

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
			import torch


a_         =      logging.get_logger(__name__)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	Dict , a							:	List[Any] , a							:	List[str]=21 , a							:	Union[str, Any]=[] , a							:	int=None		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    model
				SCREAMING_SNAKE_CASE				: List[Any]			       =    trace_batch_nums
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    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				: Tuple			       =    collections.deque([] , snake_case__		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    []
				SCREAMING_SNAKE_CASE				: Tuple			       =    0
				SCREAMING_SNAKE_CASE				: List[str]			       =    0
				SCREAMING_SNAKE_CASE				: Tuple			       =    False
				SCREAMING_SNAKE_CASE				: Any			       =    "                 "

				self.analyse_model()

				self.register_forward_hook()
			def 							__UpperCamelCase	(     self							:	List[Any] , a							:	List[str]=None		)							->    Any:

				"""simple docstring"""

				if frame is not None:
					self.expand_frame(snake_case__		)
				self.frames.append("\n".join(self.frame		)		)
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    []  # start a new frame
			def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	Any		)							->    List[str]:

				"""simple docstring"""

				self.frame.append(snake_case__		)
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    List[Any]:

				"""simple docstring"""

				print("\n".join(self.frames		)		)
				SCREAMING_SNAKE_CASE				: List[str]			       =    []
			def 							__UpperCamelCase	(     self							:	Dict		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    []
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    Any:

				"""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				: List[str]			       =    []
			def 							__UpperCamelCase	(     self							:	str		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {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							:	int , a							:	List[Any] , a							:	Union[str, Any]		)							->    Any:

				"""simple docstring"""

				if torch.is_tensor(snake_case__		):
					self.expand_frame(get_abs_min_max(snake_case__ , snake_case__		)		)
					if detect_overflow(snake_case__ , snake_case__		):
						SCREAMING_SNAKE_CASE				: 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							:	str		)							->    Union[str, 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]		)							->    Tuple:

				"""simple docstring"""

				self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"		)
			def 							__UpperCamelCase	(     self							:	int , a							:	int , a							:	Any , a							:	int		)							->    Union[str, Any]:

				"""simple docstring"""

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

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

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

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

				self.save_frame()
			def 							__UpperCamelCase	(     self							:	List[Any]		)							->    str:

				"""simple docstring"""

				self.model.apply(self._register_forward_hook		)
			def 							__UpperCamelCase	(     self							:	List[Any] , a							:	Any		)							->    Optional[Any]:

				"""simple docstring"""

				module.register_forward_hook(self.forward_hook		)






			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	Optional[Any] , a							:	List[Any] , a							:	List[str]		)							->    Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    False

				SCREAMING_SNAKE_CASE				: 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
					SCREAMING_SNAKE_CASE				: Any			       =    True

				self.create_frame(snake_case__ , snake_case__ , 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__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    var.abs()
	return f"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    False
	if torch.isnan(_lowerCAmelCase).any().item():
		SCREAMING_SNAKE_CASE				: Optional[Any]			       =    True
		print(f"{ctx} has nans")
	if torch.isinf(_lowerCAmelCase).any().item():
		SCREAMING_SNAKE_CASE				: List[Any]			       =    True
		print(f"{ctx} has infs")

	# if needed to monitor large elements can enable the following
	if 0:  # and detected:
		SCREAMING_SNAKE_CASE				: str			       =    var[torch.ge(var.abs()							,     100)]
		if naaa.numel() > 0:
			print(f"{ctx}:  n100={naaa.numel()}")
		SCREAMING_SNAKE_CASE				: Any			       =    var[torch.ge(var.abs()							,     1000)]
		if naaaa.numel() > 0:
			print(f"{ctx}: n1000={naaaa.numel()}")
		SCREAMING_SNAKE_CASE				: Tuple			       =    var[torch.ge(var.abs()							,     10000)]
		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							(							a_				):
			'''simple docstring'''







			lowerCamelCase__          ='underflow_overflow'
			lowerCamelCase__          ='tpu_metrics_debug'


from __future__ import annotations






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	List[Any] , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    vertices
				SCREAMING_SNAKE_CASE				: Tuple			       =    [[0] * vertices for _ in range(a		)]
			def 							__UpperCamelCase	(     self							:	Dict , a							:	list[int]		)							->    None:

				"""simple docstring"""

				print("Vertex \t Distance from Source"		)
				for vertex in range(self.vertices		):
					print(a , "\t\t" , distances_from_source[vertex]		)
			def 							__UpperCamelCase	(     self							:	Tuple , a							:	list[int] , a							:	list[bool]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    1e7
				SCREAMING_SNAKE_CASE				: int			       =    0

				# Search not nearest vertex not in the shortest path tree
				for vertex in range(self.vertices		):
					if distances_from_source[vertex] < minimum and visited[vertex] is False:
						SCREAMING_SNAKE_CASE				: Optional[int]			       =    distances_from_source[vertex]
						SCREAMING_SNAKE_CASE				: Optional[Any]			       =    vertex
				return min_index






			def 							__UpperCamelCase	(     self							:	Any , a							:	int		)							->    None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    [int(1e7		)] * self.vertices  # distances from the source
				SCREAMING_SNAKE_CASE				: Tuple			       =    0
				SCREAMING_SNAKE_CASE				: List[Any]			       =    [False] * self.vertices

				for _ in range(self.vertices		):
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.minimum_distance(a , a		)
					SCREAMING_SNAKE_CASE				: List[str]			       =    True

					# Update dist value of the adjacent vertices
					# of the picked vertex only if the current
					# distance is greater than new distance and
					# the vertex in not in the shortest path tree
					for v in range(self.vertices		):
						if (
						    self.graph[u][v] > 0
						    and visited[v] is False
						    and distances[v] > distances[u] + self.graph[u][v]
						):
							SCREAMING_SNAKE_CASE				: int			       =    distances[u] + self.graph[u][v]

				self.print_solution(a		)


if __name__ == "__main__":
			a_         =      Graph(9)
			a_         =      [
			    [0, 4, 0, 0, 0, 0, 0, 8, 0],
			    [4, 0, 8, 0, 0, 0, 0, 11, 0],
			    [0, 8, 0, 7, 0, 4, 0, 0, 2],
			    [0, 0, 7, 0, 9, 14, 0, 0, 0],
			    [0, 0, 0, 9, 0, 10, 0, 0, 0],
			    [0, 0, 4, 14, 10, 0, 2, 0, 0],
			    [0, 0, 0, 0, 0, 2, 0, 1, 6],
			    [8, 11, 0, 0, 0, 0, 1, 0, 7],
			    [0, 0, 2, 0, 0, 0, 6, 7, 0],
			]
			graph.dijkstra(0)