



# 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     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''Salesforce/blip-image-captioning-base'''
     UpperCamelCase__		    =				(
         '''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.'''
     )
     UpperCamelCase__		    =				'''image_captioner'''
     UpperCamelCase__		    =				AutoModelForVisionaSeq

     UpperCamelCase__		    =				['''image''']
     UpperCamelCase__		    =				['''text''']




     def __init__(				self ,						*a__ ,						**a__):
         requires_backends(self ,						['''vision'''])
         super().__init__(*a__ ,						**a__)




     def 		snake_case_		(				self ,						a__):
         return self.pre_processor(images=a__ ,						return_tensors='''pt''')




     def 		snake_case_		(				self ,						a__):
         return self.model.generate(**a__)




     def 		snake_case_		(				self ,						a__):
         return self.pre_processor.batch_decode(a__ ,						skip_special_tokens=a__)[0].strip()





import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier



def 		lowerCAmelCase__    (					UpperCamelCase_				:       dict	)->  tuple:
    return (data["data"], data["target"])



def 		lowerCAmelCase__    (					UpperCamelCase_				:       np.ndarray ,       UpperCamelCase_				:       np.ndarray	)->  XGBClassifier:
    A__		 =					XGBClassifier()
    classifier.fit(UpperCamelCase_ ,       UpperCamelCase_	)
    return classifier



def 		lowerCAmelCase__    (					)->  None:
    A__		 =					load_iris()
    A__ ,	A__		 =					data_handling(UpperCamelCase_	)
    A__ ,	A__ ,	A__ ,	A__		 =					train_test_split(
        UpperCamelCase_ ,       UpperCamelCase_ ,       test_size=0.25	)

    A__		 =					iris['''target_names''']

    # Create an XGBoost Classifier from the training data
    A__		 =					xgboost(UpperCamelCase_ ,       UpperCamelCase_	)

    # Display the confusion matrix of the classifier with both training and test sets
    ConfusionMatrixDisplay.from_estimator(
        UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       display_labels=UpperCamelCase_ ,       cmap='''Blues''' ,       normalize='''true''' ,       )
    plt.title('''Normalized Confusion Matrix - IRIS Dataset'''	)
    plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()
