"""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							:






     def __init__(		self				,     a__				,     a__=sys.maxsize  ):
       _lowerCAmelCase						:       str  			=     """bilinear"""
       _lowerCAmelCase						:       List[str]  			=     max_size
       _lowerCAmelCase						:       Optional[Any]  			=     short_edge_length






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

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

       return img_augs







class 						__A							:






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






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     tuple(max(a__  ) for s in zip(*[img.shape for img in images]  )  )
       _lowerCAmelCase						:       Optional[Any]  			=     [im.shape[-2:] for im in images]
       _lowerCAmelCase						:       Optional[Any]  			=     [
           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				,     a__				,     a__=False  ):
       with torch.no_grad():
         if not isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       Optional[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						:       Union[str, Any]  			=     torch.tensor([im.shape[:2] for im in images]  )
         _lowerCAmelCase						:       Optional[int]  			=     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						:       List[Any]  			=     [self.normalizer(a__  ) for x in images]
         # now pad them to do the following operations
         _lowerCAmelCase			,	_lowerCAmelCase						:       Any  			=     self.pad(a__  )
         # Normalize

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







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Any	)       ->      Dict:
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Tuple[int, int]	)       ->      Tuple:
  assert torch.isfinite(_lowerCamelCase	).all(), "Box tensor contains infinite or NaN!"
  _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     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	)








from argparse import ArgumentParser

from .env import EnvironmentCommand






def 							__lowercase   ( ):
							a__             =     ArgumentParser('Diffusers CLI tool'					,    usage='diffusers-cli <command> [<args>]'   )
							a__             =     parser.add_subparsers(help='diffusers-cli command helpers'   )

							# Register commands
							EnvironmentCommand.register_subcommand(__lowerCAmelCase   )

							# Let's go
							a__             =     parser.parse_args()

							if not hasattr(__lowerCAmelCase					,    'func'   ):
														parser.print_help()
														exit(1   )

							# Run
							a__             =     args.func(__lowerCAmelCase   )
							service.run()


if __name__ == "__main__":
						main()




