

import math






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list							,       UpperCAmelCase_							:     int	):




	'''simple docstring'''




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

	while arr[prev] < x:
		_UpperCamelCase   :		int			=						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}')







import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image







class 	lowercase    :


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case=3	,       __snake_case=32	,       __snake_case=3	,       __snake_case=10	,       __snake_case=[8, 16, 32, 64]	,       __snake_case=[1, 1, 2, 1]	,       __snake_case=True	,       __snake_case=True	,       __snake_case="relu"	,       __snake_case=3	,       __snake_case=None	,       __snake_case=["stage2", "stage3", "stage4"]	,       __snake_case=[2, 3, 4]	,       __snake_case=1	,       ):
			_UpperCamelCase   :		List[Any]			=						parent
			_UpperCamelCase   :		Dict			=						batch_size
			_UpperCamelCase   :		Optional[int]			=						image_size
			_UpperCamelCase   :		str			=						num_channels
			_UpperCamelCase   :		Optional[Any]			=						embeddings_size
			_UpperCamelCase   :		Tuple			=						hidden_sizes
			_UpperCamelCase   :		Dict			=						depths
			_UpperCamelCase   :		str			=						is_training
			_UpperCamelCase   :		Optional[int]			=						use_labels
			_UpperCamelCase   :		str			=						hidden_act
			_UpperCamelCase   :		Optional[int]			=						num_labels
			_UpperCamelCase   :		Optional[int]			=						scope
			_UpperCamelCase   :		Tuple			=						len(__snake_case)
			_UpperCamelCase   :		Dict			=						out_features
			_UpperCamelCase   :		Union[str, Any]			=						out_indices
			_UpperCamelCase   :		int			=						num_groups


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase   :		str			=						None
			if self.use_labels:
				_UpperCamelCase   :		Optional[int]			=						ids_tensor([self.batch_size]	,       self.num_labels)

			_UpperCamelCase   :		str			=						self.get_config()

			return config, pixel_values, labels


		def 		A__					(				self):
			return BitConfig(
			    num_channels=self.num_channels	,       embeddings_size=self.embeddings_size	,       hidden_sizes=self.hidden_sizes	,       depths=self.depths	,       hidden_act=self.hidden_act	,       num_labels=self.num_labels	,       out_features=self.out_features	,       out_indices=self.out_indices	,       num_groups=self.num_groups	,       )


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		str			=						BitModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Optional[Any]			=						model(__snake_case)
			self.parent.assertEqual(
			    result.last_hidden_state.shape	,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,       )


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Dict			=						self.num_labels
			_UpperCamelCase   :		Dict			=						BitForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Dict			=						model(__snake_case	,       labels=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						BitBackbone(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		List[Any]			=						model(__snake_case)

			# verify feature maps
			self.parent.assertEqual(len(result.feature_maps)	,       len(config.out_features))
			self.parent.assertListEqual(list(result.feature_maps[0].shape)	,       [self.batch_size, self.hidden_sizes[1], 4, 4])

			# verify channels
			self.parent.assertEqual(len(model.channels)	,       len(config.out_features))
			self.parent.assertListEqual(model.channels	,       config.hidden_sizes[1:])

			# verify backbone works with out_features=None
			_UpperCamelCase   :		Any			=						None
			_UpperCamelCase   :		str			=						BitBackbone(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(__snake_case)

			# verify feature maps
			self.parent.assertEqual(len(result.feature_maps)	,       1)
			self.parent.assertListEqual(list(result.feature_maps[0].shape)	,       [self.batch_size, self.hidden_sizes[-1], 1, 1])

			# verify channels
			self.parent.assertEqual(len(model.channels)	,       1)
			self.parent.assertListEqual(model.channels	,       [config.hidden_sizes[-1]])






		def 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						self.prepare_config_and_inputs()
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		int			=						config_and_inputs
			_UpperCamelCase   :		int			=						{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class 	lowercase    (				_lowercase    ,							_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		a__					       =  (
		    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		a__					       =  False
		a__					       =  False
		a__					       =  False
		a__					       =  False
		a__					       =  False


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						BitModelTester(self)
			_UpperCamelCase   :		Optional[Any]			=						ConfigTester(self	,       config_class=__snake_case	,       has_text_modality=__snake_case)


		def 		A__					(				self):
			self.create_and_test_config_common_properties()
			self.config_tester.create_and_test_config_to_json_string()
			self.config_tester.create_and_test_config_to_json_file()
			self.config_tester.create_and_test_config_from_and_save_pretrained()
			self.config_tester.create_and_test_config_with_num_labels()
			self.config_tester.check_config_can_be_init_without_params()
			self.config_tester.check_config_arguments_init()


		def 		A__					(				self):
			return


		@unittest.skip(reason='Bit does not output attentions')
		def 		A__					(				self):
			pass


		@unittest.skip(reason='Bit does not use inputs_embeds')
		def 		A__					(				self):
			pass


		@unittest.skip(reason='Bit does not support input and output embeddings')
		def 		A__					(				self):
			pass


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		int			=						model_class(__snake_case)
				_UpperCamelCase   :		List[Any]			=						inspect.signature(model.forward)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				_UpperCamelCase   :		Optional[int]			=						[*signature.parameters.keys()]

				_UpperCamelCase   :		List[Any]			=						['pixel_values']
				self.assertListEqual(arg_names[:1]	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase   :		Union[str, Any]			=						model_class(config=__snake_case)
				for name, module in model.named_modules():
					if isinstance(__snake_case	,       (nn.BatchNormad, nn.GroupNorm)):
						self.assertTrue(
						    torch.all(module.weight == 1)	,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''	,       )
						self.assertTrue(
						    torch.all(module.bias == 0)	,       msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''	,       )


		def 		A__					(				self):
			def check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case):
				_UpperCamelCase   :		str			=						model_class(__snake_case)
				model.to(__snake_case)
				model.eval()

				with torch.no_grad():
					_UpperCamelCase   :		Union[str, Any]			=						model(**self._prepare_for_class(__snake_case	,       __snake_case))

				_UpperCamelCase   :		List[Any]			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

				_UpperCamelCase   :		str			=						self.model_tester.num_stages
				self.assertEqual(len(__snake_case)	,       expected_num_stages + 1)

				# Bit's feature maps are of shape (batch_size, num_channels, height, width)
				self.assertListEqual(
				    list(hidden_states[0].shape[-2:])	,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,       )

			_UpperCamelCase	,		_UpperCamelCase   :		int			=						self.model_tester.prepare_config_and_inputs_for_common()
			_UpperCamelCase   :		List[str]			=						['preactivation', 'bottleneck']
			for model_class in self.all_model_classes:
				for layer_type in layers_type:
					_UpperCamelCase   :		Any			=						layer_type
					_UpperCamelCase   :		Tuple			=						True
					check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case)

					# check that output_hidden_states also work using config
					del inputs_dict["output_hidden_states"]
					_UpperCamelCase   :		List[str]			=						True

					check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case)


		@unittest.skip(reason='Bit does not use feedforward chunking')
		def 		A__					(				self):
			pass


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)






		@slow
		def 		A__					(				self):
			for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase   :		Optional[Any]			=						BitModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






def 					lowerCamelCase_     (							)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		List[Any]			=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image







@require_torch
@require_vision
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		@cached_property
		def 		A__					(				self):
			return (
			    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
			)






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(__snake_case)

			_UpperCamelCase   :		str			=						self.default_image_processor
			_UpperCamelCase   :		List[str]			=						prepare_img()
			_UpperCamelCase   :		int			=						image_processor(images=__snake_case	,       return_tensors='pt').to(__snake_case)

			# forward pass
			with torch.no_grad():
				_UpperCamelCase   :		Any			=						model(**__snake_case)

			# verify the logits
			_UpperCamelCase   :		Dict			=						torch.Size((1, 10_00))
			self.assertEqual(outputs.logits.shape	,       __snake_case)

			_UpperCamelCase   :		Optional[int]			=						torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]).to(__snake_case)

			self.assertTrue(torch.allclose(outputs.logits[0, :3]	,       __snake_case	,       atol=1e-4))







@require_torch
class 	lowercase    (				_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (BitBackbone,) if is_torch_available() else ()
		a__					       =  BitConfig

		a__					       =  False


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						BitModelTester(self)




