"""simple docstring"""







from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
	import tensorflow as tf

	from transformers import TFBlipTextModel
	from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST

class 	UpperCamelCase    :



				def __init__(self       :   List[Any]			,						_A       :   List[Any]			,						_A       :   int=12			,						_A       :   List[str]=7			,						_A       :   List[Any]=True			,						_A       :   Union[str, Any]=True			,						_A       :   Union[str, Any]=True			,						_A       :   int=99			,						_A       :   Optional[Any]=32			,						_A       :   str=32			,						_A       :   List[Any]=2			,						_A       :   Any=4			,						_A       :   Tuple=37			,						_A       :   Dict=0.1			,						_A       :   Optional[Any]=0.1			,						_A       :   Union[str, Any]=5_12			,						_A       :   Any=0.02			,						_A       :   List[Any]=0			,						_A       :   Tuple=None			,						)					->			Dict:
					__snake_case    :							Optional[int]   	=				parent
					__snake_case    :							Any   	=				batch_size
					__snake_case    :							Any   	=				seq_length
					__snake_case    :							Union[str, Any]   	=				is_training
					__snake_case    :							Any   	=				use_input_mask
					__snake_case    :							Any   	=				use_labels
					__snake_case    :							int   	=				vocab_size
					__snake_case    :							Any   	=				hidden_size
					__snake_case    :							int   	=				projection_dim
					__snake_case    :							List[Any]   	=				num_hidden_layers
					__snake_case    :							str   	=				num_attention_heads
					__snake_case    :							Tuple   	=				intermediate_size
					__snake_case    :							Optional[int]   	=				dropout
					__snake_case    :							Tuple   	=				attention_dropout
					__snake_case    :							Optional[Any]   	=				max_position_embeddings
					__snake_case    :							Any   	=				initializer_range
					__snake_case    :							Any   	=				scope
					__snake_case    :							Any   	=				bos_token_id



				def       _lowercase  (self       :   Any)					->			Any:
					__snake_case    :							Optional[Any]   	=				ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

					__snake_case    :							List[str]   	=				None
					if self.use_input_mask:
						__snake_case    :							Any   	=				random_attention_mask([self.batch_size, self.seq_length])

					if input_mask is not None:
						__snake_case    :							str   	=				input_mask.numpy()
						__snake_case	,       __snake_case    :							Optional[Any]   	=				input_mask.shape
						__snake_case    :							List[str]   	=				np.random.randint(1			,						seq_length - 1			,						size=(batch_size,))
						for batch_idx, start_index in enumerate(_A):
							__snake_case    :							Dict   	=				1
							__snake_case    :							Dict   	=				0

					__snake_case    :							Tuple   	=				self.get_config()

					return config, input_ids, tf.convert_to_tensor(_A)



				def       _lowercase  (self       :   str)					->			Optional[int]:
					return BlipTextConfig(
					    vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						projection_dim=self.projection_dim			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						dropout=self.dropout			,						attention_dropout=self.attention_dropout			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						bos_token_id=self.bos_token_id			,						)



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   Tuple			,						_A       :   List[Any]			,						_A       :   Union[str, Any])					->			Optional[Any]:
					__snake_case    :							Optional[int]   	=				TFBlipTextModel(config=_A)
					__snake_case    :							Optional[int]   	=				model(_A			,						attention_mask=_A			,						training=_A)
					__snake_case    :							List[str]   	=				model(_A			,						training=_A)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))
					self.parent.assertEqual(result.pooler_output.shape			,						(self.batch_size, self.hidden_size))






				def       _lowercase  (self       :   str)					->			Optional[int]:
					__snake_case    :							Union[str, Any]   	=				self.prepare_config_and_inputs()
					__snake_case	,       __snake_case	,       __snake_case    :							int   	=				config_and_inputs
					__snake_case    :							Any   	=				{'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict







@require_tf
class 	UpperCamelCase    (					lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Optional[Any]					=	(TFBlipTextModel,) if is_tf_available() else ()
				UpperCAmelCase       :   List[Any]					=	False
				UpperCAmelCase       :   Optional[int]					=	False
				UpperCAmelCase       :   Optional[int]					=	False



				def       _lowercase  (self       :   Optional[Any])					->			List[Any]:
					__snake_case    :							Any   	=				BlipTextModelTester(self)
					__snake_case    :							Optional[Any]   	=				ConfigTester(self			,						config_class=_A			,						hidden_size=37)



				def       _lowercase  (self       :   str)					->			Dict:
					self.config_tester.run_common_tests()



				def       _lowercase  (self       :   Tuple)					->			Any:
					__snake_case    :							Tuple   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_A)



				def       _lowercase  (self       :   Optional[Any])					->			Union[str, Any]:
					pass



				def       _lowercase  (self       :   Union[str, Any])					->			Optional[int]:
					pass



				@unittest.skip(reason='Blip does not use inputs_embeds')
				def       _lowercase  (self       :   Dict)					->			List[Any]:
					pass



				@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING')
				def       _lowercase  (self       :   int)					->			List[Any]:
					pass



				@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING')
				def       _lowercase  (self       :   str)					->			int:
					pass



				@slow
				def       _lowercase  (self       :   Tuple)					->			Optional[Any]:
					for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__snake_case    :							Union[str, Any]   	=				TFBlipTextModel.from_pretrained(_A)
						self.assertIsNotNone(_A)






				def       _lowercase  (self       :   Any			,						_A       :   Tuple=True)					->			List[str]:
					super().test_pt_tf_model_equivalence(allow_missing_keys=_A)






"""simple docstring"""







import math
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list    ,							UpperCAmelCase_					:       int						)							->    int:

	'''simple docstring'''





	__snake_case    :							List[str]   	=				len(UpperCAmelCase_						)
	__snake_case    :							List[Any]   	=				int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
	__snake_case    :							Any   	=				0
	while arr[min(UpperCAmelCase_    ,							UpperCAmelCase_						) - 1] < x:
		__snake_case    :							Tuple   	=				step
		step += int(math.floor(math.sqrt(UpperCAmelCase_						)						)						)
		if prev >= n:
			return -1

	while arr[prev] < x:
		__snake_case    :							Union[str, Any]   	=				prev + 1
		if prev == min(UpperCAmelCase_    ,							UpperCAmelCase_						):
			return -1
	if arr[prev] == x:
		return prev
	return -1


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





