


'''simple docstring'''





from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar





class       _a							(       _lowercase):




	@abstractmethod
	def __lt__(      self		:	T	,      _SCREAMING_SNAKE_CASE		:	T	)-> bool:
			pass




	@abstractmethod
	def __gt__(      self		:	T	,      _SCREAMING_SNAKE_CASE		:	T	)-> bool:
			pass




	@abstractmethod
	def __eq__(      self		:	T	,      _SCREAMING_SNAKE_CASE		:	object	)-> bool:
			pass
lowerCamelCase		=	TypeVar('''T''', bound=Comparable)





class       _a							(       Generic[T]):




	def __init__(      self		:	int	)-> None:
			lowerCAmelCase__     :    list[T]					  =      []
			lowerCAmelCase__     :    int					  =      0




	def __repr__(      self		:	Dict	)-> str:
			return str(self.h	)




	def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	int	)-> int | None:
			if child_idx > 0:
					return (child_idx - 1) // 2
			return None




	def 							UpperCAmelCase__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	int	)-> int | None:
			lowerCAmelCase__     :    Optional[int]					  =      2 * parent_idx + 1
			if left_child_index < self.heap_size:
					return left_child_index
			return None




	def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	int	)-> int | None:
			lowerCAmelCase__     :    Dict					  =      2 * parent_idx + 2
			if right_child_index < self.heap_size:
					return right_child_index
			return None




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	int	)-> None:
			if index < self.heap_size:
					lowerCAmelCase__     :    int					  =      index
					lowerCAmelCase__     :    int					  =      self.left_child_idx(_SCREAMING_SNAKE_CASE	)
					lowerCAmelCase__     :    Optional[int]					  =      self.right_child_idx(_SCREAMING_SNAKE_CASE	)
					# check which child is larger than its parent
					if left_child is not None and self.h[left_child] > self.h[violation]:
							lowerCAmelCase__     :    str					  =      left_child
					if right_child is not None and self.h[right_child] > self.h[violation]:
							lowerCAmelCase__     :    List[Any]					  =      right_child
					# if violation indeed exists
					if violation != index:
							# swap to fix the violation
							lowerCAmelCase__     :    Union[str, Any]					  =      self.h[index], self.h[violation]
							# fix the subsequent violation recursively if any
							self.max_heapify(_SCREAMING_SNAKE_CASE	)




	def 							UpperCAmelCase__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	Iterable[T]	)-> None:
			lowerCAmelCase__     :    Any					  =      list(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      len(self.h	)
			if self.heap_size > 1:
					# max_heapify from right to left but exclude leaves (last level)
					for i in range(self.heap_size // 2 - 1	,      -1	,      -1	):
							self.max_heapify(_SCREAMING_SNAKE_CASE	)




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> T:
			if self.heap_size >= 2:
					lowerCAmelCase__     :    Any					  =      self.h[0]
					lowerCAmelCase__     :    List[Any]					  =      self.h.pop(-1	)
					self.heap_size -= 1
					self.max_heapify(0	)
					return me
			elif self.heap_size == 1:
					self.heap_size -= 1
					return self.h.pop(-1	)
			else:
					raise Exception('''Empty heap'''	)




	def 							UpperCAmelCase__(      self		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	T	)-> None:
			self.h.append(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    List[Any]					  =      (self.heap_size - 1) // 2
			self.heap_size += 1
			while idx >= 0:
					self.max_heapify(_SCREAMING_SNAKE_CASE	)
					lowerCAmelCase__     :    Dict					  =      (idx - 1) // 2




	def 							UpperCAmelCase__(      self		:	Optional[int]	)-> None:
			lowerCAmelCase__     :    Tuple					  =      self.heap_size
			for j in range(size - 1	,      0	,      -1	):
					lowerCAmelCase__     :    int					  =      self.h[j], self.h[0]
					self.heap_size -= 1
					self.max_heapify(0	)
			lowerCAmelCase__     :    Optional[Any]					  =      size


if __name__ == "__main__":
		import doctest

		# run doc test
		doctest.testmod()

		# demo
		for unsorted in [
		    [0],
		    [2],
		    [3, 5],
		    [5, 3],
		    [5, 5],
		    [0, 0, 0, 0],
		    [1, 1, 1, 1],
		    [2, 2, 3, 5],
		    [0, 2, 2, 3, 5],
		    [2, 5, 3, 0, 2, 3, 0, 3],
		    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
		    [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
		    [-45, -2, -5],
		]:
				print(f'''unsorted array: {unsorted}''')

				lowerCamelCase		=	Heap()
				heap.build_max_heap(unsorted)
				print(f'''after build heap: {heap}''')

				print(f'''max value: {heap.extract_max()}''')
				print(f'''after max value removed: {heap}''')

				heap.insert(100)
				print(f'''after new value 100 inserted: {heap}''')

				heap.heap_sort()
				print(f'''heap-sorted array: {heap}\n''')








import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
		import torch

		from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
		import pyanvml.pyanvml as nvml


lowerCamelCase		=	logging.get_logger(__name__)





class       _a							(       _lowercase):
	_a						:						PyTorchBenchmarkArguments
	_a						:						PretrainedConfig
	_a						:						str   					=							"PyTorch"




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> Any:
			return torch.__version__




	def 							UpperCAmelCase__(      self		:	List[Any]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> float:
			lowerCAmelCase__     :    Optional[Any]					  =      self._prepare_inference_func(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			return self._measure_speed(_inference	)




	def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> [Memory, Optional[MemorySummary]]:
			lowerCAmelCase__     :    List[Any]					  =      self._prepare_inference_func(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			return self._measure_memory(_inference	)




	def 							UpperCAmelCase__(      self		:	Tuple	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> float:
			lowerCAmelCase__     :    Any					  =      self._prepare_train_func(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			return self._measure_speed(_train	)




	def 							UpperCAmelCase__(      self		:	Optional[Any]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> [Memory, Optional[MemorySummary]]:
			lowerCAmelCase__     :    Tuple					  =      self._prepare_train_func(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			return self._measure_memory(_train	)




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> Callable[[], None]:
			lowerCAmelCase__     :    int					  =      self.config_dict[model_name]

			if self.args.torchscript:
					lowerCAmelCase__     :    List[Any]					  =      True

			lowerCAmelCase__     :    List[Any]					  =      (
			    hasattr(_SCREAMING_SNAKE_CASE	,      '''architectures'''	)
			    and isinstance(config.architectures	,      _SCREAMING_SNAKE_CASE	)
			    and len(config.architectures	) > 0
			)
			if not self.args.only_pretrain_model and has_model_class_in_config:
					try:
							lowerCAmelCase__     :    Dict					  =      config.architectures[0]
							lowerCAmelCase__     :    Optional[Any]					  =      __import__('''transformers'''	,      fromlist=[model_class]	)
							lowerCAmelCase__     :    Any					  =      getattr(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    List[Any]					  =      model_cls(_SCREAMING_SNAKE_CASE	)
					except ImportError:
							raise ImportError(
							    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
							    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
			else:
					lowerCAmelCase__     :    Union[str, Any]					  =      MODEL_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE	)

			model.eval()
			model.to(self.args.device	)

			# encoder-decoder has vocab size saved differently
			lowerCAmelCase__     :    str					  =      config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE	,      '''vocab_size'''	) else config.encoder.vocab_size
			lowerCAmelCase__     :    Dict					  =      torch.randint(_SCREAMING_SNAKE_CASE	,      (batch_size, sequence_length)	,      dtype=torch.long	,      device=self.args.device	)

			if self.args.fpaa:
					logger.info('''Running training in Mixed Precision...'''	)
					if not self.args.is_gpu:
							raise ValueError('''Mixed precision is possible only for GPU.'''	)
					# amp seems to have memory leaks so that memory usage
					# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
					model.half()

			if self.args.torchscript:
					with torch.no_grad():
							lowerCAmelCase__     :    int					  =      torch.jit.trace(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
			else:
					lowerCAmelCase__     :    Optional[int]					  =      model

			def encoder_decoder_forward():
					with torch.no_grad():
							lowerCAmelCase__     :    str					  =      inference_model(_SCREAMING_SNAKE_CASE	,      decoder_input_ids=_SCREAMING_SNAKE_CASE	)
					return outputs

			def encoder_forward():
					with torch.no_grad():
							lowerCAmelCase__     :    List[Any]					  =      inference_model(_SCREAMING_SNAKE_CASE	)
					return outputs

			lowerCAmelCase__     :    Any					  =      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
			return _forward




	def 							UpperCAmelCase__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	int	,      _SCREAMING_SNAKE_CASE		:	int	)-> Callable[[], None]:
			lowerCAmelCase__     :    Optional[int]					  =      self.config_dict[model_name]

			lowerCAmelCase__     :    List[str]					  =      (
			    hasattr(_SCREAMING_SNAKE_CASE	,      '''architectures'''	)
			    and isinstance(config.architectures	,      _SCREAMING_SNAKE_CASE	)
			    and len(config.architectures	) > 0
			)
			if not self.args.only_pretrain_model and has_model_class_in_config:
					try:
							lowerCAmelCase__     :    Optional[Any]					  =      config.architectures[0]
							lowerCAmelCase__     :    List[str]					  =      __import__('''transformers'''	,      fromlist=[model_class]	)
							lowerCAmelCase__     :    str					  =      getattr(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    Union[str, Any]					  =      model_cls(_SCREAMING_SNAKE_CASE	)
					except ImportError:
							raise ImportError(
							    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
							    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''	)
			else:
					lowerCAmelCase__     :    Any					  =      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_SCREAMING_SNAKE_CASE	)

			if self.args.torchscript:
					raise NotImplementedError('''Training for torchscript is currently not implemented'''	)
			else:
					lowerCAmelCase__     :    Union[str, Any]					  =      model

			model.train()
			model.to(self.args.device	)

			# encoder-decoder has vocab size saved differently
			lowerCAmelCase__     :    Union[str, Any]					  =      config.vocab_size if hasattr(_SCREAMING_SNAKE_CASE	,      '''vocab_size'''	) else config.encoder.vocab_size
			lowerCAmelCase__     :    List[str]					  =      torch.randint(_SCREAMING_SNAKE_CASE	,      (batch_size, sequence_length)	,      dtype=torch.long	,      device=self.args.device	)

			if self.args.fpaa:
					logger.info('''Running training in Mixed Precision...'''	)
					if not self.args.is_gpu:
							raise ValueError('''Mixed precision is possible only for GPU.'''	)

					# amp seems to have memory leaks so that memory usage
					# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
					model.half()

			def compute_loss_and_backprob_encoder():
					lowerCAmelCase__     :    Optional[Any]					  =      train_model(_SCREAMING_SNAKE_CASE	,      labels=_SCREAMING_SNAKE_CASE	)[0]
					loss.backward()
					return loss

			def compute_loss_and_backprob_encoder_decoder():
					lowerCAmelCase__     :    List[str]					  =      train_model(_SCREAMING_SNAKE_CASE	,      decoder_input_ids=_SCREAMING_SNAKE_CASE	,      labels=_SCREAMING_SNAKE_CASE	)[0]
					loss.backward()
					return loss

			lowerCAmelCase__     :    List[Any]					  =      (
			    compute_loss_and_backprob_encoder_decoder
			    if config.is_encoder_decoder
			    else compute_loss_and_backprob_encoder
			)
			return _train




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	,      _SCREAMING_SNAKE_CASE		:	List[str]	)-> float:
			try:
					if self.args.is_tpu or self.args.torchscript:
							# run additional 10 times to stabilize compilation for tpu and torchscript
							logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''	)
							timeit.repeat(
							    _SCREAMING_SNAKE_CASE	,      repeat=1	,      number=5	,      )

					# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
					lowerCAmelCase__     :    Tuple					  =      timeit.repeat(
					    _SCREAMING_SNAKE_CASE	,      repeat=self.args.repeat	,      number=10	,      )

					if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
							import torch_xla.debug.metrics as met

							self.print_fn(met.metrics_report()	)

					return min(_SCREAMING_SNAKE_CASE	) / 10.0
			except RuntimeError as e:
					self.print_fn(F'Doesn\'t fit on GPU. {e}'	)
					return "N/A"




	def 							UpperCAmelCase__(      self		:	int	,      _SCREAMING_SNAKE_CASE		:	Callable[[], None]	)-> [Memory, MemorySummary]:
			try:
					if self.args.trace_memory_line_by_line:
							lowerCAmelCase__     :    int					  =      start_memory_tracing('''transformers'''	)

					if self.args.is_tpu:
							# tpu
							raise NotImplementedError(
							    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
							    ''' `--no-memory` or `args.memory=False`'''	)
					elif self.args.is_gpu:
							if not is_pyanvml_available():
									logger.warning(
									    '''py3nvml not installed, we won\'t log GPU memory usage. '''
									    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''	)
									lowerCAmelCase__     :    Union[str, Any]					  =      '''N/A'''
							else:
									logger.info(
									    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
									    ''' on the same GPU.'''	)
									# init nvml
									nvml.nvmlInit()
									func()
									lowerCAmelCase__     :    Optional[int]					  =      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
									lowerCAmelCase__     :    Any					  =      nvml.nvmlDeviceGetMemoryInfo(_SCREAMING_SNAKE_CASE	)
									lowerCAmelCase__     :    int					  =      meminfo.used
									lowerCAmelCase__     :    Tuple					  =      Memory(_SCREAMING_SNAKE_CASE	)
									# shutdown nvml
									nvml.nvmlShutdown()
					else:
							# cpu
							lowerCAmelCase__     :    Union[str, Any]					  =      measure_peak_memory_cpu(_SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    Union[str, Any]					  =      Memory(_SCREAMING_SNAKE_CASE	) if isinstance(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	) else memory_bytes

					if self.args.trace_memory_line_by_line:
							lowerCAmelCase__     :    int					  =      stop_memory_tracing(_SCREAMING_SNAKE_CASE	)
					else:
							lowerCAmelCase__     :    str					  =      None

					return memory, summary
			except RuntimeError as e:
					self.print_fn(F'Doesn\'t fit on GPU. {e}'	)
					return "N/A", None




