
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      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	@slow
	@require_torch
	def 				UpperCamelCase_  (       self					: List[Any] ):
					__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					: Optional[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					: Union[str, Any] ):
									__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()




'''simple docstring'''


from collections import defaultdict
from math import ceil, sqrt







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 100_0000							,      SCREAMING_SNAKE_CASE__ = 10     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       defaultdict     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				for outer_width in range(3							,      (t_limit // 4) + 2     ):
								if outer_width * outer_width > t_limit:
												_SCREAMING_SNAKE_CASE :       int     		=      max(
												    ceil(sqrt(outer_width * outer_width - t_limit     )     )							,      1     )
								else:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      1

								hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

								for hole_width in range(SCREAMING_SNAKE_CASE__							,      outer_width - 1							,      2     ):
												count[outer_width * outer_width - hole_width * hole_width] += 1

				return sum(1 for n in count.values() if 1 <= n <= 10     )


if __name__ == "__main__":
				print(F"{solution() = }")




