






'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			'''imagegpt'''
      UpperCamelCase_								=			['''past_key_values''']
      UpperCamelCase_								=			{
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }






      def __init__( self			:		List[str]     ,      UpperCAmelCase			:		Union[str, Any]=512 + 1     ,      UpperCAmelCase			:		str=32 * 32     ,      UpperCAmelCase			:		Union[str, Any]=512     ,      UpperCAmelCase			:		Optional[int]=24     ,      UpperCAmelCase			:		List[str]=8     ,      UpperCAmelCase			:		Dict=None     ,      UpperCAmelCase			:		Dict="quick_gelu"     ,      UpperCAmelCase			:		List[Any]=0.1     ,      UpperCAmelCase			:		Tuple=0.1     ,      UpperCAmelCase			:		Any=0.1     ,      UpperCAmelCase			:		Union[str, Any]=1e-5     ,      UpperCAmelCase			:		Tuple=0.0_2     ,      UpperCAmelCase			:		Optional[Any]=True     ,      UpperCAmelCase			:		Union[str, Any]=True     ,      UpperCAmelCase			:		int=False     ,      UpperCAmelCase			:		Any=False     ,      UpperCAmelCase			:		Any=False     ,      **UpperCAmelCase			:		int     ,      )  ->  Any:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =vocab_size
       lowercase					:   str				  =n_positions
       lowercase					:   Dict				  =n_embd
       lowercase					:   Optional[Any]				  =n_layer
       lowercase					:   Union[str, Any]				  =n_head
       lowercase					:   List[str]				  =n_inner
       lowercase					:   Dict				  =activation_function
       lowercase					:   List[Any]				  =resid_pdrop
       lowercase					:   Optional[Any]				  =embd_pdrop
       lowercase					:   Optional[Any]				  =attn_pdrop
       lowercase					:   Any				  =layer_norm_epsilon
       lowercase					:   List[str]				  =initializer_range
       lowercase					:   int				  =scale_attn_weights
       lowercase					:   Optional[int]				  =use_cache
       lowercase					:   Optional[Any]				  =scale_attn_by_inverse_layer_idx
       lowercase					:   List[str]				  =reorder_and_upcast_attn
       lowercase					:   Optional[int]				  =tie_word_embeddings

       super().__init__(tie_word_embeddings=UpperCAmelCase     ,      **UpperCAmelCase		)





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      @property
      def 				A__      ( self			:		Any		)  ->  Mapping[str, Mapping[int, str]]:


       '''simple docstring'''

       return OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
           ]		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		"FeatureExtractionMixin"     ,      UpperCAmelCase			:		int = 1     ,      UpperCAmelCase			:		int = -1     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		Optional["TensorType"] = None     ,      UpperCAmelCase			:		int = 3     ,      UpperCAmelCase			:		int = 32     ,      UpperCAmelCase			:		int = 32     ,      )  ->  Mapping[str, Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =self._generate_dummy_images(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)
       lowercase					:   List[Any]				  =dict(preprocessor(images=UpperCAmelCase     ,      return_tensors=UpperCAmelCase		)		)

       return inputs











'''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()



