import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
					import torch


UpperCamelCase      			=     logging.get_logger(__name__)


class _a       :

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=21							,			__UpperCAmelCase=[]							,			__UpperCAmelCase=None   ):
			__A							:   str	  =				model
			__A							:   Any	  =				trace_batch_nums
			__A							:   List[str]	  =				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
			__A							:   List[Any]	  =				collections.deque([]							,			__UpperCAmelCase   )
			__A							:   Tuple	  =				[]
			__A							:   Tuple	  =				0
			__A							:   int	  =				0
			__A							:   Union[str, Any]	  =				False
			__A							:   Union[str, Any]	  =				"                 "

			self.analyse_model()

			self.register_forward_hook()

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase=None   ):
			if frame is not None:
					self.expand_frame(__UpperCAmelCase   )
			self.frames.append("\n".join(self.frame   )   )
			__A							:   Optional[Any]	  =				[]  # start a new frame

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			self.frame.append(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			print("\n".join(self.frames   )   )
			__A							:   Optional[Any]	  =				[]

	def 					__UpperCAmelCase(       self   ):
			__A							:   Optional[int]	  =				[]

	def 					__UpperCAmelCase(       self   ):
			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"   )
			__A							:   int	  =				[]

	def 					__UpperCAmelCase(       self   ):
			# 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
			__A							:   Optional[int]	  =				{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							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			if torch.is_tensor(__UpperCAmelCase   ):
					self.expand_frame(get_abs_min_max(__UpperCAmelCase							,			__UpperCAmelCase   )   )
					if detect_overflow(__UpperCAmelCase							,			__UpperCAmelCase   ):
							__A							:   Optional[int]	  =				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   ):
			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   ):
			self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			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 					__UpperCAmelCase(       self   ):
			self.model.apply(self._register_forward_hook   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			module.register_forward_hook(self.forward_hook   )







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

			__A							:   Dict	  =				False

			__A							:   Tuple	  =				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
					__A							:   Optional[int]	  =				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    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	List[Any]:
		__A							:   str	  =				var.abs()
		return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"



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

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

	'''simple docstring'''



	lowerCamelCase_   :      int       =	"""underflow_overflow"""
	lowerCamelCase_   :      int       =	"""tpu_metrics_debug"""






import os



def    lowerCamelCase_   (  )							->	List[str]:
		__A							:   Any	  =				0
		__A							:   Any	  =				0
		with open(os.path.dirname(_lowercase    ) + "/p022_names.txt"    ) as file:
				__A							:   int	  =				str(file.readlines()[0]    )
				__A							:   Optional[Any]	  =				name.replace("\"" ,     ""    ).split(","    )

		name.sort()
		for i in range(len(_lowercase    )    ):
				for j in name[i]:
						temp_sum += ord(_lowercase    ) - ord("A"    ) + 1
				total_sum += (i + 1) * temp_sum
				__A							:   List[Any]	  =				0
		return total_sum


if __name__ == "__main__":
					print(solution())





