'''simple docstring'''





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_xlm_roberta import XLMRobertaTokenizer
else:
      UpperCamelCase    :			Optional[int]						     =  None


UpperCamelCase    :			Optional[int]						     =  logging.get_logger(__name__)

UpperCamelCase    :			List[str]						     =  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase    :			Union[str, Any]						     =  {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase    :			Any						     =  {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}



class    UpperCamelCase (    lowerCAmelCase__    ):







    """simple docstring"""





    A			:  List[Any]            =       VOCAB_FILES_NAMES
    A			:  Optional[Any]            =       PRETRAINED_VOCAB_FILES_MAP
    A			:  Optional[int]            =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    A			:  Optional[Any]            =       ["input_ids", "attention_mask"]
    A			:  Optional[int]            =       XLMRobertaTokenizer





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


         """simple docstring"""







         a :				int				=      AddedToken(a__				,				lstrip=a__				,				rstrip=a__) if isinstance(a__				,				a__) else mask_token

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

         a :				Optional[int]				=      vocab_file
         a :				Tuple				=      False if not self.vocab_file else True





    def 					SCREAMING_SNAKE_CASE_			( self			:		List[Any]				,				UpperCAmelCase_			:		Tuple				,				UpperCAmelCase_			:		Optional[Any] = None):


         """simple docstring"""







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





    def 					SCREAMING_SNAKE_CASE_			( self			:		Tuple				,				UpperCAmelCase_			:		int				,				UpperCAmelCase_			:		Optional[int] = None):


         """simple docstring"""







         a :				Optional[int]				=      [self.sep_token_id]
         a :				List[Any]				=      [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			:		int				,				UpperCAmelCase_			:		Dict				,				UpperCAmelCase_			:		List[Any] = None):


         """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(a__):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""")
              return
         a :				str				=      os.path.join(
             a__				,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

         return (out_vocab_file,)








"""simple docstring"""


from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
A_     :      List[str]								=load_iris()
iris.keys()


print(f'Target names: \n {iris.target_names} ')
print(f'\n Features: \n {iris.feature_names}')

# Train set e Test set
A_							,	A_							,	A_							,	A_     :      List[Any]								=train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

A_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

print(
    f'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    f' {iris["target_names"][prediction]}'
)






