import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


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






class 		a__						:

					def __init__(   self				:	List[str],_A				:	Any,_A				:	Any=21,_A				:	int=[],_A				:	List[Any]=None   ):







							"""simple docstring"""






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

							self.analyse_model()

							self.register_forward_hook()

					def     __UpperCamelCase       (   self				:	Any,_A				:	int=None   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	List[str],_A				:	Dict   ):







							"""simple docstring"""






							self.frame.append(snake_case__   )

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							print("\n".join(self.frames   )   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   []

					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   []

					def     __UpperCamelCase       (   self				:	Optional[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[Any]    				=   []

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_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     __UpperCamelCase       (   self				:	Optional[int],_A				:	Optional[int],_A				:	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   ):







							"""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[Any]   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Optional[Any],_A				:	List[str],_A				:	Tuple,_A				:	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[str]   ):







							"""simple docstring"""






							self.model.apply(self._register_forward_hook   )

					def     __UpperCamelCase       (   self				:	List[str],_A				:	List[Any]   ):







							"""simple docstring"""






							module.register_forward_hook(self.forward_hook   )






					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	int,_A				:	str,_A				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   False

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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_  :		Optional[int]    				=   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 				_snake_case     (       lowerCAmelCase							:       List[str]       ,			lowerCAmelCase							:       Tuple     ):
		"""simple docstring"""







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




def 				_snake_case     (       lowerCAmelCase							:       Tuple       ,			lowerCAmelCase							:       str     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Dict    				=   False
		if torch.isnan(_lowerCAmelCase     ).any().item():
				SCREAMING_SNAKE_CASE_  :		List[Any]    				=   True
				print(f'{ctx} has nans'     )
		if torch.isinf(_lowerCAmelCase     ).any().item():
				SCREAMING_SNAKE_CASE_  :		Dict    				=   True
				print(f'{ctx} has infs'     )

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				SCREAMING_SNAKE_CASE_  :		Any    				=   var[torch.ge(var.abs()       ,			1_0_0     )]
				if naaa.numel() > 0:
						print(f'{ctx}:  n100={naaa.numel()}'     )
				SCREAMING_SNAKE_CASE_  :		int    				=   var[torch.ge(var.abs()       ,			1_0_0_0     )]
				if naaaa.numel() > 0:
						print(f'{ctx}: n1000={naaaa.numel()}'     )
				SCREAMING_SNAKE_CASE_  :		Dict    				=   var[torch.ge(var.abs()       ,			1_0_0_0_0     )]
				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 		a__						( a_ ):
					A     	=							'underflow_overflow'
					A     	=							'tpu_metrics_debug'

from __future__ import annotations

import sys




def 				_snake_case     (       lowerCAmelCase							:       int     ):
		"""simple docstring"""







		if n < 0:
				raise ValueError("Negative arguments are not supported"     )
		return _fib(lowerCAmelCase     )[0]




def 				_snake_case     (       lowerCAmelCase							:       int     ):
		"""simple docstring"""







		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Dict    				=   _fib(n // 2     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		__lowerCamelCase :     str          =  int(sys.argv[1])
		print(f'''fibonacci({n}) is {fibonacci(n)}''')
