






'''simple docstring'''



import unittest

import numpy as np

from transformers import RobertaConfig, 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():
      from transformers.models.roberta.modeling_flax_roberta import (
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaModel,
      )
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      def __init__( self			:		int     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		int=13     ,      UpperCAmelCase			:		Optional[int]=7     ,      UpperCAmelCase			:		Optional[Any]=True     ,      UpperCAmelCase			:		Tuple=True     ,      UpperCAmelCase			:		Tuple=True     ,      UpperCAmelCase			:		Optional[Any]=True     ,      UpperCAmelCase			:		Dict=99     ,      UpperCAmelCase			:		Any=32     ,      UpperCAmelCase			:		Any=5     ,      UpperCAmelCase			:		Optional[int]=4     ,      UpperCAmelCase			:		Optional[Any]=37     ,      UpperCAmelCase			:		List[Any]="gelu"     ,      UpperCAmelCase			:		Tuple=0.1     ,      UpperCAmelCase			:		int=0.1     ,      UpperCAmelCase			:		str=512     ,      UpperCAmelCase			:		Optional[Any]=16     ,      UpperCAmelCase			:		Tuple=2     ,      UpperCAmelCase			:		str=0.0_2     ,      UpperCAmelCase			:		List[Any]=4     ,      )  ->  Any:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =parent
       lowercase					:   Optional[Any]				  =batch_size
       lowercase					:   Tuple				  =seq_length
       lowercase					:   Any				  =is_training
       lowercase					:   Optional[int]				  =use_attention_mask
       lowercase					:   Optional[int]				  =use_token_type_ids
       lowercase					:   Tuple				  =use_labels
       lowercase					:   Any				  =vocab_size
       lowercase					:   int				  =hidden_size
       lowercase					:   str				  =num_hidden_layers
       lowercase					:   Any				  =num_attention_heads
       lowercase					:   str				  =intermediate_size
       lowercase					:   Optional[Any]				  =hidden_act
       lowercase					:   str				  =hidden_dropout_prob
       lowercase					:   List[Any]				  =attention_probs_dropout_prob
       lowercase					:   Union[str, Any]				  =max_position_embeddings
       lowercase					:   Tuple				  =type_vocab_size
       lowercase					:   Optional[int]				  =type_sequence_label_size
       lowercase					:   List[Any]				  =initializer_range
       lowercase					:   Dict				  =num_choices






      def 				A__      ( self			:		Any		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Any				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size		)

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

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

       lowercase					:   Dict				  =RobertaConfig(
           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=UpperCAmelCase     ,      initializer_range=self.initializer_range     ,      )

       return config, input_ids, token_type_ids, attention_mask






      def 				A__      ( self			:		List[str]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[str]				  =self.prepare_config_and_inputs()
       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   str				  =config_and_inputs
       lowercase					:   Union[str, Any]				  ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict






      def 				A__      ( self			:		Union[str, Any]		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =self.prepare_config_and_inputs()
       lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Tuple				  =config_and_inputs

       lowercase					:   Dict				  =True
       lowercase					:   Union[str, Any]				  =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
       lowercase					:   Optional[Any]				  =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
class UpperCAmelCase_					(    __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			True

      UpperCamelCase_								=			(
          (
              FlaxRobertaModel,
              FlaxRobertaForCausalLM,
              FlaxRobertaForMaskedLM,
              FlaxRobertaForSequenceClassification,
              FlaxRobertaForTokenClassification,
              FlaxRobertaForMultipleChoice,
              FlaxRobertaForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )






      def 				A__      ( self			:		Union[str, Any]		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   str				  =FlaxRobertaModelTester(self		)






      @slow
      def 				A__      ( self			:		Union[str, Any]		)  ->  Tuple:


       '''simple docstring'''

       for model_class_name in self.all_model_classes:
        lowercase					:   Optional[Any]				  =model_class_name.from_pretrained('''roberta-base'''     ,      from_pt=UpperCAmelCase		)
        lowercase					:   Optional[int]				  =model(np.ones((1, 1)		)		)
        self.assertIsNotNone(UpperCAmelCase		)











'''simple docstring'''



from __future__ import annotations

import math


def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 lowercase					:   str				  =u
 for i in range(1					,      __A			):
  lowercase					:   Any				  =temp * (u - i)
 return temp


def 							lowercase_				(    )       ->      None:



 """simple docstring"""




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

 print('''enter the values of parameters in a list: '''			)
 lowercase					:   Optional[int]				  =list(map(__A					,      input().split()			)			)

 print('''enter the values of corresponding parameters: '''			)
 for i in range(__A			):
  lowercase					:   str				  =float(input()			)

 lowercase					:   int				  =int(input('''enter the value to interpolate: '''			)			)
 lowercase					:   Union[str, Any]				  =(value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1					,      __A			):
  for j in range(n - i			):
   lowercase					:   str				  =y[j + 1][i - 1] - y[j][i - 1]

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

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


if __name__ == "__main__":
      main()



