




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class     A(		lowerCamelCase_				, unittest.TestCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		XLMTokenizer
 UpperCamelCase		  =		False

 def 							a__					(							self   :    Tuple    )  ->      List[Any]:

       """simple docstring"""






       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       lowerCamelCase_							=       [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           'w</w>',
           'r</w>',
           't</w>',
           'lo',
           'low',
           'er</w>',
           'low</w>',
           'lowest</w>',
           'newer</w>',
           'wider</w>',
           '<unk>',
       ]
       lowerCamelCase_							=       dict(zip(snake_case__		,				range(len(snake_case__    )    )    )    )
       lowerCamelCase_							=       ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

       lowerCamelCase_							=       os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['vocab_file']    )
       lowerCamelCase_							=       os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['merges_file']    )
       with open(self.vocab_file		,				'w'    ) as fp:
             fp.write(json.dumps(snake_case__    )    )
       with open(self.merges_file		,				'w'    ) as fp:
             fp.write('\n'.join(snake_case__    )    )

 def 							a__					(							self   :    Optional[int]		,				A_   :    Dict    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       'lower newer'
       lowerCamelCase_							=       'lower newer'
       return input_text, output_text

 def 							a__					(							self   :    Optional[int]    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       XLMTokenizer(self.vocab_file		,				self.merges_file    )

       lowerCamelCase_							=       'lower'
       lowerCamelCase_							=       ['low', 'er</w>']
       lowerCamelCase_							=       tokenizer.tokenize(snake_case__    )
       self.assertListEqual(snake_case__		,				snake_case__    )

       lowerCamelCase_							=       tokens + ['<unk>']
       lowerCamelCase_							=       [14, 15, 20]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__    )		,				snake_case__    )





 @slow
 def 							a__					(							self   :    Optional[int]    )  ->      List[str]:

       """simple docstring"""






       lowerCamelCase_							=       XLMTokenizer.from_pretrained('xlm-mlm-en-2048'    )

       lowerCamelCase_							=       tokenizer.encode('sequence builders'		,				add_special_tokens=snake_case__    )
       lowerCamelCase_							=       tokenizer.encode('multi-sequence build'		,				add_special_tokens=snake_case__    )

       lowerCamelCase_							=       tokenizer.build_inputs_with_special_tokens(snake_case__    )
       lowerCamelCase_							=       tokenizer.build_inputs_with_special_tokens(snake_case__		,				snake_case__    )

       assert encoded_sentence == [0] + text + [1]
       assert encoded_pair == [0] + text + [1] + text_a + [1]









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()



