


'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





class   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						CanineTokenizer
  _lowerCamelCase						=						False






  def 						snake_case	(					self  ):







   """simple docstring"""
   super().setUp()
   lowerCamelCase_  				=						CanineTokenizer()
   tokenizer.save_pretrained(self.tmpdirname  )






  @cached_property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return CanineTokenizer.from_pretrained("google/canine-s"  )






  def 						snake_case	(					self    ,						**UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.tokenizer_class.from_pretrained(self.tmpdirname    ,						**UpperCamelCase  )
   lowerCamelCase_  				=						1024
   return tokenizer






  @require_torch
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.canine_tokenizer
   lowerCamelCase_  				=						["Life is like a box of chocolates.", "You never know what you're gonna get."]
   # fmt: off
   lowerCamelCase_  				=						[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
   # fmt: on
   lowerCamelCase_  				=						tokenizer(UpperCamelCase    ,						padding=UpperCamelCase    ,						return_tensors="pt"  )
   self.assertIsInstance(UpperCamelCase    ,						UpperCamelCase  )

   lowerCamelCase_  				=						list(batch.input_ids.numpy()[0]  )

   self.assertListEqual(UpperCamelCase    ,						UpperCamelCase  )

   self.assertEqual((2, 39)    ,						batch.input_ids.shape  )
   self.assertEqual((2, 39)    ,						batch.attention_mask.shape  )






  @require_torch
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.canine_tokenizer
   lowerCamelCase_  				=						["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
   lowerCamelCase_  				=						tokenizer(UpperCamelCase    ,						padding=UpperCamelCase    ,						return_tensors="pt"  )
   # check if input_ids, attention_mask and token_type_ids are returned
   self.assertIn("input_ids"    ,						UpperCamelCase  )
   self.assertIn("attention_mask"    ,						UpperCamelCase  )
   self.assertIn("token_type_ids"    ,						UpperCamelCase  )






  @require_torch
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.canine_tokenizer
   lowerCamelCase_  				=						[
       "What's the weater?",
       "It's about 25 degrees.",
   ]
   lowerCamelCase_  				=						tokenizer(
       text_target=UpperCamelCase    ,						max_length=32    ,						padding="max_length"    ,						truncation=UpperCamelCase    ,						return_tensors="pt"  )
   self.assertEqual(32    ,						targets["input_ids"].shape[1]  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   # safety check on max_len default value so we are sure the test works
   lowerCamelCase_  				=						self.get_tokenizers()
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
     self.assertNotEqual(tokenizer.model_max_length    ,						42  )

        # Now let's start the test
   lowerCamelCase_  				=						self.get_tokenizers()
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
     # Isolate this from the other tests because we save additional tokens/etc
     lowerCamelCase_  				=						tempfile.mkdtemp()

     lowerCamelCase_  				=						" He is very happy, UNwant\u00E9d,running"
     lowerCamelCase_  				=						tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )
     tokenizer.save_pretrained(UpperCamelCase  )

     lowerCamelCase_  				=						tokenizer.__class__.from_pretrained(UpperCamelCase  )
     lowerCamelCase_  				=						after_tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )
     self.assertListEqual(UpperCamelCase    ,						UpperCamelCase  )

     shutil.rmtree(UpperCamelCase  )

   lowerCamelCase_  				=						self.get_tokenizers(model_max_length=42  )
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
     # Isolate this from the other tests because we save additional tokens/etc
     lowerCamelCase_  				=						tempfile.mkdtemp()

     lowerCamelCase_  				=						" He is very happy, UNwant\u00E9d,running"

     lowerCamelCase_  				=						tokenizer.additional_special_tokens

     # We can add a new special token for Canine as follows:
     lowerCamelCase_  				=						chr(0Xe_007  )
     additional_special_tokens.append(UpperCamelCase  )
     tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}  )
     lowerCamelCase_  				=						tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )
     tokenizer.save_pretrained(UpperCamelCase  )

     lowerCamelCase_  				=						tokenizer.__class__.from_pretrained(UpperCamelCase  )
     lowerCamelCase_  				=						after_tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )
     self.assertListEqual(UpperCamelCase    ,						UpperCamelCase  )
     self.assertIn(UpperCamelCase    ,						after_tokenizer.additional_special_tokens  )
     self.assertEqual(after_tokenizer.model_max_length    ,						42  )

     lowerCamelCase_  				=						tokenizer.__class__.from_pretrained(UpperCamelCase    ,						model_max_length=43  )
     self.assertEqual(tokenizer.model_max_length    ,						43  )

     shutil.rmtree(UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_tokenizers(do_lower_case=UpperCamelCase  )
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
     lowerCamelCase_				,lowerCamelCase_  				=						self.get_clean_sequence(UpperCamelCase  )

     # a special token for Canine can be defined as follows:
     lowerCamelCase_  				=						0Xe_005
     lowerCamelCase_  				=						chr(UpperCamelCase  )

     tokenizer.add_special_tokens({"cls_token": special_token}  )
     lowerCamelCase_  				=						tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )
     self.assertEqual(len(UpperCamelCase  )    ,						1  )

     lowerCamelCase_  				=						tokenizer.decode(ids + encoded_special_token    ,						clean_up_tokenization_spaces=UpperCamelCase  )
     lowerCamelCase_  				=						tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )

     lowerCamelCase_  				=						tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )
     lowerCamelCase_  				=						tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )
     self.assertEqual(UpperCamelCase    ,						input_encoded + special_token_id  )

     lowerCamelCase_  				=						tokenizer.decode(UpperCamelCase    ,						skip_special_tokens=UpperCamelCase  )
     self.assertTrue(special_token not in decoded  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_tokenizers(do_lower_case=UpperCamelCase  )
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
     lowerCamelCase_  				=						chr(0Xe_005  )
     lowerCamelCase_  				=						chr(0Xe_006  )

     # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
     tokenizer.add_tokens([SPECIAL_TOKEN_1]    ,						special_tokens=UpperCamelCase  )
     # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
     # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
     tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}  )

     lowerCamelCase_  				=						tokenizer.tokenize(UpperCamelCase  )
     lowerCamelCase_  				=						tokenizer.tokenize(UpperCamelCase  )

     self.assertEqual(len(UpperCamelCase  )    ,						1  )
     self.assertEqual(len(UpperCamelCase  )    ,						1  )
     self.assertEqual(token_a[0]    ,						UpperCamelCase  )
     self.assertEqual(token_a[0]    ,						UpperCamelCase  )






  @require_tokenizers
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_tokenizers(do_lower_case=UpperCamelCase  )
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
     # a special token for Canine can be defined as follows:
     lowerCamelCase_  				=						0Xe_006
     lowerCamelCase_  				=						chr(UpperCamelCase  )

     lowerCamelCase_  				=						AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase  )
     tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}  )

     with tempfile.TemporaryDirectory() as tmp_dir_name:
      tokenizer.save_pretrained(UpperCamelCase  )
      tokenizer.from_pretrained(UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   if self.test_slow_tokenizer:
    tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

   if self.test_rust_tokenizer:
    tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

   for tokenizer_class, tokenizer_utils in tokenizer_list:
    with tempfile.TemporaryDirectory() as tmp_dir:
     tokenizer_utils.save_pretrained(UpperCamelCase  )

     with open(os.path.join(UpperCamelCase    ,						"special_tokens_map.json"  )    ,						encoding="utf-8"  ) as json_file:
      lowerCamelCase_  				=						json.load(UpperCamelCase  )

     with open(os.path.join(UpperCamelCase    ,						"tokenizer_config.json"  )    ,						encoding="utf-8"  ) as json_file:
      lowerCamelCase_  				=						json.load(UpperCamelCase  )

     # a special token for Canine can be defined as follows:
     lowerCamelCase_  				=						0Xe_006
     lowerCamelCase_  				=						chr(UpperCamelCase  )

     lowerCamelCase_  				=						[new_token_a]
     lowerCamelCase_  				=						[new_token_a]

     with open(os.path.join(UpperCamelCase    ,						"special_tokens_map.json"  )    ,						"w"    ,						encoding="utf-8"  ) as outfile:
      json.dump(UpperCamelCase    ,						UpperCamelCase  )
     with open(os.path.join(UpperCamelCase    ,						"tokenizer_config.json"  )    ,						"w"    ,						encoding="utf-8"  ) as outfile:
      json.dump(UpperCamelCase    ,						UpperCamelCase  )

     # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
     # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
     # "special_tokens_map.json" files
     lowerCamelCase_  				=						tokenizer_class.from_pretrained(UpperCamelCase    ,						extra_ids=0  )
     self.assertIn(UpperCamelCase    ,						tokenizer_without_change_in_init.additional_special_tokens  )
     # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
     self.assertEqual(
         [new_token_a]    ,						tokenizer_without_change_in_init.convert_ids_to_tokens(
             tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]  )  )    ,						)

     lowerCamelCase_  				=						0Xe_007
     lowerCamelCase_  				=						chr(UpperCamelCase  )
     # Now we test that we can change the value of additional_special_tokens in the from_pretrained
     lowerCamelCase_  				=						[AddedToken(UpperCamelCase    ,						lstrip=UpperCamelCase  )]
     lowerCamelCase_  				=						tokenizer_class.from_pretrained(
         UpperCamelCase    ,						additional_special_tokens=UpperCamelCase    ,						extra_ids=0  )

     self.assertIn(UpperCamelCase    ,						tokenizer.additional_special_tokens  )
     # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
     self.assertEqual(
         [new_token_a]    ,						tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]  )  )  )






  @require_tokenizers
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_tokenizers(do_lower_case=UpperCamelCase  )
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
     lowerCamelCase_  				=						"hello world"
     if self.space_between_special_tokens:
      lowerCamelCase_  				=						"[CLS] hello world [SEP]"
     else:
      lowerCamelCase_  				=						input
     lowerCamelCase_  				=						tokenizer.encode(UpperCamelCase    ,						add_special_tokens=UpperCamelCase  )
     lowerCamelCase_  				=						tokenizer.decode(UpperCamelCase    ,						spaces_between_special_tokens=self.space_between_special_tokens  )
     self.assertIn(UpperCamelCase    ,						[output, output.lower()]  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.get_tokenizers()
   for tokenizer in tokenizers:
    with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
     lowerCamelCase_  				=						[
         "bos_token",
         "eos_token",
         "unk_token",
         "sep_token",
         "pad_token",
         "cls_token",
         "mask_token",
     ]

   lowerCamelCase_  				=						"a"
   lowerCamelCase_  				=						ord(UpperCamelCase  )

   for attr in attributes_list:
    setattr(UpperCamelCase    ,						attr + "_id"    ,						UpperCamelCase  )
    self.assertEqual(getattr(UpperCamelCase    ,						UpperCamelCase  )    ,						UpperCamelCase  )
    self.assertEqual(getattr(UpperCamelCase    ,						attr + "_id"  )    ,						UpperCamelCase  )

    setattr(UpperCamelCase    ,						attr + "_id"    ,						UpperCamelCase  )
    self.assertEqual(getattr(UpperCamelCase    ,						UpperCamelCase  )    ,						UpperCamelCase  )
    self.assertEqual(getattr(UpperCamelCase    ,						attr + "_id"  )    ,						UpperCamelCase  )

   setattr(UpperCamelCase    ,						"additional_special_tokens_ids"    ,						[]  )
   self.assertListEqual(getattr(UpperCamelCase    ,						"additional_special_tokens"  )    ,						[]  )
   self.assertListEqual(getattr(UpperCamelCase    ,						"additional_special_tokens_ids"  )    ,						[]  )

   lowerCamelCase_  				=						0Xe_006
   lowerCamelCase_  				=						chr(UpperCamelCase  )
   setattr(UpperCamelCase    ,						"additional_special_tokens_ids"    ,						[additional_special_token_id]  )
   self.assertListEqual(getattr(UpperCamelCase    ,						"additional_special_tokens"  )    ,						[additional_special_token]  )
   self.assertListEqual(getattr(UpperCamelCase    ,						"additional_special_tokens_ids"  )    ,						[additional_special_token_id]  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass







  def 						snake_case	(					self  ):







   """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 __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()



