




"""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 __lowercase(      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						,	):
								__lowerCamelCase					:	int	       =				parent
								__lowerCamelCase					:	Optional[int]	       =				batch_size
								__lowerCamelCase					:	Optional[Any]	       =				seq_length
								__lowerCamelCase					:	int	       =				is_training
								__lowerCamelCase					:	Any	       =				use_attention_mask
								__lowerCamelCase					:	Tuple	       =				use_token_type_ids
								__lowerCamelCase					:	Dict	       =				use_labels
								__lowerCamelCase					:	int	       =				vocab_size
								__lowerCamelCase					:	Tuple	       =				hidden_size
								__lowerCamelCase					:	Optional[int]	       =				num_hidden_layers
								__lowerCamelCase					:	Tuple	       =				num_attention_heads
								__lowerCamelCase					:	Any	       =				intermediate_size
								__lowerCamelCase					:	int	       =				hidden_act
								__lowerCamelCase					:	List[Any]	       =				hidden_dropout_prob
								__lowerCamelCase					:	Any	       =				attention_probs_dropout_prob
								__lowerCamelCase					:	Tuple	       =				max_position_embeddings
								__lowerCamelCase					:	Union[str, Any]	       =				type_vocab_size
								__lowerCamelCase					:	List[Any]	       =				type_sequence_label_size
								__lowerCamelCase					:	Optional[Any]	       =				initializer_range
								__lowerCamelCase					:	Dict	       =				num_choices
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size     )

								__lowerCamelCase					:	List[Any]	       =				None
								if self.use_attention_mask:
											__lowerCamelCase					:	int	       =				random_attention_mask([self.batch_size, self.seq_length]     )

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

								__lowerCamelCase					:	str	       =				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=_lowerCAmelCase						,	initializer_range=self.initializer_range						,	)

								return config, input_ids, token_type_ids, attention_mask
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				self.prepare_config_and_inputs()
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				config_and_inputs
								__lowerCamelCase					:	Dict	       =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict





					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				self.prepare_config_and_inputs()
								__lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase       ,      __lowerCamelCase					:	Dict	       =				config_and_inputs

								__lowerCamelCase					:	Union[str, Any]	       =				True
								__lowerCamelCase					:	Union[str, Any]	       =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
								__lowerCamelCase					:	Dict	       =				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 __lowercase(      _lowerCAmelCase	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						int     		= True

					__a						:						Tuple     		= (
					    (
					        FlaxRobertaPreLayerNormModel,
					        FlaxRobertaPreLayerNormForCausalLM,
					        FlaxRobertaPreLayerNormForMaskedLM,
					        FlaxRobertaPreLayerNormForSequenceClassification,
					        FlaxRobertaPreLayerNormForTokenClassification,
					        FlaxRobertaPreLayerNormForMultipleChoice,
					        FlaxRobertaPreLayerNormForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				FlaxRobertaPreLayerNormModelTester(self     )





					@slow
					def     snake_case_				(    self     ):







								for model_class_name in self.all_model_classes:
											__lowerCamelCase					:	Any	       =				model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,	from_pt=_lowerCAmelCase     )
											__lowerCamelCase					:	Optional[int]	       =				model(np.ones((1, 1)     )     )
											self.assertIsNotNone(_lowerCAmelCase     )



@require_flax
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Any	       =				FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,	from_pt=_lowerCAmelCase     )

								__lowerCamelCase					:	List[Any]	       =				np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						,	dtype=jnp.intaa     )
								__lowerCamelCase					:	str	       =				model(_lowerCAmelCase     )[0]
								__lowerCamelCase					:	str	       =				[1, 11, 50265]
								self.assertEqual(list(output.shape     )						,	_lowerCAmelCase     )
								# compare the actual values for a slice.
								__lowerCamelCase					:	int	       =				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]						,	_lowerCAmelCase						,	atol=1E-4     )     )





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						,	from_pt=_lowerCAmelCase     )

								__lowerCamelCase					:	List[Any]	       =				np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]						,	dtype=jnp.intaa     )
								__lowerCamelCase					:	str	       =				model(_lowerCAmelCase     )[0]
								# compare the actual values for a slice.
								__lowerCamelCase					:	Union[str, Any]	       =				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]						,	_lowerCAmelCase						,	atol=1E-4     )     )









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations(A__:    int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations_with_dp_array(
			    A__:    int							,				A__:    list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						__lowerCamelCase					:	Dict	       =				sum(
						    count_of_possible_combinations_with_dp_array(target - item							,				A__     )
						    for item in array     )
						__lowerCamelCase					:	str	       =				answer
						return answer

			__lowerCamelCase					:	Union[str, Any]	       =				[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:
			__lowerCamelCase					:	int	       =				[0] * (target + 1)
			__lowerCamelCase					:	str	       =				1

			for i in range(1							,				target + 1     ):
						for j in range(A__     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					a_				:    Any										=     3
					a_				:    str										=     5
					a_				:    List[Any]										=     [1, 2, 5]
					print(combination_sum_iv(n, array, target))



