






'''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     A    :






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=sys.maxsize ):
       UpperCamelCase_				:   int					    =							"""bilinear"""
       UpperCamelCase_				:   int					    =							max_size
       UpperCamelCase_				:   Union[str, Any]					    =							short_edge_length





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

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

       return img_augs






class     A    :






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,     cfg.INPUT.MAX_SIZE_TEST )
       UpperCamelCase_				:   Tuple					    =							cfg.INPUT.FORMAT
       UpperCamelCase_				:   str					    =							cfg.SIZE_DIVISIBILITY
       UpperCamelCase_				:   List[Any]					    =							cfg.PAD_VALUE
       UpperCamelCase_				:   Optional[Any]					    =							cfg.INPUT.MAX_SIZE_TEST
       UpperCamelCase_				:   int					    =							cfg.MODEL.DEVICE
       UpperCamelCase_				:   Optional[int]					    =							torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )			,     1			,     1 )
       UpperCamelCase_				:   List[Any]					    =							torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD )			,     1			,     1 )
       UpperCamelCase_				:   str					    =							lambda __lowerCAmelCase : (x - self.pixel_mean) / self.pixel_std
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							tuple(max(__lowerCAmelCase ) for s in zip(*[img.shape for img in images] ) )
       UpperCamelCase_				:   List[Any]					    =							[im.shape[-2:] for im in images]
       UpperCamelCase_				:   Tuple					    =							[
           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			,     __lowerCAmelCase			,     __lowerCAmelCase=False ):
       with torch.no_grad():
             if not isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   UpperCamelCase_				:   str					    =							[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_    ,			UpperCamelCase_				:   Union[str, Any]					    =							self.pad(__lowerCAmelCase )
             # Normalize

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






def 							snake_case					(     a_							: str    ,   a_							: Dict	)       ->				List[str]:





      """simple docstring"""

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



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: Tuple[int, int]	)       ->				Any:





      """simple docstring"""

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














'''simple docstring'''

import warnings

from .generation import TFGenerationMixin




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 warnings.warn(
     '''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.''',   SCREAMING_SNAKE_CASE__,   )






