





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_							=				logging.get_logger(__name__)
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       str       =	["""pixel_values"""]






		def __init__(							self,   snake_case__ = True,   snake_case__ = None,   snake_case__ = PILImageResampling.BILINEAR,   snake_case__ = True,   snake_case__ = None,   snake_case__ = True,   snake_case__ = 1 / 2_55,   snake_case__ = True,   snake_case__ = None,   snake_case__ = None,   **snake_case__,   )       -> None:






									"""simple docstring"""
									super().__init__(**snake_case__  )
									lowercase_	:   Optional[int]  				=     size if size is not None else {"""shortest_edge""": 2_56}
									lowercase_	:   str  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									lowercase_	:   List[Any]  				=     crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
									lowercase_	:   Any  				=     get_size_dict(snake_case__  )
									lowercase_	:   List[str]  				=     do_resize
									lowercase_	:   Optional[Any]  				=     size
									lowercase_	:   str  				=     resample
									lowercase_	:   Dict  				=     do_center_crop
									lowercase_	:   Any  				=     crop_size
									lowercase_	:   int  				=     do_rescale
									lowercase_	:   Dict  				=     rescale_factor
									lowercase_	:   Optional[int]  				=     do_normalize
									lowercase_	:   Any  				=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
									lowercase_	:   List[Any]  				=     image_std if image_std is not None else IMAGENET_STANDARD_STD






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = PILImageResampling.BICUBIC,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									if "shortest_edge" not in size:
																raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
									lowercase_	:   str  				=     get_resize_output_image_size(snake_case__,   size=size["""shortest_edge"""],   default_to_square=snake_case__  )
									return resize(snake_case__,   size=snake_case__,   resample=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     get_size_dict(snake_case__  )
									return center_crop(snake_case__,   size=(size["""height"""], size["""width"""]),   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__  )       -> np.ndarray:






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






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> np.ndarray:






									"""simple docstring"""
									return normalize(snake_case__,   mean=snake_case__,   std=snake_case__,   data_format=snake_case__,   **snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   **snake_case__,   )       -> Tuple:






									"""simple docstring"""
									lowercase_	:   int  				=     do_resize if do_resize is not None else self.do_resize
									lowercase_	:   int  				=     size if size is not None else self.size
									lowercase_	:   str  				=     get_size_dict(snake_case__,   default_to_square=snake_case__  )
									lowercase_	:   str  				=     resample if resample is not None else self.resample
									lowercase_	:   int  				=     do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase_	:   Dict  				=     crop_size if crop_size is not None else self.crop_size
									lowercase_	:   Any  				=     get_size_dict(snake_case__  )
									lowercase_	:   List[Any]  				=     do_rescale if do_rescale is not None else self.do_rescale
									lowercase_	:   Optional[int]  				=     rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase_	:   Optional[Any]  				=     do_normalize if do_normalize is not None else self.do_normalize
									lowercase_	:   Optional[Any]  				=     image_mean if image_mean is not None else self.image_mean
									lowercase_	:   str  				=     image_std if image_std is not None else self.image_std

									lowercase_	:   int  				=     make_list_of_images(snake_case__  )

									if not valid_images(snake_case__  ):
																raise ValueError(
																    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

									if do_resize and size is None:
																raise ValueError("""Size must be specified if do_resize is True."""  )

									if do_center_crop and crop_size is None:
																raise ValueError("""Crop size must be specified if do_center_crop is True."""  )

									if do_rescale and rescale_factor is None:
																raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

									if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

									# All transformations expect numpy arrays.
									lowercase_	:   Tuple  				=     [to_numpy_array(snake_case__  ) for image in images]

									if do_resize:
																lowercase_	:   int  				=     [self.resize(image=snake_case__,   size=snake_case__,   resample=snake_case__  ) for image in images]

									if do_center_crop:
																lowercase_	:   List[str]  				=     [self.center_crop(image=snake_case__,   size=snake_case__  ) for image in images]

									if do_rescale:
																lowercase_	:   Union[str, Any]  				=     [self.rescale(image=snake_case__,   scale=snake_case__  ) for image in images]

									if do_normalize:
																lowercase_	:   Optional[int]  				=     [self.normalize(image=snake_case__,   mean=snake_case__,   std=snake_case__  ) for image in images]

									lowercase_	:   Optional[int]  				=     [to_channel_dimension_format(snake_case__,   snake_case__  ) for image in images]

									lowercase_	:   List[Any]  				=     {"""pixel_values""": images}
									return BatchFeature(data=snake_case__,   tensor_type=snake_case__  )






def  __magic_name__  (				lowercase				)				-> list[list]:

							"""simple docstring"""



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





def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							UpperCAmelCase_							=				[
							    [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]]))