


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      __lowerCamelCase       :







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






            '''simple docstring'''




            __UpperCamelCase      =       'bilinear'
            __UpperCamelCase      =       max_size
            __UpperCamelCase      =       short_edge_length







       def __call__(       self:      Tuple,A_:      int ):






            '''simple docstring'''




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

                 if img.dtype == np.uinta:
                      __UpperCamelCase      =       Image.fromarray(_snake_case )
                      __UpperCamelCase      =       pil_image.resize((neww, newh),PILImageResampling.BILINEAR )
                      __UpperCamelCase      =       np.asarray(_snake_case )
                 else:
                      __UpperCamelCase      =       img.permute(2,0,1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
                      __UpperCamelCase      =       nn.functional.interpolate(
                          _snake_case,(newh, neww),mode=self.interp_method,align_corners=_snake_case ).squeeze(0 )
                 img_augs.append(_snake_case )

            return img_augs



class      __lowerCamelCase       :







       def __init__(       self:      Tuple,A_:      int ):






            '''simple docstring'''




            __UpperCamelCase      =       ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],cfg.INPUT.MAX_SIZE_TEST )
            __UpperCamelCase      =       cfg.INPUT.FORMAT
            __UpperCamelCase      =       cfg.SIZE_DIVISIBILITY
            __UpperCamelCase      =       cfg.PAD_VALUE
            __UpperCamelCase      =       cfg.INPUT.MAX_SIZE_TEST
            __UpperCamelCase      =       cfg.MODEL.DEVICE
            __UpperCamelCase      =       torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ),1,1 )
            __UpperCamelCase      =       torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ),1,1 )
            __UpperCamelCase      =       lambda A_ : (x - self.pixel_mean) / self.pixel_std







       def 						snake_case_     (       self:      str,A_:      List[str] ):






            '''simple docstring'''




            __UpperCamelCase      =       tuple(max(_snake_case ) for s in zip(*[img.shape for img in images] ) )
            __UpperCamelCase      =       [im.shape[-2:] for im in images]
            __UpperCamelCase      =       [
                nn.functional.pad(
                    _snake_case,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],value=self.pad_value,)
                for size, im in zip(_snake_case,_snake_case )
            ]

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







       def __call__(       self:      str,A_:      List[str],A_:      int=False ):






            '''simple docstring'''




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

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






def 						_A						(						_lowercase ,   _lowercase      ) ->      List[Any]:


     """simple docstring"""


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






def 						_A						(						_lowercase ,   _lowercase      ) ->      Tuple:


     """simple docstring"""


     assert torch.isfinite(__A      ).all(), "Box tensor contains infinite or NaN!"
     __UpperCamelCase,  __UpperCamelCase      =       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      )








from datetime import datetime

import requests


def    A						(__A   :    str       )   ->      bytes:







     """simple docstring"""


     UpperCAmelCase_          =							'''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
     UpperCAmelCase_          =							requests.get(base_url + url       ).json()[0]['''urls'''][0]['''src''']
     return requests.get(__A       ).content


if __name__ == "__main__":
    snake_case_ : Optional[Any]    		=       input("Enter Video/IGTV url: ").strip()
    snake_case_ : Any    		=       f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
    with open(file_name, "wb") as fp:
        fp.write(download_video(url))
    print(f"Done. Video saved to disk as {file_name}.")






