
"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_					:						str				= logging.get_logger(__name__)



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				["pixel_values"]




			def __init__(				self						:					int       ,   _snake_case						:					bool = True       ,   _snake_case						:					int = 32       ,   _snake_case						:					Tuple=PILImageResampling.BILINEAR       ,   _snake_case						:					bool = True       ,   **_snake_case						:					List[Any]       ,   )      -> None:







								"""simple docstring"""
								A_				 =						do_resize
								A_				 =						do_rescale
								A_				 =						size_divisor
								A_				 =						resample
								super().__init__(**_snake_case	)




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					np.ndarray       ,   _snake_case						:					int       ,   _snake_case						:					List[str]       ,   _snake_case						:					Optional[ChannelDimension] = None       ,   **_snake_case						:					Dict	)      -> np.ndarray:







								"""simple docstring"""
								A_ ,    A_				 =						get_image_size(_snake_case	)
								# Rounds the height and width down to the closest multiple of size_divisor
								A_				 =						height // size_divisor * size_divisor
								A_				 =						width // size_divisor * size_divisor
								A_				 =						resize(_snake_case       ,   (new_h, new_w)       ,   resample=_snake_case       ,   data_format=_snake_case       ,   **_snake_case	)
								return image




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					np.ndarray       ,   _snake_case						:					float       ,   _snake_case						:					Optional[ChannelDimension] = None       ,   **_snake_case						:					str	)      -> np.ndarray:







								"""simple docstring"""
								return rescale(image=_snake_case       ,   scale=_snake_case       ,   data_format=_snake_case       ,   **_snake_case	)






			def      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   _snake_case						:					Optional[bool] = None       ,   _snake_case						:					Optional[int] = None       ,   _snake_case						:					int=None       ,   _snake_case						:					Optional[bool] = None       ,   _snake_case						:					Optional[Union[TensorType, str]] = None       ,   _snake_case						:					ChannelDimension = ChannelDimension.FIRST       ,   **_snake_case						:					Tuple       ,   )      -> BatchFeature:







								"""simple docstring"""
								A_				 =						do_resize if do_resize is not None else self.do_resize
								A_				 =						do_rescale if do_rescale is not None else self.do_rescale
								A_				 =						size_divisor if size_divisor is not None else self.size_divisor
								A_				 =						resample if resample is not None else self.resample

								if do_resize and size_divisor is None:
													raise ValueError("size_divisor is required for resizing"	)

								A_				 =						make_list_of_images(_snake_case	)

								if not valid_images(_snake_case	):
													raise ValueError("Invalid image(s)"	)

								# All transformations expect numpy arrays.
								A_				 =						[to_numpy_array(_snake_case	) for img in images]

								if do_resize:
													A_				 =						[self.resize(_snake_case       ,   size_divisor=_snake_case       ,   resample=_snake_case	) for image in images]

								if do_rescale:
													A_				 =						[self.rescale(_snake_case       ,   scale=1 / 255	) for image in images]

								A_				 =						[to_channel_dimension_format(_snake_case       ,   _snake_case	) for image in images]

								A_				 =						{"pixel_values": images}
								return BatchFeature(data=_snake_case       ,   tensor_type=_snake_case	)






"""simple docstring"""





def 							A_     (__a				):

					'''simple docstring'''





					A_				 =						current_set.copy()
					for row_index, row in enumerate(__a				):
										A_				 =						row[0]
										for column_index, column in enumerate(__a				):
															if magnitude == 0:
																				A_				 =						column
																				continue
															A_				 =						column / magnitude
    # Subtract to cancel term
					A_				 =						current_set[0]
					A_				 =						[first_row]
					A_				 =						current_set[1::]
					for row in current_set:
										A_				 =						[]
										# If first term is 0, it is already in form we want, so we preserve it
										if row[0] == 0:
															final_set.append(__a				)
															continue
										for column_index in range(len(__a				)				):
															temp_row.append(first_row[column_index] - row[column_index]				)
										final_set.append(__a				)
					# Create next recursion iteration set
					if len(final_set[0]				) != 3:
										A_				 =						final_set[0]
										A_				 =						[]
										A_				 =						[]
										for row in final_set[1::]:
															current_first_column.append(row[0]				)
															next_iteration.append(row[1::]				)
										A_				 =						simplify(__a				)
										for i in range(len(__a				)				):
															resultant[i].insert(0				,   current_first_column[i]				)
										resultant.insert(0				,   __a				)
										A_				 =						resultant
					return final_set
def 							A_     (__a				):

					'''simple docstring'''





					if len(__a				) == 0:
										raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
					A_				 =						len(__a				) + 1
					if any(len(__a				) != _length for item in equations				):
										raise IndexError("solve_simultaneous() requires n lists of length n+1"				)
					for row in equations:
										if any(not isinstance(__a				,   (int, float)				) for column in row				):
															raise ValueError("solve_simultaneous() requires lists of integers"				)
					if len(__a				) == 1:
										return [equations[0][-1] / equations[0][0]]
					A_				 =						equations.copy()
					if any(0 in row for row in data_set				):
										A_				 =						data_set.copy()
										A_				 =						[]
										for row_index, row in enumerate(__a				):
															if 0 not in row:
																				A_				 =						data_set.pop(__a				)
																				break
										if not full_row:
															raise ValueError("solve_simultaneous() requires at least 1 full equation"				)
										data_set.insert(0				,   __a				)
					A_				 =						data_set.copy()
					A_				 =						simplify(__a				)
					A_				 =						simplified[::-1]
					A_				 =						[]
					for row in simplified:
										A_				 =						row[-1]
										if not solutions:
															if row[-2] == 0:
																				solutions.append(0				)
																				continue
															solutions.append(current_solution / row[-2]				)
															continue
										A_				 =						row.copy()[: len(__a				) - 1 :]
										while temp_row[0] == 0:
															temp_row.pop(0				)
										if len(__a				) == 0:
															solutions.append(0				)
															continue
										A_				 =						temp_row[1::]
										A_				 =						temp_row[::-1]
										for column_index, column in enumerate(__a				):
															current_solution -= column * solutions[column_index]
										solutions.append(__a				)
					A_				 =						[]
					for item in solutions:
										final.append(float(round(__a				,   5				)				)				)
					return final[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		UpperCamelCase_					:						List[Any]				= [
		    [2, 1, 1, 1, 1, 4],
		    [1, 2, 1, 1, 1, 5],
		    [1, 1, 2, 1, 1, 6],
		    [1, 1, 1, 2, 1, 7],
		    [1, 1, 1, 1, 2, 8],
		]
		print(solve_simultaneous(eq))
		print(solve_simultaneous([[4, 2]]))




