





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """CTRLForSequenceClassification""",
						    """CTRLLMHeadModel""",
						    """CTRLModel""",
						    """CTRLPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFCTRLForSequenceClassification""",
						    """TFCTRLLMHeadModel""",
						    """TFCTRLModel""",
						    """TFCTRLPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
						from .tokenization_ctrl import CTRLTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_ctrl import (
												    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CTRLForSequenceClassification,
												    CTRLLMHeadModel,
												    CTRLModel,
												    CTRLPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_ctrl import (
												    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCTRLForSequenceClassification,
												    TFCTRLLMHeadModel,
												    TFCTRLModel,
												    TFCTRLPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








from __future__ import annotations

import math


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     Optional[int]     =		u
	for i in range(1				,	__a				):
		snake_case_						:     Optional[Any]     =		temp * (u - i)
	return temp


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Dict     =		int(input('enter the numbers of values: '				)				)
	snake_case_						:     list[list[float]]     =		[]
	for _ in range(__a				):
		y.append([]				)
	for i in range(__a				):
		for j in range(__a				):
			y[i].append(__a				)
			snake_case_						:     str     =		0

	print('enter the values of parameters in a list: '				)
	snake_case_						:     int     =		list(map(__a				,	input().split()				)				)

	print('enter the values of corresponding parameters: '				)
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		float(input()				)

	snake_case_						:     int     =		int(input('enter the value to interpolate: '				)				)
	snake_case_						:     List[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				):
			snake_case_						:     int     =		y[j + 1][i - 1] - y[j][i - 1]

	snake_case_						:     str     =		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()

