






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


__a					:				str      =      logging.get_logger(__name__)


class 					__lowercase					:







     '''simple docstring'''




     def __init__( self				:	Dict       ,		UpperCamelCase_				:	Optional[int]       ,		UpperCamelCase_				:	Dict=21       ,		UpperCamelCase_				:	Dict=[]       ,		UpperCamelCase_				:	List[str]=None ):


         """simple docstring"""



         __A   			=		model
         __A   			=		trace_batch_nums
         __A   			=		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
         __A   			=		collections.deque([]       ,		lowercase_ )
         __A   			=		[]
         __A   			=		0
         __A   			=		0
         __A   			=		False
         __A   			=		"""                 """

         self.analyse_model()

         self.register_forward_hook()




     def 						lowerCAmelCase_   ( self				:	Union[str, Any]       ,		UpperCamelCase_				:	Union[str, Any]=None ):


         """simple docstring"""



         if frame is not None:
             self.expand_frame(lowercase_ )
         self.frames.append("""\n""".join(self.frame ) )
         __A   			=		[]  # start a new frame




     def 						lowerCAmelCase_   ( self				:	str       ,		UpperCamelCase_				:	Tuple ):


         """simple docstring"""



         self.frame.append(lowercase_ )




     def 						lowerCAmelCase_   ( self				:	Tuple ):


         """simple docstring"""



         print("""\n""".join(self.frames ) )
         __A   			=		[]




     def 						lowerCAmelCase_   ( self				:	Optional[Any] ):


         """simple docstring"""



         __A   			=		[]




     def 						lowerCAmelCase_   ( self				:	List[Any] ):


         """simple docstring"""



         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""" )
         __A   			=		[]




     def 						lowerCAmelCase_   ( self				:	Union[str, Any] ):


         """simple docstring"""



         __A   			=		{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 						lowerCAmelCase_   ( self				:	Dict       ,		UpperCamelCase_				:	Dict       ,		UpperCamelCase_				:	Tuple ):


         """simple docstring"""



         if torch.is_tensor(lowercase_ ):
             self.expand_frame(get_abs_min_max(lowercase_       ,		lowercase_ ) )
             if detect_overflow(lowercase_       ,		lowercase_ ):
                 __A   			=		True
         elif var is None:
             self.expand_frame(F"{'None':>17} {ctx}" )
         else:
             self.expand_frame(F"{'not a tensor':>17} {ctx}" )




     def 						lowerCAmelCase_   ( self				:	Optional[int] ):


         """simple docstring"""



         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 						lowerCAmelCase_   ( self				:	Union[str, Any] ):


         """simple docstring"""



         self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n" )




     def 						lowerCAmelCase_   ( self				:	Optional[Any]       ,		UpperCamelCase_				:	List[str]       ,		UpperCamelCase_				:	Optional[int]       ,		UpperCamelCase_				:	str ):


         """simple docstring"""



         self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}" )

         # params
         for name, p in module.named_parameters(recurse=lowercase_ ):
             self.analyse_variable(lowercase_       ,		lowercase_ )

         # inputs
         if isinstance(lowercase_       ,		lowercase_ ):
             for i, x in enumerate(lowercase_ ):
                 self.analyse_variable(lowercase_       ,		F"input[{i}]" )
         else:
             self.analyse_variable(lowercase_       ,		"""input""" )

         # outputs
         if isinstance(lowercase_       ,		lowercase_ ):
             for i, x in enumerate(lowercase_ ):
                 # possibly a tuple of tuples
                 if isinstance(lowercase_       ,		lowercase_ ):
                     for j, y in enumerate(lowercase_ ):
                         self.analyse_variable(lowercase_       ,		F"output[{i}][{j}]" )
                 else:
                     self.analyse_variable(lowercase_       ,		F"output[{i}]" )
         else:
             self.analyse_variable(lowercase_       ,		"""output""" )

         self.save_frame()




     def 						lowerCAmelCase_   ( self				:	Optional[int] ):


         """simple docstring"""



         self.model.apply(self._register_forward_hook )




     def 						lowerCAmelCase_   ( self				:	str       ,		UpperCamelCase_				:	List[str] ):


         """simple docstring"""



         module.register_forward_hook(self.forward_hook )





     def 						lowerCAmelCase_   ( self				:	Union[str, Any]       ,		UpperCamelCase_				:	int       ,		UpperCamelCase_				:	int       ,		UpperCamelCase_				:	Any ):


         """simple docstring"""



         __A   			=		False

         __A   			=		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
             __A   			=		True

         self.create_frame(lowercase_       ,		lowercase_       ,		lowercase_ )

         # 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 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Tuple	, __lowercase      :	int			)  ->					Tuple:





    """simple docstring"""

    __A   			=		var.abs()
    return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"



def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Any	, __lowercase      :	Any			)  ->					Optional[Any]:





    """simple docstring"""

    __A   			=		False
    if torch.isnan(a__			).any().item():
        __A   			=		True
        print(f"{ctx} has nans"			)
    if torch.isinf(a__			).any().item():
        __A   			=		True
        print(f"{ctx} has infs"			)

    # if needed to monitor large elements can enable the following
    if 0:  # and detected:
        __A   			=		var[torch.ge(var.abs()	, 1_0_0			)]
        if naaa.numel() > 0:
            print(f"{ctx}:  n100={naaa.numel()}"			)
        __A   			=		var[torch.ge(var.abs()	, 1_0_0_0			)]
        if naaaa.numel() > 0:
            print(f"{ctx}: n1000={naaaa.numel()}"			)
        __A   			=		var[torch.ge(var.abs()	, 1_0_0_0_0			)]
        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 					__lowercase					(		lowercase_     ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE        = "underflow_overflow"
     SCREAMING_SNAKE_CASE        = "tpu_metrics_debug"





import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCamelCase__   = 180


def 							lowerCAmelCase__    (       a__  ,			a__      )	->List[str]:




  '''simple docstring'''

  _UpperCamelCase				=  k_size // 2
  _UpperCamelCase	,		_UpperCamelCase				=  np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _UpperCamelCase				=  (
      1
      / (2 * np.pi * sigma)
      * np.exp(-(np.square(a__      ) + np.square(a__      )) / (2 * np.square(a__      ))      )
  )
  return g


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  np.zeros(a__      )

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      _UpperCamelCase				=  gradient_direction[row, col]

      if (
          0 <= direction < PI / 8
          or 15 * PI / 8 <= direction <= 2 * PI
          or 7 * PI / 8 <= direction <= 9 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row, col - 1]
        _UpperCamelCase				=  sobel_grad[row, col + 1]
        if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          PI / 8 <= direction < 3 * PI / 8
          or 9 * PI / 8 <= direction < 11 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row + 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row - 1, col + 1]
        if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          3 * PI / 8 <= direction < 5 * PI / 8
          or 11 * PI / 8 <= direction < 13 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col]
        _UpperCamelCase				=  sobel_grad[row + 1, col]
        if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
          _UpperCamelCase				=  sobel_grad[row, col]

      elif (
          5 * PI / 8 <= direction < 7 * PI / 8
          or 13 * PI / 8 <= direction < 15 * PI / 8
      ):
        _UpperCamelCase				=  sobel_grad[row - 1, col - 1]
        _UpperCamelCase				=  sobel_grad[row + 1, col + 1]
        if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
          _UpperCamelCase				=  sobel_grad[row, col]

  return destination


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )	->Union[str, Any]:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0] - 1      ):
    for col in range(1  ,			image_shape[1] - 1      ):
      if destination[row, col] >= threshold_high:
        _UpperCamelCase				=  strong
      elif destination[row, col] <= threshold_low:
        _UpperCamelCase				=  0
      else:
        _UpperCamelCase				=  weak


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__  ,			a__      )	->str:




  '''simple docstring'''

  for row in range(1  ,			image_shape[0]      ):
    for col in range(1  ,			image_shape[1]      ):
      if destination[row, col] == weak:
        if 255 in (
            destination[row, col + 1],
            destination[row, col - 1],
            destination[row - 1, col],
            destination[row + 1, col],
            destination[row - 1, col - 1],
            destination[row + 1, col - 1],
            destination[row - 1, col + 1],
            destination[row + 1, col + 1],
        ):
          _UpperCamelCase				=  strong
        else:
          _UpperCamelCase				=  0


def 							lowerCAmelCase__    (       a__  ,			a__=15  ,			a__=30  ,			a__=128  ,			a__=255      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  img_convolve(a__  ,			gen_gaussian_kernel(9  ,			sigma=1.4      )      )
  # get the gradient and degree by sobel_filter
  _UpperCamelCase	,		_UpperCamelCase				=  sobel_filter(a__      )
  _UpperCamelCase				=  PI + np.radadeg(a__      )

  _UpperCamelCase				=  suppress_non_maximum(image.shape  ,			a__  ,			a__      )

  detect_high_low_threshold(
      image.shape  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )

  track_edge(image.shape  ,			a__  ,			a__  ,			a__      )

  return destination


if __name__ == "__main__":
   # read original image in gray mode
   lowerCamelCase__   = cva.imread(R'''../image_data/lena.jpg''', 0)
   # canny edge detection
   lowerCamelCase__   = canny(lena)
   cva.imshow('''canny''', canny_destination)
   cva.waitKey(0)




