






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					from transformers.models.bert.modeling_flax_bert import (
					    FlaxBertForMaskedLM,
					    FlaxBertForMultipleChoice,
					    FlaxBertForNextSentencePrediction,
					    FlaxBertForPreTraining,
					    FlaxBertForQuestionAnswering,
					    FlaxBertForSequenceClassification,
					    FlaxBertForTokenClassification,
					    FlaxBertModel,
					)
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		def __init__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Dict=1_3						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=7						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=True						,_SCREAMING_SNAKE_CASE		:       Any=True						,_SCREAMING_SNAKE_CASE		:       str=True						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=True						,_SCREAMING_SNAKE_CASE		:       Dict=9_9						,_SCREAMING_SNAKE_CASE		:       Tuple=3_2						,_SCREAMING_SNAKE_CASE		:       str=5						,_SCREAMING_SNAKE_CASE		:       List[str]=4						,_SCREAMING_SNAKE_CASE		:       List[str]=3_7						,_SCREAMING_SNAKE_CASE		:       Optional[int]="gelu"						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[int]=5_1_2						,_SCREAMING_SNAKE_CASE		:       List[Any]=1_6						,_SCREAMING_SNAKE_CASE		:       str=2						,_SCREAMING_SNAKE_CASE		:       Dict=0.02						,_SCREAMING_SNAKE_CASE		:       Any=4						,)    ->   Tuple:


						'''simple docstring'''
						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       A(  self		:       Tuple		)    ->   List[Any]:


						'''simple docstring'''
						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	 =      BertConfig(
						    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       A(  self		:       Optional[Any]		)    ->   str:


						'''simple docstring'''
						A	 =      self.prepare_config_and_inputs()
						A ,		A ,		A ,		A	 =      config_and_inputs
						A	 =      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict







		def       A(  self		:       List[Any]		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      self.prepare_config_and_inputs()
						A ,		A ,		A ,		A	 =      config_and_inputs

						A	 =      True
						A	 =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
						A	 =      ids_tensor([self.batch_size, self.seq_length]						,vocab_size=2		)

						return (
						    config,
						    input_ids,
						    attention_mask,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)


@require_flax
class      UpperCamelCase (				snake_case__			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   True

		snake_case    =   (
		    (
		        FlaxBertModel,
		        FlaxBertForPreTraining,
		        FlaxBertForMaskedLM,
		        FlaxBertForMultipleChoice,
		        FlaxBertForQuestionAnswering,
		        FlaxBertForNextSentencePrediction,
		        FlaxBertForSequenceClassification,
		        FlaxBertForTokenClassification,
		        FlaxBertForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)
		def       A(  self		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      FlaxBertModelTester(self		)







		@slow
		def       A(  self		:       List[Any]		)    ->   Optional[int]:


						'''simple docstring'''
						# Only check this for base model, not necessary for all model classes.
						# This will also help speed-up tests.
						A	 =      FlaxBertModel.from_pretrained('bert-base-cased'		)
						A	 =      model(np.ones((1, 1)		)		)
						self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





