


def      _lowercase(				__a							:	list[list[int]]						,    __a							:	list[list[int]]    ):
							a__     						=[]
							a__     						=len(__lowerCamelCase    )
							for i in range(__lowerCamelCase    ):
														a__     						=[]
														for j in range(__lowerCamelCase    ):
																					a__     						=0
																					for k in range(__lowerCamelCase    ):
																												a__     						=val + matrix_a[i][k] * matrix_b[k][j]
																					list_a.append(__lowerCamelCase    )
														matrix_c.append(__lowerCamelCase    )
							return matrix_c






def      _lowercase(				__a							:	int    ):
							return [[int(row == column    ) for column in range(__lowerCamelCase    )] for row in range(__lowerCamelCase    )]






def      _lowercase(				__a							:	int    ):
							if n <= 1:
														return n
							a__     						=identity(2    )
							a__     						=[[1, 1], [1, 0]]
							a__     						=n - 1
							while n > 0:
														if n % 2 == 1:
																					a__     						=multiply(__lowerCamelCase						,    __lowerCamelCase    )
														a__     						=multiply(__lowerCamelCase						,    __lowerCamelCase    )
														a__     						=int(n / 2    )
							return res_matrix[0][0]






def      _lowercase(				__a							:	int    ):
							if n <= 1:
														return n
							a__     						=0
							a__     						=1
							for _ in range(2						,    n + 1    ):
														a__						,    a__     						=fiba, fiba + fiba
							return fiba






def      _lowercase(				):
							for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
														a__     						=int(''.join(c for c in ordinal if c in '0123456789'    )    )  # 1000th --> 1000
														print(
														    f"""{ordinal} fibonacci number using matrix exponentiation is """
														    f"""{nth_fibonacci_matrix(__lowerCamelCase    )} and using bruteforce is """
														    f"""{nth_fibonacci_bruteforce(__lowerCamelCase    )}\n"""    )
							# from timeit import timeit
							# print(timeit("nth_fibonacci_matrix(1000000)",
							#              "from main import nth_fibonacci_matrix", number=5))
							# print(timeit("nth_fibonacci_bruteforce(1000000)",
							#              "from main import nth_fibonacci_bruteforce", number=5))
							# 2.3342058970001744
							# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





# 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


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




class 			lowercase_				(lowercase__      ):
				snake_case  						='google/flan-t5-base'
				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.'
				)
				snake_case  						='text_qa'
				snake_case  						=AutoTokenizer
				snake_case  						=AutoModelForSeqaSeqLM

				snake_case  						=['text', 'text']
				snake_case  						=['text']





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_)     ->   Optional[int]:
											a__     						=QA_PROMPT.format(text=lowercase_					,     question=lowercase_)
											return self.pre_processor(lowercase_					,     return_tensors='pt')





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Dict:
											a__     						=self.model.generate(**lowercase_)

											a__						,    a__     						=inputs['input_ids'].shape
											a__     						=output_ids.shape[0]

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





				def 	__UpperCamelCase						(							self					,     lowercase_)     ->   Union[str, Any]:
											return self.pre_processor.decode(lowercase_					,     skip_special_tokens=lowercase_					,     clean_up_tokenization_spaces=lowercase_)

