






'''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 UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		str     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Union[str, Any]=sys.maxsize		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   Optional[int]				  ='''bilinear'''
       lowercase					:   Optional[Any]				  =max_size
       lowercase					:   List[Any]				  =short_edge_length






      def __call__( self			:		int     ,      UpperCAmelCase			:		Dict		)  ->  Dict:


       '''simple docstring'''

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

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

       return img_augs





class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Tuple     ,      UpperCAmelCase			:		Dict		)  ->  Any:


       '''simple docstring'''

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






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		List[Any]		)  ->  List[Any]:


       '''simple docstring'''

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

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






      def __call__( self			:		List[Any]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Union[str, Any]=False		)  ->  List[Any]:


       '''simple docstring'''

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

        if self.size_divisibility > 0:
         raise NotImplementedError()
        # pad
        lowercase					:   Union[str, Any]				  =torch.true_divide(UpperCAmelCase     ,      UpperCAmelCase		)
        if single_image:
         return images[0], sizes[0], scales_yx[0]
        else:
         return images, sizes, scales_yx





def 							lowercase_				(    __A :       Tuple					,      __A :       Union[str, Any]			)       ->      Optional[int]:



 """simple docstring"""




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





def 							lowercase_				(    __A :       Dict					,      __A :       Tuple[int, int]			)       ->      Union[str, Any]:



 """simple docstring"""




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











'''simple docstring'''



import re


def 							lowercase_				(    __A :       str			)       ->      bool:



 """simple docstring"""




 lowercase					:   Any				  =re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$'''			)
 if match := re.search(__A					,      __A			):
  return match.string == phone
 return False


if __name__ == "__main__":
      print(indian_phone_validator('+918827897895'))



