


'''simple docstring'''

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets


class       a__				( _lowerCAmelCase      ):







				"""simple docstring"""



				@slow
				@require_torch
				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''       ,			'''prajjwal1/bert-tiny'''		)
										__lowerCAmelCase							       =				BertTokenizer.from_pretrained('''bert-base-uncased'''		)

										__lowerCAmelCase							       =				bertabert.config.encoder.vocab_size
										__lowerCAmelCase							       =				tokenizer.sep_token_id
										__lowerCAmelCase							       =				tokenizer.cls_token_id
										__lowerCAmelCase							       =				1_28

										__lowerCAmelCase							       =				datasets.load_dataset('''cnn_dailymail'''       ,			'''3.0.0'''       ,			split='''train[:1%]'''		)
										__lowerCAmelCase							       =				datasets.load_dataset('''cnn_dailymail'''       ,			'''3.0.0'''       ,			split='''validation[:1%]'''		)

										__lowerCAmelCase							       =				train_dataset.select(range(32		)		)
										__lowerCAmelCase							       =				val_dataset.select(range(16		)		)

										__lowerCAmelCase							       =				4

										def _map_to_encoder_decoder_inputs(__lowercase		):
																# Tokenizer will automatically set [BOS] <text> [EOS]
																__lowerCAmelCase							       =				tokenizer(batch['''article''']       ,			padding='''max_length'''       ,			truncation=_lowerCAmelCase       ,			max_length=5_12		)
																__lowerCAmelCase							       =				tokenizer(batch['''highlights''']       ,			padding='''max_length'''       ,			truncation=_lowerCAmelCase       ,			max_length=1_28		)
																__lowerCAmelCase							       =				inputs.input_ids
																__lowerCAmelCase							       =				inputs.attention_mask

																__lowerCAmelCase							       =				outputs.input_ids
																__lowerCAmelCase							       =				outputs.input_ids.copy()
																__lowerCAmelCase							       =				[
																    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
																]
																__lowerCAmelCase							       =				outputs.attention_mask

																assert all(len(_lowerCAmelCase		) == 5_12 for x in inputs.input_ids		)
																assert all(len(_lowerCAmelCase		) == 1_28 for x in outputs.input_ids		)

																return batch

										def _compute_metrics(__lowercase		):
																__lowerCAmelCase							       =				pred.label_ids
																__lowerCAmelCase							       =				pred.predictions

																# all unnecessary tokens are removed
																__lowerCAmelCase							       =				tokenizer.batch_decode(_lowerCAmelCase       ,			skip_special_tokens=_lowerCAmelCase		)
																__lowerCAmelCase							       =				tokenizer.batch_decode(_lowerCAmelCase       ,			skip_special_tokens=_lowerCAmelCase		)

																__lowerCAmelCase							       =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(_lowerCAmelCase		)		)]		) / len(_lowerCAmelCase		)

																return {"accuracy": accuracy}

										# map train dataset
										__lowerCAmelCase							       =				train_dataset.map(
										    _map_to_encoder_decoder_inputs       ,			batched=_lowerCAmelCase       ,			batch_size=_lowerCAmelCase       ,			remove_columns=['''article''', '''highlights''']       ,			)
										train_dataset.set_format(
										    type='''torch'''       ,			columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']       ,			)

										# same for validation dataset
										__lowerCAmelCase							       =				val_dataset.map(
										    _map_to_encoder_decoder_inputs       ,			batched=_lowerCAmelCase       ,			batch_size=_lowerCAmelCase       ,			remove_columns=['''article''', '''highlights''']       ,			)
										val_dataset.set_format(
										    type='''torch'''       ,			columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']       ,			)

										__lowerCAmelCase							       =				self.get_auto_remove_tmp_dir()

										__lowerCAmelCase							       =				SeqaSeqTrainingArguments(
										    output_dir=_lowerCAmelCase       ,			per_device_train_batch_size=_lowerCAmelCase       ,			per_device_eval_batch_size=_lowerCAmelCase       ,			predict_with_generate=_lowerCAmelCase       ,			evaluation_strategy='''steps'''       ,			do_train=_lowerCAmelCase       ,			do_eval=_lowerCAmelCase       ,			warmup_steps=0       ,			eval_steps=2       ,			logging_steps=2       ,			)

										# instantiate trainer
										__lowerCAmelCase							       =				SeqaSeqTrainer(
										    model=_lowerCAmelCase       ,			args=_lowerCAmelCase       ,			compute_metrics=_compute_metrics       ,			train_dataset=_lowerCAmelCase       ,			eval_dataset=_lowerCAmelCase       ,			tokenizer=_lowerCAmelCase       ,			)

										# start training
										trainer.train()





'''simple docstring'''

from string import ascii_uppercase

_UpperCAmelCase				: Optional[int]							    =				{char: i for i, char in enumerate(ascii_uppercase)}
_UpperCAmelCase				: Optional[int]							    =				dict(enumerate(ascii_uppercase))

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				len(lowerCamelCase)
						__lowerCAmelCase							       =				0
						while True:
												if x == i:
																		__lowerCAmelCase							       =				0
												if len(lowerCamelCase) == len(lowerCamelCase):
																		break
												key += key[i]
												i += 1
						return key

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in message:
												if letter == " ":
																		cipher_text += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] - dicta[key_new[i]]) % 2_6
																		i += 1
																		cipher_text += dicta[x]
						return cipher_text

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				''''''
						__lowerCAmelCase							       =				0
						for letter in cipher_text:
												if letter == " ":
																		or_txt += " "
												else:
																		__lowerCAmelCase							       =				(dicta[letter] + dicta[key_new[i]] + 2_6) % 2_6
																		i += 1
																		or_txt += dicta[x]
						return or_txt

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''THE GERMAN ATTACK'''
						__lowerCAmelCase							       =				'''SECRET'''
						__lowerCAmelCase							       =				generate_key(lowerCamelCase,				lowerCamelCase)
						__lowerCAmelCase							       =				cipher_text(lowerCamelCase,				lowerCamelCase)
						print(F"""Encrypted Text = {s}""")
						print(F"""Original Text = {original_text(lowerCamelCase,				lowerCamelCase)}""")


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()

