


'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class   snake_case					(					lowercase    ,		unittest.TestCase    ):





		"""simple docstring"""




		_lowerCamelCase						=						BlenderbotSmallTokenizer
		_lowerCamelCase						=						False






		def 						snake_case	(					self  ):







			"""simple docstring"""
			super().setUp()

			lowerCamelCase_  				=						["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
			lowerCamelCase_  				=						dict(zip(UpperCamelCase    ,						range(len(UpperCamelCase  )  )  )  )

			lowerCamelCase_  				=						["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
			lowerCamelCase_  				=						{"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

			lowerCamelCase_  				=						os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"]  )
			lowerCamelCase_  				=						os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["merges_file"]  )
			with open(self.vocab_file    ,						"w"    ,						encoding="utf-8"  ) as fp:
				fp.write(json.dumps(UpperCamelCase  ) + "\n"  )
			with open(self.merges_file    ,						"w"    ,						encoding="utf-8"  ) as fp:
				fp.write("\n".join(UpperCamelCase  )  )






		def 						snake_case	(					self    ,						**UpperCamelCase  ):







			"""simple docstring"""
			kwargs.update(self.special_tokens_map  )
			return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    ,						**UpperCamelCase  )






		def 						snake_case	(					self    ,						UpperCamelCase  ):







			"""simple docstring"""
			lowerCamelCase_  				=						"adapt act apte"
			lowerCamelCase_  				=						"adapt act apte"
			return input_text, output_text






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						BlenderbotSmallTokenizer(self.vocab_file    ,						self.merges_file    ,						**self.special_tokens_map  )
			lowerCamelCase_  				=						"adapt act apte"
			lowerCamelCase_  				=						["adapt", "act", "ap@@", "te"]
			lowerCamelCase_  				=						tokenizer.tokenize(UpperCamelCase  )
			self.assertListEqual(UpperCamelCase    ,						UpperCamelCase  )

			lowerCamelCase_  				=						[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

			lowerCamelCase_  				=						[0, 1, 2, 3, 4, 5]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase  )    ,						UpperCamelCase  )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"  )
			assert tok("sam"  ).input_ids == [1384]
			lowerCamelCase_  				=						"I am a small frog."
			lowerCamelCase_  				=						tok([src_text]    ,						padding=UpperCamelCase    ,						truncation=UpperCamelCase  )["input_ids"]
			lowerCamelCase_  				=						tok.batch_decode(UpperCamelCase    ,						skip_special_tokens=UpperCamelCase    ,						clean_up_tokenization_spaces=UpperCamelCase  )[0]
			assert src_text != decoded  # I wish it did!
			assert decoded == "i am a small frog ."







		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_  				=						BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"  )
			lowerCamelCase_  				=						"I am a small frog ."
			lowerCamelCase_  				=						"."
			lowerCamelCase_  				=						tok(UpperCamelCase  )["input_ids"]
			lowerCamelCase_  				=						tok(UpperCamelCase  )["input_ids"]

			assert encoded[-1] == encoded_dot[0]







'''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 __snake_case      (   UpperCAmelCase_		:		dict	):
 return (data["data"], data["target"])





def __snake_case      (   UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		np.ndarray	):
 lowerCamelCase_  				=						XGBClassifier()
 classifier.fit(UpperCAmelCase_      ,       UpperCAmelCase_	)
 return classifier





def __snake_case      (   ):
 lowerCamelCase_  				=						load_iris()
 lowerCamelCase_				,lowerCamelCase_  				=						data_handling(UpperCAmelCase_	)
 lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						train_test_split(
     UpperCAmelCase_      ,       UpperCAmelCase_      ,       test_size=0.25	)

 lowerCamelCase_  				=						iris["target_names"]

 # Create an XGBoost Classifier from the training data
 lowerCamelCase_  				=						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()



