




from __future__ import annotations

import math



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase      ):
  lowercase			:Tuple							=  u
  for i in range(1,	lowerCamelCase      ):
    lowercase			:Union[str, Any]							=  temp * (u - i)
  return temp



def 					UpperCAmelCase__     (   ):
  lowercase			:List[Any]							=  int(input("enter the numbers of values: "      )      )
  lowercase			:list[list[float]]							=  []
  for _ in range(lowerCamelCase      ):
    y.append([]      )
  for i in range(lowerCamelCase      ):
    for j in range(lowerCamelCase      ):
      y[i].append(lowerCamelCase      )
      lowercase			:Tuple							=  0

  print("enter the values of parameters in a list: "      )
  lowercase			:int							=  list(map(lowerCamelCase,	input().split()      )      )

  print("enter the values of corresponding parameters: "      )
  for i in range(lowerCamelCase      ):
    lowercase			:Dict							=  float(input()      )

  lowercase			:Dict							=  int(input("enter the value to interpolate: "      )      )
  lowercase			:str							=  (value - x[0]) / (x[1] - x[0])

  # for calculating forward difference table

  for i in range(1,	lowerCamelCase      ):
    for j in range(n - i      ):
      lowercase			:Optional[int]							=  y[j + 1][i - 1] - y[j][i - 1]

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

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


if __name__ == "__main__":
     main()









import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel




class       __lowerCAmelCase				:






       def __init__(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str]=13				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Tuple=True				,    _lowerCAmelCase:    Dict=True				,    _lowerCAmelCase:    Optional[Any]=True				,    _lowerCAmelCase:    Union[str, Any]=True				,    _lowerCAmelCase:    str=99				,    _lowerCAmelCase:    Dict=32				,    _lowerCAmelCase:    Dict=5				,    _lowerCAmelCase:    int=4				,    _lowerCAmelCase:    Optional[Any]=4				,    _lowerCAmelCase:    Any="gelu"				,    _lowerCAmelCase:    Any=0.0				,    _lowerCAmelCase:    List[Any]=0.1				,    _lowerCAmelCase:    Optional[int]=True				,    _lowerCAmelCase:    Any=5_12				,    _lowerCAmelCase:    Optional[Any]=16				,    _lowerCAmelCase:    str=2				,    _lowerCAmelCase:    Tuple=0.02				,    _lowerCAmelCase:    str=3				,    _lowerCAmelCase:    List[str]=4				,    _lowerCAmelCase:    Optional[Any]=None				,    ):
         lowercase			:List[str]							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:List[str]							=  seq_length
         lowercase			:Dict							=  is_training
         lowercase			:Union[str, Any]							=  use_input_mask
         lowercase			:Optional[int]							=  use_token_type_ids
         lowercase			:Dict							=  use_labels
         lowercase			:Any							=  vocab_size
         lowercase			:List[Any]							=  hidden_size
         lowercase			:Optional[int]							=  num_hidden_layers
         lowercase			:Union[str, Any]							=  num_attention_heads
         lowercase			:Any							=  intermediate_multiple_size
         lowercase			:str							=  hidden_act
         lowercase			:List[Any]							=  hidden_dropout
         lowercase			:Optional[Any]							=  attention_dropout
         lowercase			:int							=  weight_tying
         lowercase			:Tuple							=  max_position_embeddings
         lowercase			:str							=  type_vocab_size
         lowercase			:Union[str, Any]							=  type_sequence_label_size
         lowercase			:Dict							=  initializer_range
         lowercase			:Dict							=  num_labels
         lowercase			:Dict							=  num_choices
         lowercase			:Any							=  scope






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:List[Any]							=  ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

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

         lowercase			:int							=  None
         if self.use_labels:
           lowercase			:Dict							=  ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels )

         lowercase			:Tuple							=  self.get_config()

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         return GPTNeoXJapaneseConfig(
             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_multiple_size=self.intermediate_multiple_size				,    hidden_act=self.hidden_act				,    hidden_dropout=self.hidden_dropout				,    attention_dropout=self.attention_dropout				,    weight_tying=self.weight_tying				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    is_decoder=_lowerCAmelCase				,    initializer_range=self.initializer_range				,    )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Any							=  self.prepare_config_and_inputs()

         lowercase			:Tuple							=  True

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str] ):
         lowercase			:List[str]							=  GPTNeoXJapaneseModel(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Optional[int]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         lowercase			:Dict							=  model(_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int ):
         lowercase			:Dict							=  True
         lowercase			:Optional[Any]							=  GPTNeoXJapaneseModel(_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:List[str]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    List[Any] ):
         lowercase			:str							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Tuple							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    labels=_lowerCAmelCase )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    Any ):
         lowercase			:Optional[Any]							=  True
         lowercase			:List[str]							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()

         # first forward pass
         lowercase			:str							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    use_cache=_lowerCAmelCase )
         lowercase			:Tuple							=  outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         lowercase			:Optional[int]							=  ids_tensor((self.batch_size, 3)				,    config.vocab_size )
         lowercase			:Tuple							=  ids_tensor((self.batch_size, 3)				,    vocab_size=2 )

         # append to next input_ids and
         lowercase			:Tuple							=  torch.cat([input_ids, next_tokens]				,    dim=-1 )
         lowercase			:List[Any]							=  torch.cat([input_mask, next_mask]				,    dim=-1 )

         lowercase			:Dict							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase )
         lowercase			:List[str]							=  output_from_no_past["hidden_states"][0]
         lowercase			:str							=  model(
             _lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    past_key_values=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase				,    )["hidden_states"][0]

         # select random slice
         lowercase			:Dict							=  ids_tensor((1,)				,    output_from_past.shape[-1] ).item()
         lowercase			:str							=  output_from_no_past[:, -3:, random_slice_idx].detach()
         lowercase			:List[str]							=  output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(_lowerCAmelCase				,    _lowerCAmelCase				,    atol=1e-3 ) )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:str							=  self.prepare_config_and_inputs()
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  config_and_inputs
         lowercase			:Union[str, Any]							=  {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







@require_torch
class       __lowerCAmelCase				(							lowerCAmelCase					,       lowerCAmelCase					,       unittest.TestCase):
       _a           =  (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
       _a           =  (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
       _a           =  (
           {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
           if is_torch_available()
           else {}
       )
       _a           =  False
       _a           =  False
       _a           =  False
       _a           =  False






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Any							=  GPTNeoXJapaneseModelTester(self )
         lowercase			:List[str]							=  ConfigTester(self				,    config_class=_lowerCAmelCase				,    hidden_size=37 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         self.config_tester.run_common_tests()






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         # This regression test was failing with PyTorch < 1.3
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs_for_decoder()

         lowercase			:Union[str, Any]							=  None

         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:List[str]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[int]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*_lowerCAmelCase )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:int							=  "abeja/gpt-neox-japanese-2.7b"

         lowercase			:Optional[int]							=  ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

         lowercase			:int							=  [
             "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
             "100年後に必要とされる会社は、「人」が中心の会社です。",
             "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
             "国境の長いトンネルを抜けると、そこは雪国だった。",
             "美味しい日本食といえば、やっぱりお寿司ですよね。",
         ]

         lowercase			:List[str]							=  GPTNeoXJapaneseTokenizer.from_pretrained(_lowerCAmelCase )
         lowercase			:Tuple							=  GPTNeoXJapaneseForCausalLM.from_pretrained(_lowerCAmelCase )

         lowercase			:List[str]							=  []
         for prompt in prompts:
           lowercase			:Optional[int]							=  tokenizer(_lowerCAmelCase				,    return_tensors="pt" ).input_ids
           lowercase			:Union[str, Any]							=  model.generate(_lowerCAmelCase				,    max_length=50 )
           lowercase			:List[str]							=  tokenizer.batch_decode(_lowerCAmelCase				,    skip_special_tokens=_lowerCAmelCase )
           predicted_outputs += generated_string
         self.assertListEqual(_lowerCAmelCase				,    _lowerCAmelCase )



