






'''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	:




       def __init__(					self      : Any	,    A__      : List[str]	,    A__      : List[Any]=sys.maxsize					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				"""bilinear"""
            __lowerCamelCase						:       Optional[int]				   =				max_size
            __lowerCamelCase						:       List[str]				   =				short_edge_length




       def __call__(					self      : Optional[Any]	,    A__      : Any					):


            """simple docstring"""







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

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

            return img_augs







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : List[Any]	,    A__      : Any					):


            """simple docstring"""







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




       def 					a_					(					self      : Optional[int]	,    A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				tuple(max(A__					) for s in zip(*[img.shape for img in images]					)					)
            __lowerCamelCase						:       Tuple				   =				[im.shape[-2:] for im in images]
            __lowerCamelCase						:       Dict				   =				[
                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      : Tuple	,    A__      : Optional[Any]	,    A__      : Optional[Any]=False					):


            """simple docstring"""







            with torch.no_grad():
                 if not isinstance(A__	,    A__					):
                      __lowerCamelCase						:       List[Any]				   =				[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
                 __lowerCamelCase						:       str				   =				torch.tensor([im.shape[:2] for im in images]					)
                 __lowerCamelCase						:       str				   =				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
                 __lowerCamelCase						:       Optional[Any]				   =				[self.normalizer(A__					) for x in images]
                 # now pad them to do the following operations
                 __lowerCamelCase   ,      __lowerCamelCase						:       Optional[int]				   =				self.pad(A__					)
                 # Normalize

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

def     __lowercase  (_lowercase,						_lowercase     )   ->     Dict:


     """simple docstring"""
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes
def     __lowercase  (_lowercase,						_lowercase     )   ->     List[str]:


     """simple docstring"""
     assert torch.isfinite(_lowercase     ).all(), "Box tensor contains infinite or NaN!"
     __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				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     )











'''simple docstring'''

from ..utils import DummyObject, requires_backends







class 	SCREAMING_SNAKE_CASE	(      metaclass=lowerCAmelCase_  ):
       snake_case__    :     Union[str, Any]   		=			['torch', 'torchsde']




       def __init__(					self      : int	,    *A__      : Optional[Any]	,    **A__      : Dict					):


            """simple docstring"""







            requires_backends(self	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Optional[Any]	,    *A__      : str	,    **A__      : Optional[Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)




       @classmethod
       def 					a_					(					cls      : Union[str, Any]	,    *A__      : Any	,    **A__      : Union[str, Any]					):


            """simple docstring"""







            requires_backends(cls	,    ["""torch""", """torchsde"""]					)



