



import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
				import torch


_lowercase:					str      =						logging.get_logger(__name__)



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						,			lowerCamelCase_=21						,			lowerCamelCase_=[]						,			lowerCamelCase_=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([]						,			lowerCamelCase_						)
						a   	=  []
						a   	=  0
						a   	=  0
						a   	=  False
						a   	=  "                 "

						self.analyse_model()

						self.register_forward_hook()




	def 	UpperCamelCase_			(self						,			lowerCamelCase_=None						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.frame.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  []




	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"						)
						a   	=  []




	def 	UpperCamelCase_			(self						):
						"""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 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						if torch.is_tensor(lowerCamelCase_						):
											self.expand_frame(get_abs_min_max(lowerCamelCase_						,			lowerCamelCase_						)						)
											if detect_overflow(lowerCamelCase_						,			lowerCamelCase_						):
																a   	=  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						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""simple docstring"""

						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						):
						"""simple docstring"""

						self.model.apply(self._register_forward_hook						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						module.register_forward_hook(self.forward_hook						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_						,			lowerCamelCase_						):
						"""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(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        a( A			:       Optional[Any]					,	A			:       Optional[int]			)						->			List[Any]:
					"""simple docstring"""





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






def        a( A			:       List[str]					,	A			:       Optional[int]			)						->			int:
					"""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()					,	100			)]
										if naaa.numel() > 0:
															print(f'''{ctx}:  n100={naaa.numel()}'''			)
										a   	=  var[torch.ge(var.abs()					,	1000			)]
										if naaaa.numel() > 0:
															print(f'''{ctx}: n1000={naaaa.numel()}'''			)
										a   	=  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       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				"underflow_overflow"
	__A		      =				"tpu_metrics_debug"





_lowercase:					Optional[Any]      =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  name
						a   	=  []
						a   	=  []




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.inbound.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.outbound.append(lowerCamelCase_						)




	def __repr__(self						):
						"""simple docstring"""

						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def        a( A			:       Optional[Any]					,	A			:       Optional[int]=3					,	A			:       str=0.85			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  {}
					for node in nodes:
										a   	=  1

					a   	=  {}
					for node in nodes:
										a   	=  len(node.outbound			)

					for i in range(A			):
										print(f'''======= Iteration {i + 1} ======='''			)
										for _, node in enumerate(A			):
															a   	=  (1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound			)
										print(A			)






def        a( )						->			List[Any]:
					"""simple docstring"""





					a   	=  list(input("Enter Names of the Nodes: "			).split()			)

					a   	=  [Node(A			) for name in names]

					for ri, row in enumerate(A			):
										for ci, col in enumerate(A			):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]			)
																				nodes[ri].add_outbound(names[ci]			)

					print("======= Nodes ======="			)
					for node in nodes:
										print(A			)

					page_rank(A			)


if __name__ == "__main__":
				main()
