





from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


_a									=				"Here is a text containing a lot of information: \'\'\'{text}\'\'\'.\n\nCan you answer this question about the text: \'{question}\'"






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						'google/flan-t5-base'
			lowerCAmelCase_												=						(
			    '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.'
			)
			lowerCAmelCase_												=						'text_qa'
			lowerCAmelCase_												=						AutoTokenizer
			lowerCAmelCase_												=						AutoModelForSeqaSeqLM

			lowerCAmelCase_												=						['text', 'text']
			lowerCAmelCase_												=						['text']





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		QA_PROMPT.format(text=__lowerCAmelCase	,  question=__lowerCAmelCase							)
				return self.pre_processor(__lowerCAmelCase	,  return_tensors='''pt'''							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.model.generate(**__lowerCAmelCase							)

				lowerCamelCase__        =		inputs["input_ids"].shape
				lowerCamelCase__        =		output_ids.shape[0]

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







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










from __future__ import annotations

import math
import random





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case							)    ->				list[int]:







	'''simple docstring'''







	lowerCamelCase__        =		d * e - 1
	lowerCamelCase__        =		0
	lowerCamelCase__        =		0
	while p == 0:
		lowerCamelCase__        =		random.randint(2	,n - 1							)
		lowerCamelCase__        =		k
		while True:
			if t % 2 == 0:
				lowerCamelCase__        =		t // 2
				lowerCamelCase__        =		(g**t) % n
				lowerCamelCase__        =		math.gcd(x - 1	,__snake_case							)
				if x > 1 and y > 1:
					lowerCamelCase__        =		y
					lowerCamelCase__        =		n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



