



"""simple docstring"""
from typing import Any

import numpy as np
class    a		:



  def __init__(  self	:   str    ,						lowerCAmelCase	:   np.ndarray    ,						lowerCAmelCase	:   int						)       ->   None:


     '''simple docstring'''




     if np.any(tableau[:, -1]    ,						where=tableau[:, -1] < 0						):
        raise ValueError("""RHS must be > 0"""						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =tableau
     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[str]            =tableau.shape

     # Number of decision variables x1, x2, x3...
     SCREAMING_SNAKE_CASE_:	List[str]            =n_vars

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

     # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
     SCREAMING_SNAKE_CASE_:	List[Any]            =(self.n_art_vars > 0) + 1

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

     # Objectives for each stage
     SCREAMING_SNAKE_CASE_:	List[Any]            =["""max"""]

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

     SCREAMING_SNAKE_CASE_:	str            =[""""""]

     # Index of current pivot row and column
     SCREAMING_SNAKE_CASE_:	int            =None
     SCREAMING_SNAKE_CASE_:	Dict            =None

     # Does objective row only contain (non)-negative values?
     SCREAMING_SNAKE_CASE_:	Optional[int]            =False



  @staticmethod
  def 		lowerCamelCase__    (  *lowerCAmelCase	:   int						)       ->   list[str]:


     '''simple docstring'''




     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
     SCREAMING_SNAKE_CASE_:	List[Any]            =["""x""", """s""", """a"""]
     SCREAMING_SNAKE_CASE_:	List[str]            =[]
     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	:   List[Any]    ,						lowerCAmelCase	:   np.ndarray						)       ->   tuple[Any, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.objectives[-1]

     # Find entries of highest magnitude in objective rows
     SCREAMING_SNAKE_CASE_:	Dict            =(objective == """min""") - (objective == """max""")
     SCREAMING_SNAKE_CASE_:	Dict            =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:
        SCREAMING_SNAKE_CASE_:	List[str]            =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
     SCREAMING_SNAKE_CASE_:	Tuple            =slice(self.n_stages    ,						self.n_rows						)

     # RHS
     SCREAMING_SNAKE_CASE_:	Any            =tableau[s, -1]

     # Elements of pivot column within slice
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =tableau[s, col_idx]

     # Array filled with nans
     SCREAMING_SNAKE_CASE_:	Any            =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
     SCREAMING_SNAKE_CASE_:	Any            =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
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =np.nanargmin(lowerCAmelCase						) + self.n_stages
     return row_idx, col_idx



  def 		lowerCamelCase__    (  self	:   Optional[int]    ,						lowerCAmelCase	:   np.ndarray    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   int						)       ->   np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =tableau[row_idx].copy()

     SCREAMING_SNAKE_CASE_:	List[str]            =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
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =piv_row
     return tableau



  def 		lowerCamelCase__    (  self	:   str    ,						lowerCAmelCase	:   np.ndarray						)       ->   np.ndarray:


     '''simple docstring'''




     self.objectives.pop()

     if not self.objectives:
        return tableau

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

     # Delete the artificial variable columns
     SCREAMING_SNAKE_CASE_:	Tuple            =np.delete(lowerCAmelCase    ,						lowerCAmelCase    ,						axis=1						)

     # Delete the objective row of the first stage
     SCREAMING_SNAKE_CASE_:	Optional[int]            =np.delete(lowerCAmelCase    ,						0    ,						axis=0						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =1
     self.n_rows -= 1
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =0
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =False
     return tableau



  def 		lowerCamelCase__    (  self	:   Dict						)       ->   dict[Any, Any]:


     '''simple docstring'''




     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:
           SCREAMING_SNAKE_CASE_:	List[Any]            =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						)

        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Dict            =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
           SCREAMING_SNAKE_CASE_:	Union[str, Any]            =self.change_stage(self.tableau						)
        else:
           SCREAMING_SNAKE_CASE_:	Any            =self.pivot(self.tableau    ,						lowerCAmelCase    ,						lowerCAmelCase						)
     return {}





  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   np.ndarray    ,						lowerCAmelCase	:   list[str]						)       ->   dict[str, float]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            ={"""P""": abs(tableau[0, -1]						)}

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

        # First entry in the nonzero ids
        SCREAMING_SNAKE_CASE_:	Tuple            =nonzero[0][0]
        SCREAMING_SNAKE_CASE_:	Tuple            =tableau[nonzero_rowidx, i]

        # If there is only one nonzero value in column, which is one
        if n_nonzero == nonzero_val == 1:
           SCREAMING_SNAKE_CASE_:	Tuple            =tableau[nonzero_rowidx, -1]
           SCREAMING_SNAKE_CASE_:	List[str]            =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"""
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class    a		:



  def __init__(  self	:   str    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Dict=sys.maxsize						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            ="""bilinear"""
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =max_size
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =short_edge_length





  def __call__(  self	:   Tuple    ,						lowerCAmelCase	:   List[str]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =[]
     for img in imgs:
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[str]            =img.shape[:2]
        # later: provide list and randomly choose index for resize
        SCREAMING_SNAKE_CASE_:	Dict            =np.random.randint(self.short_edge_length[0]    ,						self.short_edge_length[1] + 1						)
        if size == 0:
           return img
        SCREAMING_SNAKE_CASE_:	Tuple            =size * 1.0 / min(lowerCAmelCase    ,						lowerCAmelCase						)
        if h < w:
           SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Tuple            =size, scale * w
        else:
           SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[Any]            =scale * h, size
        if max(lowerCAmelCase    ,						lowerCAmelCase						) > self.max_size:
           SCREAMING_SNAKE_CASE_:	Tuple            =self.max_size * 1.0 / max(lowerCAmelCase    ,						lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	Union[str, Any]            =newh * scale
           SCREAMING_SNAKE_CASE_:	str            =neww * scale
        SCREAMING_SNAKE_CASE_:	Dict            =int(neww + 0.5						)
        SCREAMING_SNAKE_CASE_:	List[Any]            =int(newh + 0.5						)

        if img.dtype == np.uinta:
           SCREAMING_SNAKE_CASE_:	str            =Image.fromarray(lowerCAmelCase						)
           SCREAMING_SNAKE_CASE_:	List[str]            =pil_image.resize((neww, newh)    ,						PILImageResampling.BILINEAR						)
           SCREAMING_SNAKE_CASE_:	Dict            =np.asarray(lowerCAmelCase						)
        else:
           SCREAMING_SNAKE_CASE_:	str            =img.permute(2    ,						0    ,						1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
           SCREAMING_SNAKE_CASE_:	Optional[Any]            =nn.functional.interpolate(
               lowerCAmelCase    ,						(newh, neww)    ,						mode=self.interp_method    ,						align_corners=lowerCAmelCase						).squeeze(0						)
        img_augs.append(lowerCAmelCase						)

     return img_augs







class    a		:



  def __init__(  self	:   Any    ,						lowerCAmelCase	:   int						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,						cfg.INPUT.MAX_SIZE_TEST						)
     SCREAMING_SNAKE_CASE_:	int            =cfg.INPUT.FORMAT
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =cfg.SIZE_DIVISIBILITY
     SCREAMING_SNAKE_CASE_:	Dict            =cfg.PAD_VALUE
     SCREAMING_SNAKE_CASE_:	List[str]            =cfg.INPUT.MAX_SIZE_TEST
     SCREAMING_SNAKE_CASE_:	Optional[int]            =cfg.MODEL.DEVICE
     SCREAMING_SNAKE_CASE_:	Any            =torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)    ,						1    ,						1						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						)    ,						1    ,						1						)
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =lambda lowerCAmelCase						: (x - self.pixel_mean) / self.pixel_std



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   List[str]						)       ->   Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =tuple(max(lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[im.shape[-2:] for im in images]
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[
         nn.functional.pad(
             lowerCAmelCase    ,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,						value=self.pad_value    ,						)
         for size, im in zip(lowerCAmelCase    ,						lowerCAmelCase						)
     ]

     return torch.stack(lowerCAmelCase						), torch.tensor(lowerCAmelCase						)





  def __call__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Union[str, Any]=False						)       ->   Optional[Any]:


     '''simple docstring'''




     with torch.no_grad():
        if not isinstance(lowerCAmelCase    ,						lowerCAmelCase						):
           SCREAMING_SNAKE_CASE_:	Optional[int]            =[images]
        if single_image:
           assert len(lowerCAmelCase						) == 1
        for i in range(len(lowerCAmelCase						)						):
           if isinstance(images[i]    ,						torch.Tensor						):
              images.insert(lowerCAmelCase    ,						images.pop(lowerCAmelCase						).to(self.device						).float()						)
           elif not isinstance(images[i]    ,						torch.Tensor						):
              images.insert(
                  lowerCAmelCase    ,						torch.as_tensor(img_tensorize(images.pop(lowerCAmelCase						)    ,						input_format=self.input_format						)						)
                  .to(self.device						)
                  .float()    ,						)
            # resize smallest edge
        SCREAMING_SNAKE_CASE_:	int            =torch.tensor([im.shape[:2] for im in images]						)
        SCREAMING_SNAKE_CASE_:	Dict            =self.aug(lowerCAmelCase						)
        # transpose images and convert to torch tensors
        # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
        # now normalize before pad to avoid useless arithmetic
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[self.normalizer(lowerCAmelCase						) for x in images]
        # now pad them to do the following operations
        SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[Any]            =self.pad(lowerCAmelCase						)
        # Normalize

        if self.size_divisibility > 0:
           raise NotImplementedError()
        # pad
        SCREAMING_SNAKE_CASE_:	List[Any]            =torch.true_divide(lowerCAmelCase    ,						lowerCAmelCase						)
        if single_image:
           return images[0], sizes[0], scales_yx[0]
        else:
           return images, sizes, scales_yx







def        __magic_name__			(							lowercase							,     lowercase	):
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes





def        __magic_name__			(							lowercase							,     lowercase	):
   assert torch.isfinite(lowercase	).all(), "Box tensor contains infinite or NaN!"
   SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Optional[int]            =box_size
   tensor[:, 0].clamp_(min=0							,     max=lowercase	)
   tensor[:, 1].clamp_(min=0							,     max=lowercase	)
   tensor[:, 2].clamp_(min=0							,     max=lowercase	)
   tensor[:, 3].clamp_(min=0							,     max=lowercase	)



