





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	# Initialise PyTorch model
	snake_case_						:     Any     =		TaConfig.from_json_file(__a				)
	print(f"""Building PyTorch model from configuration: {config}"""				)
	snake_case_						:     Dict     =		TaForConditionalGeneration(__a				)

	# Load weights from tf checkpoint
	load_tf_weights_in_ta(__a				,	__a				,	__a				)

	# Save pytorch-model
	print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
	model.save_pretrained(__a				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
						)
						parser.add_argument(
						    """--config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)








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()

