


'''simple docstring'''






def   UpperCAmelCase   (		lowerCamelCase_	:str					,   lowerCamelCase_	:int			):







		'''simple docstring'''
		snake_case_		:				List[str]      							=    word.split()

		def justify(lowerCamelCase_	:list					,   lowerCamelCase_	:int					,   lowerCamelCase_	:int			) -> str:
				snake_case_		:				Optional[int]      							=    max_width - width
				snake_case_		:				Dict      							=    len(lowerCamelCase_			)
				if len(lowerCamelCase_			) == 1:
						# if there is only word in line
						# just insert overall_spaces_count for the remainder of line
						return line[0] + " " * overall_spaces_count
				else:
						snake_case_		:				Optional[int]      							=    words_count - 1
						# num_spaces_between_words_list[i] : tells you to insert
						# num_spaces_between_words_list[i] spaces
						# after word on line[i]
						snake_case_		:				Optional[Any]      							=    spaces_to_insert_between_words * [
						    overall_spaces_count // spaces_to_insert_between_words
						]
						snake_case_		:				Optional[Any]      							=    (
						    overall_spaces_count % spaces_to_insert_between_words
						)
						# distribute spaces via round robin to the left words
						for i in range(lowerCamelCase_			):
								num_spaces_between_words_list[i] += 1
						snake_case_		:				Dict      							=    []
						for i in range(lowerCamelCase_			):
								# add the word
								aligned_words_list.append(line[i]			)
								# add the spaces to insert
								aligned_words_list.append(num_spaces_between_words_list[i] * """ """			)
						# just add the last word to the sentence
						aligned_words_list.append(line[-1]			)
						# join the aligned words list to form a justified line
						return "".join(lowerCamelCase_			)

		snake_case_		:				Optional[Any]      							=    []
		snake_case_		:				Optional[int]      							=    []
		snake_case_		:				Any      							=    0
		for word in words:
				if width + len(lowerCamelCase_			) + len(lowerCamelCase_			) <= max_width:
						# keep adding words until we can fill out max_width
						# width = sum of length of all words (without overall_spaces_count)
						# len(word) = length of current word
						# len(line) = number of overall_spaces_count to insert between words
						line.append(lowerCamelCase_			)
						width += len(lowerCamelCase_			)
				else:
						# justify the line and add it to result
						answer.append(justify(lowerCamelCase_					,   lowerCamelCase_					,   lowerCamelCase_			)			)
						# reset new line and new width
						snake_case_							,   snake_case_		:				Optional[int]      							=    [word], len(lowerCamelCase_			)
		snake_case_		:				str      							=    max_width - width - len(lowerCamelCase_			)
		answer.append(""" """.join(lowerCamelCase_			) + (remaining_spaces + 1) * """ """			)
		return answer


if __name__ == "__main__":
							from doctest import testmod

							testmod()



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     lowerCamelCase__	(self       :		int						)		->  Tuple:




							"""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(_UpperCAmelCase       :		Any						):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									lowercase__	     =						tokenizer(batch["""article"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   max_length=512						)
									lowercase__	     =						tokenizer(batch["""highlights"""] ,   padding="""max_length""" ,   truncation=_UpperCAmelCase ,   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(_UpperCAmelCase						) == 512 for x in inputs.input_ids						)
									assert all(len(_UpperCAmelCase						) == 128 for x in outputs.input_ids						)

									return batch

							def _compute_metrics(_UpperCAmelCase       :		List[Any]						):
									lowercase__	     =						pred.label_ids
									lowercase__	     =						pred.predictions

									# all unnecessary tokens are removed
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)
									lowercase__	     =						tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase						)

									lowercase__	     =						sum([int(pred_str[i] == label_str[i]						) for i in range(len(_UpperCAmelCase						)						)]						) / len(_UpperCAmelCase						)

									return {"accuracy": accuracy}

							# map train dataset
							lowercase__	     =						train_dataset.map(
							    _map_to_encoder_decoder_inputs ,   batched=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   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=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   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=_UpperCAmelCase ,   per_device_train_batch_size=_UpperCAmelCase ,   per_device_eval_batch_size=_UpperCAmelCase ,   predict_with_generate=_UpperCAmelCase ,   evaluation_strategy="""steps""" ,   do_train=_UpperCAmelCase ,   do_eval=_UpperCAmelCase ,   warmup_steps=0 ,   eval_steps=2 ,   logging_steps=2 ,   )

							# instantiate trainer
							lowercase__	     =						SeqaSeqTrainer(
							    model=_UpperCAmelCase ,   args=_UpperCAmelCase ,   compute_metrics=_compute_metrics ,   train_dataset=_UpperCAmelCase ,   eval_dataset=_UpperCAmelCase ,   tokenizer=_UpperCAmelCase ,   )

							# start training
							trainer.train()

