
"""simple docstring"""
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowercase		:						Optional[Any] =					"""Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""




class    _snake_case  (						lowerCAmelCase_       ):
							SCREAMING_SNAKE_CASE		     =						"""google/flan-t5-base"""
							SCREAMING_SNAKE_CASE		     =						(
							    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
							    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
							)
							SCREAMING_SNAKE_CASE		     =						"""text_qa"""
							SCREAMING_SNAKE_CASE		     =						AutoTokenizer
							SCREAMING_SNAKE_CASE		     =						AutoModelForSeqaSeqLM

							SCREAMING_SNAKE_CASE		     =						["""text""", """text"""]
							SCREAMING_SNAKE_CASE		     =						["""text"""]






							def 							__lowercase  (					self						:			int						, snake_case__						:			List[Any]						, snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					QA_PROMPT.format(text=__SCREAMING_SNAKE_CASE						, question=__SCREAMING_SNAKE_CASE    )
												return self.pre_processor(__SCREAMING_SNAKE_CASE						, return_tensors="""pt"""    )






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			List[str]    ):






												"""simple docstring"""



												__a:						int		    =					self.model.generate(**__SCREAMING_SNAKE_CASE    )

												__a    ,				__a:						Dict		    =					inputs["""input_ids"""].shape
												__a:						List[Any]		    =					output_ids.shape[0]

												return output_ids.reshape(__SCREAMING_SNAKE_CASE						, out_b // in_b						, *output_ids.shape[1:]    )[0][0]






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			List[str]    ):






												"""simple docstring"""



												return self.pre_processor.decode(__SCREAMING_SNAKE_CASE						, skip_special_tokens=__SCREAMING_SNAKE_CASE						, clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE    )


"""simple docstring"""
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					for index, item in enumerate(lowerCamelCase_ ):
										if item == target:
															return index
					return -1


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				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__":
			lowercase		:						str =					input('Enter numbers separated by comma:\n').strip()
			lowercase		:						int =					[int(item.strip()) for item in user_input.split(',')]

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