
"""simple docstring"""
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


_a   :      Union[str, Any]       =				logging.get_logger(__name__)






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			PyTorchBenchmarkArguments
    __lowercase				:			PretrainedConfig
    __lowercase				:			str	=       "PyTorch"
    @property
    def        lowerCamelCase__						(     self  ):
      return torch.__version__
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Union[str, Any]            = self._prepare_inference_func(snake_case_    ,     snake_case_    ,     snake_case_  )
      return self._measure_speed(_inference  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = self._prepare_inference_func(snake_case_    ,     snake_case_    ,     snake_case_  )
      return self._measure_memory(_inference  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Dict            = self._prepare_train_func(snake_case_    ,     snake_case_    ,     snake_case_  )
      return self._measure_speed(_train  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Optional[int]            = self._prepare_train_func(snake_case_    ,     snake_case_    ,     snake_case_  )
      return self._measure_memory(_train  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    List[Any]            = self.config_dict[model_name]

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

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

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

      # encoder-decoder has vocab size saved differently
      _snake_case  :    Tuple            = config.vocab_size if hasattr(snake_case_    ,     "vocab_size"  ) else config.encoder.vocab_size
      _snake_case  :    int            = torch.randint(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():
          _snake_case  :    str            = torch.jit.trace(snake_case_    ,     snake_case_  )
      else:
        _snake_case  :    Optional[Any]            = model

      def encoder_decoder_forward():
        with torch.no_grad():
          _snake_case  :    Dict            = inference_model(snake_case_    ,     decoder_input_ids=snake_case_  )
        return outputs

      def encoder_forward():
        with torch.no_grad():
          _snake_case  :    Optional[Any]            = inference_model(snake_case_  )
        return outputs

      _snake_case  :    str            = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
      return _forward
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Any            = self.config_dict[model_name]

      _snake_case  :    Union[str, Any]            = (
          hasattr(snake_case_    ,     "architectures"  )
          and isinstance(config.architectures    ,     snake_case_  )
          and len(config.architectures  ) > 0
      )
      if not self.args.only_pretrain_model and has_model_class_in_config:
        try:
          _snake_case  :    Union[str, Any]            = config.architectures[0]
          _snake_case  :    Optional[int]            = __import__("transformers"    ,     fromlist=[model_class]  )
          _snake_case  :    int            = getattr(snake_case_    ,     snake_case_  )
          _snake_case  :    Any            = model_cls(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:
        _snake_case  :    Optional[int]            = MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case_  )

      if self.args.torchscript:
        raise NotImplementedError("Training for torchscript is currently not implemented"  )
      else:
        _snake_case  :    Dict            = model

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

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

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

      _snake_case  :    Optional[int]            = (
          compute_loss_and_backprob_encoder_decoder
          if config.is_encoder_decoder
          else compute_loss_and_backprob_encoder
      )
      return _train
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      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(
              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
        _snake_case  :    Tuple            = timeit.repeat(
            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(snake_case_  ) / 10.0
      except RuntimeError as e:
        self.print_fn(F'Doesn\'t fit on GPU. {e}'  )
        return "N/A"


    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      try:
        if self.args.trace_memory_line_by_line:
          _snake_case  :    Dict            = 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."  )
            _snake_case  :    Tuple            = "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()
            _snake_case  :    Union[str, Any]            = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
            _snake_case  :    int            = nvml.nvmlDeviceGetMemoryInfo(snake_case_  )
            _snake_case  :    Optional[int]            = meminfo.used
            _snake_case  :    Optional[Any]            = Memory(snake_case_  )
            # shutdown nvml
            nvml.nvmlShutdown()
        else:
          # cpu
          _snake_case  :    str            = measure_peak_memory_cpu(snake_case_  )
          _snake_case  :    Tuple            = Memory(snake_case_  ) if isinstance(snake_case_    ,     snake_case_  ) else memory_bytes

        if self.args.trace_memory_line_by_line:
          _snake_case  :    List[str]            = stop_memory_tracing(snake_case_  )
        else:
          _snake_case  :    List[Any]            = 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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			str      )				->   bool:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 42
  SCREAMING_SNAKE_CASE__     				= list(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= _vector[:-1], int(_vector[-1]      )
  SCREAMING_SNAKE_CASE__     				= [int(__UpperCamelCase      ) for digit in __vector]

  vector.reverse()
  for i, digit in enumerate(__UpperCamelCase      ):
    if i & 1 == 0:
      SCREAMING_SNAKE_CASE__     				= digit * 2
      if doubled > 9:
        doubled -= 9
      check_digit += doubled
    else:
      check_digit += digit

  return check_digit % 10 == 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   assert is_luhn('''79927398713''')
   assert not is_luhn('''79927398714''')
