
'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


UpperCAmelCase__   : Union[str, Any]					=		logging.get_logger(__name__)






class    A     :





	def __init__(					self			:       Dict      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       int=21      ,							__magic_name__			:       Optional[int]=[]      ,							__magic_name__			:       List[str]=None							):


				"""simple docstring"""

				lowerCAmelCase__								=							model
				lowerCAmelCase__								=							trace_batch_nums
				lowerCAmelCase__								=							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
				lowerCAmelCase__								=							collections.deque([]      ,							__UpperCamelCase							)
				lowerCAmelCase__								=							[]
				lowerCAmelCase__								=							0
				lowerCAmelCase__								=							0
				lowerCAmelCase__								=							False
				lowerCAmelCase__								=							"                 "

				self.analyse_model()

				self.register_forward_hook()





	def 							__SCREAMING_SNAKE_CASE  (					self			:       str      ,							__magic_name__			:       Any=None							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       Union[str, Any]							):


				"""simple docstring"""

				self.frame.append(__UpperCamelCase							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				print("\n".join(self.frames							)							)
				lowerCAmelCase__								=							[]





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				lowerCAmelCase__								=							[]





	def 							__SCREAMING_SNAKE_CASE  (					self			:       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"							)
				lowerCAmelCase__								=							[]





	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__								=							{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 							__SCREAMING_SNAKE_CASE  (					self			:       str      ,							__magic_name__			:       int      ,							__magic_name__			:       str							):


				"""simple docstring"""

				if torch.is_tensor(__UpperCamelCase							):
							self.expand_frame(get_abs_min_max(__UpperCamelCase      ,							__UpperCamelCase							)							)
							if detect_overflow(__UpperCamelCase      ,							__UpperCamelCase							):
										lowerCAmelCase__								=							True
				elif var is None:
							self.expand_frame(f"""{"None":>17} {ctx}"""							)
				else:
							self.expand_frame(f"""{"not a tensor":>17} {ctx}"""							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple							):


				"""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 							__SCREAMING_SNAKE_CASE  (					self			:       List[Any]							):


				"""simple docstring"""

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





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       Tuple      ,							__magic_name__			:       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=__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 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				self.model.apply(self._register_forward_hook							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							__magic_name__			:       int							):


				"""simple docstring"""

				module.register_forward_hook(self.forward_hook							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       Optional[int]      ,							__magic_name__			:       int      ,							__magic_name__			:       Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__								=							False

				lowerCAmelCase__								=							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
							lowerCAmelCase__								=							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   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     List[str]      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							var.abs()
			return F"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Any      )					->       Dict:







			'''simple docstring'''



			lowerCAmelCase__								=							False
			if torch.isnan(lowercase_      ).any().item():
						lowerCAmelCase__								=							True
						print(F"""{ctx} has nans"""      )
			if torch.isinf(lowercase_      ).any().item():
						lowerCAmelCase__								=							True
						print(F"""{ctx} has infs"""      )

			# if needed to monitor large elements can enable the following
			if 0:  # and detected:
						lowerCAmelCase__								=							var[torch.ge(var.abs()						,	1_00      )]
						if naaa.numel() > 0:
									print(F"""{ctx}:  n100={naaa.numel()}"""      )
						lowerCAmelCase__								=							var[torch.ge(var.abs()						,	10_00      )]
						if naaaa.numel() > 0:
									print(F"""{ctx}: n1000={naaaa.numel()}"""      )
						lowerCAmelCase__								=							var[torch.ge(var.abs()						,	1_00_00      )]
						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     (	UpperCAmelCase_    ):
	snake_case__      :List[str]        =     'underflow_overflow'
	snake_case__      :Optional[int]        =     'tpu_metrics_debug'







'''simple docstring'''

from __future__ import annotations







def   A     (      UpperCamelCase_					:     int      )					->       list[int]:







			'''simple docstring'''



			assert (
			    isinstance(UpperCamelCase_						,	UpperCamelCase_      ) and mask > 0
			), F"""mask needs to be positive integer, your input {mask}"""
			lowerCAmelCase__								=							[]
			lowerCAmelCase__								=							mask

			while submask:
						all_submasks.append(UpperCamelCase_      )
						lowerCAmelCase__								=							(submask - 1) & mask

			return all_submasks


if __name__ == "__main__":
		import doctest

		doctest.testmod()





