



'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


_A					:     Optional[Any]      =logging.get_logger(__name__)



class 		lowerCamelCase__   :




					'''simple docstring'''



					def __init__(	self					:						Optional[int]  ,	UpperCamelCase_					:						int  ,	UpperCamelCase_					:						str=21  ,	UpperCamelCase_					:						str=[]  ,	UpperCamelCase_					:						Optional[int]=None       )	->							Dict:



						'''simple docstring'''





						_lowercase       :							str  	=		model
						_lowercase       :							List[str]  	=		trace_batch_nums
						_lowercase       :							Union[str, 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
						_lowercase       :							Dict  	=		collections.deque([]  ,	a_       )
						_lowercase       :							Tuple  	=		[]
						_lowercase       :							List[Any]  	=		0
						_lowercase       :							Dict  	=		0
						_lowercase       :							List[str]  	=		False
						_lowercase       :							Optional[int]  	=		'                 '

						self.analyse_model()

						self.register_forward_hook()




					def     __UpperCAmelCase  (	self					:						List[str]  ,	UpperCamelCase_					:						Union[str, Any]=None       )	->							Optional[Any]:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Optional[Any]       )	->							str:



						'''simple docstring'''





						self.frame.append(a_       )




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							List[str]:



						'''simple docstring'''





						print('\n'.join(self.frames       )       )
						_lowercase       :							Any  	=		[]




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



						'''simple docstring'''





						_lowercase       :							Any  	=		[]




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							Union[str, 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'       )
						_lowercase       :							List[str]  	=		[]




					def     __UpperCAmelCase  (	self					:						int       )	->							List[str]:



						'''simple docstring'''





						_lowercase       :							Optional[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  ,	UpperCamelCase_					:						Union[str, Any]  ,	UpperCamelCase_					:						str       )	->							Union[str, Any]:



						'''simple docstring'''





						if torch.is_tensor(a_       ):
							self.expand_frame(get_abs_min_max(a_  ,	a_       )       )
							if detect_overflow(a_  ,	a_       ):
								_lowercase       :							Any  	=		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					:						Union[str, Any]       )	->							Dict:



						'''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]       )	->							List[str]:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						List[str]  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						Union[str, Any]  ,	UpperCamelCase_					:						Dict       )	->							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=a_       ):
							self.analyse_variable(a_  ,	a_       )

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

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

						self.save_frame()




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							List[str]:



						'''simple docstring'''





						self.model.apply(self._register_forward_hook       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						str       )	->							int:



						'''simple docstring'''





						module.register_forward_hook(self.forward_hook       )




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	UpperCamelCase_					:						Any  ,	UpperCamelCase_					:						Optional[int]  ,	UpperCamelCase_					:						Any       )	->							Dict:



						'''simple docstring'''





						_lowercase       :							Optional[Any]  	=		False

						_lowercase       :							str  	=		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
							_lowercase       :							Any  	=		True

						self.create_frame(a_  ,	a_  ,	a_       )

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







def     __UpperCamelCase					(				_lowercase,					_lowercase       )			->      List[str]:
	_lowercase       :							Optional[Any]  	=		False
	if torch.isnan(__a       ).any().item():
		_lowercase       :							Optional[Any]  	=		True
		print(f'''{ctx} has nans'''       )
	if torch.isinf(__a       ).any().item():
		_lowercase       :							Union[str, Any]  	=		True
		print(f'''{ctx} has infs'''       )

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




					'''simple docstring'''



					A_         =    """underflow_overflow"""
					A_         =    """tpu_metrics_debug"""





from math import gcd

def       A_   (						__a  :					int = 12_000		):

				"""simple docstring"""
				a__					   =		0
				for d in range(max_d + 1		):
								for n in range(d // 3 + 1			,		(d + 1) // 2		):
												if gcd(__a			,		__a		) == 1:
																fractions_number += 1
				return fractions_number


if __name__ == "__main__":
	print(f"""{solution() = }""")

