
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       __A(				unittest.TestCase       ):

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[Any]=1_3   ,					__UpperCamelCase					:     Union[str, Any]=7   ,					__UpperCamelCase					:     Tuple=True   ,					__UpperCamelCase					:     int=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     Any=True   ,					__UpperCamelCase					:     Optional[Any]=9_9   ,					__UpperCamelCase					:     List[Any]=3_2   ,					__UpperCamelCase					:     Tuple=5   ,					__UpperCamelCase					:     Optional[int]=4   ,					__UpperCamelCase					:     Dict=3_7   ,					__UpperCamelCase					:     str="gelu"   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     Tuple=5_1_2   ,					__UpperCamelCase					:     Tuple=1_6   ,					__UpperCamelCase					:     List[str]=2   ,					__UpperCamelCase					:     int=0.02   ,					__UpperCamelCase					:     List[Any]=4   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					seq_length
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_attention_mask
												lowerCamelCase_							  =					use_token_type_ids
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					intermediate_size
												lowerCamelCase_							  =					hidden_act
												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					max_position_embeddings
												lowerCamelCase_							  =					type_vocab_size
												lowerCamelCase_							  =					type_sequence_label_size
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					num_choices

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size   )

												lowerCamelCase_							  =					None
												if self.use_attention_mask:
																			lowerCamelCase_							  =					random_attention_mask([self.batch_size, self.seq_length]   )

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

												lowerCamelCase_							  =					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=__lowercase   ,					initializer_range=self.initializer_range   ,					)

												return config, input_ids, token_type_ids, attention_mask

					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs
												lowerCamelCase_							  =					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
												return config, inputs_dict



					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					config_and_inputs

												lowerCamelCase_							  =					True
												lowerCamelCase_							  =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
												lowerCamelCase_							  =					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       __A(				lowercase__     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    True

					SCREAMING_SNAKE_CASE			    =    (
					    (
					        FlaxRobertaPreLayerNormModel,
					        FlaxRobertaPreLayerNormForCausalLM,
					        FlaxRobertaPreLayerNormForMaskedLM,
					        FlaxRobertaPreLayerNormForSequenceClassification,
					        FlaxRobertaPreLayerNormForTokenClassification,
					        FlaxRobertaPreLayerNormForMultipleChoice,
					        FlaxRobertaPreLayerNormForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					FlaxRobertaPreLayerNormModelTester(self   )



					@slow
					def    lowercase__      (	self					:     Tuple   ):
												for model_class_name in self.all_model_classes:
																			lowerCamelCase_							  =					model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,					from_pt=__lowercase   )
																			lowerCamelCase_							  =					model(np.ones((1, 1)   )   )
																			self.assertIsNotNone(__lowercase   )





@require_flax
class       __A(				unittest.TestCase       ):

					@slow
					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,					from_pt=__lowercase   )

												lowerCamelCase_							  =					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,					dtype=jnp.intaa   )
												lowerCamelCase_							  =					model(__lowercase   )[0]
												lowerCamelCase_							  =					[1, 1_1, 5_0_2_6_5]
												self.assertEqual(list(output.shape   )   ,					__lowercase   )
												# compare the actual values for a slice.
												lowerCamelCase_							  =					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]   ,					__lowercase   ,					atol=1E-4   )   )



					@slow
					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""   ,					from_pt=__lowercase   )

												lowerCamelCase_							  =					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,					dtype=jnp.intaa   )
												lowerCamelCase_							  =					model(__lowercase   )[0]
												# compare the actual values for a slice.
												lowerCamelCase_							  =					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]   ,					__lowercase   ,					atol=1E-4   )   )







def      __lowerCAmelCase						(							UpperCAmelCase__				:      int = 4_0_0_0_0_0_0       )  ->    int:
							lowerCamelCase_							  =					[]
							lowerCamelCase_  ,   lowerCamelCase_							  =					0, 1
							while b <= n:
														if b % 2 == 0:
																					even_fibs.append(UpperCAmelCase__       )
														lowerCamelCase_  ,   lowerCamelCase_							  =					b, a + b
							return sum(UpperCAmelCase__       )


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





