

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
			import jax.numpy as jnp

			from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
			    FlaxRobertaPreLayerNormForCausalLM,
			    FlaxRobertaPreLayerNormForMaskedLM,
			    FlaxRobertaPreLayerNormForMultipleChoice,
			    FlaxRobertaPreLayerNormForQuestionAnswering,
			    FlaxRobertaPreLayerNormForSequenceClassification,
			    FlaxRobertaPreLayerNormForTokenClassification,
			    FlaxRobertaPreLayerNormModel,
			)






class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			def __init__(			self							: Union[str, Any]      ,      a							: Optional[Any]      ,      a							: List[Any]=13      ,      a							: Dict=7      ,      a							: str=True      ,      a							: Optional[int]=True      ,      a							: int=True      ,      a							: Optional[int]=True      ,      a							: Tuple=99      ,      a							: Any=32      ,      a							: Optional[Any]=5      ,      a							: str=4      ,      a							: Dict=37      ,      a							: Dict="gelu"      ,      a							: str=0.1      ,      a							: Dict=0.1      ,      a							: Dict=512      ,      a							: int=16      ,      a							: List[Any]=2      ,      a							: Any=0.02      ,      a							: Dict=4      ,      )							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       parent
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       batch_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       seq_length
				SCREAMING_SNAKE_CASE   :      str				     =       is_training
				SCREAMING_SNAKE_CASE   :      Dict				     =       use_attention_mask
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       use_token_type_ids
				SCREAMING_SNAKE_CASE   :      str				     =       use_labels
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       vocab_size
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       hidden_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       num_hidden_layers
				SCREAMING_SNAKE_CASE   :      Any				     =       num_attention_heads
				SCREAMING_SNAKE_CASE   :      List[str]				     =       intermediate_size
				SCREAMING_SNAKE_CASE   :      int				     =       hidden_act
				SCREAMING_SNAKE_CASE   :      Any				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      Any				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       max_position_embeddings
				SCREAMING_SNAKE_CASE   :      str				     =       type_vocab_size
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       type_sequence_label_size
				SCREAMING_SNAKE_CASE   :      List[str]				     =       initializer_range
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       num_choices
			def 							__UpperCamelCase (			self							: Any	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      str				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       None
				if self.use_attention_mask:
					SCREAMING_SNAKE_CASE   :      str				     =       random_attention_mask([self.batch_size, self.seq_length]	)

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

				SCREAMING_SNAKE_CASE   :      int				     =       RobertaPreLayerNormConfig(
				    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 							__UpperCamelCase (			self							: int	)							->       List[str]:

				"""simple docstring"""

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






			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       config_and_inputs

				SCREAMING_SNAKE_CASE   :      List[str]				     =       True
				SCREAMING_SNAKE_CASE   :      List[str]				     =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class _UpperCamelCase							(							_UpperCamelCase							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =True

			lowerCamelCase__           =(
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			def 							__UpperCamelCase (			self							: Dict	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       FlaxRobertaPreLayerNormModelTester(self	)






			@slow
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       Any:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"      ,      from_pt=_UpperCAmelCase	)
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       model(np.ones((1, 1)	)	)
					self.assertIsNotNone(_UpperCAmelCase	)






@require_flax
class _UpperCamelCase							(							unittest.TestCase   ):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"      ,      from_pt=_UpperCAmelCase	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      ,      dtype=jnp.intaa	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       model(_UpperCAmelCase	)[0]
				SCREAMING_SNAKE_CASE   :      Any				     =       [1, 11, 5_0265]
				self.assertEqual(list(output.shape	)      ,      _UpperCAmelCase	)
				# compare the actual values for a slice.
				SCREAMING_SNAKE_CASE   :      int				     =       np.array(
				    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]      ,      dtype=np.floataa	)
				self.assertTrue(np.allclose(output[:, :3, :3]      ,      _UpperCAmelCase      ,      atol=1e-4	)	)






			@slow
			def 							__UpperCamelCase (			self							: Tuple	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"      ,      from_pt=_UpperCAmelCase	)

				SCREAMING_SNAKE_CASE   :      str				     =       np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      ,      dtype=jnp.intaa	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       model(_UpperCAmelCase	)[0]
				# compare the actual values for a slice.
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.array(
				    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]      ,      dtype=np.floataa	)
				self.assertTrue(np.allclose(output[:, :3, :3]      ,      _UpperCAmelCase      ,      atol=1e-4	)	)









import math

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * power_factor

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * math.sqrt(1 - power_factor**2)


if __name__ == "__main__":
			import doctest

			doctest.testmod()