






from __future__ import annotations

from collections.abc import Callable

lowercase  							=					list[list[float | int]]





def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							len(a_)
							snake_case_				=							[[0 for _ in range(size + 1)] for _ in range(a_)]
							snake_case_				=							42
							snake_case_				=							42
							snake_case_				=							42
							snake_case_				=							42
							snake_case_				=							42
							snake_case_				=							42

							for row in range(a_):
														for col in range(a_):
																					snake_case_				=							matrix[row][col]

														snake_case_				=							vector[row][0]

							snake_case_				=							0
							snake_case_				=							0
							while row < size and col < size:
														# pivoting
														snake_case_				=							max((abs(augmented[rowa][col]), rowa) for rowa in range(a_							,     a_))[
														    1
														]
														if augmented[pivot_row][col] == 0:
																					col += 1
																					continue
														else:
																					snake_case_			,		snake_case_				=							augmented[pivot_row], augmented[row]

														for rowa in range(row + 1							,     a_):
																					snake_case_				=							augmented[rowa][col] / augmented[row][col]
																					snake_case_				=							0
																					for cola in range(col + 1							,     size + 1):
																												augmented[rowa][cola] -= augmented[row][cola] * ratio

														row += 1
														col += 1

							# back substitution
							for col in range(1							,     a_):
														for row in range(a_):
																					snake_case_				=							augmented[row][col] / augmented[col][col]
																					for cola in range(a_							,     size + 1):
																												augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
							return [
							    [round(augmented[row][size] / augmented[row][row]							,     10)] for row in range(a_)
							]





def 						__UpperCAmelCase			(     a_):
							snake_case_				=							len(a_)
							snake_case_				=							[[0 for _ in range(a_)] for _ in range(a_)]
							snake_case_				=							[[0] for _ in range(a_)]
							snake_case_				=							42
							snake_case_				=							42
							snake_case_				=							42
							snake_case_				=							42

							for x_val, y_val in enumerate(a_):
														for col in range(a_):
																					snake_case_				=							(x_val + 1) ** (size - col - 1)
														snake_case_				=							y_val

							snake_case_				=							solve(a_							,     a_)

							def interpolated_func(a_) -> int:
														return sum(
														    round(coeffs[x_val][0]) * (var ** (size - x_val - 1))
														    for x_val in range(a_))

							return interpolated_func





def 						__UpperCAmelCase			(     a_):
							return (
							    1
							    - variable
							    + variable**2
							    - variable**3
							    + variable**4
							    - variable**5
							    + variable**6
							    - variable**7
							    + variable**8
							    - variable**9
							    + variable**10
							)





def 						__UpperCAmelCase			(     a_ = question_function							,     a_ = 10):
							snake_case_				=							[func(a_) for x_val in range(1							,     order + 1)]

							snake_case_				=							[
							    interpolate(data_points[:max_coeff]) for max_coeff in range(1							,     order + 1)
							]

							snake_case_				=							0
							snake_case_				=							42
							snake_case_				=							42

							for poly in polynomials:
														snake_case_				=							1
														while func(a_) == poly(a_):
																					x_val += 1

														ret += poly(a_)

							return ret


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













from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''BlipImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							False
								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								if images is None and text is None:
															raise ValueError('You have to specify either images or text.'					)

								# Get only text
								if images is None:
															snake_case_				=							self.tokenizer
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
															return text_encoding

								# add pixel_values
								snake_case_				=							self.image_processor(a       ,			return_tensors=a					)

								if text is not None:
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
								else:
															snake_case_				=							None

								if text_encoding is not None:
															encoding_image_processor.update(a					)

								return encoding_image_processor




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    int:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    Any:
								return self.tokenizer.decode(*a       ,			**a					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





