



"""simple docstring"""
import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 					a	(  UpperCAmelCase__	,      unittest.TestCase       ):
  UpperCamelCase	:				Dict     						=					MgpstrTokenizer
  UpperCamelCase	:				Dict     						=					False
  UpperCamelCase	:				Optional[int]     						=					{}
  UpperCamelCase	:				Optional[int]     						=					False



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Union[str, Any]:


     '''simple docstring'''




     super().setUp()

     # fmt: off
     SCREAMING_SNAKE_CASE_: Optional[int]     			=["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
     # fmt: on
     SCREAMING_SNAKE_CASE_: Optional[int]     			=dict(zip(lowerCAmelCase       ,		range(len(lowerCAmelCase		)		)		)		)

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



  def 	lowerCamelCase__       (						self :					Any       ,		**lowerCAmelCase :					int		)     ->					Optional[int]:


     '''simple docstring'''




     return MgpstrTokenizer.from_pretrained(self.tmpdirname       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					List[str]		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			="""tester"""
     SCREAMING_SNAKE_CASE_: Tuple     			="""tester"""
     return input_text, output_text



  @unittest.skip("""MGP-STR always lower cases letters."""		)
  def 	lowerCamelCase__       (						self :					Tuple		)     ->					str:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.get_tokenizers(do_lower_case=lowerCAmelCase		)
     for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
           SCREAMING_SNAKE_CASE_: int     			="""[SPECIAL_TOKEN]"""

           tokenizer.add_special_tokens({"""cls_token""": special_token}		)
           SCREAMING_SNAKE_CASE_: Optional[int]     			=tokenizer.encode([special_token]       ,		add_special_tokens=lowerCAmelCase		)
           self.assertEqual(len(lowerCAmelCase		)       ,		1		)

           SCREAMING_SNAKE_CASE_: Optional[int]     			=tokenizer.decode(lowerCAmelCase       ,		skip_special_tokens=lowerCAmelCase		)
           self.assertTrue(special_token not in decoded		)



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=self.get_tokenizers()
     for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
           SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=self.get_input_output_texts(lowerCAmelCase		)

           SCREAMING_SNAKE_CASE_: List[str]     			=tokenizer.tokenize(lowerCAmelCase		)
           SCREAMING_SNAKE_CASE_: Optional[Any]     			=tokenizer.convert_tokens_to_ids(lowerCAmelCase		)
           SCREAMING_SNAKE_CASE_: Optional[int]     			=tokenizer.encode(lowerCAmelCase       ,		add_special_tokens=lowerCAmelCase		)
           self.assertListEqual(lowerCAmelCase       ,		lowerCAmelCase		)

           SCREAMING_SNAKE_CASE_: int     			=tokenizer.convert_ids_to_tokens(lowerCAmelCase		)
           self.assertNotEqual(len(lowerCAmelCase		)       ,		0		)
           SCREAMING_SNAKE_CASE_: Any     			=tokenizer.decode(lowerCAmelCase		)
           self.assertIsInstance(lowerCAmelCase       ,		lowerCAmelCase		)

           self.assertEqual(text_a.replace(""" """       ,		""""""		)       ,		lowerCAmelCase		)



  @unittest.skip("""MGP-STR tokenizer only handles one sequence."""		)
  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Union[str, Any]:


     '''simple docstring'''




     pass





  @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""		)
  def 	lowerCamelCase__       (						self :					Dict		)     ->					List[str]:


     '''simple docstring'''




     pass








"""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      __magic_name__				(							lowercase ):
   return (data["data"], data["target"])





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=XGBClassifier()
   classifier.fit(lowercase							,			lowercase )
   return classifier





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_iris()
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=data_handling(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=train_test_split(
       lowercase							,			lowercase							,			test_size=0.25 )

   SCREAMING_SNAKE_CASE_: Tuple     			=iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   SCREAMING_SNAKE_CASE_: Optional[int]     			=xgboost(lowercase							,			lowercase )

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


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()



