






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_	:	Tuple				=				logging.get_logger(__name__)




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	PyTorchBenchmarkArguments
       a       :	PretrainedConfig
       a       :	str								="PyTorch"





       @property
       def     _a					(      self							):
         return torch.__version__





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      List[Any]					   =       self._prepare_inference_func(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)
         return self._measure_speed(_inference							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      List[Any]					   =       self._prepare_inference_func(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)
         return self._measure_memory(_inference							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       self._prepare_train_func(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)
         return self._measure_speed(_train							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      Any					   =       self._prepare_train_func(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							)
         return self._measure_memory(_train							)





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      Union[str, Any]					   =       self.config_dict[model_name]

         if self.args.torchscript:
           UpperCamelCase_:      str					   =       True

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

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

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

         def encoder_decoder_forward():
           with torch.no_grad():
             UpperCamelCase_:      Union[str, Any]					   =       inference_model(_lowerCamelCase						,    decoder_input_ids=_lowerCamelCase							)
           return outputs

         def encoder_forward():
           with torch.no_grad():
             UpperCamelCase_:      Tuple					   =       inference_model(_lowerCamelCase							)
           return outputs

         UpperCamelCase_:      List[Any]					   =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
         return _forward





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase							):
         UpperCamelCase_:      Union[str, Any]					   =       self.config_dict[model_name]

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

         if self.args.torchscript:
           raise NotImplementedError('Training for torchscript is currently not implemented'							)
         else:
           UpperCamelCase_:      Tuple					   =       model

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

         # encoder-decoder has vocab size saved differently
         UpperCamelCase_:      Any					   =       config.vocab_size if hasattr(_lowerCamelCase						,    'vocab_size'							) else config.encoder.vocab_size
         UpperCamelCase_:      List[Any]					   =       torch.randint(_lowerCamelCase						,    (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():
           UpperCamelCase_:      List[Any]					   =       train_model(_lowerCamelCase						,    labels=_lowerCamelCase							)[0]
           loss.backward()
           return loss

         def compute_loss_and_backprob_encoder_decoder():
           UpperCamelCase_:      Optional[Any]					   =       train_model(_lowerCamelCase						,    decoder_input_ids=_lowerCamelCase						,    labels=_lowerCamelCase							)[0]
           loss.backward()
           return loss

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





       def     _a					(      self						,    _lowerCamelCase							):
         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(
                 _lowerCamelCase						,    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
           UpperCamelCase_:      Union[str, Any]					   =       timeit.repeat(
               _lowerCamelCase						,    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(_lowerCamelCase							) / 1_0.0
         except RuntimeError as e:
           self.print_fn(f'''Doesn\'t fit on GPU. {e}'''							)
           return "N/A"





       def     _a					(      self						,    _lowerCamelCase							):
         try:
           if self.args.trace_memory_line_by_line:
             UpperCamelCase_:      Union[str, 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.'							)
               UpperCamelCase_:      Optional[int]					   =       '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()
               UpperCamelCase_:      Any					   =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
               UpperCamelCase_:      Optional[Any]					   =       nvml.nvmlDeviceGetMemoryInfo(_lowerCamelCase							)
               UpperCamelCase_:      Dict					   =       meminfo.used
               UpperCamelCase_:      Union[str, Any]					   =       Memory(_lowerCamelCase							)
               # shutdown nvml
               nvml.nvmlShutdown()
           else:
             # cpu
             UpperCamelCase_:      Optional[Any]					   =       measure_peak_memory_cpu(_lowerCamelCase							)
             UpperCamelCase_:      List[str]					   =       Memory(_lowerCamelCase							) if isinstance(_lowerCamelCase						,    _lowerCamelCase							) else memory_bytes

           if self.args.trace_memory_line_by_line:
             UpperCamelCase_:      Optional[int]					   =       stop_memory_tracing(_lowerCamelCase							)
           else:
             UpperCamelCase_:      Any					   =       None

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







def 						snake_case      (UpperCAmelCase__ = 4_0_0_0_0_0_0						) ->       int:

  if n <= 1:
    return 0
  UpperCamelCase_:      Dict					   =       0
  UpperCamelCase_:      Union[str, Any]					   =       2
  UpperCamelCase_:      int					   =       0
  while 4 * b + a <= n:
    UpperCamelCase_   ,UpperCamelCase_:      List[str]					   =       b, 4 * b + a
    count += a
  return count + b


if __name__ == "__main__":
   print(F'''{solution() = }''')