
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 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	def __init__(       self					: str							,A					: Dict							,A					: Any=13							,A					: int=7							,A					: List[Any]=True							,A					: Tuple=True							,A					: List[Any]=True							,A					: Union[str, Any]=True							,A					: str=99							,A					: Dict=32							,A					: Optional[Any]=5							,A					: List[str]=4							,A					: Optional[Any]=37							,A					: Optional[int]="gelu"							,A					: str=0.1							,A					: Tuple=0.1							,A					: Tuple=5_12							,A					: Dict=16							,A					: Optional[int]=2							,A					: Optional[Any]=0.02							,A					: str=4							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= seq_length
					__A   			= is_training
					__A   			= use_attention_mask
					__A   			= use_token_type_ids
					__A   			= use_labels
					__A   			= vocab_size
					__A   			= hidden_size
					__A   			= num_hidden_layers
					__A   			= num_attention_heads
					__A   			= intermediate_size
					__A   			= hidden_act
					__A   			= hidden_dropout_prob
					__A   			= attention_probs_dropout_prob
					__A   			= max_position_embeddings
					__A   			= type_vocab_size
					__A   			= type_sequence_label_size
					__A   			= initializer_range
					__A   			= num_choices






	def 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size )

					__A   			= None
					if self.use_attention_mask:
									__A   			= random_attention_mask([self.batch_size, self.seq_length] )

					__A   			= None
					if self.use_token_type_ids:
									__A   			= ids_tensor([self.batch_size, self.seq_length]							,self.type_vocab_size )

					__A   			= 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=SCREAMING_SNAKE_CASE_							,initializer_range=self.initializer_range							,)

					return config, input_ids, token_type_ids, attention_mask







	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.prepare_config_and_inputs()
					__A   			= config_and_inputs
					__A   			= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
					return config, inputs_dict





@require_flax
class 							UpperCAmelCase      (  a__			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     True

	snake_case_					    =     (
	    (
	        FlaxRoFormerModel,
	        FlaxRoFormerForMaskedLM,
	        FlaxRoFormerForSequenceClassification,
	        FlaxRoFormerForTokenClassification,
	        FlaxRoFormerForMultipleChoice,
	        FlaxRoFormerForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= FlaxRoFormerModelTester(self )







	@slow
	def 				UpperCamelCase_  (       self					: List[str] ):
					for model_class_name in self.all_model_classes:
									__A   			= model_class_name.from_pretrained("junnyu/roformer_chinese_small"							,from_pt=SCREAMING_SNAKE_CASE_ )
									__A   			= model(np.ones((1, 1) ) )
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )





@require_flax
class 							UpperCAmelCase      (  unittest.TestCase  ):




	'''simple docstring'''





	@slow
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base" )
					__A   			= jnp.array([[0, 1, 2, 3, 4, 5]] )
					__A   			= model(SCREAMING_SNAKE_CASE_ )[0]

					__A   			= 5_00_00

					__A   			= (1, 6, vocab_size)
					self.assertEqual(output.shape							,SCREAMING_SNAKE_CASE_ )

					__A   			= jnp.array(
					    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )

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





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def 		UpperCamelCase	(   )	->     tuple[list[int], int]:
   UpperCamelCase    :  int							=	[randint(-1000       ,     1000	) for i in range(10	)]
   UpperCamelCase    :  Dict							=	randint(-5000       ,     5000	)
   return (arr, r)


__UpperCAmelCase          =       make_dataset()
def 		UpperCamelCase	(   snake_case__       :    list[int]       ,     snake_case__       :    int	)	->     tuple[int, ...]:
   for triplet in permutations(snake_case__       ,     3	):
      if sum(snake_case__	) == target:
         return tuple(sorted(snake_case__	)	)
   return (0, 0, 0)
def 		UpperCamelCase	(   snake_case__       :    list[int]       ,     snake_case__       :    int	)	->     tuple[int, int, int]:
   arr.sort()
   UpperCamelCase    :  List[str]							=	len(snake_case__	)
   for i in range(n - 1	):
      UpperCamelCase	,     UpperCamelCase    :  Optional[Any]							=	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 		UpperCamelCase	(   )	->     tuple[float, float]:
   UpperCamelCase    :  Any							=	'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
   UpperCamelCase    :  Optional[Any]							=	'\ntriplet_sum1(*dataset)\n'
   UpperCamelCase    :  Dict							=	'\ntriplet_sum2(*dataset)\n'
   UpperCamelCase    :  Optional[int]							=	repeat(setup=snake_case__       ,     stmt=snake_case__       ,     repeat=5       ,     number=10000	)
   UpperCamelCase    :  Any							=	repeat(setup=snake_case__       ,     stmt=snake_case__       ,     repeat=5       ,     number=10000	)
   return (min(snake_case__	), min(snake_case__	))


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

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



