




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


UpperCamelCase__ :   Dict						=							logging.get_logger(__name__)

UpperCamelCase__ :   List[str]						=							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   List[str]						=							{
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase__ :   int						=							{
    '''yjernite/retribert-base-uncased''': 5_12,
}


UpperCamelCase__ :   str						=							{
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Union[str, Any]						=     VOCAB_FILES_NAMES
				__a     :       List[str]						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       Optional[Any]						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       Union[str, Any]						=     PRETRAINED_INIT_CONFIGURATION
				__a     :       Tuple						=     RetriBertTokenizer
				__a     :       List[str]						=     ["input_ids", "attention_mask"]






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

							SCREAMING_SNAKE_CASE_   : List[str]					  =      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
							if (
							    normalizer_state.get('lowercase'      ,snake_case__ ) != do_lower_case
							    or normalizer_state.get('strip_accents'      ,snake_case__ ) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'      ,snake_case__ ) != tokenize_chinese_chars
							):
										SCREAMING_SNAKE_CASE_   : int					  =      getattr(snake_case__      ,normalizer_state.pop('type' ) )
										SCREAMING_SNAKE_CASE_   : Tuple					  =      do_lower_case
										SCREAMING_SNAKE_CASE_   : int					  =      strip_accents
										SCREAMING_SNAKE_CASE_   : int					  =      tokenize_chinese_chars
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      normalizer_class(**snake_case__ )

							SCREAMING_SNAKE_CASE_   : Any					  =      do_lower_case






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=None ):
							SCREAMING_SNAKE_CASE_   : 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 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : List[str]					  =      [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 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self._tokenizer.model.save(snake_case__      ,name=snake_case__ )
							return tuple(snake_case__ )









from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  float:







			"""simple docstring"""





			return math.sqrt(sum(pow(a - b  ,							2  ) for a, b in zip(lowerCamelCase_  ,							lowerCamelCase_  )  )  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  list[list[list[float] | float]]:







			"""simple docstring"""





			if dataset.ndim != value_array.ndim:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      (
						    'Wrong input data\'s dimensions... '
						    F'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
						)
						raise ValueError(lowerCamelCase_  )

			try:
						if dataset.shape[1] != value_array.shape[1]:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      (
									    'Wrong input data\'s shape... '
									    F'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
									)
									raise ValueError(lowerCamelCase_  )
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError('Wrong shape'  )

			if dataset.dtype != value_array.dtype:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      (
						    'Input data have different datatype... '
						    F'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
						)
						raise TypeError(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      []

			for value in value_array:
						SCREAMING_SNAKE_CASE_   : str					  =      euclidean(lowerCamelCase_  ,							dataset[0]  )
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      dataset[0].tolist()

						for dataset_value in dataset[1:]:
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      euclidean(lowerCamelCase_  ,							lowerCamelCase_  )

									if dist > temp_dist:
												SCREAMING_SNAKE_CASE_   : Optional[int]					  =      temp_dist
												SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      dataset_value.tolist()

						answer.append([vector, dist]  )

			return answer





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  float:







			"""simple docstring"""





			return np.dot(lowerCamelCase_  ,							lowerCamelCase_  ) / (norm(lowerCamelCase_  ) * norm(lowerCamelCase_  ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



