






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 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							@slow
							@require_torch
							def       __A (  self:   Union[str, Any]   )    ->				Tuple:
												_A								= EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''		,     '''prajjwal1/bert-tiny'''   )
												_A								= BertTokenizer.from_pretrained('''bert-base-uncased'''   )

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

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

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

												_A								= 4

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

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

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

																	return batch

												def _compute_metrics(__A:   int   ):
																	_A								= pred.label_ids
																	_A								= pred.predictions

																	# all unnecessary tokens are removed
																	_A								= tokenizer.batch_decode(__A		,     skip_special_tokens=__A   )
																	_A								= tokenizer.batch_decode(__A		,     skip_special_tokens=__A   )

																	_A								= sum([int(pred_str[i] == label_str[i]   ) for i in range(len(__A   )   )]   ) / len(__A   )

																	return {"accuracy": accuracy}

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

												_A								= self.get_auto_remove_tmp_dir()

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

												# instantiate trainer
												_A								= SeqaSeqTrainer(
												    model=__A		,     args=__A		,     compute_metrics=_compute_metrics		,     train_dataset=__A		,     eval_dataset=__A		,     tokenizer=__A		,     )

												# start training
												trainer.train()











import itertools
import string
from collections.abc import Generator, Iterable




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= iter(_lowercase )
					while True:
										_A								= tuple(itertools.islice(_lowercase					,			_lowercase ) )
										if not chunk:
															return
										yield chunk




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
					_A								= ''''''

					if len(_lowercase ) < 2:
										return dirty

					for i in range(len(_lowercase ) - 1 ):
										clean += dirty[i]

										if dirty[i] == dirty[i + 1]:
															clean += "X"

					clean += dirty[-1]

					if len(_lowercase ) & 1:
										clean += "X"

					return clean




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
					# we're using a list instead of a '2d' array because it makes the math
					# for setting up the table and doing the actual encoding/decoding simpler
					_A								= []

					# copy key chars into the table if they are in `alphabet` ignoring duplicates
					for char in key.upper():
										if char not in table and char in alphabet:
															table.append(_lowercase )

    # fill the rest of the table in with the remaining alphabet chars
					for char in alphabet:
										if char not in table:
															table.append(_lowercase )

					return table




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= prepare_input(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
										elif cola == cola:
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
										else:  # rectangle
															ciphertext += table[rowa * 5 + cola]
															ciphertext += table[rowa * 5 + cola]

					return ciphertext




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															plaintext += table[rowa * 5 + (cola - 1) % 5]
															plaintext += table[rowa * 5 + (cola - 1) % 5]
										elif cola == cola:
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
										else:  # rectangle
															plaintext += table[rowa * 5 + cola]
															plaintext += table[rowa * 5 + cola]

					return plaintext



