





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
						from PIL import Image
class SCREAMING_SNAKE_CASE_  (   _a   ):
						__magic_name__:     str	 =    """Salesforce/blip-image-captioning-base"""
						__magic_name__:     List[str]	 =    (
						    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
						    """image to caption, and returns a text that contains the description in English."""
						)
						__magic_name__:     Dict	 =    """image_captioner"""
						__magic_name__:     Union[str, Any]	 =    AutoModelForVisionaSeq

						__magic_name__:     Optional[Any]	 =    ["""image"""]
						__magic_name__:     Union[str, Any]	 =    ["""text"""]






						def __init__( self : Any						,				*_A : str						,				**_A : List[str]		)							->      int:







							"""simple docstring"""


							requires_backends(self						,				['vision']		)
							super().__init__(*__lowerCamelCase						,				**__lowerCamelCase		)






						def UpperCAmelCase_			( self : Tuple						,				_A : "Image"		)							->      List[Any]:







							"""simple docstring"""


							return self.pre_processor(images=__lowerCamelCase						,				return_tensors='pt'		)






						def UpperCAmelCase_			( self : Any						,				_A : Any		)							->      List[str]:







							"""simple docstring"""


							return self.model.generate(**__lowerCamelCase		)






						def UpperCAmelCase_			( self : Optional[int]						,				_A : Tuple		)							->      Optional[Any]:







							"""simple docstring"""


							return self.pre_processor.batch_decode(__lowerCamelCase						,				skip_special_tokens=__lowerCamelCase		)[0].strip()








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()

