


"""simple docstring"""


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					,     __a					,     __a=13					,     __a=7					,     __a=True					,     __a=True					,     __a=False					,     __a=True					,     __a=99					,     __a=64					,     __a=5					,     __a=4					,     __a=64					,     __a="gelu"					,     __a=0.1					,     __a=0.1					,     __a=5_12					,     __a=16					,     __a=2					,     __a=0.0_2					,     __a=3					,     __a=4					,     __a=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					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    MPNetModel(config=__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     __a			)
										__lowerCAmelCase       					=    model(__a			)
										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					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    MPNetForQuestionAnswering(config=__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(
										    __a					,     attention_mask=__a					,     start_positions=__a					,     end_positions=__a					,     )
										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					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.num_labels
										__lowerCAmelCase       					=    MPNetForSequenceClassification(__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     attention_mask=__a					,     labels=__a			)
										self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_labels)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.num_choices
										__lowerCAmelCase       					=    MPNetForMultipleChoice(config=__a			)
										model.to(__a			)
										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(
										    __a					,     attention_mask=__a					,     labels=__a					,     )
										self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.num_choices)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.num_labels
										__lowerCAmelCase       					=    MPNetForTokenClassification(config=__a			)
										model.to(__a			)
										model.eval()
										__lowerCAmelCase       					=    model(__a					,     attention_mask=__a					,     labels=__a			)
										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						(     lowerCAmelCase__ ,lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







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

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    MPNetModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=__a					,     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(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_sequence_classification(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_multiple_choice(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_token_classification(*__a			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_mpnet_for_question_answering(*__a			)
@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(__a			)[0]
										__lowerCAmelCase       					=    torch.Size((1, 11, 7_68)			)
										self.assertEqual(output.shape					,     __a			)
										__lowerCAmelCase       					=    torch.tensor(
										    [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]			)
										# compare the actual values for a slice.
										self.assertTrue(torch.allclose(output[:, :3, :3]					,     __a					,     atol=1e-4			)			)








"""simple docstring"""


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    len(_UpperCamelCase   )
			print("The following activities are selected:"   )

			# The first activity is always selected
			__lowerCAmelCase       					=    0
			print(_UpperCamelCase					,				end=","   )

			# Consider rest of the activities
			for j in range(_UpperCamelCase   ):
						# If this activity has start time greater than
						# or equal to the finish time of previously
						# selected activity, then select it
						if start[j] >= finish[i]:
									print(_UpperCamelCase					,				end=","   )
									__lowerCAmelCase       					=    j


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A  :     Dict    					=    [1, 3, 0, 5, 8, 5]
	A  :     int    					=    [2, 4, 6, 7, 9, 9]
	print_max_activities(start, finish)




