






from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Union[str, Any]							       =[randint(-1000		,  1000	) for i in range(10	)]
 snake_case__  :    Any							       =randint(-5000		,  5000	)
 return (arr, r)


__lowerCamelCase	:		Tuple										=   make_dataset()







def 			A__							(      _a  :					list[int]		,  _a  :					int	):





 '''simple docstring'''







 for triplet in permutations(_a		,  3	):
  if sum(_a	) == target:
   return tuple(sorted(_a	)	)
 return (0, 0, 0)







def 			A__							(      _a  :					list[int]		,  _a  :					int	):





 '''simple docstring'''







 arr.sort()
 snake_case__  :    Optional[int]							       =len(_a	)
 for i in range(n - 1	):
  snake_case__       ,   snake_case__  :    Dict							       =i + 1, n - 1
  while left < right:
   if arr[i] + arr[left] + arr[right] == target:
    return (arr[i], arr[left], arr[right])
   elif arr[i] + arr[left] + arr[right] < target:
    left += 1
   elif arr[i] + arr[left] + arr[right] > target:
    right -= 1
 return (0, 0, 0)







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       ="""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
 snake_case__  :    str							       ="""
triplet_sum1(*dataset)
"""
 snake_case__  :    List[Any]							       ="""
triplet_sum2(*dataset)
"""
 snake_case__  :    Union[str, Any]							       =repeat(setup=_a		,  stmt=_a		,  repeat=5		,  number=10000	)
 snake_case__  :    Dict							       =repeat(setup=_a		,  stmt=_a		,  repeat=5		,  number=10000	)
 return (min(_a	), min(_a	))


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       __lowerCamelCase	:		int										=   solution_times()
       print(F"The time for naive implementation is {times[0]}.")
       print(F"The time for optimized implementation is {times[1]}.")













import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )







class  _lowercase   (   unittest.TestCase						):
      def __init__(     self				,			a				,			a=1_3				,			a=7				,			a=True				,			a=True				,			a=True				,			a=True				,			a=9_9				,			a=3_2				,			a=5				,			a=4				,			a=3_7				,			a="gelu"				,			a=0.1				,			a=0.1				,			a=5_1_2				,			a=1_6				,			a=2				,			a=0.02				,			a=4				,			):
       snake_case__  :    Any							       =parent
       snake_case__  :    Dict							       =batch_size
       snake_case__  :    List[Any]							       =seq_length
       snake_case__  :    str							       =is_training
       snake_case__  :    Union[str, Any]							       =use_attention_mask
       snake_case__  :    str							       =use_token_type_ids
       snake_case__  :    int							       =use_labels
       snake_case__  :    Tuple							       =vocab_size
       snake_case__  :    List[str]							       =hidden_size
       snake_case__  :    Dict							       =num_hidden_layers
       snake_case__  :    Optional[Any]							       =num_attention_heads
       snake_case__  :    List[str]							       =intermediate_size
       snake_case__  :    str							       =hidden_act
       snake_case__  :    Union[str, Any]							       =hidden_dropout_prob
       snake_case__  :    Tuple							       =attention_probs_dropout_prob
       snake_case__  :    Tuple							       =max_position_embeddings
       snake_case__  :    str							       =type_vocab_size
       snake_case__  :    Optional[Any]							       =type_sequence_label_size
       snake_case__  :    str							       =initializer_range
       snake_case__  :    List[Any]							       =num_choices
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size   )

       snake_case__  :    Tuple							       =None
       if self.use_attention_mask:
        snake_case__  :    Union[str, Any]							       =random_attention_mask([self.batch_size, self.seq_length]   )

       snake_case__  :    Union[str, Any]							       =None
       if self.use_token_type_ids:
        snake_case__  :    Optional[Any]							       =ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size   )

       snake_case__  :    Tuple							       =RoFormerConfig(
           vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			is_decoder=a				,			initializer_range=self.initializer_range				,			)

       return config, input_ids, token_type_ids, attention_mask



      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =self.prepare_config_and_inputs()
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Optional[Any]							       =config_and_inputs
       snake_case__  :    Dict							       ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
       return config, inputs_dict







@require_flax
class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						str										=							True

      _a							:						Optional[Any]										=							(
          (
              FlaxRoFormerModel,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[Any]							       =FlaxRoFormerModelTester(self   )



      @slow
      def 						lowercase__					(     self   ):
       for model_class_name in self.all_model_classes:
        snake_case__  :    Tuple							       =model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""				,			from_pt=a   )
        snake_case__  :    List[Any]							       =model(np.ones((1, 1)   )   )
        self.assertIsNotNone(a   )







@require_flax
class  _lowercase   (   unittest.TestCase						):
      @slow
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
       snake_case__  :    Tuple							       =jnp.array([[0, 1, 2, 3, 4, 5]]   )
       snake_case__  :    str							       =model(a   )[0]

       snake_case__  :    List[str]							       =5_0_0_0_0

       snake_case__  :    str							       =(1, 6, vocab_size)
       self.assertEqual(output.shape				,			a   )

       snake_case__  :    Optional[int]							       =jnp.array(
           [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]   )

       self.assertTrue(jnp.allclose(output[:, :3, :3]				,			a				,			atol=1e-4   )   )





