"""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   :    List[Any]						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Any=sys.maxsize     )      -> Dict:
      __a			      =						"""bilinear"""
      __a			      =						max_size
      __a			      =						short_edge_length



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

          if img.dtype == np.uinta:
              __a			      =						Image.fromarray(lowerCamelCase_     )
              __a			      =						pil_image.resize((neww, newh)						,  PILImageResampling.BILINEAR     )
              __a			      =						np.asarray(lowerCamelCase_     )
          else:
              __a			      =						img.permute(2						,  0						,  1     ).unsqueeze(0     )  # 3, 0, 1)  # hw(c) -> nchw
              __a			      =						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   :    List[Any]						,  lowerCamelCase_   :    Any     )      -> int:
      __a			      =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]						,  cfg.INPUT.MAX_SIZE_TEST     )
      __a			      =						cfg.INPUT.FORMAT
      __a			      =						cfg.SIZE_DIVISIBILITY
      __a			      =						cfg.PAD_VALUE
      __a			      =						cfg.INPUT.MAX_SIZE_TEST
      __a			      =						cfg.MODEL.DEVICE
      __a			      =						torch.tensor(cfg.MODEL.PIXEL_STD     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,  1						,  1     )
      __a			      =						torch.tensor(cfg.MODEL.PIXEL_MEAN     ).to(self.device     ).view(len(cfg.MODEL.PIXEL_STD     )						,  1						,  1     )
      __a			      =						lambda lowerCamelCase_     : (x - self.pixel_mean) / self.pixel_std



  def        lowerCAmelCase_				(		self   :    Dict						,  lowerCamelCase_   :    Tuple     )      -> Any:
      __a			      =						tuple(max(lowerCamelCase_     ) for s in zip(*[img.shape for img in images]     )     )
      __a			      =						[im.shape[-2:] for im in images]
      __a			      =						[
          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   :    Tuple						,  lowerCamelCase_   :    Union[str, Any]						,  lowerCamelCase_   :    Any=False     )      -> Any:
      with torch.no_grad():
          if not isinstance(lowerCamelCase_						,  lowerCamelCase_     ):
              __a			      =						[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
          __a			      =						torch.tensor([im.shape[:2] for im in images]     )
          __a			      =						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
          __a			      =						[self.normalizer(lowerCamelCase_     ) for x in images]
          # now pad them to do the following operations
          __a      ,  __a			      =						self.pad(lowerCamelCase_     )
          # Normalize

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

def 						UpperCamelCase				(	_lowerCAmelCase   :							Optional[int]				, _lowerCAmelCase   :							Any						):
    boxes[:, 0::2] *= scale_yx[:, 1]
    boxes[:, 1::2] *= scale_yx[:, 0]
    return boxes






def 						UpperCamelCase				(	_lowerCAmelCase   :							List[Any]				, _lowerCAmelCase   :							Tuple[int, int]						):
    assert torch.isfinite(_lowerCAmelCase						).all(), "Box tensor contains infinite or NaN!"
    __a      ,  __a			      =						box_size
    tensor[:, 0].clamp_(min=0				, max=_lowerCAmelCase						)
    tensor[:, 1].clamp_(min=0				, max=_lowerCAmelCase						)
    tensor[:, 2].clamp_(min=0				, max=_lowerCAmelCase						)
    tensor[:, 3].clamp_(min=0				, max=_lowerCAmelCase						)

"""simple docstring"""


def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Tuple						):
    __a			      =						len(_lowerCAmelCase						)
    # Create a stack and push index of fist element to it
    __a			      =						[]
    st.append(0						)

    # Span value of first element is always 1
    __a			      =						1

    # Calculate span values for rest of the elements
    for i in range(1				, _lowerCAmelCase						):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(_lowerCAmelCase						) > 0 and price[st[0]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        __a			      =						i + 1 if len(_lowerCAmelCase						) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Optional[Any]						):
    for i in range(0				, _lowerCAmelCase						):
        print(arr[i]				, end=""" """						)


# Driver program to test above function
__A       			=      [10, 4, 5, 90, 1_20, 80]
__A       			=      [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)






print_array(S, len(price))
