


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			int:
             a_					:		List[str]							=  FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'     )
             a_					:		Tuple							=  AutoTokenizer.from_pretrained('google/mt5-small'     )

             a_					:		List[Any]							=  tokenizer('Hello there'     ,							return_tensors='np'     ).input_ids
             a_					:		Optional[int]							=  tokenizer('Hi I am'     ,							return_tensors='np'     ).input_ids

             a_					:		Optional[Any]							=  shift_tokens_right(SCREAMING_SNAKE_CASE__     ,							model.config.pad_token_id     ,							model.config.decoder_start_token_id     )

             a_					:		List[str]							=  model(SCREAMING_SNAKE_CASE__     ,							decoder_input_ids=SCREAMING_SNAKE_CASE__     ).logits
             a_					:		Optional[Any]							=  optax.softmax_cross_entropy(SCREAMING_SNAKE_CASE__     ,							onehot(SCREAMING_SNAKE_CASE__     ,							logits.shape[-1]     )     ).mean()

             a_					:		Any							=  -(labels.shape[-1] * loss.item())

             a_					:		Any							=  -84.9127
             self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )







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     a(      A       :    dict       )		->       tuple:
					"""simple docstring"""





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






def     a(      A       :    np.ndarray ,     A       :    np.ndarray       )		->       XGBClassifier:
					"""simple docstring"""





					a					     =   XGBClassifier()
					classifier.fit(A ,     A       )
					return classifier






def     a(      )		->       None:
					"""simple docstring"""





					a					     =   load_iris()
					a     ,				a					     =   data_handling(A       )
					a     ,				a     ,				a     ,				a					     =   train_test_split(
					    A ,     A ,     test_size=0.25       )

					a					     =   iris["target_names"]

					# Create an XGBoost Classifier from the training data
					a					     =   xgboost(A ,     A       )

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


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()
