




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:






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=sys.maxsize    )					->						Optional[int]:
         UpperCAmelCase_     :				Union[str, Any]  =     '''bilinear'''
         UpperCAmelCase_     :				Union[str, Any]  =     max_size
         UpperCAmelCase_     :				int  =     short_edge_length





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

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

         return img_augs


class     __a:






    """simple docstring"""





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





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE    )					->						int:
         UpperCAmelCase_     :				List[Any]  =     tuple(max(_SCREAMING_SNAKE_CASE    ) for s in zip(*[img.shape for img in images]    )    )
         UpperCAmelCase_     :				List[Any]  =     [im.shape[-2:] for im in images]
         UpperCAmelCase_     :				Dict  =     [
             nn.functional.pad(
                 _SCREAMING_SNAKE_CASE  ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]  ,value=self.pad_value  ,)
             for size, im in zip(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         ]

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





    def __call__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=False    )					->						List[Any]:
         with torch.no_grad():
              if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                   UpperCAmelCase_     :				List[str]  =     [images]
              if single_image:
                   assert len(_SCREAMING_SNAKE_CASE    ) == 1
              for i in range(len(_SCREAMING_SNAKE_CASE    )    ):
                   if isinstance(images[i]  ,torch.Tensor    ):
                        images.insert(_SCREAMING_SNAKE_CASE  ,images.pop(_SCREAMING_SNAKE_CASE    ).to(self.device    ).float()    )
                   elif not isinstance(images[i]  ,torch.Tensor    ):
                        images.insert(
                            _SCREAMING_SNAKE_CASE  ,torch.as_tensor(img_tensorize(images.pop(_SCREAMING_SNAKE_CASE    )  ,input_format=self.input_format    )    )
                            .to(self.device    )
                            .float()  ,)
            # resize smallest edge
              UpperCAmelCase_     :				Optional[int]  =     torch.tensor([im.shape[:2] for im in images]    )
              UpperCAmelCase_     :				Dict  =     self.aug(_SCREAMING_SNAKE_CASE    )
              # 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
              UpperCAmelCase_     :				List[Any]  =     [self.normalizer(_SCREAMING_SNAKE_CASE    ) for x in images]
              # now pad them to do the following operations
              UpperCAmelCase_,       UpperCAmelCase_     :				Any  =     self.pad(_SCREAMING_SNAKE_CASE    )
              # Normalize

              if self.size_divisibility > 0:
                   raise NotImplementedError()
              # pad
              UpperCAmelCase_     :				Tuple  =     torch.true_divide(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
              if single_image:
                   return images[0], sizes[0], scales_yx[0]
              else:
                   return images, sizes, scales_yx







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     assert torch.isfinite(SCREAMING_SNAKE_CASE__ ).all(), "Box tensor contains infinite or NaN!"
     UpperCAmelCase_,       UpperCAmelCase_     :				str  =     box_size
     tensor[:, 0].clamp_(min=0					,		max=SCREAMING_SNAKE_CASE__ )
     tensor[:, 1].clamp_(min=0					,		max=SCREAMING_SNAKE_CASE__ )
     tensor[:, 2].clamp_(min=0					,		max=SCREAMING_SNAKE_CASE__ )
     tensor[:, 3].clamp_(min=0					,		max=SCREAMING_SNAKE_CASE__ )







'''simple docstring'''



from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__UpperCamelCase   :							Optional[int]         =   Lock()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[Any]     )	->      int:

   """simple docstring"""
   global process_lock

   # we perform n swaps since after n swaps we know we are sorted
   # we *could* stop early if we are sorted already, but it takes as long to
   # find out we are sorted as it does to sort the list with this algorithm
   for i in range(0,       10     ):
      if (i + position) % 2 == 0 and r_send is not None:
         # send your value to your right neighbor
         process_lock.acquire()
         r_send[1].send(SCREAMING_SNAKE_CASE__     )
         process_lock.release()

         # receive your right neighbor's value
         process_lock.acquire()
         __a					  =     rr_cv[0].recv()
         process_lock.release()

         # take the lower value since you are on the left
         __a					  =     min(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
      elif (i + position) % 2 != 0 and l_send is not None:
         # send your value to your left neighbor
         process_lock.acquire()
         l_send[1].send(SCREAMING_SNAKE_CASE__     )
         process_lock.release()

         # receive your left neighbor's value
         process_lock.acquire()
         __a					  =     lr_cv[0].recv()
         process_lock.release()

         # take the higher value since you are on the right
         __a					  =     max(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
    # after all swaps are performed, send the values back to main
   result_pipe[1].send(SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     []
   __a					  =     []
   # initialize the list of pipes where the values will be retrieved
   for _ in arr:
      result_pipe.append(Pipe()     )
   # creates the processes
   # the first and last process only have one neighbor so they are made outside
   # of the loop
   __a					  =     Pipe()
   __a					  =     Pipe()
   process_array_.append(
       Process(
           target=SCREAMING_SNAKE_CASE__,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),       )     )
   __a					  =     temp_rs
   __a					  =     temp_rr

   for i in range(1,       len(SCREAMING_SNAKE_CASE__     ) - 1     ):
      __a					  =     Pipe()
      __a					  =     Pipe()
      process_array_.append(
          Process(
              target=SCREAMING_SNAKE_CASE__,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),       )     )
      __a					  =     temp_rs
      __a					  =     temp_rr

   process_array_.append(
       Process(
           target=SCREAMING_SNAKE_CASE__,       args=(
               len(SCREAMING_SNAKE_CASE__     ) - 1,
               arr[len(SCREAMING_SNAKE_CASE__     ) - 1],
               temp_ls,
               None,
               temp_lr,
               None,
               result_pipe[len(SCREAMING_SNAKE_CASE__     ) - 1],
           ),       )     )

   # start the processes
   for p in process_array_:
      p.start()

   # wait for the processes to end and write their values to the list
   for p in range(0,       len(SCREAMING_SNAKE_CASE__     )     ):
      __a					  =     result_pipe[p][0].recv()
      process_array_[p].join()
   return arr



def  __UpperCAmelCase						(					)	->      Any:

   """simple docstring"""
   __a					  =     list(range(10,       0,       -1     )     )
   print('Initial List'     )
   print(*SCREAMING_SNAKE_CASE__     )
   __a					  =     odd_even_transposition(SCREAMING_SNAKE_CASE__     )
   print('Sorted List\n'     )
   print(*SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()