'''simple docstring'''

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   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=13		,						__A=7		,						__A=True		,						__A=True		,						__A=True		,						__A=True		,						__A=99		,						__A=32		,						__A=5		,						__A=4		,						__A=37		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=16		,						__A=2		,						__A=0.02		,						__A=4		,						) ->     str:
								_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 		UpperCamelCase__       (   self				) ->     Tuple:
								_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=__A		,						initializer_range=self.initializer_range		,						)

								return config, input_ids, token_type_ids, attention_mask






		def 		UpperCamelCase__       (   self				) ->     int:
								_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 		UpperCamelCase__       (   self				) ->     List[str]:
								_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   SCREAMING_SNAKE_CASE			(						__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      Any       =							True

		lowercase   :      Any       =							(
		    (
		        FlaxRobertaPreLayerNormModel,
		        FlaxRobertaPreLayerNormForCausalLM,
		        FlaxRobertaPreLayerNormForMaskedLM,
		        FlaxRobertaPreLayerNormForSequenceClassification,
		        FlaxRobertaPreLayerNormForTokenClassification,
		        FlaxRobertaPreLayerNormForMultipleChoice,
		        FlaxRobertaPreLayerNormForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=FlaxRobertaPreLayerNormModelTester(self				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     int:
								for model_class_name in self.all_model_classes:
														_lowerCAmelCase										=model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'		,						from_pt=__A				)
														_lowerCAmelCase										=model(np.ones((1, 1)				)				)
														self.assertIsNotNone(__A				)

@require_flax
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		@slow
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'		,						from_pt=__A				)

								_lowerCAmelCase										=np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		,						dtype=jnp.intaa				)
								_lowerCAmelCase										=model(__A				)[0]
								_lowerCAmelCase										=[1, 11, 5_0265]
								self.assertEqual(list(output.shape				)		,						__A				)
								# compare the actual values for a slice.
								_lowerCAmelCase										=np.array(
								    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]		,						dtype=np.floataa				)
								self.assertTrue(np.allclose(output[:, :3, :3]		,						__A		,						atol=1E-4				)				)






		@slow
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'		,						from_pt=__A				)

								_lowerCAmelCase										=np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		,						dtype=jnp.intaa				)
								_lowerCAmelCase										=model(__A				)[0]
								# compare the actual values for a slice.
								_lowerCAmelCase										=np.array(
								    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]		,						dtype=np.floataa				)
								self.assertTrue(np.allclose(output[:, :3, :3]		,						__A		,						atol=1E-4				)				)






'''simple docstring'''

import unittest

from knapsack import knapsack as k
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=0
								_lowerCAmelCase										=[0]
								_lowerCAmelCase										=[0]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						0				)

								_lowerCAmelCase										=[60]
								_lowerCAmelCase										=[10]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						0				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=3
								_lowerCAmelCase										=[1, 2, 3]
								_lowerCAmelCase										=[3, 2, 1]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						5				)






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										=50
								_lowerCAmelCase										=[60, 100, 120]
								_lowerCAmelCase										=[10, 20, 30]
								_lowerCAmelCase										=len(__A				)
								self.assertEqual(k.knapsack(__A		,						__A		,						__A		,						__A				)		,						220				)


if __name__ == "__main__":
							unittest.main()





