


import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


_UpperCAmelCase      :	str  =logging.get_logger(__name__)







class 					snake_case__:







						'''simple docstring'''




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

										self.analyse_model()

										self.register_forward_hook()




						def     lowercase_			(					self ,    __lowercase=None				)       ->     Any:
										if frame is not None:
														self.expand_frame(__lowercase				)
										self.frames.append('''\n'''.join(self.frame				)				)
										lowerCAmelCase_			:   Optional[int]     					=					[]  # start a new frame




						def     lowercase_			(					self ,    __lowercase				)       ->     Dict:
										self.frame.append(__lowercase				)




						def     lowercase_			(					self				)       ->     Tuple:
										print('''\n'''.join(self.frames				)				)
										lowerCAmelCase_			:   Optional[int]     					=					[]




						def     lowercase_			(					self				)       ->     Union[str, Any]:
										lowerCAmelCase_			:   Any     					=					[]




						def     lowercase_			(					self				)       ->     Union[str, Any]:
										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_			:   str     					=					[]




						def     lowercase_			(					self				)       ->     str:
										# 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
										lowerCAmelCase_			:   Dict     					=					{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 ,    __lowercase ,    __lowercase				)       ->     List[Any]:
										if torch.is_tensor(__lowercase				):
														self.expand_frame(get_abs_min_max(__lowercase ,    __lowercase				)				)
														if detect_overflow(__lowercase ,    __lowercase				):
																		lowerCAmelCase_			:   str     					=					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				)       ->     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				)       ->     List[Any]:
										self.expand_frame(f"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     Dict:
										self.expand_frame(f"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""				)

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

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

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

										self.save_frame()




						def     lowercase_			(					self				)       ->     str:
										self.model.apply(self._register_forward_hook				)




						def     lowercase_			(					self ,    __lowercase				)       ->     List[str]:
										module.register_forward_hook(self.forward_hook				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     int:
										# - input is a tuple of packed inputs (could be non-Tensors)
										# - output could be a Tensor or a tuple of Tensors and non-Tensors

										lowerCAmelCase_			:   str     					=					False

										lowerCAmelCase_			:   Dict     					=					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_			:   Dict     					=					True

										self.create_frame(__lowercase ,    __lowercase ,    __lowercase				)

										# 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       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			Union[str, Any]:
				lowerCAmelCase_			:   Optional[Any]     					=					var.abs()
				return f"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_			)->			Optional[Any]:
				lowerCAmelCase_			:   Any     					=					False
				if torch.isnan(lowerCAmelCase_			).any().item():
								lowerCAmelCase_			:   Any     					=					True
								print(f"""{ctx} has nans"""			)
				if torch.isinf(lowerCAmelCase_			).any().item():
								lowerCAmelCase_			:   List[Any]     					=					True
								print(f"""{ctx} has infs"""			)

				# if needed to monitor large elements can enable the following
				if 0:  # and detected:
								lowerCAmelCase_			:   int     					=					var[torch.ge(var.abs()			, 100			)]
								if naaa.numel() > 0:
												print(f"""{ctx}:  n100={naaa.numel()}"""			)
								lowerCAmelCase_			:   int     					=					var[torch.ge(var.abs()			, 1_000			)]
								if naaaa.numel() > 0:
												print(f"""{ctx}: n1000={naaaa.numel()}"""			)
								lowerCAmelCase_			:   Any     					=					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 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[Any]       =				"""underflow_overflow"""
						SCREAMING_SNAKE_CASE__   :	Optional[Any]       =				"""tpu_metrics_debug"""



from transformers import PretrainedConfig







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     Dict:
										lowerCAmelCase_			:   List[Any]     					=					attribute
										super().__init__(**__lowercase				)







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	int       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     List[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					attribute