


"""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 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	@slow
	@require_torch
	def   UpperCAmelCase_				(			self      :    Dict       )	->     Any:
						__SCREAMING_SNAKE_CASE						       =    EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"      ,      "prajjwal1/bert-tiny"       )
						__SCREAMING_SNAKE_CASE						       =    BertTokenizer.from_pretrained("bert-base-uncased"       )

						__SCREAMING_SNAKE_CASE						       =    bertabert.config.encoder.vocab_size
						__SCREAMING_SNAKE_CASE						       =    tokenizer.sep_token_id
						__SCREAMING_SNAKE_CASE						       =    tokenizer.cls_token_id
						__SCREAMING_SNAKE_CASE						       =    1_2_8

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

						__SCREAMING_SNAKE_CASE						       =    train_dataset.select(range(3_2       )       )
						__SCREAMING_SNAKE_CASE						       =    val_dataset.select(range(1_6       )       )

						__SCREAMING_SNAKE_CASE						       =    4

						def _map_to_encoder_decoder_inputs(UpperCAmelCase__      :    Tuple       ):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											__SCREAMING_SNAKE_CASE						       =    tokenizer(batch["article"]      ,      padding="max_length"      ,      truncation=UpperCAmelCase__      ,      max_length=5_1_2       )
											__SCREAMING_SNAKE_CASE						       =    tokenizer(batch["highlights"]      ,      padding="max_length"      ,      truncation=UpperCAmelCase__      ,      max_length=1_2_8       )
											__SCREAMING_SNAKE_CASE						       =    inputs.input_ids
											__SCREAMING_SNAKE_CASE						       =    inputs.attention_mask

											__SCREAMING_SNAKE_CASE						       =    outputs.input_ids
											__SCREAMING_SNAKE_CASE						       =    outputs.input_ids.copy()
											__SCREAMING_SNAKE_CASE						       =    [
											    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
											]
											__SCREAMING_SNAKE_CASE						       =    outputs.attention_mask

											assert all(len(UpperCAmelCase__       ) == 5_1_2 for x in inputs.input_ids       )
											assert all(len(UpperCAmelCase__       ) == 1_2_8 for x in outputs.input_ids       )

											return batch

						def _compute_metrics(UpperCAmelCase__      :    Optional[int]       ):
											__SCREAMING_SNAKE_CASE						       =    pred.label_ids
											__SCREAMING_SNAKE_CASE						       =    pred.predictions

											# all unnecessary tokens are removed
											__SCREAMING_SNAKE_CASE						       =    tokenizer.batch_decode(UpperCAmelCase__      ,      skip_special_tokens=UpperCAmelCase__       )
											__SCREAMING_SNAKE_CASE						       =    tokenizer.batch_decode(UpperCAmelCase__      ,      skip_special_tokens=UpperCAmelCase__       )

											__SCREAMING_SNAKE_CASE						       =    sum([int(pred_str[i] == label_str[i]       ) for i in range(len(UpperCAmelCase__       )       )]       ) / len(UpperCAmelCase__       )

											return {"accuracy": accuracy}

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

						__SCREAMING_SNAKE_CASE						       =    self.get_auto_remove_tmp_dir()

						__SCREAMING_SNAKE_CASE						       =    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
						__SCREAMING_SNAKE_CASE						       =    SeqaSeqTrainer(
						    model=UpperCAmelCase__      ,      args=UpperCAmelCase__      ,      compute_metrics=_compute_metrics      ,      train_dataset=UpperCAmelCase__      ,      eval_dataset=UpperCAmelCase__      ,      tokenizer=UpperCAmelCase__      ,      )

						# start training
						trainer.train()




"""simple docstring"""







def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    [0 for i in range(r + 1       )]
					# nc0 = 1
					__SCREAMING_SNAKE_CASE						       =    1
					for i in range(1  ,						n + 1       ):
										# to compute current row from previous row.
										__SCREAMING_SNAKE_CASE						       =    min(lowerCAmelCase_  ,						lowerCAmelCase_       )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]



print(binomial_coefficient(n=1_0, r=5))
