
import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


__a        = logging.get_logger(__name__)
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Dict=2_1	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=[]	,							SCREAMING_SNAKE_CASE_  :      int=None	)	->					Union[str, Any]:
					lowercase_					  = model
					lowercase_					  = trace_batch_nums
					lowercase_					  = 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_					  = collections.deque([]	,							SCREAMING_SNAKE_CASE_	)
					lowercase_					  = []
					lowercase_					  = 0
					lowercase_					  = 0
					lowercase_					  = False
					lowercase_					  = '''                 '''

					self.analyse_model()

					self.register_forward_hook()
	def 						_lowercase  (	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      str=None	)	->					Optional[Any]:
					if frame is not None:
									self.expand_frame(SCREAMING_SNAKE_CASE_	)
					self.frames.append('''\n'''.join(self.frame	)	)
					lowercase_					  = []  # start a new frame
	def 						_lowercase  (	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					Any:
					self.frame.append(SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      str	)	->					Union[str, Any]:
					print('''\n'''.join(self.frames	)	)
					lowercase_					  = []
	def 						_lowercase  (	self  :      Optional[Any]	)	->					Union[str, Any]:
					lowercase_					  = []
	def 						_lowercase  (	self  :      str	)	->					str:
					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_					  = []
	def 						_lowercase  (	self  :      int	)	->					Dict:
					# extract the fully qualified module names, to be able to report at run time. e.g.:
					# encoder.block.2.layer.0.SelfAttention.o
					#
					# for shared weights only the first shared module name will be registered
					lowercase_					  = {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 						_lowercase  (	self  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Dict	)	->					Tuple:
					if torch.is_tensor(SCREAMING_SNAKE_CASE_	):
									self.expand_frame(get_abs_min_max(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)	)
									if detect_overflow(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	):
													lowercase_					  = True
					elif var is None:
									self.expand_frame(f'''{'None':>17} {ctx}'''	)
					else:
									self.expand_frame(f'''{'not a tensor':>17} {ctx}'''	)
	def 						_lowercase  (	self  :      Union[str, Any]	)	->					List[str]:
					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 						_lowercase  (	self  :      Optional[int]	)	->					List[str]:
					self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''	)
	def 						_lowercase  (	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Tuple	)	->					Any:
					self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''	)

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

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

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

					self.save_frame()
	def 						_lowercase  (	self  :      List[Any]	)	->					str:
					self.model.apply(self._register_forward_hook	)
	def 						_lowercase  (	self  :      int	,							SCREAMING_SNAKE_CASE_  :      Tuple	)	->					int:
					module.register_forward_hook(self.forward_hook	)




	def 						_lowercase  (	self  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Dict:
					# - input is a tuple of packed inputs (could be non-Tensors)
					# - output could be a Tensor or a tuple of Tensors and non-Tensors

					lowercase_					  = False

					lowercase_					  = 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_					  = True

					self.create_frame(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_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     a						(							snake_case__: Any						,       snake_case__: Tuple ):


				'''simple docstring'''
				lowercase_					  = var.abs()
				return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''




def     a						(							snake_case__: Union[str, Any]						,       snake_case__: int ):


				'''simple docstring'''
				lowercase_					  = False
				if torch.isnan(snake_case__ ).any().item():
								lowercase_					  = True
								print(F'''{ctx} has nans''' )
				if torch.isinf(snake_case__ ).any().item():
								lowercase_					  = True
								print(F'''{ctx} has infs''' )

				# if needed to monitor large elements can enable the following
				if 0:  # and detected:
								lowercase_					  = var[torch.ge(var.abs()						,       100 )]
								if naaa.numel() > 0:
												print(F'''{ctx}:  n100={naaa.numel()}''' )
								lowercase_					  = var[torch.ge(var.abs()						,       1_000 )]
								if naaaa.numel() > 0:
												print(F'''{ctx}: n1000={naaaa.numel()}''' )
								lowercase_					  = 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							):



	"""simple docstring"""




	a      :Optional[Any]   					=     'underflow_overflow'
	a      :str   					=     'tpu_metrics_debug'






class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	)	->					None:
					lowercase_					  = [0, 1]




	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      int	)	->					list:
					if (difference := index - (len(self.sequence	) - 2)) >= 1:
									for _ in range(SCREAMING_SNAKE_CASE_	):
													self.sequence.append(self.sequence[-1] + self.sequence[-2]	)
					return self.sequence[:index]




def     a						(							):


				'''simple docstring'''
				print(
				    '''Fibonacci Series Using Dynamic Programming\n'''						,       '''Enter the index of the Fibonacci number you want to calculate '''						,       '''in the prompt below. (To exit enter exit or Ctrl-C)\n'''						,       sep=''''''						,       )
				lowercase_					  = Fibonacci()

				while True:
								lowercase_					  = input('''>> ''' )
								if prompt in {"exit", "quit"}:
												break

								try:
												lowercase_					  = int(snake_case__ )
								except ValueError:
												print('''Enter a number or \'exit\'''' )
												continue

								print(fibonacci.get(snake_case__ ) )


if __name__ == "__main__":
						main()




