




'''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   _snake_case:






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






    """simple docstring"""


    A__         =    'bilinear'
    A__         =    max_size
    A__         =    short_edge_length






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






    """simple docstring"""


    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(a						,		a						)
       if h < w:
          A__     , A__         =    size, scale * w
       else:
          A__     , A__         =    scale * h, size
       if max(a						,		a						) > self.max_size:
          A__         =    self.max_size * 1.0 / max(a						,		a						)
          A__         =    newh * scale
          A__         =    neww * scale
       A__         =    int(neww + 0.5						)
       A__         =    int(newh + 0.5						)

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

    return img_augs




class   _snake_case:






 def __init__(self					:     Union[str, Any]						,		a					:     Any						)					->      str:






    """simple docstring"""


    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 a						: (x - self.pixel_mean) / self.pixel_std






 def 	_UpperCamelCase   (self					:     List[str]						,		a					:     Tuple						)					->      List[str]:






    """simple docstring"""


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

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






 def __call__(self					:     Dict						,		a					:     int						,		a					:     List[str]=False						)					->      Any:






    """simple docstring"""


    with torch.no_grad():
       if not isinstance(a						,		a						):
          A__         =    [images]
       if single_image:
          assert len(a						) == 1
       for i in range(len(a						)						):
          if isinstance(images[i]						,		torch.Tensor						):
             images.insert(a						,		images.pop(a						).to(self.device						).float()						)
          elif not isinstance(images[i]						,		torch.Tensor						):
             images.insert(
                 a						,		torch.as_tensor(img_tensorize(images.pop(a						)						,		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(a						)
       # 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(a						) for x in images]
       # now pad them to do the following operations
       A__     , A__         =    self.pad(a						)
       # Normalize

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







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







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







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







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








'''simple docstring'''







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase_    	=							2 << 2_0  # 2mb
lowerCAmelCase_    	=							get_logger(__name__)

class   _snake_case( UpperCAmelCase			):






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    self._options.get_all_options().get('runner'						)
    return runner in [None, "DirectRunner", "PortableRunner"]







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=False	):






   '''simple docstring'''







   A__         =    FileSystems
   if fs.exists(UpperCAmelCase	):
      if force_upload:
         logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
      else:
         logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
         return
   with fs.create(UpperCAmelCase	) as remote_file:
      with open(UpperCAmelCase			,'rb'	) as local_file:
         A__         =    local_file.read(UpperCAmelCase	)
         while chunk:
            remote_file.write(UpperCAmelCase	)
            A__         =    local_file.read(UpperCAmelCase	)







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=False	):






   '''simple docstring'''







   A__         =    FileSystems
   if os.path.exists(UpperCAmelCase	):
      if force_download:
         logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
      else:
         logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
         return
   with fs.open(UpperCAmelCase	) as remote_file:
      with open(UpperCAmelCase			,'wb'	) as local_file:
         A__         =    remote_file.read(UpperCAmelCase	)
         while chunk:
            local_file.write(UpperCAmelCase	)
            A__         =    remote_file.read(UpperCAmelCase	)


