





'''simple docstring'''






from __future__ import annotations

import math







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				float:



			"""simple docstring"""


			__snake_case					:      Any							      =     u
			for i in range(1    ,	_lowerCamelCase ):
						__snake_case					:      Optional[Any]							      =     temp * (u - i)
			return temp







def 					_a						(       ) ->				None:



			"""simple docstring"""


			__snake_case					:      str							      =     int(input("""enter the numbers of values: """ ) )
			__snake_case					:      list[list[float]]							      =     []
			for _ in range(_lowerCamelCase ):
						y.append([] )
			for i in range(_lowerCamelCase ):
						for j in range(_lowerCamelCase ):
									y[i].append(_lowerCamelCase )
									__snake_case					:      Any							      =     0

			print("""enter the values of parameters in a list: """ )
			__snake_case					:      Dict							      =     list(map(_lowerCamelCase    ,	input().split() ) )

			print("""enter the values of corresponding parameters: """ )
			for i in range(_lowerCamelCase ):
						__snake_case					:      Optional[int]							      =     float(input() )

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

			# for calculating forward difference table

			for i in range(1    ,	_lowerCamelCase ):
						for j in range(n - i ):
									__snake_case					:      List[Any]							      =     y[j + 1][i - 1] - y[j][i - 1]

			__snake_case					:      Union[str, Any]							      =     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()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A							:			Dict         =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			["BlenderbotTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			str         =			[
     "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BlenderbotForCausalLM",
     "BlenderbotForConditionalGeneration",
     "BlenderbotModel",
     "BlenderbotPreTrainedModel",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Union[str, Any]         =			[
     "TFBlenderbotForConditionalGeneration",
     "TFBlenderbotModel",
     "TFBlenderbotPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[Any]         =			[
     "FlaxBlenderbotForConditionalGeneration",
     "FlaxBlenderbotModel",
     "FlaxBlenderbotPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_blenderbot import (
     BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlenderbotConfig,
     BlenderbotOnnxConfig,
 )
 from .tokenization_blenderbot import BlenderbotTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blenderbot import (
      BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlenderbotForCausalLM,
      BlenderbotForConditionalGeneration,
      BlenderbotModel,
      BlenderbotPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_blenderbot import (
      TFBlenderbotForConditionalGeneration,
      TFBlenderbotModel,
      TFBlenderbotPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      FlaxBlenderbotPreTrainedModel,
  )

else:
 import sys

 __A							:			str         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





