
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


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


class 						__magic_name__				(  snake_case      ):
		_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  _A							(							self      :		Optional[Any]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		str			):
							lowerCAmelCase						:   List[str]							=       QA_PROMPT.format(text=lowerCamelCase__  ,					question=lowerCamelCase__			)
							return self.pre_processor(lowerCamelCase__  ,					return_tensors='''pt'''			)





		def  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		int			):
							lowerCAmelCase						:   Optional[int]							=       self.model.generate(**lowerCamelCase__			)

							lowerCAmelCase		,							lowerCAmelCase						:   Optional[Any]							=       inputs['''input_ids'''].shape
							lowerCAmelCase						:   List[str]							=       output_ids.shape[0]

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





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		List[Any]			):
							return self.pre_processor.decode(lowerCamelCase__  ,					skip_special_tokens=lowerCamelCase__  ,					clean_up_tokenization_spaces=lowerCamelCase__			)


from math import floor, sqrt
def       UpperCAmelCase__							(    __magic_name__  :       int					):


					'''simple docstring'''

					lowerCAmelCase						:   int							=       0.0
					lowerCAmelCase						:   int							=       1.0
					lowerCAmelCase						:   Any							=       int(sqrt(__magic_name__					)					)
					lowerCAmelCase						:   Optional[int]							=       root
					lowerCAmelCase						:   Dict							=       0
					while integer_part != 2 * root:
										lowerCAmelCase						:   str							=       denominator * integer_part - numerator
										lowerCAmelCase						:   Dict							=       (n - numerator**2) / denominator
										lowerCAmelCase						:   str							=       int((root + numerator) / denominator					)
										period += 1
					return period
def       UpperCAmelCase__							(    __magic_name__  :       int = 1_00_00					):


					'''simple docstring'''

					lowerCAmelCase						:   List[str]							=       0
					for i in range(2							,					n + 1					):
										lowerCAmelCase						:   Any							=       sqrt(__magic_name__					)
										if sr - floor(__magic_name__					) != 0 and continuous_fraction_period(__magic_name__					) % 2 == 1:
															count_odd_periods += 1
					return count_odd_periods


if __name__ == "__main__":
		print(f"""{solution(int(input().strip()))}""")
