





"""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       (		a__		):






							@slow
							@require_torch
							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"   ,      "prajjwal1/bert-tiny"   )
								a   :	List[Any]			=       BertTokenizer.from_pretrained("bert-base-uncased"   )

								a   :	str			=       bertabert.config.encoder.vocab_size
								a   :	Optional[int]			=       tokenizer.sep_token_id
								a   :	Any			=       tokenizer.cls_token_id
								a   :	str			=       128

								a   :	str			=       datasets.load_dataset("cnn_dailymail"   ,      "3.0.0"   ,      split="train[:1%]"   )
								a   :	Union[str, Any]			=       datasets.load_dataset("cnn_dailymail"   ,      "3.0.0"   ,      split="validation[:1%]"   )

								a   :	List[str]			=       train_dataset.select(range(32   )   )
								a   :	List[Any]			=       val_dataset.select(range(16   )   )

								a   :	List[str]			=       4

								def _map_to_encoder_decoder_inputs(lowerCAmelCase__   ):
									# Tokenizer will automatically set [BOS] <text> [EOS]
									a   :	List[str]			=       tokenizer(batch["article"]   ,      padding="max_length"   ,      truncation=lowerCAmelCase__   ,      max_length=512   )
									a   :	List[str]			=       tokenizer(batch["highlights"]   ,      padding="max_length"   ,      truncation=lowerCAmelCase__   ,      max_length=128   )
									a   :	str			=       inputs.input_ids
									a   :	Tuple			=       inputs.attention_mask

									a   :	Optional[Any]			=       outputs.input_ids
									a   :	List[str]			=       outputs.input_ids.copy()
									a   :	Union[str, Any]			=       [
									    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
									]
									a   :	str			=       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__   ):
									a   :	List[str]			=       pred.label_ids
									a   :	str			=       pred.predictions

									# all unnecessary tokens are removed
									a   :	Union[str, Any]			=       tokenizer.batch_decode(lowerCAmelCase__   ,      skip_special_tokens=lowerCAmelCase__   )
									a   :	Optional[Any]			=       tokenizer.batch_decode(lowerCAmelCase__   ,      skip_special_tokens=lowerCAmelCase__   )

									a   :	int			=       sum([int(pred_str[i] == label_str[i]   ) for i in range(len(lowerCAmelCase__   )   )]   ) / len(lowerCAmelCase__   )

									return {"accuracy": accuracy}

								# map train dataset
								a   :	Optional[int]			=       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
								a   :	int			=       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"]   ,      )

								a   :	int			=       self.get_auto_remove_tmp_dir()

								a   :	int			=       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
								a   :	Optional[Any]			=       SeqaSeqTrainer(
								    model=lowerCAmelCase__   ,      args=lowerCAmelCase__   ,      compute_metrics=_compute_metrics   ,      train_dataset=lowerCAmelCase__   ,      eval_dataset=lowerCAmelCase__   ,      tokenizer=lowerCAmelCase__   ,      )

								# start training
								trainer.train()









"""simple docstring"""




a		:				str  			=    8.314_4598






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					float					, _lowercase		:					float      )				->float:






	'''simple docstring'''






	if temperature < 0:
		raise Exception("Temperature cannot be less than 0 K"      )
	if molar_mass <= 0:
		raise Exception("Molar mass cannot be less than or equal to 0 kg/mol"      )
	else:
		return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		a		:				Any  			=    300
		a		:				Dict  			=    28
		a		:				Dict  			=    rms_speed_of_molecule(temperature, molar_mass)
		print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


