





from __future__ import annotations

import math


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


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

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

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

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

	snake_case_						:	Union[str, 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()







from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={"""help""": """The output directory where the model will be written."""}    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The encoder model checkpoint for weights initialization."""
				            """Don't set if you want to train an encoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    metadata={
				        """help""": (
				            """The decoder model checkpoint for weights initialization."""
				            """Don't set if you want to train a decoder model from scratch."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained encoder config name or path if not the same as encoder_model_name"""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained decoder config name or path if not the same as decoder_model_name"""}   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			HfArgumentParser((ModelArguments,)   )
			((a)    ,		)    =			parser.parse_args_into_dataclasses()

			# Load pretrained model and tokenizer

			# Use explicit specified encoder config
			if model_args.encoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.encoder_config_name   )
			# Use pretrained encoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path   )

			# Use explicit specified decoder config
			if model_args.decoder_config_name:
						a    =			AutoConfig.from_pretrained(model_args.decoder_config_name   )
			# Use pretrained decoder model's config
			else:
						a    =			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path   )

			# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
			a    =			True
			a    =			True

			a    =			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
			    encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path,  decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path,  encoder_config=A,  decoder_config=A,  )

			# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
			a    =			decoder_config.decoder_start_token_id
			a    =			decoder_config.pad_token_id
			if decoder_start_token_id is None:
						a    =			decoder_config.bos_token_id
			if pad_token_id is None:
						a    =			decoder_config.eos_token_id

			# This is necessary to make Flax's generate() work
			a    =			decoder_config.eos_token_id
			a    =			decoder_start_token_id
			a    =			pad_token_id

			a    =			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path   )

			a    =			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path   )
			a    =			tokenizer.convert_ids_to_tokens(model.config.pad_token_id   )

			model.save_pretrained(model_args.output_dir   )
			image_processor.save_pretrained(model_args.output_dir   )
			tokenizer.save_pretrained(model_args.output_dir   )


if __name__ == "__main__":
		main()






