





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
				import torch


snake_case_				=      logging.get_logger(__name__)
class 							snake_case_		:







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

				self.analyse_model()

				self.register_forward_hook()







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







		def       __lowercase (		self      ,  __lowercase )					-> List[str]:
				self.frame.append(__lowercase )







		def       __lowercase (		self )					-> Union[str, Any]:
				print('''\n'''.join(self.frames ) )
				lowerCamelCase :			Dict    		=[]







		def       __lowercase (		self )					-> Optional[Any]:
				lowerCamelCase :			Tuple    		=[]







		def       __lowercase (		self )					-> Optional[int]:
				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 :			Tuple    		=[]







		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 :			Tuple    		={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 )					-> Optional[Any]:
				if torch.is_tensor(__lowercase ):
						self.expand_frame(get_abs_min_max(__lowercase      ,  __lowercase ) )
						if detect_overflow(__lowercase      ,  __lowercase ):
								lowerCamelCase :			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       __lowercase (		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       __lowercase (		self )					-> Tuple:
				self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n" )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> List[Any]:
				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 )					-> Optional[int]:
				module.register_forward_hook(self.forward_hook )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase )					-> Tuple:
				# - 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 :			Any    		=False

				lowerCamelCase :			Any    		=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 :			int    		=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        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		lowerCamelCase :			Tuple    		=var.abs()
		return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Any:
		lowerCamelCase :			Dict    		=False
		if torch.isnan(SCREAMING_SNAKE_CASE_		).any().item():
				lowerCamelCase :			List[Any]    		=True
				print(F"{ctx} has nans"		)
		if torch.isinf(SCREAMING_SNAKE_CASE_		).any().item():
				lowerCamelCase :			Any    		=True
				print(F"{ctx} has infs"		)

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				lowerCamelCase :			Dict    		=var[torch.ge(var.abs()				,							1_0_0		)]
				if naaa.numel() > 0:
						print(F"{ctx}:  n100={naaa.numel()}"		)
				lowerCamelCase :			List[Any]    		=var[torch.ge(var.abs()				,							1_0_0_0		)]
				if naaaa.numel() > 0:
						print(F"{ctx}: n1000={naaaa.numel()}"		)
				lowerCamelCase :			int    		=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 							snake_case_		(      _A):
		lowerCamelCase     :List[Any]							=  "underflow_overflow"
		lowerCamelCase     :str							=  "tpu_metrics_debug"








def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Dict:
		assert b != 0, "This cannot accept modulo that is == 0"
		if n == 0:
				return 1

		if n % 2 == 1:
				return (bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n - 1				,							SCREAMING_SNAKE_CASE_		) * a) % b

		lowerCamelCase :			Tuple    		=bin_exp_mod(SCREAMING_SNAKE_CASE_				,							n / 2				,							SCREAMING_SNAKE_CASE_		)
		return (r * r) % b


if __name__ == "__main__":
				try:
								snake_case_				=      int(input('''Enter Base : ''').strip())
								snake_case_				=      int(input('''Enter Power : ''').strip())
								snake_case_				=      int(input('''Enter Modulo : ''').strip())
				except ValueError:
								print('''Invalid literal for integer''')

				print(bin_exp_mod(BASE, POWER, MODULO))

