"""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      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=sys.maxsize	):


      '''simple docstring'''

      __lowerCamelCase    		=					'bilinear'
      __lowerCamelCase    		=					max_size
      __lowerCamelCase    		=					short_edge_length







   def __call__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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

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

      return img_augs






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

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

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







   def __call__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=False	):


      '''simple docstring'''

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

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



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	int						)  ->							int:
   boxes[:, 0::2] *= scale_yx[:, 1]
   boxes[:, 1::2] *= scale_yx[:, 0]
   return boxes



def       A__    (	lowercase_     :	Tuple      , lowercase_     :	Tuple[int, int]						)  ->							str:
   assert torch.isfinite(lowercase_						).all(), "Box tensor contains infinite or NaN!"
   __lowerCamelCase		,__lowerCamelCase    		=					box_size
   tensor[:, 0].clamp_(min=0      , max=lowercase_						)
   tensor[:, 1].clamp_(min=0      , max=lowercase_						)
   tensor[:, 2].clamp_(min=0      , max=lowercase_						)
   tensor[:, 3].clamp_(min=0      , max=lowercase_						)













import warnings

from .generation import GenerationMixin
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						# warning at import time
						warnings.warn(
						    "Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will "
						    "be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."							,					snake_case_							,					)

