





'''simple docstring'''







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
	import torch


_a      :				Any											=					logging.get_logger(__name__)





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



					'''simple docstring'''

					__lowerCAmelCase      		=       model
					__lowerCAmelCase      		=       trace_batch_nums
					__lowerCAmelCase      		=       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
					__lowerCAmelCase      		=       collections.deque([],__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       []
					__lowerCAmelCase      		=       0
					__lowerCAmelCase      		=       0
					__lowerCAmelCase      		=       False
					__lowerCAmelCase      		=       """                 """

					self.analyse_model()

					self.register_forward_hook()
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE=None    ):



					'''simple docstring'''

					if frame is not None:
								self.expand_frame(__SCREAMING_SNAKE_CASE    )
					self.frames.append("""\n""".join(self.frame    )    )
					__lowerCAmelCase      		=       []  # start a new frame
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					self.frame.append(__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					print("""\n""".join(self.frames    )    )
					__lowerCAmelCase      		=       []
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       []
		def 						lowerCamelCase__    (						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"""    )
					__lowerCAmelCase      		=       []
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       {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,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					if torch.is_tensor(__SCREAMING_SNAKE_CASE    ):
								self.expand_frame(get_abs_min_max(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )    )
								if detect_overflow(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
											__lowerCAmelCase      		=       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    ):



					'''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    ):



					'''simple docstring'''

					self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					self.expand_frame(f'{self.prefix} {self.module_names[module]} {module.__class__.__name__}'    )

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

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

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

					self.save_frame()
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					self.model.apply(self._register_forward_hook    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					module.register_forward_hook(self.forward_hook    )







		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       False

					__lowerCAmelCase      		=       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
								__lowerCAmelCase      		=       True

					self.create_frame(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )

					# 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		(		lowercase      ,			lowercase			)      ->      Optional[Any]:
			__lowerCAmelCase      		=       var.abs()
			return f'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
			__lowerCAmelCase      		=       False
			if torch.isnan(lowercase			).any().item():
						__lowerCAmelCase      		=       True
						print(f'{ctx} has nans'			)
			if torch.isinf(lowercase			).any().item():
						__lowerCAmelCase      		=       True
						print(f'{ctx} has infs'			)

			# if needed to monitor large elements can enable the following
			if 0:  # and detected:
						__lowerCAmelCase      		=       var[torch.ge(var.abs()      ,			100			)]
						if naaa.numel() > 0:
									print(f'{ctx}:  n100={naaa.numel()}'			)
						__lowerCAmelCase      		=       var[torch.ge(var.abs()      ,			1000			)]
						if naaaa.numel() > 0:
									print(f'{ctx}: n1000={naaaa.numel()}'			)
						__lowerCAmelCase      		=       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_     ):
		a     :     str     						="""underflow_overflow"""
		a     :     List[Any]     						="""tpu_metrics_debug"""








'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
			__lowerCAmelCase		, __lowerCAmelCase      		=       img.shape[0], img.shape[1]

			def to_grayscale(lowercase      ,			lowercase      ,			lowercase			):
						return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue

			def normalize(lowercase			):
						return min(lowercase      ,			255			)

			for i in range(lowercase			):
						for j in range(lowercase			):
									__lowerCAmelCase      		=       int(to_grayscale(*img[i][j]			)			)
									__lowerCAmelCase      		=       [
									    normalize(lowercase			),
									    normalize(greyscale + factor			),
									    normalize(greyscale + 2 * factor			),
									]

			return img


if __name__ == "__main__":
	# read original image
	_a      :				Any											=					{
	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
	}

	for percentage, img in images.items():
		make_sepia(img, percentage)

	for percentage, img in images.items():
		imshow(f'Original image with sepia (factor: {percentage})', img)

	waitKey(0)
	destroyAllWindows()

