



'''simple docstring'''





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
			import torch


lowerCAmelCase__	:  Any 		=logging.get_logger(__name__)

class      UpperCAmelCase_						:

						'''simple docstring'''




						def __init__(					self   ,							_A   ,							_A=21   ,							_A=[]   ,							_A=None	):







													'''simple docstring'''

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

													self.analyse_model()

													self.register_forward_hook()






						def  _A			(					self   ,							_A=None	):







													'''simple docstring'''

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






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													self.frame.append(__SCREAMING_SNAKE_CASE	)






						def  _A			(					self	):







													'''simple docstring'''

													print('\n'.join(self.frames	)	)
													__SCREAMING_SNAKE_CASE								=      []






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      []






						def  _A			(					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'	)
													__SCREAMING_SNAKE_CASE								=      []






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      {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  _A			(					self   ,							_A   ,							_A	):







													'''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	):
																											__SCREAMING_SNAKE_CASE								=      True
													elif var is None:
																				self.expand_frame(f"""{"None":>17} {ctx}"""	)
													else:
																				self.expand_frame(f"""{"not a tensor":>17} {ctx}"""	)






						def  _A			(					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  _A			(					self	):







													'''simple docstring'''

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






						def  _A			(					self   ,							_A   ,							_A   ,							_A	):







													'''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  _A			(					self	):







													'''simple docstring'''

													self.model.apply(self._register_forward_hook	)






						def  _A			(					self   ,							_A	):







													'''simple docstring'''

													module.register_forward_hook(self.forward_hook	)






						def  _A			(					self   ,							_A   ,							_A   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      False

													__SCREAMING_SNAKE_CASE								=      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
																				__SCREAMING_SNAKE_CASE								=      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 			__lowercase     (				a__							,     a__     )       ->				Optional[Any]:
							__SCREAMING_SNAKE_CASE								=      var.abs()
							return f"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""






def 			__lowercase     (				a__							,     a__     )       ->				int:
							__SCREAMING_SNAKE_CASE								=      False
							if torch.isnan(a__     ).any().item():
														__SCREAMING_SNAKE_CASE								=      True
														print(f"""{ctx} has nans"""     )
							if torch.isinf(a__     ).any().item():
														__SCREAMING_SNAKE_CASE								=      True
														print(f"""{ctx} has infs"""     )

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														__SCREAMING_SNAKE_CASE								=      var[torch.ge(var.abs()							,     1_00     )]
														if naaa.numel() > 0:
																					print(f"""{ctx}:  n100={naaa.numel()}"""     )
														__SCREAMING_SNAKE_CASE								=      var[torch.ge(var.abs()							,     10_00     )]
														if naaaa.numel() > 0:
																					print(f"""{ctx}: n1000={naaaa.numel()}"""     )
														__SCREAMING_SNAKE_CASE								=      var[torch.ge(var.abs()							,     1_00_00     )]
														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_ ):

						'''simple docstring'''




						UpperCamelCase__    :				str       						=						"""underflow_overflow"""
						UpperCamelCase__    :				List[Any]       						=						"""tpu_metrics_debug"""











from __future__ import annotations

class      UpperCAmelCase_						:

						'''simple docstring'''




						def __init__(					self   ,							_A	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      value
													__SCREAMING_SNAKE_CASE								=      None
													__SCREAMING_SNAKE_CASE								=      None







class      UpperCAmelCase_						:

						'''simple docstring'''




						UpperCamelCase__    :				int






						def __init__(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      0






						def  _A			(					self   ,							_A   ,							_A	):







													'''simple docstring'''

													if node is None:
																				return

													if path_sum == self.target:
																				self.paths += 1

													if node.left:
																				self.depth_first_search(node.left   ,							path_sum + node.left.value	)
													if node.right:
																				self.depth_first_search(node.right   ,							path_sum + node.right.value	)






						def  _A			(					self   ,							_A   ,							_A = None	):







													'''simple docstring'''

													if node is None:
																				return 0
													if target is not None:
																				__SCREAMING_SNAKE_CASE								=      target

													self.depth_first_search(_A   ,							node.value	)
													self.path_sum(node.left	)
													self.path_sum(node.right	)

													return self.paths


if __name__ == "__main__":
			import doctest

			doctest.testmod()






