code: 
  
  
  
  
  
  
  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_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=sys.maxsize		):
            UpperCAmelCase__			:     str										= """bilinear"""
            UpperCAmelCase__			:     Dict										= max_size
            UpperCAmelCase__			:     List[str]										= short_edge_length
  
  
  
  
       def __call__(							self  ,	_lowerCAmelCase		):
            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_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase		):
            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 _lowerCAmelCase		: (x - self.pixel_mean) / self.pixel_std
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            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  ,	_lowerCAmelCase  ,	_lowerCAmelCase=False		):
            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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:
  
       '''simple docstring'''
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes
  
  
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> 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					)
  
  
  
  
style_context: 
  
  
  
  
  
  
  import collections
  
  from .utils import ExplicitEnum, is_torch_available, logging
  
  
  if is_torch_available():
        import torch
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							logging.get_logger(__name__)
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     :
  
  
  
  
       def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=21  ,	_lowerCAmelCase=[]  ,	_lowerCAmelCase=None		):
            UpperCAmelCase__			:     Union[str, Any]										= model
            UpperCAmelCase__			:     Union[str, Any]										= trace_batch_nums
            UpperCAmelCase__			:     Optional[Any]										= abort_after_batch_num
  
            # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
            UpperCAmelCase__			:     int										= collections.deque([]  ,	_lowerCAmelCase		)
            UpperCAmelCase__			:     str										= []
            UpperCAmelCase__			:     Optional[int]										= 0
            UpperCAmelCase__			:     int										= 0
            UpperCAmelCase__			:     int										= False
            UpperCAmelCase__			:     List[str]										= """                 """
  
            self.analyse_model()
  
            self.register_forward_hook()
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase=None		):
            if frame is not None:
                 self.expand_frame(_lowerCAmelCase		)
            self.frames.append("""\n""".join(self.frame		)		)
            UpperCAmelCase__			:     str										= []  # start a new frame
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            self.frame.append(_lowerCAmelCase		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            print("""\n""".join(self.frames		)		)
            UpperCAmelCase__			:     List[Any]										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            UpperCAmelCase__			:     Optional[int]										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            print(f"\nDetected inf/nan during batch_number={self.batch_number}"		)
            print(f"Last {len(self.frames		)} forward frames:"		)
            print(f"{'abs min':8} {'abs max':8} metadata"		)
            print("""\n""".join(self.frames		)		)
            print("""\n\n"""		)
            UpperCAmelCase__			:     Any										= []
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            # extract the fully qualified module names, to be able to report at run time. e.g.:
            # encoder.block.2.layer.0.SelfAttention.o
            #
            # for shared weights only the first shared module name will be registered
            UpperCAmelCase__			:     str										= {m: name for name, m in self.model.named_modules()}
            # self.longest_module_name = max(len(v) for v in self.module_names.values())
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            if torch.is_tensor(_lowerCAmelCase		):
                 self.expand_frame(get_abs_min_max(_lowerCAmelCase  ,	_lowerCAmelCase		)		)
                 if detect_overflow(_lowerCAmelCase  ,	_lowerCAmelCase		):
                      UpperCAmelCase__			:     Optional[int]										= True
            elif var is None:
                 self.expand_frame(f"{'None':>17} {ctx}"		)
            else:
                 self.expand_frame(f"{'not a tensor':>17} {ctx}"		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.expand_frame(f"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"		)
            self.expand_frame(f"{'abs min':8} {'abs max':8} metadata"		)
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"		)
  
            # params
            for name, p in module.named_parameters(recurse=_lowerCAmelCase		):
                 self.analyse_variable(_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            # inputs
            if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                 for i, x in enumerate(_lowerCAmelCase		):
                      self.analyse_variable(_lowerCAmelCase  ,	f"input[{i}]"		)
            else:
                 self.analyse_variable(_lowerCAmelCase  ,	"""input"""		)
  
            # outputs
            if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                 for i, x in enumerate(_lowerCAmelCase		):
                      # possibly a tuple of tuples
                      if isinstance(_lowerCAmelCase  ,	_lowerCAmelCase		):
                           for j, y in enumerate(_lowerCAmelCase		):
                                self.analyse_variable(_lowerCAmelCase  ,	f"output[{i}][{j}]"		)
                      else:
                           self.analyse_variable(_lowerCAmelCase  ,	f"output[{i}]"		)
            else:
                 self.analyse_variable(_lowerCAmelCase  ,	"""output"""		)
  
            self.save_frame()
  
  
  
  
       def      __UpperCAmelCase     (							self		):
            self.model.apply(self._register_forward_hook		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
            module.register_forward_hook(self.forward_hook		)
  
  
  
  
       def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		):
            # - input is a tuple of packed inputs (could be non-Tensors)
            # - output could be a Tensor or a tuple of Tensors and non-Tensors
  
            UpperCAmelCase__			:     Union[str, Any]										= False
  
            UpperCAmelCase__			:     List[str]										= True if self.batch_number in self.trace_batch_nums else False
            if trace_mode:
                 self.reset_saved_frames()
  
            if self.total_calls == 0:
                 self.batch_start_frame()
            self.total_calls += 1
  
            # count batch numbers - the very first forward hook of the batch will be called when the
            # batch completes - i.e. it gets called very last - we know this batch has finished
            if module == self.model:
                 self.batch_number += 1
                 UpperCAmelCase__			:     Dict										= True
  
            self.create_frame(_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase		)
  
            # if last_frame_of_batch:
            #     self.batch_end_frame()
  
            if trace_mode:
                 self.trace_frames()
  
            if last_frame_of_batch:
                 self.batch_start_frame()
  
            if self.detected_overflow and not trace_mode:
                 self.dump_saved_frames()
  
                 # now we can abort, as it's pointless to continue running
                 raise ValueError(
                     """DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. """
                     """Please scroll up above this traceback to see the activation values prior to this event."""		)
  
            # abort after certain batch if requested to do so
            if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
                 raise ValueError(
                     f"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
                     f" `abort_after_batch_num={self.abort_after_batch_num}` arg"		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[Any]:
  
       '''simple docstring'''
       UpperCAmelCase__			:     Dict										= var.abs()
       return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> int:
  
       '''simple docstring'''
       UpperCAmelCase__			:     str										= False
       if torch.isnan(__lowerCamelCase					).any().item():
            UpperCAmelCase__			:     List[Any]										= True
            print(F"{ctx} has nans"					)
       if torch.isinf(__lowerCamelCase					).any().item():
            UpperCAmelCase__			:     List[str]										= True
            print(F"{ctx} has infs"					)
  
       # if needed to monitor large elements can enable the following
       if 0:  # and detected:
            UpperCAmelCase__			:     Dict										= var[torch.ge(var.abs()   ,       100					)]
            if naaa.numel() > 0:
                 print(F"{ctx}:  n100={naaa.numel()}"					)
            UpperCAmelCase__			:     Tuple										= var[torch.ge(var.abs()   ,       1000					)]
            if naaaa.numel() > 0:
                 print(F"{ctx}: n1000={naaaa.numel()}"					)
            UpperCAmelCase__			:     Optional[int]										= var[torch.ge(var.abs()   ,       1_0000					)]
            if naaaaa.numel() > 0:
                 print(F"{ctx}: n10000={naaaaa.numel()}"					)
  
       if 0:
            print(F"min={var.min():9.2e} max={var.max():9.2e}"					)
  
       if 0:
            print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"					)
  
       return detected
  
  
  
  
  
  
  
  class 							UpperCAmelCase_     (		__lowerCamelCase		):
       __lowerCamelCase										=     'underflow_overflow'
       __lowerCamelCase										=     'tpu_metrics_debug'
  
  
  
  
  
  
  