






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
	import torch


a_							=      logging.get_logger(__name__)


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=21						,      __UpperCAmelCase=[]						,      __UpperCAmelCase=None    )->   Tuple:







							'''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([]						,      __UpperCAmelCase    )
							lowerCAmelCase__	 =  []
							lowerCAmelCase__	 =  0
							lowerCAmelCase__	 =  0
							lowerCAmelCase__	 =  False
							lowerCAmelCase__	 =  "                 "

							self.analyse_model()

							self.register_forward_hook()

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None    )->   int:







							'''simple docstring'''





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

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Union[str, Any]:







							'''simple docstring'''





							self.frame.append(__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





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

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  []

	def 						UpperCAmelCase					(							self    )->   Tuple:







							'''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 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''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 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							if torch.is_tensor(__UpperCAmelCase    ):
													self.expand_frame(get_abs_min_max(__UpperCAmelCase						,      __UpperCAmelCase    )    )
													if detect_overflow(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  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    )->   List[Any]:







							'''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    )->   List[str]:







							'''simple docstring'''





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

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __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=__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    )->   Dict:







							'''simple docstring'''





							self.model.apply(self._register_forward_hook    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   str:







							'''simple docstring'''





							module.register_forward_hook(self.forward_hook    )


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   int:







							'''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(__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     _a					(       UpperCamelCase_   :			int      ,				UpperCamelCase_   :			int				)	->				Union[str, Any]:
						"""simple docstring"""







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


def     _a					(       UpperCamelCase_   :			Optional[Any]      ,				UpperCamelCase_   :			int				)	->				Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__	 =  False
						if torch.isnan(UpperCamelCase_				).any().item():
												lowerCAmelCase__	 =  True
												print(F"{ctx} has nans"				)
						if torch.isinf(UpperCamelCase_				).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()      ,				1_000				)]
												if naaaa.numel() > 0:
																		print(F"{ctx}: n1000={naaaa.numel()}"				)
												lowerCAmelCase__	 =  var[torch.ge(var.abs()      ,				10_000				)]
												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__       (     _UpperCAmelCase		):
	a_             ="""underflow_overflow"""
	a_             ="""tpu_metrics_debug"""














from __future__ import annotations

import random

# Adjacency list representation of this graph:
# https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
a_							=      {
    '''1''': ['''2''', '''3''', '''4''', '''5'''],
    '''2''': ['''1''', '''3''', '''4''', '''5'''],
    '''3''': ['''1''', '''2''', '''4''', '''5''', '''10'''],
    '''4''': ['''1''', '''2''', '''3''', '''5''', '''6'''],
    '''5''': ['''1''', '''2''', '''3''', '''4''', '''7'''],
    '''6''': ['''7''', '''8''', '''9''', '''10''', '''4'''],
    '''7''': ['''6''', '''8''', '''9''', '''10''', '''5'''],
    '''8''': ['''6''', '''7''', '''9''', '''10'''],
    '''9''': ['''6''', '''7''', '''8''', '''10'''],
    '''10''': ['''6''', '''7''', '''8''', '''9''', '''3'''],
}


def     _a					(       UpperCamelCase_   :			dict[str, list[str]]				)	->				set[tuple[str, str]]:
						"""simple docstring"""







						lowerCAmelCase__	 =  {node: {node} for node in graph}

						lowerCAmelCase__	 =  {node: graph[node][:] for node in graph}

						while len(UpperCamelCase_				) > 2:
												# Choose a random edge.
												lowerCAmelCase__	 =  random.choice(list(graph_copy.keys()				)				)
												lowerCAmelCase__	 =  random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												lowerCAmelCase__	 =  u + v
												lowerCAmelCase__	 =  list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(UpperCamelCase_				)
												uv_neighbors.remove(UpperCamelCase_				)
												lowerCAmelCase__	 =  uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(UpperCamelCase_				)

												lowerCAmelCase__	 =  set(contracted_nodes[u].union(contracted_nodes[v]				)				)

												# Remove nodes u and v.
												del graph_copy[u]
												del graph_copy[v]
												for neighbor in uv_neighbors:
																		if u in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(UpperCamelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(UpperCamelCase_				)

    # Find cutset.
						lowerCAmelCase__	 =  [contracted_nodes[node] for node in graph_copy]
						return {
						    (node, neighbor)
						    for node in groups[0]
						    for neighbor in graph[node]
						    if neighbor in groups[1]
						}


if __name__ == "__main__":
	print(partition_graph(TEST_GRAPH))






