




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 (     _UpperCAmelCase  ):
						"""simple docstring"""

						@slow
						@require_torch
						def     UpperCAmelCase_      ( self :     Any				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''				,							'''prajjwal1/bert-tiny'''				)
													__lowercase      =    BertTokenizer.from_pretrained('''bert-base-uncased'''				)

													__lowercase      =    bertabert.config.encoder.vocab_size
													__lowercase      =    tokenizer.sep_token_id
													__lowercase      =    tokenizer.cls_token_id
													__lowercase      =    128

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

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

													__lowercase      =    4

													def _map_to_encoder_decoder_inputs(lowerCamelCase__ :     Optional[int]				):
																				# Tokenizer will automatically set [BOS] <text> [EOS]
																				__lowercase      =    tokenizer(batch['''article''']				,							padding='''max_length'''				,							truncation=A_				,							max_length=512				)
																				__lowercase      =    tokenizer(batch['''highlights''']				,							padding='''max_length'''				,							truncation=A_				,							max_length=128				)
																				__lowercase      =    inputs.input_ids
																				__lowercase      =    inputs.attention_mask

																				__lowercase      =    outputs.input_ids
																				__lowercase      =    outputs.input_ids.copy()
																				__lowercase      =    [
																				    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
																				]
																				__lowercase      =    outputs.attention_mask

																				assert all(len(A_				) == 512 for x in inputs.input_ids				)
																				assert all(len(A_				) == 128 for x in outputs.input_ids				)

																				return batch

													def _compute_metrics(lowerCamelCase__ :     str				):
																				__lowercase      =    pred.label_ids
																				__lowercase      =    pred.predictions

																				# all unnecessary tokens are removed
																				__lowercase      =    tokenizer.batch_decode(A_				,							skip_special_tokens=A_				)
																				__lowercase      =    tokenizer.batch_decode(A_				,							skip_special_tokens=A_				)

																				__lowercase      =    sum([int(pred_str[i] == label_str[i]				) for i in range(len(A_				)				)]				) / len(A_				)

																				return {"accuracy": accuracy}

													# map train dataset
													__lowercase      =    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
													__lowercase      =    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''']				,							)

													__lowercase      =    self.get_auto_remove_tmp_dir()

													__lowercase      =    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
													__lowercase      =    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(       UpperCamelCase__							:			Iterable[str]				,       UpperCamelCase__							:			int				)    -> Generator[tuple[str, ...], None, None]:
							'''simple docstring'''


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







def 		_A(       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


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

							if len(UpperCamelCase__				) < 2:
														return dirty

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

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

							clean += dirty[-1]

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

							return clean







def 		_A(       UpperCamelCase__							:			str				)    -> list[str]:
							'''simple docstring'''


							__lowercase      =    '''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
							__lowercase      =    []

							# 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(UpperCamelCase__				)

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

							return table







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    prepare_input(UpperCamelCase__				)
							__lowercase      =    ''''''

							# https://en.wikipedia.org/wiki/Playfair_cipher#Description
							for chara, chara in chunker(UpperCamelCase__				,       2				):
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       5				)
														__lowercase     ,     __lowercase      =    divmod(table.index(UpperCamelCase__				)				,       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(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    generate_table(UpperCamelCase__				)
							__lowercase      =    ''''''

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






