
"""simple docstring"""






import math
from decimal import Decimal, getcontext






def 		_snake_case			(      lowercase__						:    int = 4_0_0_0_0_0_0						)    ->							int:


			'''simple docstring'''





			try:
						lowerCAmelCase_       :Optional[Any]     	=    int(_UpperCAmelCase						)
			except (TypeError, ValueError):
						raise TypeError("""Parameter n must be int or castable to int."""						)
			if n <= 0:
						raise ValueError("""Parameter n must be greater than or equal to one."""						)
			lowerCAmelCase_       :List[str]     	=    1_0_0
			lowerCAmelCase_       :Tuple     	=    (Decimal(5						) ** Decimal(0.5						) + 1) / Decimal(2						)

			lowerCAmelCase_       :str     	=    (math.floor(math.log(n * (phi + 2) ,	_UpperCAmelCase						) - 1						) // 3) * 3 + 2
			lowerCAmelCase_       :List[str]     	=    Decimal(round(phi ** Decimal(index + 1						)						)						) / (phi + 2)
			lowerCAmelCase_       :List[Any]     	=    num // 2
			return int(_UpperCAmelCase						)


if __name__ == "__main__":
				print(F"""{solution() = }""")










"""simple docstring"""






# 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


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

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


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     List[Any]				  =  '''google/flan-t5-base'''
							__magic_name__					:     Dict				  =  (
							    '''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.'''
							)
							__magic_name__					:     Union[str, Any]				  =  '''text_qa'''
							__magic_name__					:     Union[str, Any]				  =  AutoTokenizer
							__magic_name__					:     List[Any]				  =  AutoModelForSeqaSeqLM

							__magic_name__					:     Tuple				  =  ['''text''', '''text''']
							__magic_name__					:     Tuple				  =  ['''text''']




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						str		=      QA_PROMPT.format(text=lowerCamelCase__	,					question=lowerCamelCase__)
								return self.pre_processor(lowerCamelCase__	,					return_tensors="pt")




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Tuple		=      self.model.generate(**lowerCamelCase__)

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

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







							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




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





