




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 :  str						,							A_ :  Union[str, Any]						,							A_ :  int=sys.maxsize  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   'bilinear'
       lowerCamelCase_							=   max_size
       lowerCamelCase_							=   short_edge_length





 def __call__(    self :  List[str]						,							A_ :  Dict  )       ->     Dict:

       """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(A_						,							A_  )
             if h < w:
                   lowerCamelCase_					,    lowerCamelCase_							=   size, scale * w
             else:
                   lowerCamelCase_					,    lowerCamelCase_							=   scale * h, size
             if max(A_						,							A_  ) > self.max_size:
                   lowerCamelCase_							=   self.max_size * 1.0 / max(A_						,							A_  )
                   lowerCamelCase_							=   newh * scale
                   lowerCamelCase_							=   neww * scale
             lowerCamelCase_							=   int(neww + 0.5  )
             lowerCamelCase_							=   int(newh + 0.5  )

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

       return img_augs







class   A:



 '''simple docstring'''


 def __init__(    self :  int						,							A_ :  int  )       ->     Dict:

       """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 A_  : (x - self.pixel_mean) / self.pixel_std

 def     a__			(    self :  Any						,							A_ :  List[str]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   tuple(max(A_  ) for s in zip(*[img.shape for img in images]  )  )
       lowerCamelCase_							=   [im.shape[-2:] for im in images]
       lowerCamelCase_							=   [
           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 :  str						,							A_ :  Optional[int]						,							A_ :  str=False  )       ->     List[str]:

       """simple docstring"""






       with torch.no_grad():
             if not isinstance(A_						,							A_  ):
                   lowerCamelCase_							=   [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_							=   torch.tensor([im.shape[:2] for im in images]  )
             lowerCamelCase_							=   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_							=   [self.normalizer(A_  ) for x in images]
             # now pad them to do the following operations
             lowerCamelCase_					,    lowerCamelCase_							=   self.pad(A_  )
             # Normalize

             if self.size_divisibility > 0:
                   raise NotImplementedError()
             # pad
             lowerCamelCase_							=   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      (							lowercase     :	int     ,							lowercase     :	int					):


      '''simple docstring'''



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






def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict     ,							lowercase     :	Tuple[int, int]					):


      '''simple docstring'''



      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 os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase    :		int			   =       "1"
lowerCamelCase    :		int			   =       "0"
lowerCamelCase    :		Union[str, Any]			   =       "1"

lowerCamelCase    :		List[Any]			   =       ort.SessionOptions()
lowerCamelCase    :		Optional[Any]			   =       ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
lowerCamelCase    :		Union[str, Any]			   =       ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
lowerCamelCase    :		Tuple			   =       ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
lowerCamelCase    :		List[Any]			   =       ort.RunOptions()

lowerCamelCase    :		List[str]			   =       128
lowerCamelCase    :		List[Any]			   =       1
lowerCamelCase    :		Union[str, Any]			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Dict			   =       np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase    :		Optional[Any]			   =       np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
lowerCamelCase    :		int			   =       time.time()
lowerCamelCase    :		Dict			   =       2_000
lowerCamelCase    :		Any			   =       {}
for iter in range(max_iters):
 lowerCamelCase    :		Union[str, Any]			   =       sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1_000 / max_iters))



