





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
				import torch


__lowerCAmelCase     =			logging.get_logger(__name__)




class   _UpperCAmelCase	:




					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase=21    ,      __lowerCAmelCase=[]    ,      __lowerCAmelCase=None       )						->  List[Any]:
							UpperCamelCase__         =	model
							UpperCamelCase__         =	trace_batch_nums
							UpperCamelCase__         =	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
							UpperCamelCase__         =	collections.deque([]    ,      __lowerCAmelCase       )
							UpperCamelCase__         =	[]
							UpperCamelCase__         =	0
							UpperCamelCase__         =	0
							UpperCamelCase__         =	False
							UpperCamelCase__         =	"""                 """

							self.analyse_model()

							self.register_forward_hook()




					def 	snake_case_		(						self    ,      __lowerCAmelCase=None       )						->  Tuple:
							if frame is not None:
									self.expand_frame(__lowerCAmelCase       )
							self.frames.append("""\n""".join(self.frame       )       )
							UpperCamelCase__         =	[]  # start a new frame




					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  Tuple:
							self.frame.append(__lowerCAmelCase       )




					def 	snake_case_		(						self       )						->  Union[str, Any]:
							print("""\n""".join(self.frames       )       )
							UpperCamelCase__         =	[]




					def 	snake_case_		(						self       )						->  Tuple:
							UpperCamelCase__         =	[]




					def 	snake_case_		(						self       )						->  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"""       )
							UpperCamelCase__         =	[]




					def 	snake_case_		(						self       )						->  int:
							# 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
							UpperCamelCase__         =	{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 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  List[Any]:
							if torch.is_tensor(__lowerCAmelCase       ):
									self.expand_frame(get_abs_min_max(__lowerCAmelCase    ,      __lowerCAmelCase       )       )
									if detect_overflow(__lowerCAmelCase    ,      __lowerCAmelCase       ):
											UpperCamelCase__         =	True
							elif var is None:
									self.expand_frame(F"""{'None':>17} {ctx}"""       )
							else:
									self.expand_frame(F"""{'not a tensor':>17} {ctx}"""       )




					def 	snake_case_		(						self       )						->  Optional[Any]:
							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 	snake_case_		(						self       )						->  Optional[Any]:
							self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  List[Any]:
							self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""       )

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

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

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

							self.save_frame()




					def 	snake_case_		(						self       )						->  Tuple:
							self.model.apply(self._register_forward_hook       )




					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  Tuple:
							module.register_forward_hook(self.forward_hook       )




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

							UpperCamelCase__         =	False

							UpperCamelCase__         =	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
									UpperCamelCase__         =	True

							self.create_frame(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )

							# 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						(      _lowercase   :     List[Any] ,						_lowercase   :     List[Any]       )     ->Union[str, Any]:
		UpperCamelCase__         =	var.abs()
		return F"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""

def     a						(      _lowercase   :     Any ,						_lowercase   :     Any       )     ->Optional[int]:
		UpperCamelCase__         =	False
		if torch.isnan(_lowercase       ).any().item():
				UpperCamelCase__         =	True
				print(F"""{ctx} has nans"""       )
		if torch.isinf(_lowercase       ).any().item():
				UpperCamelCase__         =	True
				print(F"""{ctx} has infs"""       )

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				UpperCamelCase__         =	var[torch.ge(var.abs() ,						1_0_0       )]
				if naaa.numel() > 0:
						print(F"""{ctx}:  n100={naaa.numel()}"""       )
				UpperCamelCase__         =	var[torch.ge(var.abs() ,						1_0_0_0       )]
				if naaaa.numel() > 0:
						print(F"""{ctx}: n1000={naaaa.numel()}"""       )
				UpperCamelCase__         =	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   _UpperCAmelCase	(   _lowercase					):
					__A          =   "underflow_overflow"
					__A          =   "tpu_metrics_debug"












def     a						(      _lowercase   :     List[str]       )     ->List[str]:
		UpperCamelCase__         =	[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
		for gap in gaps:
				for i in range(_lowercase ,						len(_lowercase       )       ):
						UpperCamelCase__         =	collection[i]
						UpperCamelCase__         =	i
						while j >= gap and collection[j - gap] > insert_value:
								UpperCamelCase__         =	collection[j - gap]
								j -= gap
						if j != i:
								UpperCamelCase__         =	insert_value
		return collection


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				__lowerCAmelCase     =			input("Enter numbers separated by a comma:\n").strip()
				__lowerCAmelCase     =			[int(item) for item in user_input.split(",")]
				print(shell_sort(unsorted))





