

"""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        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_=sys.maxsize       ):
      __magic_name__     =      '''bilinear'''
      __magic_name__     =      max_size
      __magic_name__     =      short_edge_length







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

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

      return img_augs


class        _lowercase					:







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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      tuple(max(UpperCamelCase_       ) for s in zip(*[img.shape for img in images]       )       )
      __magic_name__     =      [im.shape[-2:] for im in images]
      __magic_name__     =      [
          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 ,						UpperCamelCase_ ,						UpperCamelCase_=False       ):
      with torch.no_grad():
       if not isinstance(UpperCamelCase_ ,						UpperCamelCase_       ):
        __magic_name__     =      [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
       __magic_name__     =      torch.tensor([im.shape[:2] for im in images]       )
       __magic_name__     =      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
       __magic_name__     =      [self.normalizer(UpperCamelCase_       ) for x in images]
       # now pad them to do the following operations
       __magic_name__   ,						__magic_name__     =      self.pad(UpperCamelCase_       )
       # Normalize

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[str]:
 boxes[:, 0::2] *= scale_yx[:, 1]
 boxes[:, 1::2] *= scale_yx[:, 0]
 return boxes





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      int:
 assert torch.isfinite(__UpperCamelCase						).all(), "Box tensor contains infinite or NaN!"
 __magic_name__   ,						__magic_name__     =      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 pytest

import datasets


# Import fixture modules as plugins
__lowerCamelCase										=		["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 # Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
 for item in items:
  if any(marker in item.keywords for marker in ['''integration''', '''unit''']						):
   continue
  item.add_marker(pytest.mark.unit						)





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 config.addinivalue_line('''markers'''      ,   '''torchaudio_latest: mark test to run with torchaudio>=0.12'''						)





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Dict:
 # test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
 __magic_name__     =      tmp_path_factory.getbasetemp() / '''cache'''
 __magic_name__     =      test_hf_cache_home / '''datasets'''
 __magic_name__     =      test_hf_cache_home / '''metrics'''
 __magic_name__     =      test_hf_cache_home / '''modules'''
 monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''      ,   str(__UpperCamelCase						)						)
 monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads'''
 monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)
 __magic_name__     =      test_hf_datasets_cache / '''downloads''' / '''extracted'''
 monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''      ,   str(__UpperCamelCase						)						)





@pytest.fixture(autouse=__UpperCamelCase      ,   scope='''session'''						)
def 	lowercase      (   ) ->      Any:
 datasets.disable_progress_bar()





@pytest.fixture(autouse=__UpperCamelCase						)
def 	lowercase      (   __UpperCamelCase						) ->      str:
 # don't take tests into account when counting downloads
 monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''      ,   __UpperCamelCase						)





@pytest.fixture
def 	lowercase      (   __UpperCamelCase						) ->      int:
 # Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
 # To be removed once SQLAlchemy 2.0 supported
 monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''      ,   __UpperCamelCase						)

