,				_lowerCamelCase				:					int=None	,				_lowerCamelCase				:					int="classification"	,				_lowerCamelCase				:					Tuple=False  )							->					Optional[Any]:
										__magic_name__	  = [] if labels is None else labels
										__magic_name__	  = [] if examples is None else examples
										__magic_name__	  = mode
										__magic_name__	  = verbose
						def __len__( self				:					List[str]  )							->					List[str]:
										return len(self.examples  )
						def __getitem__( self				:					Any	,				_lowerCamelCase				:					Optional[Any]  )							->					str:
										if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
														return SingleSentenceClassificationProcessor(labels=self.labels	,				examples=self.examples[idx]  )
										return self.examples[idx]
						@classmethod
						def 					__A					( cls				:					str	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Any=""	,				_lowerCamelCase				:					Dict=0	,				_lowerCamelCase				:					str=1	,				_lowerCamelCase				:					str=None	,				_lowerCamelCase				:					str=False	,				**_lowerCamelCase				:					Optional[Any]  )							->					Any:
										__magic_name__	  = 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 					__A					( cls				:					Union[str, Any]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					List[Any]=None	,				**_lowerCamelCase				:					List[Any]  )							->					Union[str, Any]:
										__magic_name__	  = cls(**_lowerCamelCase  )
										processor.add_examples(_lowerCamelCase	,				labels=_lowerCamelCase  )
										return processor
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Dict=""	,				_lowerCamelCase				:					Optional[int]=0	,				_lowerCamelCase				:					Union[str, Any]=1	,				_lowerCamelCase				:					str=None	,				_lowerCamelCase				:					List[str]=False	,				_lowerCamelCase				:					int=False	,				_lowerCamelCase				:					int=False	,				)							->					int:
										__magic_name__	  = self._read_tsv(_lowerCamelCase  )
										if skip_first_row:
														__magic_name__	  = lines[1:]
										__magic_name__	  = []
										__magic_name__	  = []
										__magic_name__	  = []
										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:
																		__magic_name__	  = 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 					__A					( self				:					str	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Tuple=None	,				_lowerCamelCase				:					int=None	,				_lowerCamelCase				:					Union[str, Any]=False	,				_lowerCamelCase				:					List[str]=False  )							->					Any:
										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:
														__magic_name__	  = [None] * len(_lowerCamelCase  )
										if labels is None:
														__magic_name__	  = [None] * len(_lowerCamelCase  )
										__magic_name__	  = []
										__magic_name__	  = set()
										for text_or_text_and_label, label, guid in zip(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  ):
														if isinstance(_lowerCamelCase	,				(tuple, list)  ) and label is None:
																		__magic_name__  ,      __magic_name__	  = text_or_text_and_label
														else:
																		__magic_name__	  = 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:
														__magic_name__	  = examples
										else:
														self.examples.extend(_lowerCamelCase  )

										# Update labels
										if overwrite_labels:
														__magic_name__	  = list(_lowerCamelCase  )
										else:
														__magic_name__	  = list(set(self.labels  ).union(_lowerCamelCase  )  )

										return self.examples

						def 					__A					( self				:					List[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					List[Any]=None	,				_lowerCamelCase				:					List[str]=False	,				_lowerCamelCase				:					Union[str, Any]=0	,				_lowerCamelCase				:					Optional[Any]=True	,				_lowerCamelCase				:					Any=None	,				)							->					Any:
										if max_length is None:
														__magic_name__	  = tokenizer.max_len

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

										__magic_name__	  = []
										for ex_index, example in enumerate(self.examples  ):
														if ex_index % 1_00_00 == 0:
																		logger.info(f'Tokenizing example {ex_index}'  )

														__magic_name__	  = tokenizer.encode(
														    example.text_a	,				add_special_tokens=_lowerCamelCase	,				max_length=min(_lowerCamelCase	,				tokenizer.max_len  )	,				)
														all_input_ids.append(_lowerCamelCase  )

										__magic_name__	  = max(len(_lowerCamelCase  ) for input_ids in all_input_ids  )

										__magic_name__	  = []
										for ex_index, (input_ids, example) in enumerate(zip(_lowerCamelCase	,				self.examples  )  ):
														if ex_index % 1_00_00 == 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.
														__magic_name__	  = [1 if mask_padding_with_zero else 0] * len(_lowerCamelCase  )

														# Zero-pad up to the sequence length.
														__magic_name__	  = batch_length - len(_lowerCamelCase  )
														if pad_on_left:
																		__magic_name__	  = ([pad_token] * padding_length) + input_ids
																		__magic_name__	  = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
														else:
																		__magic_name__	  = input_ids + ([pad_token] * padding_length)
																		__magic_name__	  = 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":
																		__magic_name__	  = label_map[example.label]
														elif self.mode == "regression":
																		__magic_name__	  = 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)

														__magic_name__	  = 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

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

														__magic_name__	  = TensorDataset(_lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase  )
														return dataset
										else:
														raise ValueError("return_tensors should be one of 'tf' or 'pt'"  )







'''simple docstring'''
import random
import sys

__magic_name__   :   Optional[Any]  							='ABCDEFGHIJKLMNOPQRSTUVWXYZ'




def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = input("Enter message: "						)
				__magic_name__	  = "LFWOAYUISVKMNXPBDCRJTQEGHZ"
				__magic_name__	  = input("Encrypt/Decrypt [e/d]: "						)

				check_valid_key(lowerCamelCase_						)

				if resp.lower().startswith("e"						):
								__magic_name__	  = "encrypt"
								__magic_name__	  = encrypt_message(lowerCamelCase_	,  lowerCamelCase_						)
				elif resp.lower().startswith("d"						):
								__magic_name__	  = "decrypt"
								__magic_name__	  = decrypt_message(lowerCamelCase_	,  lowerCamelCase_						)

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




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				__magic_name__	  = list(lowerCamelCase_						)
				__magic_name__	  = 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_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




				return translate_message(lowerCamelCase_	,  lowerCamelCase_	,  "encrypt"						)




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




				return translate_message(lowerCamelCase_	,  lowerCamelCase_	,  "decrypt"						)




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




				__magic_name__	  = ""
				__magic_name__	  = LETTERS
				__magic_name__	  = key

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

				for symbol in message:
								if symbol.upper() in chars_a:
												__magic_name__	  = 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'''




				__magic_name__	  = list(lowerCamelCase_						)
				random.shuffle(lowerCamelCase_						)
				return "".join(lowerCamelCase_						)


if __name__ == "__main__":
						main()



