




import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MPNetForMaskedLM,
		    MPNetForMultipleChoice,
		    MPNetForQuestionAnswering,
		    MPNetForSequenceClassification,
		    MPNetForTokenClassification,
		    MPNetModel,
		)


class 							__UpperCamelCase			:





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=13       , UpperCAmelCase_=7       , UpperCAmelCase_=True       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=True       , UpperCAmelCase_=99       , UpperCAmelCase_=64       , UpperCAmelCase_=5       , UpperCAmelCase_=4       , UpperCAmelCase_=64       , UpperCAmelCase_="gelu"       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.1       , UpperCAmelCase_=5_12       , UpperCAmelCase_=16       , UpperCAmelCase_=2       , UpperCAmelCase_=0.02       , UpperCAmelCase_=3       , UpperCAmelCase_=4       , UpperCAmelCase_=None       , ):
											lowerCAmelCase   =		parent
											lowerCAmelCase   =		batch_size
											lowerCAmelCase   =		seq_length
											lowerCAmelCase   =		is_training
											lowerCAmelCase   =		use_input_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_labels
											lowerCAmelCase   =		num_choices
											lowerCAmelCase   =		scope



				def       __snake_case		( self						):
											return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size						)

											lowerCAmelCase   =		None
											if self.use_input_mask:
																		lowerCAmelCase   =		random_attention_mask([self.batch_size, self.seq_length]						)

											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											if self.use_labels:
																		lowerCAmelCase   =		ids_tensor([self.batch_size]       , self.type_sequence_label_size						)
																		lowerCAmelCase   =		ids_tensor([self.batch_size, self.seq_length]       , self.num_labels						)
																		lowerCAmelCase   =		ids_tensor([self.batch_size]       , self.num_choices						)

											lowerCAmelCase   =		self.get_config()
											return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



				def       __snake_case		( self						):
											return MPNetConfig(
											    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       , initializer_range=self.initializer_range       , )



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		MPNetModel(config=UpperCAmelCase_						)
											model.to(UpperCAmelCase_						)
											model.eval()
											lowerCAmelCase   =		model(UpperCAmelCase_       , UpperCAmelCase_						)
											lowerCAmelCase   =		model(UpperCAmelCase_						)
											self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)						)
											self.parent.assertEqual(result.pooler_output.shape       , (self.batch_size, self.hidden_size)						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		MPNetForQuestionAnswering(config=UpperCAmelCase_						)
											model.to(UpperCAmelCase_						)
											model.eval()
											lowerCAmelCase   =		model(
											    UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , start_positions=UpperCAmelCase_       , end_positions=UpperCAmelCase_       , )
											self.parent.assertEqual(result.start_logits.shape       , (self.batch_size, self.seq_length)						)
											self.parent.assertEqual(result.end_logits.shape       , (self.batch_size, self.seq_length)						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		self.num_labels
											lowerCAmelCase   =		MPNetForSequenceClassification(UpperCAmelCase_						)
											model.to(UpperCAmelCase_						)
											model.eval()
											lowerCAmelCase   =		model(UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , labels=UpperCAmelCase_						)
											self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels)						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		self.num_choices
											lowerCAmelCase   =		MPNetForMultipleChoice(config=UpperCAmelCase_						)
											model.to(UpperCAmelCase_						)
											model.eval()
											lowerCAmelCase   =		input_ids.unsqueeze(1						).expand(-1       , self.num_choices       , -1						).contiguous()
											lowerCAmelCase   =		input_mask.unsqueeze(1						).expand(-1       , self.num_choices       , -1						).contiguous()
											lowerCAmelCase   =		model(
											    UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , labels=UpperCAmelCase_       , )
											self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices)						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
											lowerCAmelCase   =		self.num_labels
											lowerCAmelCase   =		MPNetForTokenClassification(config=UpperCAmelCase_						)
											model.to(UpperCAmelCase_						)
											model.eval()
											lowerCAmelCase   =		model(UpperCAmelCase_       , attention_mask=UpperCAmelCase_       , labels=UpperCAmelCase_						)
											self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels)						)






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.prepare_config_and_inputs()
											((lowerCAmelCase)   ,     (lowerCAmelCase)   ,     (lowerCAmelCase)   ,     (lowerCAmelCase)   ,     (lowerCAmelCase)   ,     (lowerCAmelCase))   =		config_and_inputs
											lowerCAmelCase   =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
											return config, inputs_dict




@require_torch
class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: int										=(
				    (
				        MPNetForMaskedLM,
				        MPNetForMultipleChoice,
				        MPNetForQuestionAnswering,
				        MPNetForSequenceClassification,
				        MPNetForTokenClassification,
				        MPNetModel,
				    )
				    if is_torch_available()
				    else ()
				)
				__a							: Any										=(
				    {
				        """feature-extraction""": MPNetModel,
				        """fill-mask""": MPNetForMaskedLM,
				        """question-answering""": MPNetForQuestionAnswering,
				        """text-classification""": MPNetForSequenceClassification,
				        """token-classification""": MPNetForTokenClassification,
				        """zero-shot""": MPNetForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__a							: int										=False
				__a							: List[str]										=True



				def       __snake_case		( self						):
											lowerCAmelCase   =		MPNetModelTester(self						)
											lowerCAmelCase   =		ConfigTester(self       , config_class=UpperCAmelCase_       , hidden_size=37						)



				def       __snake_case		( self						):
											self.config_tester.run_common_tests()



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase_						)






				def       __snake_case		( self						):
											lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase_						)




@require_torch
class 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				@slow
				def       __snake_case		( self						):
											lowerCAmelCase   =		MPNetModel.from_pretrained('''microsoft/mpnet-base'''						)
											lowerCAmelCase   =		torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]						)
											lowerCAmelCase   =		model(UpperCAmelCase_						)[0]
											lowerCAmelCase   =		torch.Size((1, 11, 7_68)						)
											self.assertEqual(output.shape       , UpperCAmelCase_						)
											lowerCAmelCase   =		torch.tensor(
											    [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]						)
											# compare the actual values for a slice.
											self.assertTrue(torch.allclose(output[:, :3, :3]       , UpperCAmelCase_       , atol=1E-4						)						)









def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations(_snake_case							) -> 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(_snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):

							def count_of_possible_combinations_with_dp_array(
							    _snake_case						, _snake_case							) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														lowerCAmelCase   =		sum(
														    count_of_possible_combinations_with_dp_array(target - item						, _snake_case							)
														    for item in array							)
														lowerCAmelCase   =		answer
														return answer

							lowerCAmelCase   =		[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_snake_case						, _snake_case							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							lowerCAmelCase   =		[0] * (target + 1)
							lowerCAmelCase   =		1

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

							return dp_array[target]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCAmelCase_						=3
		UpperCAmelCase_						=5
		UpperCAmelCase_						=[1, 2, 5]
		print(combination_sum_iv(n, array, target))



