import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
			import torch


_SCREAMING_SNAKE_CASE:						str    	=       logging.get_logger(__name__)

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







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






					"""simple docstring"""
					_snake_case              =      model
					_snake_case              =      trace_batch_nums
					_snake_case              =      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
					_snake_case              =      collections.deque([]  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					_snake_case              =      0
					_snake_case              =      0
					_snake_case              =      False
					_snake_case              =      "                 "

					self.analyse_model()

					self.register_forward_hook()



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict=None   ):






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



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):






					"""simple docstring"""
					self.frame.append(__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				int   ):






					"""simple docstring"""
					print("\n".join(self.frames   )   )
					_snake_case              =      []



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					_snake_case              =      []



		def 				_a				(			self    :				List[str]   ):






					"""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"   )
					_snake_case              =      []



		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					# 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
					_snake_case              =      {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 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					if torch.is_tensor(__SCREAMING_SNAKE_CASE   ):
								self.expand_frame(get_abs_min_max(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )   )
								if detect_overflow(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
											_snake_case              =      True
					elif var is None:
								self.expand_frame(F'{"None":>17} {ctx}'   )
					else:
								self.expand_frame(F'{"not a tensor":>17} {ctx}'   )



		def 				_a				(			self    :				Optional[int]   ):






					"""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 				_a				(			self    :				Optional[int]   ):






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



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					self.expand_frame(F'{self.prefix} {self.module_names[module]} {module.__class__.__name__}'   )

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

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

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

					self.save_frame()



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					self.model.apply(self._register_forward_hook   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					module.register_forward_hook(self.forward_hook   )







		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					# - input is a tuple of packed inputs (could be non-Tensors)
					# - output could be a Tensor or a tuple of Tensors and non-Tensors

					_snake_case              =      False

					_snake_case              =      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
								_snake_case              =      True

					self.create_frame(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





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






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      False
			if torch.isnan(lowerCamelCase_  ).any().item():
						_snake_case              =      True
						print(f'{ctx} has nans'  )
			if torch.isinf(lowerCamelCase_  ).any().item():
						_snake_case              =      True
						print(f'{ctx} has infs'  )

			# if needed to monitor large elements can enable the following
			if 0:  # and detected:
						_snake_case              =      var[torch.ge(var.abs()							,	100  )]
						if naaa.numel() > 0:
									print(f'{ctx}:  n100={naaa.numel()}'  )
						_snake_case              =      var[torch.ge(var.abs()							,	1000  )]
						if naaaa.numel() > 0:
									print(f'{ctx}: n1000={naaaa.numel()}'  )
						_snake_case              =      var[torch.ge(var.abs()							,	10000  )]
						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 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      '''underflow_overflow'''
		A_						 =      '''tpu_metrics_debug'''


def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      sorted(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[2]							,	reverse=lowerCamelCase_  )

			# Create a list of size equal to the maximum deadline
			# and initialize it with -1
			_snake_case              =      max(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[1]  )[1]
			_snake_case              =      [-1] * max_deadline

			# Finding the maximum profit and the count of jobs
			_snake_case              =      0
			_snake_case              =      0
			for job in jobs:
						# Find a free time slot for this job
						# (Note that we start from the last possible slot)
						for i in range(job[1] - 1							,	-1							,	-1  ):
									if time_slots[i] == -1:
												_snake_case              =      job[0]
												count += 1
												max_profit += job[2]
												break
			return [count, max_profit]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

