
"""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 		UpperCAmelCase_	:
	"""simple docstring"""




	def __init__( self       : List[Any]     ,   a_       : Dict     ,   a_       : List[Any]=sys.maxsize )->					List[str]:



						"""simple docstring"""

						UpperCAmelCase_						:    str    					=  """bilinear"""
						UpperCAmelCase_						:    Dict    					=  max_size
						UpperCAmelCase_						:    List[str]    					=  short_edge_length






	def __call__( self       : int     ,   a_       : Union[str, Any] )->					List[Any]:



						"""simple docstring"""

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

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

						return img_augs



class 		UpperCAmelCase_	:
	"""simple docstring"""




	def __init__( self       : Dict     ,   a_       : Tuple )->					int:



						"""simple docstring"""

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




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



						"""simple docstring"""

						UpperCAmelCase_						:    int    					=  tuple(max(_lowerCAmelCase ) for s in zip(*[img.shape for img in images] ) )
						UpperCAmelCase_						:    List[Any]    					=  [im.shape[-2:] for im in images]
						UpperCAmelCase_						:    Dict    					=  [
						    nn.functional.pad(
						        _lowerCAmelCase     ,   [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]     ,   value=self.pad_value     ,   )
						    for size, im in zip(_lowerCAmelCase     ,   _lowerCAmelCase )
						]

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






	def __call__( self       : Optional[Any]     ,   a_       : str     ,   a_       : Tuple=False )->					List[str]:



						"""simple docstring"""

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

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




def 			A_			(					lowercase						, lowercase      )     ->							Optional[int]:


					"""simple docstring"""







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




def 			A_			(					lowercase						, lowercase      )     ->							List[Any]:


					"""simple docstring"""







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



"""simple docstring"""







import warnings

from .generation import GenerationMixin



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 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."""  , lowerCamelCase_  , )

