





'''simple docstring'''

from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm





def 		UpperCamelCase							(				_lowerCamelCase   :   np.ndarray		,			_lowerCamelCase   :   np.ndarray			):
    return math.sqrt(sum(pow(a - b		,			2			) for a, b in zip(_lowerCamelCase		,			_lowerCamelCase			)			)			)





def 		UpperCamelCase							(				_lowerCamelCase   :   np.ndarray		,			_lowerCamelCase   :   np.ndarray			):

    if dataset.ndim != value_array.ndim:
        A__							=     (
            "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]:
            A__							=     (
                "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:
        A__							=     (
            "Input data have different datatype... "
            F"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
        )
        raise TypeError(_lowerCamelCase			)

    A__							=     []

    for value in value_array:
        A__							=     euclidean(_lowerCamelCase		,			dataset[0]			)
        A__							=     dataset[0].tolist()

        for dataset_value in dataset[1:]:
            A__							=     euclidean(_lowerCamelCase		,			_lowerCamelCase			)

            if dist > temp_dist:
                A__							=     temp_dist
                A__							=     dataset_value.tolist()

        answer.append([vector, dist]			)

    return answer





def 		UpperCamelCase							(				_lowerCamelCase   :   np.ndarray		,			_lowerCamelCase   :   np.ndarray			):
    return np.dot(_lowerCamelCase		,			_lowerCamelCase			) / (norm(_lowerCamelCase			) * norm(_lowerCamelCase			))


if __name__ == "__main__":
  import doctest

  doctest.testmod()










'''simple docstring'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers.generation import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )






@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :str				)->				Any:
     A__							=     3
     A__							=     2_50

     A__							=     ids_tensor((batch_size, length)		,			lowercase_				)
     A__							=     torch.ones((batch_size, length)		,			device=lowercase_		,			dtype=torch.float				) / length
     return input_ids, scores
 def 	UpperCAmelCase_				(							self :str				)->				Dict:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     StoppingCriteriaList(
         [
             MaxLengthCriteria(max_length=10				),
             MaxTimeCriteria(max_time=0.1				),
         ]				)

     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[Any]:
     A__							=     MaxLengthCriteria(max_length=10				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)
 def 	UpperCAmelCase_				(							self :Tuple				)->				int:
     A__							=     MaxNewTokensCriteria(start_length=5		,			max_new_tokens=5				)

     A__,      A__							=     self._get_tensors(5				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(9				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__,      A__							=     self._get_tensors(10				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)

     A__							=     StoppingCriteriaList([criteria]				)
     self.assertEqual(criteria_list.max_length		,			10				)
 def 	UpperCAmelCase_				(							self :Dict				)->				int:
     A__,      A__							=     self._get_tensors(5				)

     A__							=     MaxTimeCriteria(max_time=0.1				)
     self.assertFalse(criteria(lowercase_		,			lowercase_				)				)

     A__							=     MaxTimeCriteria(max_time=0.1		,			initial_timestamp=time.time() - 0.2				)
     self.assertTrue(criteria(lowercase_		,			lowercase_				)				)



 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				str:
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			10				)

     with self.assertWarns(lowercase_				):
         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)		,			11				)

     A__							=     validate_stopping_criteria(StoppingCriteriaList()		,			11				)

     self.assertEqual(len(lowercase_				)		,			1				)



