




import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
	import torch


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






class 					__lowerCAmelCase				:







			def __init__( self					:    Tuple					,					snake_case__					:    str					,					snake_case__					:    Tuple=21					,					snake_case__					:    Dict=[]					,					snake_case__					:    Optional[Any]=None   ):




									"""simple docstring"""


									_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([]					,					snake_case__   )
									_UpperCAmelCase									=  []
									_UpperCAmelCase									=  0
									_UpperCAmelCase									=  0
									_UpperCAmelCase									=  False
									_UpperCAmelCase									=  "                 "

									self.analyse_model()

									self.register_forward_hook()







			def UpperCamelCase    ( self					:    Union[str, Any]					,					snake_case__					:    Union[str, Any]=None   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    str					,					snake_case__					:    Optional[Any]   ):




									"""simple docstring"""


									self.frame.append(snake_case__   )







			def UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    str   ):




									"""simple docstring"""


									_UpperCAmelCase									=  []







			def UpperCamelCase    ( 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"   )
									_UpperCAmelCase									=  []







			def UpperCamelCase    ( self					:    str   ):




									"""simple docstring"""


									_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 UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    Any					,					snake_case__					:    List[str]   ):




									"""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__   ):
																					_UpperCAmelCase									=  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					:    Union[str, Any]   ):




									"""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					:    Any   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    List[Any]					,					snake_case__					:    Optional[int]					,					snake_case__					:    List[Any]					,					snake_case__					:    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					:    Union[str, Any]					,					snake_case__					:    str   ):




									"""simple docstring"""


									module.register_forward_hook(self.forward_hook   )







			def UpperCamelCase    ( self					:    Union[str, Any]					,					snake_case__					:    Union[str, Any]					,					snake_case__					:    List[Any]					,					snake_case__					:    List[str]   ):




									"""simple docstring"""


									_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(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 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


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


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  False
						if torch.isnan(snake_case_		).any().item():
												_UpperCAmelCase									=  True
												print(f"""{ctx} has nans"""		)
						if torch.isinf(snake_case_		).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()			,					100		)]
												if naaa.numel() > 0:
																		print(f"""{ctx}:  n100={naaa.numel()}"""		)
												_UpperCAmelCase									=  var[torch.ge(var.abs()			,					1000		)]
												if naaaa.numel() > 0:
																		print(f"""{ctx}: n1000={naaaa.numel()}"""		)
												_UpperCAmelCase									=  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 					__lowerCAmelCase				( UpperCAmelCase__				):
			snake_case_   :				Any				  =  "underflow_overflow"
			snake_case_   :				List[str]				  =  "tpu_metrics_debug"










def 							__SCREAMING_SNAKE_CASE						(    snake_case_ = 99_8001		):

						'''simple docstring'''


						for number in range(n - 1			,					9999			,					-1		):
												_UpperCAmelCase									=  str(snake_case_		)

												# checks whether 'str_number' is a palindrome.
												if str_number == str_number[::-1]:
																		_UpperCAmelCase									=  999

																		# if 'number' is a product of two 3-digit numbers
																		# then number is the answer otherwise fetch next number.
																		while divisor != 99:
																								if (number % divisor == 0) and (len(str(number // divisor		)		) == 3.0):
																														return number
																								divisor -= 1
						raise ValueError("That number is larger than our acceptable range."		)


if __name__ == "__main__":
				print(f"""{solution() = }""")




