






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


snake_case     					=		logging.get_logger(__name__)

snake_case     					=		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case     					=		{
    """vocab_file""": {
        """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_char_small""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_chinese_char_base""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_discriminator""": (
            """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_generator""": (
            """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"""
        ),
    }
}

snake_case     					=		{
    """junnyu/roformer_chinese_small""": 1_536,
    """junnyu/roformer_chinese_base""": 1_536,
    """junnyu/roformer_chinese_char_small""": 512,
    """junnyu/roformer_chinese_char_base""": 512,
    """junnyu/roformer_small_discriminator""": 128,
    """junnyu/roformer_small_generator""": 128,
}


snake_case     					=		{
    """junnyu/roformer_chinese_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_base""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_base""": {"""do_lower_case""": True},
    """junnyu/roformer_small_discriminator""": {"""do_lower_case""": True},
    """junnyu/roformer_small_generator""": {"""do_lower_case""": True},
}


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Optional[Any]								=						VOCAB_FILES_NAMES
     UpperCamelCase_				:		int								=						PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_				:		Tuple								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_				:		List[Any]								=						PRETRAINED_INIT_CONFIGURATION
     UpperCamelCase_				:		Any								=						RoFormerTokenizer


     def __init__(					self							:						Tuple       ,			UpperCAmelCase_							:						List[Any]=None       ,			UpperCAmelCase_							:						List[Any]=None       ,			UpperCAmelCase_							:						Optional[Any]=True       ,			UpperCAmelCase_							:						List[str]="[UNK]"       ,			UpperCAmelCase_							:						Any="[SEP]"       ,			UpperCAmelCase_							:						Any="[PAD]"       ,			UpperCAmelCase_							:						List[str]="[CLS]"       ,			UpperCAmelCase_							:						str="[MASK]"       ,			UpperCAmelCase_							:						Any=True       ,			UpperCAmelCase_							:						Optional[Any]=None       ,			**UpperCAmelCase_							:						List[str]       ,			):
       super().__init__(
           UpperCAmelCase_       ,			tokenizer_file=UpperCAmelCase_       ,			do_lower_case=UpperCAmelCase_       ,			unk_token=UpperCAmelCase_       ,			sep_token=UpperCAmelCase_       ,			pad_token=UpperCAmelCase_       ,			cls_token=UpperCAmelCase_       ,			mask_token=UpperCAmelCase_       ,			tokenize_chinese_chars=UpperCAmelCase_       ,			strip_accents=UpperCAmelCase_       ,			**UpperCAmelCase_       ,			)

       SCREAMING_SNAKE_CASE							:   int						    =     json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
       if (
           pre_tok_state.get("lowercase"       ,			UpperCAmelCase_   ) != do_lower_case
           or pre_tok_state.get("strip_accents"       ,			UpperCAmelCase_   ) != strip_accents
       ):
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     getattr(UpperCAmelCase_       ,			pre_tok_state.pop("type"   )   )
         SCREAMING_SNAKE_CASE							:   Any						    =     do_lower_case
         SCREAMING_SNAKE_CASE							:   List[str]						    =     strip_accents
         SCREAMING_SNAKE_CASE							:   Tuple						    =     pre_tok_class(**UpperCAmelCase_   )

       SCREAMING_SNAKE_CASE							:   int						    =     do_lower_case


     def __getstate__(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     self.__dict__.copy()
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     BertPreTokenizer()
       return state


     def __setstate__(					self							:						Tuple       ,			UpperCAmelCase_							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     d
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.__dict__["_tokenizer"].get_vocab()
       SCREAMING_SNAKE_CASE							:   Any						    =     PreTokenizer.custom(JiebaPreTokenizer(UpperCAmelCase_   )   )


     def 					_A							(					self							:						str       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Union[str, Any]=None   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

       return output


     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						List[int]       ,			UpperCAmelCase_							:						Optional[List[int]] = None   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     [self.sep_token_id]
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     [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   ) * [0] + len(token_ids_a + sep   ) * [1]


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[str] = None   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self._tokenizer.model.save(UpperCAmelCase_       ,			name=UpperCAmelCase_   )
       return tuple(UpperCAmelCase_   )






     def 					_A							(					self							:						Tuple       ,			UpperCAmelCase_							:						List[Any]       ,			UpperCAmelCase_							:						Any=None       ,			UpperCAmelCase_							:						str=None       ,			UpperCAmelCase_							:						Any=False       ,			**UpperCAmelCase_							:						str       ,			):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     BertPreTokenizer()
       return super().save_pretrained(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			**UpperCAmelCase_   )











from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =Counter(__lowerCamelCase						).most_common(1						)[0][0]
					return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))




