


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AlignProcessor, EfficientNetImageProcessor







@require_vision
class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Tuple:
												a__             =     tempfile.mkdtemp()

												a__             =     [
												    "[UNK]",
												    "[CLS]",
												    "[SEP]",
												    "[PAD]",
												    "[MASK]",
												    "want",
												    "##want",
												    "##ed",
												    "wa",
												    "un",
												    "runn",
												    "##ing",
												    ",",
												    "low",
												    "lowest",
												]
												a__             =     os.path.join(self.tmpdirname			,VOCAB_FILES_NAMES['vocab_file']      )
												with open(self.vocab_file			,'w'			,encoding='utf-8'      ) as vocab_writer:
																			vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )

												a__             =     {
												    "do_resize": True,
												    "size": 20,
												    "do_center_crop": True,
												    "crop_size": 18,
												    "do_normalize": True,
												    "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
												    "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
												}
												a__             =     os.path.join(self.tmpdirname			,__snake_case      )
												with open(self.image_processor_file			,'w'			,encoding='utf-8'      ) as fp:
																			json.dump(__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,**__snake_case   :List[Any]      )					->    Optional[Any]:
												return BertTokenizer.from_pretrained(self.tmpdirname			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[str]			,**__snake_case   :Tuple      )					->    Any:
												return BertTokenizerFast.from_pretrained(self.tmpdirname			,**__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,**__snake_case   :Dict      )					->    Tuple:
												return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,**__snake_case      )
					def  lowerCamelCase__(      self   :str      )					->    List[Any]:
												shutil.rmtree(self.tmpdirname      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    Tuple:
												a__             =     [np.random.randint(2_55			,size=(3, 30, 4_00)			,dtype=np.uinta      )]
												a__             =     [Image.fromarray(np.moveaxis(__snake_case			,0			,-1      )      ) for x in image_inputs]
												return image_inputs
					def  lowerCamelCase__(      self   :List[str]      )					->    int:
												a__             =     self.get_tokenizer()
												a__             =     self.get_rust_tokenizer()
												a__             =     self.get_image_processor()

												a__             =     AlignProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )
												processor_slow.save_pretrained(self.tmpdirname      )
												a__             =     AlignProcessor.from_pretrained(self.tmpdirname			,use_fast=__snake_case      )

												a__             =     AlignProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )
												processor_fast.save_pretrained(self.tmpdirname      )
												a__             =     AlignProcessor.from_pretrained(self.tmpdirname      )

												self.assertEqual(processor_slow.tokenizer.get_vocab()			,tokenizer_slow.get_vocab()      )
												self.assertEqual(processor_fast.tokenizer.get_vocab()			,tokenizer_fast.get_vocab()      )
												self.assertEqual(tokenizer_slow.get_vocab()			,tokenizer_fast.get_vocab()      )
												self.assertIsInstance(processor_slow.tokenizer			,__snake_case      )
												self.assertIsInstance(processor_fast.tokenizer			,__snake_case      )

												self.assertEqual(processor_slow.image_processor.to_json_string()			,image_processor.to_json_string()      )
												self.assertEqual(processor_fast.image_processor.to_json_string()			,image_processor.to_json_string()      )
												self.assertIsInstance(processor_slow.image_processor			,__snake_case      )
												self.assertIsInstance(processor_fast.image_processor			,__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]      )					->    Any:
												a__             =     AlignProcessor(tokenizer=self.get_tokenizer()			,image_processor=self.get_image_processor()      )
												processor.save_pretrained(self.tmpdirname      )

												a__             =     self.get_tokenizer(bos_token='(BOS)'			,eos_token='(EOS)'      )
												a__             =     self.get_image_processor(do_normalize=__snake_case			,padding_value=1.0      )

												a__             =     AlignProcessor.from_pretrained(
												    self.tmpdirname			,bos_token='(BOS)'			,eos_token='(EOS)'			,do_normalize=__snake_case			,padding_value=1.0      )

												self.assertEqual(processor.tokenizer.get_vocab()			,tokenizer_add_kwargs.get_vocab()      )
												self.assertIsInstance(processor.tokenizer			,__snake_case      )

												self.assertEqual(processor.image_processor.to_json_string()			,image_processor_add_kwargs.to_json_string()      )
												self.assertIsInstance(processor.image_processor			,__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     AlignProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     self.prepare_image_inputs()

												a__             =     image_processor(__snake_case			,return_tensors='np'      )
												a__             =     processor(images=__snake_case			,return_tensors='np'      )

												for key in input_image_proc.keys():
																			self.assertAlmostEqual(input_image_proc[key].sum()			,input_processor[key].sum()			,delta=1E-2      )
					def  lowerCamelCase__(      self   :Tuple      )					->    List[Any]:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     AlignProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     "lower newer"

												a__             =     processor(text=__snake_case      )

												a__             =     tokenizer(__snake_case			,padding='max_length'			,max_length=64      )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]			,encoded_processor[key]      )
					def  lowerCamelCase__(      self   :int      )					->    int:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     AlignProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     "lower newer"
												a__             =     self.prepare_image_inputs()

												a__             =     processor(text=__snake_case			,images=__snake_case      )

												self.assertListEqual(list(inputs.keys()      )			,['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']      )

												# test if it raises when no input is passed
												with pytest.raises(__snake_case      ):
																			processor()
					def  lowerCamelCase__(      self   :int      )					->    Optional[int]:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     AlignProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												a__             =     processor.batch_decode(__snake_case      )
												a__             =     tokenizer.batch_decode(__snake_case      )

												self.assertListEqual(__snake_case			,__snake_case      )



					def  lowerCamelCase__(      self   :Tuple      )					->    List[str]:
												a__             =     self.get_image_processor()
												a__             =     self.get_tokenizer()

												a__             =     AlignProcessor(tokenizer=__snake_case			,image_processor=__snake_case      )

												a__             =     "lower newer"
												a__             =     self.prepare_image_inputs()

												a__             =     processor(text=__snake_case			,images=__snake_case      )

												self.assertListEqual(list(inputs.keys()      )			,processor.model_input_names      )





'''simple docstring'''




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 	UpperCamelCase_							(					_UpperCAmelCase  :      dict    )    ->    tuple:


       """simple docstring"""







       return (data["data"], data["target"])

def 	UpperCamelCase_							(					_UpperCAmelCase  :      np.ndarray			,	_UpperCAmelCase  :      np.ndarray    )    ->    XGBClassifier:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    XGBClassifier()
       classifier.fit(_UpperCAmelCase			,	_UpperCAmelCase    )
       return classifier

def 	UpperCamelCase_							(					)    ->    None:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    load_iris()
       _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    data_handling(_UpperCAmelCase    )
       _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    train_test_split(
           _UpperCAmelCase			,	_UpperCAmelCase			,	test_size=0.2_5    )

       _UpperCAmelCase     :  Optional[Any]							      =    iris["target_names"]

       # Create an XGBoost Classifier from the training data
       _UpperCAmelCase     :  Tuple							      =    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()

