






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        __UpperCamelCase    (     _lowerCAmelCase   ):







	@slow
	@require_torch
	def  _a   (  self      :							List[Any]			)		-> List[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      :							Any			):
			# Tokenizer will automatically set [BOS] <text> [EOS]
			__lowercase             =  tokenizer(batch["""article"""]       ,				padding="""max_length"""       ,				truncation=_lowerCAmelCase       ,				max_length=512			)
			__lowercase             =  tokenizer(batch["""highlights"""]       ,				padding="""max_length"""       ,				truncation=_lowerCAmelCase       ,				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(_lowerCAmelCase			) == 512 for x in inputs.input_ids			)
			assert all(len(_lowerCAmelCase			) == 128 for x in outputs.input_ids			)

			return batch

		def _compute_metrics(_lowerCAmelCase      :							Tuple			):
			__lowercase             =  pred.label_ids
			__lowercase             =  pred.predictions

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

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

			return {"accuracy": accuracy}

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

		__lowercase             =  self.get_auto_remove_tmp_dir()

		__lowercase             =  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
		__lowercase             =  SeqaSeqTrainer(
		    model=_lowerCAmelCase       ,				args=_lowerCAmelCase       ,				compute_metrics=_compute_metrics       ,				train_dataset=_lowerCAmelCase       ,				eval_dataset=_lowerCAmelCase       ,				tokenizer=_lowerCAmelCase       ,				)

		# start training
		trainer.train()









def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  """"""
	for ch in key:
		if ch == " " or ch not in key_no_dups and ch.isalpha():
			key_no_dups += ch
	return key_no_dups





def 				snake_case  (     lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  [chr(i + 65					) for i in range(26					)]
	# Remove duplicate characters from key
	__lowercase             =  remove_duplicates(key.upper()					)
	__lowercase             =  len(lowerCamelCase					)
	# First fill cipher with key characters
	__lowercase             =  {alphabet[i]: char for i, char in enumerate(lowerCamelCase					)}
	# Then map remaining characters in alphabet to
	# the alphabet from the beginning
	for i in range(len(lowerCamelCase					)				,  26					):
		__lowercase             =  alphabet[i - offset]
		# Ensure we are not mapping letters to letters previously mapped
		while char in key:
			offset -= 1
			__lowercase             =  alphabet[i - offset]
		__lowercase             =  char
	return cipher_alphabet





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	return "".join(cipher_map.get(lowerCamelCase				,  lowerCamelCase					) for ch in message.upper()					)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  {v: k for k, v in cipher_map.items()}
	return "".join(rev_cipher_map.get(lowerCamelCase				,  lowerCamelCase					) for ch in message.upper()					)





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  input("""Enter message to encode or decode: """					).strip()
	__lowercase             =  input("""Enter keyword: """					).strip()
	__lowercase             =  input("""Encipher or decipher? E/D:"""					).strip()[0].lower()
	try:
		__lowercase             =  {"""e""": encipher, """d""": decipher}[option]
	except KeyError:
		raise KeyError("""invalid input option"""					)
	__lowercase             =  create_cipher_map(lowerCamelCase					)
	print(func(lowerCamelCase				,  lowerCamelCase					)					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()

