



'''simple docstring'''



import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
				import torch


_lowercase       :						List[str]						=logging.get_logger(__name__)






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						Optional[int]    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						Union[str, Any]=21    ,			__UpperCamelCase		:						Dict=[]    ,			__UpperCamelCase		:						Any=None			)      ->      Any:
							"""simple docstring"""





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

							self.analyse_model()

							self.register_forward_hook()



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						Optional[Any]=None			)      ->      Optional[Any]:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						Optional[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							self.frame.append(UpperCamelCase_			)



			def 				lowerCAmelCase (     self		:						str			)      ->      List[str]:
							"""simple docstring"""





							print('''\n'''.join(self.frames			)			)
							snake_case__      :   Union[str, Any]      =    []



			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    []



			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      Optional[int]:
							"""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'''			)
							snake_case__      :   List[Any]      =    []



			def 				lowerCAmelCase (     self		:						List[str]			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   int      =    {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 				lowerCAmelCase (     self		:						Dict    ,			__UpperCamelCase		:						str    ,			__UpperCamelCase		:						Optional[int]			)      ->      int:
							"""simple docstring"""





							if torch.is_tensor(UpperCamelCase_			):
											self.expand_frame(get_abs_min_max(UpperCamelCase_    ,			UpperCamelCase_			)			)
											if detect_overflow(UpperCamelCase_    ,			UpperCamelCase_			):
															snake_case__      :   Any      =    True
							elif var is None:
											self.expand_frame(F'''{"None":>17} {ctx}'''			)
							else:
											self.expand_frame(F'''{"not a tensor":>17} {ctx}'''			)



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      int:
							"""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 				lowerCAmelCase (     self		:						str			)      ->      Any:
							"""simple docstring"""





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



			def 				lowerCAmelCase (     self		:						Tuple    ,			__UpperCamelCase		:						Any    ,			__UpperCamelCase		:						Union[str, Any]    ,			__UpperCamelCase		:						Optional[int]			)      ->      Dict:
							"""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 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Dict:
							"""simple docstring"""





							self.model.apply(self._register_forward_hook			)



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						int			)      ->      List[str]:
							"""simple docstring"""





							module.register_forward_hook(self.forward_hook			)







			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						Dict    ,			__UpperCamelCase		:						List[Any]			)      ->      List[Any]:
							"""simple docstring"""





							snake_case__      :   str      =    False

							snake_case__      :   Dict      =    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
											snake_case__      :   Union[str, Any]      =    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 							__UpperCAmelCase				(				UpperCamelCase__				:Any   ,				UpperCamelCase__				:Dict			)		->						Any:
				snake_case__      :   Union[str, Any]      =    var.abs()
				return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:Any			)		->						int:
				snake_case__      :   List[Any]      =    False
				if torch.isnan(snake_case_			).any().item():
								snake_case__      :   Any      =    True
								print(F'''{ctx} has nans'''			)
				if torch.isinf(snake_case_			).any().item():
								snake_case__      :   Optional[Any]      =    True
								print(F'''{ctx} has infs'''			)

				# if needed to monitor large elements can enable the following
				if 0:  # and detected:
								snake_case__      :   List[Any]      =    var[torch.ge(var.abs()   ,				100			)]
								if naaa.numel() > 0:
												print(F'''{ctx}:  n100={naaa.numel()}'''			)
								snake_case__      :   List[str]      =    var[torch.ge(var.abs()   ,				1000			)]
								if naaaa.numel() > 0:
												print(F'''{ctx}: n1000={naaaa.numel()}'''			)
								snake_case__      :   Tuple      =    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 			_SCREAMING_SNAKE_CASE				(__A      ):
			A__								=  'underflow_overflow'
			A__								=  'tpu_metrics_debug'









'''simple docstring'''






import math as m





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any] ,       snake_case_    :     int ,       snake_case_    :     Optional[Any]=0.0_0_1  )		->Tuple:






							'''simple docstring'''
							return (f(a + h  ) - f(a - h  )) / (2 * h)





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Dict ,       snake_case_    :     List[Any]=0 ,       snake_case_    :     Dict=1_00 ,       snake_case_    :     List[str]=0.0_0_0_1 ,       snake_case_    :     str=1E-6 ,       snake_case_    :     Tuple=False  )		->List[Any]:






							'''simple docstring'''
							_lowercase							:		Tuple				=   xa  # set the initial guess
							_lowercase							:		List[str]				=   [a]
							_lowercase							:		str				=   abs(f(snake_case_  )  )
							_lowercase							:		Dict				=   lambda snake_case_  : calc_derivative(snake_case_ ,       snake_case_ ,       h=snake_case_  )  # noqa: E731  Derivative of f(x)
							for _ in range(snake_case_  ):
														if fa(snake_case_  ) == 0:
																					raise ValueError('''No converging solution found'''  )
														_lowercase							:		Tuple				=   a - f(snake_case_  ) / fa(snake_case_  )  # Calculate the next estimate
														if logsteps:
																					steps.append(snake_case_  )
														if error < maxerror:
																					break
							else:
														raise ValueError('''Iteration limit reached, no converging solution found'''  )
							if logsteps:
														# If logstep is true, then log intermediate steps
														return a, error, steps
							return a, error


if __name__ == "__main__":
						from matplotlib import pyplot as plt

						lowerCamelCase__	  =		lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
						lowerCamelCase__  ,      lowerCamelCase__  ,      lowerCamelCase__	  =		newton_raphson(
						    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
						)
						plt.plot([abs(f(x)) for x in steps])
						plt.xlabel('step')
						plt.ylabel('error')
						plt.show()
						print(f'''solution = {{{solution:f}}}, error = {{{error:f}}}''')



