





"""simple docstring"""

import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		from transformers.models.roberta.modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		)

class        lowercase      (    unittest.TestCase):







				def __init__(							self   :					str      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					Dict=13      ,     _lowerCamelCase   :					List[Any]=7      ,     _lowerCamelCase   :					List[Any]=True      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					Tuple=True      ,     _lowerCamelCase   :					Union[str, Any]=True      ,     _lowerCamelCase   :					Any=99      ,     _lowerCamelCase   :					int=32      ,     _lowerCamelCase   :					str=5      ,     _lowerCamelCase   :					List[str]=4      ,     _lowerCamelCase   :					Union[str, Any]=37      ,     _lowerCamelCase   :					Tuple="gelu"      ,     _lowerCamelCase   :					Union[str, Any]=0.1      ,     _lowerCamelCase   :					Dict=0.1      ,     _lowerCamelCase   :					Optional[Any]=5_12      ,     _lowerCamelCase   :					Any=16      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					List[str]=0.02      ,     _lowerCamelCase   :					Dict=4      ,     ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		parent
					A_  :				Optional[Any]	   =		batch_size
					A_  :				Dict	   =		seq_length
					A_  :				List[str]	   =		is_training
					A_  :				Tuple	   =		use_attention_mask
					A_  :				Tuple	   =		use_token_type_ids
					A_  :				Optional[int]	   =		use_labels
					A_  :				str	   =		vocab_size
					A_  :				List[Any]	   =		hidden_size
					A_  :				Union[str, Any]	   =		num_hidden_layers
					A_  :				List[str]	   =		num_attention_heads
					A_  :				str	   =		intermediate_size
					A_  :				Tuple	   =		hidden_act
					A_  :				Tuple	   =		hidden_dropout_prob
					A_  :				List[str]	   =		attention_probs_dropout_prob
					A_  :				int	   =		max_position_embeddings
					A_  :				Any	   =		type_vocab_size
					A_  :				str	   =		type_sequence_label_size
					A_  :				Union[str, Any]	   =		initializer_range
					A_  :				Optional[Any]	   =		num_choices







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				int	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

					A_  :				Dict	   =		None
					if self.use_attention_mask:
						A_  :				Union[str, Any]	   =		random_attention_mask([self.batch_size, self.seq_length]       )

					A_  :				Union[str, Any]	   =		None
					if self.use_token_type_ids:
						A_  :				List[Any]	   =		ids_tensor([self.batch_size, self.seq_length]      ,     self.type_vocab_size       )

					A_  :				Union[str, Any]	   =		RobertaConfig(
					    vocab_size=self.vocab_size      ,     hidden_size=self.hidden_size      ,     num_hidden_layers=self.num_hidden_layers      ,     num_attention_heads=self.num_attention_heads      ,     intermediate_size=self.intermediate_size      ,     hidden_act=self.hidden_act      ,     hidden_dropout_prob=self.hidden_dropout_prob      ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,     max_position_embeddings=self.max_position_embeddings      ,     type_vocab_size=self.type_vocab_size      ,     is_decoder=__UpperCamelCase      ,     initializer_range=self.initializer_range      ,     )

					return config, input_ids, token_type_ids, attention_mask







				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		self.prepare_config_and_inputs()
					A_   , A_   , A_   , A_  :				str	   =		config_and_inputs
					A_  :				Any	   =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		self.prepare_config_and_inputs()
					A_   , A_   , A_   , A_  :				Dict	   =		config_and_inputs

					A_  :				List[Any]	   =		True
					A_  :				Any	   =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
					A_  :				Optional[Any]	   =		ids_tensor([self.batch_size, self.seq_length]      ,     vocab_size=2       )

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)

@require_flax
class        lowercase      (    __snake_case     ,	unittest.TestCase):
				__lowerCAmelCase    :    Union[str, Any]         =   True

				__lowerCAmelCase    :    int         =   (
				    (
				        FlaxRobertaModel,
				        FlaxRobertaForCausalLM,
				        FlaxRobertaForMaskedLM,
				        FlaxRobertaForSequenceClassification,
				        FlaxRobertaForTokenClassification,
				        FlaxRobertaForMultipleChoice,
				        FlaxRobertaForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				List[str]	   =		FlaxRobertaModelTester(self       )







				@slow
				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					for model_class_name in self.all_model_classes:
						A_  :				List[str]	   =		model_class_name.from_pretrained('''roberta-base'''      ,     from_pt=__UpperCamelCase       )
						A_  :				List[Any]	   =		model(np.ones((1, 1)       )       )
						self.assertIsNotNone(__UpperCamelCase       )











"""simple docstring"""

from __future__ import annotations

from collections import namedtuple

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		namedtuple('''result'''  ,      '''name value'''	)
	if (voltage, current, power).count(0	) != 1:
		raise ValueError('''Only one argument must be 0'''	)
	elif power < 0:
		raise ValueError(
		    '''Power cannot be negative in any electrical/electronics system'''	)
	elif voltage == 0:
		return result('''voltage'''  ,      power / current	)
	elif current == 0:
		return result('''current'''  ,      power / voltage	)
	elif power == 0:
		return result('''power'''  ,      float(round(abs(voltage * current	)  ,      2	)	)	)
	else:
		raise ValueError('''Exactly one argument must be 0'''	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





