"""simple docstring"""






from typing import Any

import numpy as np
class 							snake_case			:




					"""simple docstring"""

					def __init__(							self  :      Optional[Any]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      int     ):
									# Check if RHS is negative
									if np.any(tableau[:, -1]       ,where=tableau[:, -1] < 0     ):
													raise ValueError('RHS must be > 0'     )

									UpperCAmelCase__			 =				tableau
									UpperCAmelCase__ ,      UpperCAmelCase__			 =				tableau.shape

									# Number of decision variables x1, x2, x3...
									UpperCAmelCase__			 =				n_vars

									# Number of artificial variables to be minimised
									UpperCAmelCase__			 =				len(np.where(tableau[self.n_vars : -1] == -1     )[0]     )

									# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
									UpperCAmelCase__			 =				(self.n_art_vars > 0) + 1

									# Number of slack variables added to make inequalities into equalities
									UpperCAmelCase__			 =				self.n_rows - self.n_stages

									# Objectives for each stage
									UpperCAmelCase__			 =				['max']

									# In two stage simplex, first minimise then maximise
									if self.n_art_vars:
													self.objectives.append('min'     )

									UpperCAmelCase__			 =				['']

									# Index of current pivot row and column
									UpperCAmelCase__			 =				None
									UpperCAmelCase__			 =				None

									# Does objective row only contain (non)-negative values?
									UpperCAmelCase__			 =				False







					@staticmethod
					def 						__lowerCAmelCase		(							*lowerCamelCase__  :      int     ):
									if len(lowerCamelCase__     ) != 3:
													raise ValueError('Must provide n_vars, n_slack, and n_art_vars'     )

									if not all(x >= 0 and isinstance(lowerCamelCase__       ,lowerCamelCase__     ) for x in args     ):
													raise ValueError('All arguments must be non-negative integers'     )

									# decision | slack | artificial
									UpperCAmelCase__			 =				['x', 's', 'a']
									UpperCAmelCase__			 =				[]
									for i in range(3     ):
													for j in range(args[i]     ):
																	titles.append(string_starts[i] + str(j + 1     )     )
									titles.append('RHS'     )
									return titles







					def 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      np.ndarray     ):
									UpperCAmelCase__			 =				self.objectives[-1]

									# Find entries of highest magnitude in objective rows
									UpperCAmelCase__			 =				(objective == 'min') - (objective == 'max')
									UpperCAmelCase__			 =				np.argmax(sign * tableau[0, : self.n_vars]     )

									# Choice is only valid if below 0 for maximise, and above for minimise
									if sign * self.tableau[0, col_idx] <= 0:
													UpperCAmelCase__			 =				True
													return 0, 0

									# Pivot row is chosen as having the lowest quotient when elements of
									# the pivot column divide the right-hand side

									# Slice excluding the objective rows
									UpperCAmelCase__			 =				slice(self.n_stages       ,self.n_rows     )

									# RHS
									UpperCAmelCase__			 =				tableau[s, -1]

									# Elements of pivot column within slice
									UpperCAmelCase__			 =				tableau[s, col_idx]

									# Array filled with nans
									UpperCAmelCase__			 =				np.full(self.n_rows - self.n_stages       ,np.nan     )

									# If element in pivot column is greater than zeron_stages, return
									# quotient or nan otherwise
									UpperCAmelCase__			 =				np.divide(lowerCamelCase__       ,lowerCamelCase__       ,out=lowerCamelCase__       ,where=divisor > 0     )

									# Arg of minimum quotient excluding the nan values. n_stages is added
									# to compensate for earlier exclusion of objective columns
									UpperCAmelCase__			 =				np.nanargmin(lowerCamelCase__     ) + self.n_stages
									return row_idx, col_idx







					def 						__lowerCAmelCase		(							self  :      List[str]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      int       ,lowerCamelCase__  :      int     ):
									UpperCAmelCase__			 =				tableau[row_idx].copy()

									UpperCAmelCase__			 =				piv_row[col_idx]

									# Entry becomes 1
									piv_row *= 1 / piv_val

									# Variable in pivot column becomes basic, ie the only non-zero entry
									for idx, coeff in enumerate(tableau[:, col_idx]     ):
													tableau[idx] += -coeff * piv_row
									UpperCAmelCase__			 =				piv_row
									return tableau







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      np.ndarray     ):
									self.objectives.pop()

									if not self.objectives:
													return tableau

									# Slice containing ids for artificial columns
									UpperCAmelCase__			 =				slice(-self.n_art_vars - 1       ,-1     )

									# Delete the artificial variable columns
									UpperCAmelCase__			 =				np.delete(lowerCamelCase__       ,lowerCamelCase__       ,axis=1     )

									# Delete the objective row of the first stage
									UpperCAmelCase__			 =				np.delete(lowerCamelCase__       ,0       ,axis=0     )

									UpperCAmelCase__			 =				1
									self.n_rows -= 1
									UpperCAmelCase__			 =				0
									UpperCAmelCase__			 =				False
									return tableau







					def 						__lowerCAmelCase		(							self  :      Optional[int]     ):
									# Stop simplex algorithm from cycling.
									for _ in range(100     ):
													# Completion of each stage removes an objective. If both stages
													# are complete, then no objectives are left
													if not self.objectives:
																	UpperCAmelCase__			 =				self.generate_col_titles(
																	    self.n_vars       ,self.n_slack       ,self.n_art_vars     )

																	# Find the values of each variable at optimal solution
																	return self.interpret_tableau(self.tableau       ,self.col_titles     )

													UpperCAmelCase__ ,      UpperCAmelCase__			 =				self.find_pivot(self.tableau     )

													# If there are no more negative values in objective row
													if self.stop_iter:
																	# Delete artificial variable columns and rows. Update attributes
																	UpperCAmelCase__			 =				self.change_stage(self.tableau     )
													else:
																	UpperCAmelCase__			 =				self.pivot(self.tableau       ,lowerCamelCase__       ,lowerCamelCase__     )
									return {}







					def 						__lowerCAmelCase		(							self  :      List[str]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      list[str]     ):
									UpperCAmelCase__			 =				{'P': abs(tableau[0, -1]     )}

									for i in range(self.n_vars     ):
													# Gives ids of nonzero entries in the ith column
													UpperCAmelCase__			 =				np.nonzero(tableau[:, i]     )
													UpperCAmelCase__			 =				len(nonzero[0]     )

													# First entry in the nonzero ids
													UpperCAmelCase__			 =				nonzero[0][0]
													UpperCAmelCase__			 =				tableau[nonzero_rowidx, i]

													# If there is only one nonzero value in column, which is one
													if n_nonzero == nonzero_val == 1:
																	UpperCAmelCase__			 =				tableau[nonzero_rowidx, -1]
																	UpperCAmelCase__			 =				rhs_val

        # Check for basic variables
									for title in col_titles:
													# Don't add RHS or slack variables to output dict
													if title[0] not in "R-s-a":
																	output_dict.setdefault(lowerCamelCase__       ,0     )
									return output_dict


if __name__ == "__main__":
						import doctest

						doctest.testmod()




"""simple docstring"""






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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCAmelCase__    :    Tuple			=      logging.get_logger(__name__)
class 							snake_case			(					__UpperCAmelCase							):




					"""simple docstring"""

					snake_case__							       =					["pixel_values"]







					def __init__(							self  :      str       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Dict[str, int] = None       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Dict[str, int] = None       ,lowerCamelCase__  :      PILImageResampling = PILImageResampling.BICUBIC       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Union[int, float] = 1 / 255       ,lowerCamelCase__  :      bool = True       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,**lowerCamelCase__  :      List[Any]       ,):
									super().__init__(**lowerCamelCase__     )
									UpperCAmelCase__			 =				crop_size if crop_size is not None else {'height': 256, 'width': 256}
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )
									UpperCAmelCase__			 =				size if size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )

									UpperCAmelCase__			 =				do_center_crop
									UpperCAmelCase__			 =				crop_size
									UpperCAmelCase__			 =				do_resize
									UpperCAmelCase__			 =				size
									UpperCAmelCase__			 =				resample
									UpperCAmelCase__			 =				do_rescale
									UpperCAmelCase__			 =				rescale_factor
									UpperCAmelCase__			 =				do_normalize
									UpperCAmelCase__			 =				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									UpperCAmelCase__			 =				image_std if image_std is not None else IMAGENET_DEFAULT_STD







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      Optional[int] = None       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      List[str]       ,):
									UpperCAmelCase__			 =				self.size if size is None else size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )

									UpperCAmelCase__ ,      UpperCAmelCase__			 =				get_image_size(lowerCamelCase__     )
									UpperCAmelCase__			 =				min(lowerCamelCase__       ,lowerCamelCase__     )
									UpperCAmelCase__			 =				(size['height'] / crop_size['height']) * min_dim
									UpperCAmelCase__			 =				(size['width'] / crop_size['width']) * min_dim
									return center_crop(lowerCamelCase__       ,size=(cropped_height, cropped_width)       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Any       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Dict[str, int]       ,lowerCamelCase__  :      PILImageResampling = PIL.Image.BICUBIC       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      Any       ,):
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									if "height" not in size or "width" not in size:
													raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''     )
									return resize(
									    lowerCamelCase__       ,size=(size['height'], size['width'])       ,resample=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Optional[int]       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Union[int, float]       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      int       ,):
									return rescale(lowerCamelCase__       ,scale=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      int       ,lowerCamelCase__  :      np.ndarray       ,lowerCamelCase__  :      Union[float, List[float]]       ,lowerCamelCase__  :      Union[float, List[float]]       ,lowerCamelCase__  :      Optional[Union[str, ChannelDimension]] = None       ,**lowerCamelCase__  :      List[Any]       ,):
									return normalize(lowerCamelCase__       ,mean=lowerCamelCase__       ,std=lowerCamelCase__       ,data_format=lowerCamelCase__       ,**lowerCamelCase__     )







					def 						__lowerCAmelCase		(							self  :      Optional[Any]       ,lowerCamelCase__  :      ImageInput       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Dict[str, int]] = None       ,lowerCamelCase__  :      PILImageResampling = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[float] = None       ,lowerCamelCase__  :      Optional[bool] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[float, List[float]]] = None       ,lowerCamelCase__  :      Optional[Union[str, TensorType]] = None       ,lowerCamelCase__  :      ChannelDimension = ChannelDimension.FIRST       ,**lowerCamelCase__  :      Optional[int]       ,):
									UpperCAmelCase__			 =				do_center_crop if do_center_crop is not None else self.do_center_crop
									UpperCAmelCase__			 =				crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__       ,param_name='crop_size'     )
									UpperCAmelCase__			 =				do_resize if do_resize is not None else self.do_resize
									UpperCAmelCase__			 =				size if size is not None else self.size
									UpperCAmelCase__			 =				get_size_dict(lowerCamelCase__     )
									UpperCAmelCase__			 =				resample if resample is not None else self.resample
									UpperCAmelCase__			 =				do_rescale if do_rescale is not None else self.do_rescale
									UpperCAmelCase__			 =				rescale_factor if rescale_factor is not None else self.rescale_factor
									UpperCAmelCase__			 =				do_normalize if do_normalize is not None else self.do_normalize
									UpperCAmelCase__			 =				image_mean if image_mean is not None else self.image_mean
									UpperCAmelCase__			 =				image_std if image_std is not None else self.image_std

									UpperCAmelCase__			 =				make_list_of_images(lowerCamelCase__     )

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

									if do_center_crop and crop_size is None:
													raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'     )

									if do_resize and size is None:
													raise ValueError('Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'     )

									# All transformations expect numpy arrays.
									UpperCAmelCase__			 =				[to_numpy_array(lowerCamelCase__     ) for image in images]

									if do_center_crop:
													UpperCAmelCase__			 =				[self.center_crop(lowerCamelCase__       ,lowerCamelCase__       ,size=lowerCamelCase__     ) for image in images]

									if do_resize:
													UpperCAmelCase__			 =				[self.resize(image=lowerCamelCase__       ,size=lowerCamelCase__       ,resample=lowerCamelCase__     ) for image in images]

									if do_rescale:
													UpperCAmelCase__			 =				[self.rescale(image=lowerCamelCase__       ,scale=lowerCamelCase__     ) for image in images]

									if do_normalize:
													UpperCAmelCase__			 =				[self.normalize(image=lowerCamelCase__       ,mean=lowerCamelCase__       ,std=lowerCamelCase__     ) for image in images]

									UpperCAmelCase__			 =				[to_channel_dimension_format(lowerCamelCase__       ,lowerCamelCase__     ) for image in images]

									UpperCAmelCase__			 =				{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__       ,tensor_type=lowerCamelCase__     )



