






"""simple docstring"""







# 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

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
  from PIL import Image







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      '''dandelin/vilt-b32-finetuned-vqa'''
    SCREAMING_SNAKE_CASE		=      (
        '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
        '''image containing the information, as well as a `question` which should be the question in English. It '''
        '''returns a text that is the answer to the question.'''
    )
    SCREAMING_SNAKE_CASE		=      '''image_qa'''
    SCREAMING_SNAKE_CASE		=      AutoProcessor
    SCREAMING_SNAKE_CASE		=      AutoModelForVisualQuestionAnswering

    SCREAMING_SNAKE_CASE		=      ['''image''', '''text''']
    SCREAMING_SNAKE_CASE		=      ['''text''']






    def __init__(			self     ,*A     ,**A ):
          requires_backends(self     ,["""vision"""] )
          super().__init__(*A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A ):
          return self.pre_processor(A     ,A     ,return_tensors="""pt""" )






    def     _UpperCamelCase							(			self     ,A ):
          with torch.no_grad():
                return self.model(**A ).logits






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	outputs.argmax(-1 ).item()
          return self.model.config.idalabel[idx]











"""simple docstring"""







import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_UpperCamelCase					      =  pd.read_csv(
    """https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/"""
    """position_salaries.csv"""
)
_UpperCamelCase					      =  dataset.iloc[:, 1:2].values
_UpperCamelCase					      =  dataset.iloc[:, 2].values


_UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase					      =  train_test_split(X, y, test_size=0.2, random_state=0)


_UpperCamelCase					      =  PolynomialFeatures(degree=4)
_UpperCamelCase					      =  poly_reg.fit_transform(X)
_UpperCamelCase					      =  LinearRegression()
pol_reg.fit(X_poly, y)

def _a			(		):







      """simple docstring"""






      plt.scatter(_snake_case				,	_snake_case				,	color="""red"""							)
      plt.plot(_snake_case				,	pol_reg.predict(poly_reg.fit_transform(_snake_case							)							)				,	color="""blue"""							)
      plt.title("""Truth or Bluff (Linear Regression)"""							)
      plt.xlabel("""Position level"""							)
      plt.ylabel("""Salary"""							)
      plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003



