





"""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:
      A_     :      Optional[int]								=None


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

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

A_     :      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"""
        ),
    },
}

A_     :      Any								={
    """xlm-roberta-base""": 5_1_2,
    """xlm-roberta-large""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}







class 	__a   (  lowerCAmelCase__		):
     SCREAMING_SNAKE_CASE__ :      List[Any]       		=			VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__ :      Optional[Any]       		=			PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__ :      Optional[int]       		=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__ :      Optional[Any]       		=			["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE__ :      Optional[int]       		=			XLMRobertaTokenizer



     def __init__(       self					,     a__=None					,     a__=None					,     a__="<s>"					,     a__="</s>"					,     a__="</s>"					,     a__="<s>"					,     a__="<unk>"					,     a__="<pad>"					,     a__="<mask>"					,     **a__					,     ):
            # Mask token behave like a normal word, i.e. include the space before it
            _lowerCamelCase    	=					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__					,     )

            _lowerCamelCase    	=					vocab_file
            _lowerCamelCase    	=					False if not self.vocab_file else True



     def 						snake_case_			(       self					,     a__					,     a__ = None   ):

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



     def 						snake_case_			(       self					,     a__					,     a__ = None   ):
            _lowerCamelCase    	=					[self.sep_token_id]
            _lowerCamelCase    	=					[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 						snake_case_			(       self					,     a__					,     a__ = None   ):
            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
            _lowerCamelCase    	=					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]}'
)






