



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__SCREAMING_SNAKE_CASE :      Tuple             ={
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE :      Optional[Any]             =['MobileNetV1FeatureExtractor']
					__SCREAMING_SNAKE_CASE :      Tuple             =['MobileNetV1ImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE :      Optional[Any]             =[
					    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MobileNetV1ForImageClassification',
					    'MobileNetV1Model',
					    'MobileNetV1PreTrainedModel',
					    'load_tf_weights_in_mobilenet_v1',
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    MobileNetVaConfig,
					    MobileNetVaOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
										from .image_processing_mobilenet_va import MobileNetVaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mobilenet_va import (
										    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					__SCREAMING_SNAKE_CASE :      List[str]             =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










'''simple docstring'''
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			for index, item in enumerate(lowerCamelCase__				):
						if item == target:
									return index
			return -1



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			if not (0 <= high < len(lowerCamelCase__				) and 0 <= low < len(lowerCamelCase__				)):
						raise Exception("""Invalid upper or lower bound!"""				)
			if high < low:
						return -1
			if sequence[low] == target:
						return low
			if sequence[high] == target:
						return high
			return rec_linear_search(lowerCamelCase__							,							low + 1							,							high - 1							,							lowerCamelCase__				)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE :      List[str]             =input('Enter numbers separated by comma:\n').strip()
					__SCREAMING_SNAKE_CASE :      str             =[int(item.strip()) for item in user_input.split(',')]

					__SCREAMING_SNAKE_CASE :      List[str]             =int(input('Enter a single number to be found in the list:\n').strip())
					__SCREAMING_SNAKE_CASE :      List[Any]             =linear_search(sequence, target)
					if result != -1:
										print(f"""linear_search({sequence}, {target}) = {result}""")
					else:
										print(f"""{target} was not found in {sequence}""")





