"""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 lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				Tuple   , _snake_case			:				List[str]=13   , _snake_case			:				Optional[int]=7   , _snake_case			:				List[Any]=True   , _snake_case			:				Dict=True   , _snake_case			:				Any=True   , _snake_case			:				str=True   , _snake_case			:				Dict=99   , _snake_case			:				str=32   , _snake_case			:				Tuple=5   , _snake_case			:				int=4   , _snake_case			:				str=37   , _snake_case			:				Dict="gelu"   , _snake_case			:				int=0.1   , _snake_case			:				Any=0.1   , _snake_case			:				Union[str, Any]=512   , _snake_case			:				List[str]=16   , _snake_case			:				Union[str, Any]=2   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				str=4   , )							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_attention_mask
									SCREAMING_SNAKE_CASE__		  =				use_token_type_ids
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				type_vocab_size
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				num_choices



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

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

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

									SCREAMING_SNAKE_CASE__		  =				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=_snake_case   , initializer_range=self.initializer_range   , )

									return config, input_ids, token_type_ids, attention_mask



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
									return config, inputs_dict



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				config_and_inputs

									SCREAMING_SNAKE_CASE__		  =				True
									SCREAMING_SNAKE_CASE__		  =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
									SCREAMING_SNAKE_CASE__		  =				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 lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		True

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



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				FlaxRobertaPreLayerNormModelTester(self		)



					@slow
					def 			lowerCAmelCase_					(						self			:				int		)							->       Dict:
									for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE__		  =				model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   , from_pt=_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				model(np.ones((1, 1)		)		)
													self.assertIsNotNone(_snake_case		)



@require_flax
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   , from_pt=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   , dtype=jnp.intaa		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]
									SCREAMING_SNAKE_CASE__		  =				[1, 11, 50265]
									self.assertEqual(list(output.shape		)   , _snake_case		)
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE__		  =				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]   , _snake_case   , atol=1e-4		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   , from_pt=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   , dtype=jnp.intaa		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE__		  =				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]   , _snake_case   , atol=1e-4		)		)





"""simple docstring"""






import sys




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]
				SCREAMING_SNAKE_CASE__		  =				[[0 for x in range(__UpperCAmelCase       )] for x in range(__UpperCAmelCase       )]

				for chain_length in range(2  ,					__UpperCAmelCase       ):
								for a in range(1  ,					n - chain_length + 1       ):
												SCREAMING_SNAKE_CASE__		  =				a + chain_length - 1

												SCREAMING_SNAKE_CASE__		  =				sys.maxsize
												for c in range(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																SCREAMING_SNAKE_CASE__		  =				(
																    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																)
																if cost < matrix[a][b]:
																				SCREAMING_SNAKE_CASE__		  =				cost
																				SCREAMING_SNAKE_CASE__		  =				c
				return matrix, sol




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   Union[str, Any]:
				if i == j:
								print("A" + str(__UpperCAmelCase       )  ,					end=" "       )
				else:
								print("("  ,					end=" "       )
								print_optiomal_solution(__UpperCAmelCase  ,					__UpperCAmelCase  ,					optimal_solution[i][j]       )
								print_optiomal_solution(__UpperCAmelCase  ,					optimal_solution[i][j] + 1  ,					__UpperCAmelCase       )
								print(")"  ,					end=" "       )




def 						SCREAMING_SNAKE_CASE					(  )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				[30, 35, 15, 5, 10, 20, 25]
				SCREAMING_SNAKE_CASE__		  =				len(__UpperCAmelCase       )
				# Size of matrix created from above array will be
				# 30*35 35*15 15*5 5*10 10*20 20*25
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				matrix_chain_order(__UpperCAmelCase       )

				print("No. of Operation required: " + str(matrix[1][n - 1]       )       )
				print_optiomal_solution(__UpperCAmelCase  ,					1  ,					n - 1       )


if __name__ == "__main__":
						main()




