

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


UpperCAmelCase__	:       str			      =      logging.get_logger(__name__)







class     a__    (				UpperCAmelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	PyTorchBenchmarkArguments
  UpperCAmelCase__ :	PretrainedConfig
  UpperCAmelCase__ :	str         ="PyTorch"


  @property
  def 	_lowercase (     self      :	Union[str, Any]						)   ->Tuple:
        """simple docstring"""







        return torch.__version__


  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	int						)   ->float:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				self._prepare_inference_func(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)
        return self._measure_speed(_inference						)


  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	int						)   ->[Memory, Optional[MemorySummary]]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				self._prepare_inference_func(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)
        return self._measure_memory(_inference						)


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	int						)   ->float:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				self._prepare_train_func(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)
        return self._measure_speed(_train						)


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	int						)   ->[Memory, Optional[MemorySummary]]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				self._prepare_train_func(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						)
        return self._measure_memory(_train						)


  def 	_lowercase (     self      :	Tuple			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	int						)   ->Callable[[], None]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				self.config_dict[model_name]

        if self.args.torchscript:
              SCREAMING_SNAKE_CASE	: List[str]					       =				True

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				(
            hasattr(UpperCAmelCase__			,			"""architectures"""						)
            and isinstance(config.architectures			,			UpperCAmelCase__						)
            and len(config.architectures						) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
              try:
                    SCREAMING_SNAKE_CASE	: Tuple					       =				config.architectures[0]
                    SCREAMING_SNAKE_CASE	: List[str]					       =				__import__("""transformers"""			,			fromlist=[model_class]						)
                    SCREAMING_SNAKE_CASE	: int					       =				getattr(UpperCAmelCase__			,			UpperCAmelCase__						)
                    SCREAMING_SNAKE_CASE	: List[Any]					       =				model_cls(UpperCAmelCase__						)
              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:
              SCREAMING_SNAKE_CASE	: Any					       =				MODEL_MAPPING[config.__class__](UpperCAmelCase__						)

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

        # encoder-decoder has vocab size saved differently
        SCREAMING_SNAKE_CASE	: Tuple					       =				config.vocab_size if hasattr(UpperCAmelCase__			,			"""vocab_size"""						) else config.encoder.vocab_size
        SCREAMING_SNAKE_CASE	: Dict					       =				torch.randint(UpperCAmelCase__			,			(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():
                    SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				torch.jit.trace(UpperCAmelCase__			,			UpperCAmelCase__						)
        else:
              SCREAMING_SNAKE_CASE	: Dict					       =				model

        def encoder_decoder_forward():
              with torch.no_grad():
                    SCREAMING_SNAKE_CASE	: List[str]					       =				inference_model(UpperCAmelCase__			,			decoder_input_ids=UpperCAmelCase__						)
              return outputs

        def encoder_forward():
              with torch.no_grad():
                    SCREAMING_SNAKE_CASE	: str					       =				inference_model(UpperCAmelCase__						)
              return outputs

        SCREAMING_SNAKE_CASE	: str					       =				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward


  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	str			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	int						)   ->Callable[[], None]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				self.config_dict[model_name]

        SCREAMING_SNAKE_CASE	: str					       =				(
            hasattr(UpperCAmelCase__			,			"""architectures"""						)
            and isinstance(config.architectures			,			UpperCAmelCase__						)
            and len(config.architectures						) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
              try:
                    SCREAMING_SNAKE_CASE	: Tuple					       =				config.architectures[0]
                    SCREAMING_SNAKE_CASE	: Dict					       =				__import__("""transformers"""			,			fromlist=[model_class]						)
                    SCREAMING_SNAKE_CASE	: List[Any]					       =				getattr(UpperCAmelCase__			,			UpperCAmelCase__						)
                    SCREAMING_SNAKE_CASE	: Tuple					       =				model_cls(UpperCAmelCase__						)
              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:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCAmelCase__						)

        if self.args.torchscript:
              raise NotImplementedError("""Training for torchscript is currently not implemented"""						)
        else:
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				model

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

        # encoder-decoder has vocab size saved differently
        SCREAMING_SNAKE_CASE	: Dict					       =				config.vocab_size if hasattr(UpperCAmelCase__			,			"""vocab_size"""						) else config.encoder.vocab_size
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				torch.randint(UpperCAmelCase__			,			(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():
              SCREAMING_SNAKE_CASE	: Dict					       =				train_model(UpperCAmelCase__			,			labels=UpperCAmelCase__						)[0]
              loss.backward()
              return loss

        def compute_loss_and_backprob_encoder_decoder():
              SCREAMING_SNAKE_CASE	: int					       =				train_model(UpperCAmelCase__			,			decoder_input_ids=UpperCAmelCase__			,			labels=UpperCAmelCase__						)[0]
              loss.backward()
              return loss

        SCREAMING_SNAKE_CASE	: int					       =				(
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	Tuple						)   ->float:
        """simple docstring"""







        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(
                        UpperCAmelCase__			,			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
              SCREAMING_SNAKE_CASE	: Tuple					       =				timeit.repeat(
                  UpperCAmelCase__			,			repeat=self.args.repeat			,			number=1_0			,			)

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




  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Callable[[], None]						)   ->[Memory, MemorySummary]:
        """simple docstring"""







        try:
              if self.args.trace_memory_line_by_line:
                    SCREAMING_SNAKE_CASE	: List[Any]					       =				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."""						)
                          SCREAMING_SNAKE_CASE	: Optional[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()
                          SCREAMING_SNAKE_CASE	: Any					       =				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx						)
                          SCREAMING_SNAKE_CASE	: Optional[Any]					       =				nvml.nvmlDeviceGetMemoryInfo(UpperCAmelCase__						)
                          SCREAMING_SNAKE_CASE	: List[str]					       =				meminfo.used
                          SCREAMING_SNAKE_CASE	: int					       =				Memory(UpperCAmelCase__						)
                          # shutdown nvml
                          nvml.nvmlShutdown()
              else:
                    # cpu
                    SCREAMING_SNAKE_CASE	: Optional[Any]					       =				measure_peak_memory_cpu(UpperCAmelCase__						)
                    SCREAMING_SNAKE_CASE	: Tuple					       =				Memory(UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else memory_bytes

              if self.args.trace_memory_line_by_line:
                    SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				stop_memory_tracing(UpperCAmelCase__						)
              else:
                    SCREAMING_SNAKE_CASE	: Dict					       =				None

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







from __future__ import annotations

UpperCAmelCase__	:       Optional[int]			      =      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]







class     a__    :







  """simple docstring"""




  def __init__(     self      :	Optional[Any]			,			UpperCAmelCase__      :	int						)   ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				-1
        SCREAMING_SNAKE_CASE	: int					       =				-1
        SCREAMING_SNAKE_CASE	: list[int]					       =				[-1] * length
        SCREAMING_SNAKE_CASE	: Node | None					       =				None
        SCREAMING_SNAKE_CASE	: Node | None					       =				None




  def __repr__(     self      :	Dict						)   ->str:
        """simple docstring"""







        return f"Node(min_value={self.minn} max_value={self.maxx})"






def    __lowercase (       _A     )       ->  Node | None:
      SCREAMING_SNAKE_CASE	: Tuple					       =				Node(len(_A     )     )
      SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: int					       =				min(_A     ), max(_A     )
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      SCREAMING_SNAKE_CASE	: int					       =				(root.minn + root.maxx) // 2

      SCREAMING_SNAKE_CASE	: list[int]					       =				[]
      SCREAMING_SNAKE_CASE	: list[int]					       =				[]

      for index, num in enumerate(_A     ):
            if num <= pivot:
                  left_arr.append(_A     )
            else:
                  right_arr.append(_A     )
            SCREAMING_SNAKE_CASE	: int					       =				len(_A     )
      SCREAMING_SNAKE_CASE	: int					       =				build_tree(_A     )
      SCREAMING_SNAKE_CASE	: Tuple					       =				build_tree(_A     )
      return root






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  int:
      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      SCREAMING_SNAKE_CASE	: List[str]					       =				(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left  ,      _A  ,      node.map_left[index] - 1     )
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right  ,      _A  ,      index - node.map_left[index]     )






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  int:
      if start > end:
            return 0
      SCREAMING_SNAKE_CASE	: Any					       =				rank_till_index(_A  ,      _A  ,      _A     )
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				rank_till_index(_A  ,      _A  ,      start - 1     )
      return rank_till_end - rank_before_start






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  int:
      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      SCREAMING_SNAKE_CASE	: Tuple					       =				node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left  ,      _A  ,      (node.map_left[start - 1] if start else 0)  ,      node.map_left[end] - 1  ,      )
      else:
            return quantile(
                node.right  ,      index - num_elements_in_left_tree  ,      start - (node.map_left[start - 1] if start else 0)  ,      end - node.map_left[end]  ,      )






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A  ,      _A     )       ->  int:
      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      SCREAMING_SNAKE_CASE	: Dict					       =				range_counting(
          node.left  ,      (node.map_left[start - 1] if start else 0)  ,      node.map_left[end] - 1  ,      _A  ,      _A  ,      )
      SCREAMING_SNAKE_CASE	: List[str]					       =				range_counting(
          node.right  ,      start - (node.map_left[start - 1] if start else 0)  ,      end - node.map_left[end]  ,      _A  ,      _A  ,      )
      return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()




