


import numpy as np
from PIL import Image
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Union[str, Any]				)       ->  np.array:
						__magic_name__		, __magic_name__		, __magic_name__				:							Union[str, Any]				=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
						return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[Any]				)       ->  np.array:
						return (gray > 1_2_7) & (gray <= 2_5_5)
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :Union[str, Any]				)       ->  np.array:
						__magic_name__				:							Tuple				=      np.zeros_like(snake_case__				)
						__magic_name__				:							Optional[Any]				=      np.zeros(
						    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)				)

						# Copy image to padded image
						__magic_name__				:							Union[str, Any]				=      image

						# Iterate over image & apply kernel
						for x in range(image.shape[1]				):
												for y in range(image.shape[0]				):
																		__magic_name__				:							List[str]				=      (
																		    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																		).sum()
																		__magic_name__				:							Optional[Any]				=      int(summation == 5				)
						return output


# kernel to be applied
snake_case__     							= np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
			# read original image
			snake_case__     							= np.array(Image.open(R'..\image_data\lena.jpg'))
			# Apply erosion operation to a binary image
			snake_case__     							= erosion(grayabinary(rgbagray(image)), structuring_element)
			# Save the output image
			snake_case__     							= Image.fromarray(output).convert('RGB')
			pil_img.save('result_erosion.png')






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
			import torch


snake_case__     							= logging.get_logger(__name__)



class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase=21     ,      _UpperCAmelCase=[]     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										__magic_name__				:							Tuple				=      model
										__magic_name__				:							List[str]				=      trace_batch_nums
										__magic_name__				:							int				=      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
										__magic_name__				:							Union[str, Any]				=      collections.deque([]     ,      _UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      []
										__magic_name__				:							List[str]				=      0
										__magic_name__				:							Optional[int]				=      0
										__magic_name__				:							Any				=      False
										__magic_name__				:							str				=      '                 '

										self.analyse_model()

										self.register_forward_hook()
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase=None      ):




										"""simple docstring"""




										if frame is not None:
																self.expand_frame(_UpperCAmelCase      )
										self.frames.append('\n'.join(self.frame      )      )
										__magic_name__				:							List[Any]				=      []  # start a new frame
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										self.frame.append(_UpperCAmelCase      )
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										print('\n'.join(self.frames      )      )
										__magic_name__				:							List[str]				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      []
				def  UpperCamelCase_						(  self      ):




										"""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'      )
										__magic_name__				:							Any				=      []
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							int				=      {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     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										if torch.is_tensor(_UpperCAmelCase      ):
																self.expand_frame(get_abs_min_max(_UpperCAmelCase     ,      _UpperCAmelCase      )      )
																if detect_overflow(_UpperCAmelCase     ,      _UpperCAmelCase      ):
																						__magic_name__				:							List[str]				=      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      ):




										"""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  UpperCamelCase_						(  self      ):




										"""simple docstring"""




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




										"""simple docstring"""




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

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

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

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

										self.save_frame()
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										self.model.apply(self._register_forward_hook      )
				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										module.register_forward_hook(self.forward_hook      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							Optional[int]				=      False

										__magic_name__				:							Any				=      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
																__magic_name__				:							str				=      True

										self.create_frame(_UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase      )

										# 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__  :Union[str, Any]      ,						lowercase__  :Any				)       ->  Any:
						__magic_name__				:							List[Any]				=      var.abs()
						return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Optional[int]				)       ->  Optional[Any]:
						__magic_name__				:							str				=      False
						if torch.isnan(lowercase__				).any().item():
												__magic_name__				:							Optional[Any]				=      True
												print(f"{ctx} has nans"				)
						if torch.isinf(lowercase__				).any().item():
												__magic_name__				:							str				=      True
												print(f"{ctx} has infs"				)

						# if needed to monitor large elements can enable the following
						if 0:  # and detected:
												__magic_name__				:							Any				=      var[torch.ge(var.abs()      ,						1_0_0				)]
												if naaa.numel() > 0:
																		print(f"{ctx}:  n100={naaa.numel()}"				)
												__magic_name__				:							Union[str, Any]				=      var[torch.ge(var.abs()      ,						1_0_0_0				)]
												if naaaa.numel() > 0:
																		print(f"{ctx}: n1000={naaaa.numel()}"				)
												__magic_name__				:							Any				=      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     snake_case__  (      _A  ):
				__lowerCAmelCase		=      '''underflow_overflow'''
				__lowerCAmelCase		=      '''tpu_metrics_debug'''


