





'''simple docstring'''

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      if dataset.ndim != value_array.ndim:
            _snake_case		:	Tuple					     =     (
                '''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]:
                  _snake_case		:	Union[str, 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:
            _snake_case		:	Optional[Any]					     =     (
                '''Input data have different datatype... '''
                f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
            )
            raise TypeError(lowerCAmelCase_	)

      _snake_case		:	Union[str, Any]					     =     []

      for value in value_array:
            _snake_case		:	Tuple					     =     euclidean(lowerCAmelCase_			,      dataset[0]	)
            _snake_case		:	Optional[Any]					     =     dataset[0].tolist()

            for dataset_value in dataset[1:]:
                  _snake_case		:	Optional[Any]					     =     euclidean(lowerCAmelCase_			,      lowerCAmelCase_	)

                  if dist > temp_dist:
                        _snake_case		:	List[Any]					     =     temp_dist
                        _snake_case		:	str					     =     dataset_value.tolist()

            answer.append([vector, dist]	)

      return answer




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



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


if __name__ == "__main__":
   import doctest

   doctest.testmod()









'''simple docstring'''

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


