




"""simple docstring"""






from __future__ import annotations

import math







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Any       =       u
				for i in range(1				,       lowercase__						):
								_lowerCamelCase	:     Tuple       =       temp * (u - i)
				return temp







def 	_snake_case     (							):
				_lowerCamelCase	:     List[str]       =       int(input('enter the numbers of values: '						)						)
				_lowerCamelCase	:     list[list[float]]       =       []
				for _ in range(lowercase__						):
								y.append([]						)
				for i in range(lowercase__						):
								for j in range(lowercase__						):
												y[i].append(lowercase__						)
												_lowerCamelCase	:     List[str]       =       0

				print('enter the values of parameters in a list: '						)
				_lowerCamelCase	:     Union[str, Any]       =       list(map(lowercase__				,       input().split()						)						)

				print('enter the values of corresponding parameters: '						)
				for i in range(lowercase__						):
								_lowerCamelCase	:     Tuple       =       float(input()						)

				_lowerCamelCase	:     Optional[int]       =       int(input('enter the value to interpolate: '						)						)
				_lowerCamelCase	:     int       =       (value - x[0]) / (x[1] - x[0])

				# for calculating forward difference table

				for i in range(1				,       lowercase__						):
								for j in range(n - i						):
												_lowerCamelCase	:     int       =       y[j + 1][i - 1] - y[j][i - 1]

				_lowerCamelCase	:     List[Any]       =       y[0][0]
				for i in range(1				,       lowercase__						):
								summ += (ucal(lowercase__				,       lowercase__						) * y[0][i]) / math.factorial(lowercase__						)

				print(f'''the value at {value} is {summ}'''						)


if __name__ == "__main__":
			main()





"""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 lowerCAmelCase__      :






			'''simple docstring'''



			def __init__(    self    , lowercase    , lowercase=13    , lowercase=7    , lowercase=True    , lowercase=True    , lowercase=False    , lowercase=True    , lowercase=99    , lowercase=64    , lowercase=5    , lowercase=4    , lowercase=64    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=16    , lowercase=2    , lowercase=0.02    , lowercase=3    , lowercase=4    , lowercase=None    , ):
							_lowerCamelCase	:     List[str]       =       parent
							_lowerCamelCase	:     List[str]       =       batch_size
							_lowerCamelCase	:     Tuple       =       seq_length
							_lowerCamelCase	:     List[Any]       =       is_training
							_lowerCamelCase	:     int       =       use_input_mask
							_lowerCamelCase	:     Optional[Any]       =       use_token_type_ids
							_lowerCamelCase	:     List[Any]       =       use_labels
							_lowerCamelCase	:     str       =       vocab_size
							_lowerCamelCase	:     Optional[int]       =       hidden_size
							_lowerCamelCase	:     List[str]       =       num_hidden_layers
							_lowerCamelCase	:     Optional[int]       =       num_attention_heads
							_lowerCamelCase	:     List[str]       =       intermediate_size
							_lowerCamelCase	:     int       =       hidden_act
							_lowerCamelCase	:     Optional[int]       =       hidden_dropout_prob
							_lowerCamelCase	:     List[Any]       =       attention_probs_dropout_prob
							_lowerCamelCase	:     List[str]       =       max_position_embeddings
							_lowerCamelCase	:     List[Any]       =       type_vocab_size
							_lowerCamelCase	:     Optional[int]       =       type_sequence_label_size
							_lowerCamelCase	:     Union[str, Any]       =       initializer_range
							_lowerCamelCase	:     Tuple       =       num_labels
							_lowerCamelCase	:     int       =       num_choices
							_lowerCamelCase	:     int       =       scope
			def    A_       (    self    ):
							return MPNetConfig.from_pretrained('microsoft/mpnet-base'    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size    )

							_lowerCamelCase	:     Dict       =       None
							if self.use_input_mask:
											_lowerCamelCase	:     Optional[int]       =       random_attention_mask([self.batch_size, self.seq_length]    )

							_lowerCamelCase	:     int       =       None
							_lowerCamelCase	:     List[str]       =       None
							_lowerCamelCase	:     Any       =       None
							if self.use_labels:
											_lowerCamelCase	:     List[str]       =       ids_tensor([self.batch_size]    , self.type_sequence_label_size    )
											_lowerCamelCase	:     str       =       ids_tensor([self.batch_size, self.seq_length]    , self.num_labels    )
											_lowerCamelCase	:     Dict       =       ids_tensor([self.batch_size]    , self.num_choices    )

							_lowerCamelCase	:     int       =       self.get_config()
							return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def    A_       (    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    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Optional[Any]       =       MPNetModel(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Tuple       =       model(lowercase    , lowercase    )
							_lowerCamelCase	:     Tuple       =       model(lowercase    )
							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    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     str       =       MPNetForQuestionAnswering(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     str       =       model(
							    lowercase    , attention_mask=lowercase    , start_positions=lowercase    , end_positions=lowercase    , )
							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    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Optional[int]       =       self.num_labels
							_lowerCamelCase	:     int       =       MPNetForSequenceClassification(lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Optional[Any]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     List[str]       =       self.num_choices
							_lowerCamelCase	:     List[str]       =       MPNetForMultipleChoice(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Union[str, Any]       =       input_ids.unsqueeze(1    ).expand(-1    , self.num_choices    , -1    ).contiguous()
							_lowerCamelCase	:     List[str]       =       input_mask.unsqueeze(1    ).expand(-1    , self.num_choices    , -1    ).contiguous()
							_lowerCamelCase	:     Any       =       model(
							    lowercase    , attention_mask=lowercase    , labels=lowercase    , )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_choices)    )
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    , lowercase    ):
							_lowerCamelCase	:     Any       =       self.num_labels
							_lowerCamelCase	:     Union[str, Any]       =       MPNetForTokenClassification(config=lowercase    )
							model.to(lowercase    )
							model.eval()
							_lowerCamelCase	:     Union[str, Any]       =       model(lowercase    , attention_mask=lowercase    , labels=lowercase    )
							self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.num_labels)    )






			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.prepare_config_and_inputs()
							((_lowerCamelCase),					(_lowerCamelCase),					(_lowerCamelCase),					(_lowerCamelCase),					(_lowerCamelCase),					(_lowerCamelCase))	:     List[Any]       =       config_and_inputs
							_lowerCamelCase	:     str       =       {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict






@require_torch
class lowerCAmelCase__      (	lowercase,      lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     (
			    (
			        MPNetForMaskedLM,
			        MPNetForMultipleChoice,
			        MPNetForQuestionAnswering,
			        MPNetForSequenceClassification,
			        MPNetForTokenClassification,
			        MPNetModel,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__ =     (
			    {
			        """feature-extraction""": MPNetModel,
			        """fill-mask""": MPNetForMaskedLM,
			        """question-answering""": MPNetForQuestionAnswering,
			        """text-classification""": MPNetForSequenceClassification,
			        """token-classification""": MPNetForTokenClassification,
			        """zero-shot""": MPNetForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__ =     False
			lowerCamelCase__ =     True
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       MPNetModelTester(self    )
							_lowerCamelCase	:     List[Any]       =       ConfigTester(self    , config_class=lowercase    , hidden_size=37    )
			def    A_       (    self    ):
							self.config_tester.run_common_tests()
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_model(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_for_token_classification(*lowercase    )






			def    A_       (    self    ):
							_lowerCamelCase	:     str       =       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_mpnet_for_question_answering(*lowercase    )






@require_torch
class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       MPNetModel.from_pretrained('microsoft/mpnet-base'    )
							_lowerCamelCase	:     Optional[Any]       =       torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
							_lowerCamelCase	:     Any       =       model(lowercase    )[0]
							_lowerCamelCase	:     Dict       =       torch.Size((1, 11, 768)    )
							self.assertEqual(output.shape    , lowercase    )
							_lowerCamelCase	:     List[Any]       =       torch.tensor(
							    [[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]]    )
							# compare the actual values for a slice.
							self.assertTrue(torch.allclose(output[:, :3, :3]    , lowercase    , atol=1E-4    )    )