import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_camembert import CamembertTokenizer
else:
   a_    					=       None


a_    					=       logging.get_logger(__name__)

a_    					=       {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

a_    					=       {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

a_    					=       {
    "camembert-base": 512,
}

a_    					=       "▁"





class 		_lowercase						(					snake_case_       ):
 lowercase				     =   VOCAB_FILES_NAMES
 lowercase				     =   PRETRAINED_VOCAB_FILES_MAP
 lowercase				     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase				     =   ['input_ids', 'attention_mask']
 lowercase				     =   CamembertTokenizer

 def __init__( self	:							List[Any]   ,      snake_case	:							Optional[int]=None   ,      snake_case	:							Tuple=None   ,      snake_case	:							Tuple="<s>"   ,      snake_case	:							Optional[Any]="</s>"   ,      snake_case	:							Union[str, Any]="</s>"   ,      snake_case	:							List[str]="<s>"   ,      snake_case	:							Tuple="<unk>"   ,      snake_case	:							Optional[int]="<pad>"   ,      snake_case	:							Optional[int]="<mask>"   ,      snake_case	:							int=["<s>NOTUSED", "</s>NOTUSED"]   ,      **snake_case	:							int   ,      )   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					AddedToken(lowerCamelCase_   ,      lstrip=lowerCamelCase_   ,      rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_   ,      lowerCamelCase_			) else mask_token

  super().__init__(
      lowerCamelCase_   ,      tokenizer_file=lowerCamelCase_   ,      bos_token=lowerCamelCase_   ,      eos_token=lowerCamelCase_   ,      sep_token=lowerCamelCase_   ,      cls_token=lowerCamelCase_   ,      unk_token=lowerCamelCase_   ,      pad_token=lowerCamelCase_   ,      mask_token=lowerCamelCase_   ,      additional_special_tokens=lowerCamelCase_   ,      **lowerCamelCase_   ,      )

  UpperCamelCase_					:						List[Any] =					vocab_file
  UpperCamelCase_					:						int =					False if not self.vocab_file else True

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							Dict   ,      snake_case	:							Any = None			)   ->						Tuple:

  """simple docstring"""





  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  UpperCamelCase_					:						Tuple =					[self.cls_token_id]
  UpperCamelCase_					:						Optional[int] =					[self.sep_token_id]
  return cls + token_ids_a + sep + sep + token_ids_a + sep

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							List[str]   ,      snake_case	:							str = None			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					[self.sep_token_id]
  UpperCamelCase_					:						Tuple =					[self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep			) * [0]
  return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]


 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Any   ,      snake_case	:							Optional[Any] = None			)   ->						int:

  """simple docstring"""





  if not self.can_save_slow_tokenizer:
   raise ValueError(
       'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
       'tokenizer.'			)

  if not os.path.isdir(lowerCamelCase_			):
   logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
   return
  UpperCamelCase_					:						str =					os.path.join(
      lowerCamelCase_   ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

  if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase_			):
   copyfile(self.vocab_file   ,      lowerCamelCase_			)

  return (out_vocab_file,)








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()
