



'''simple docstring'''






def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     list ,       snake_case_    :     int  )		->int:






							'''simple docstring'''



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





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     list ,       snake_case_    :     int ,       snake_case_    :     int ,       snake_case_    :     int  )		->int:






							'''simple docstring'''



							if not (0 <= high < len(a__  ) and 0 <= low < len(a__  )):
														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(a__ ,       low + 1 ,       high - 1 ,       a__  )


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

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






"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a__			:						Tuple							=			{
    """configuration_segformer""": ["""SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SegformerConfig""", """SegformerOnnxConfig"""]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[str]							=			["""SegformerFeatureExtractor"""]
							a__			:						str							=			["""SegformerImageProcessor"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						Optional[Any]							=			[
							    """SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """SegformerDecodeHead""",
							    """SegformerForImageClassification""",
							    """SegformerForSemanticSegmentation""",
							    """SegformerLayer""",
							    """SegformerModel""",
							    """SegformerPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a__			:						List[Any]							=			[
							    """TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFSegformerDecodeHead""",
							    """TFSegformerForImageClassification""",
							    """TFSegformerForSemanticSegmentation""",
							    """TFSegformerModel""",
							    """TFSegformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

							a__			:						Any							=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


