
'''simple docstring'''






def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		for index, item in enumerate(_SCREAMING_SNAKE_CASE  ):
				if item == target:
						return index
		return -1







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


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

							__lowercase	=    int(input('''Enter a single number to be found in the list:\n''').strip())
							__lowercase	=    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_sentencepiece_available, is_torch_available


__lowercase	=    {
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''XLMProphetNetTokenizer''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMProphetNetDecoder''',
							    '''XLMProphetNetEncoder''',
							    '''XLMProphetNetForCausalLM''',
							    '''XLMProphetNetForConditionalGeneration''',
							    '''XLMProphetNetModel''',
							    '''XLMProphetNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_prophetnet import (
														    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMProphetNetDecoder,
														    XLMProphetNetEncoder,
														    XLMProphetNetForCausalLM,
														    XLMProphetNetForConditionalGeneration,
														    XLMProphetNetModel,
														    XLMProphetNetPreTrainedModel,
														)

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






