






"""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  :						str					       =							logging.get_logger(__name__)


class 	_UpperCamelCase ( lowerCamelCase_							):



    '''simple docstring'''


    __lowercase    :   List[Any]											=						42
    __lowercase    :   int											=						42
    __lowercase    :   str											=						'PyTorch'


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


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				self._prepare_inference_func(__lowercase   ,			__lowercase   ,			__lowercase			)
     return self._measure_speed(_inference			)


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				self._prepare_inference_func(__lowercase   ,			__lowercase   ,			__lowercase			)
     return self._measure_memory(_inference			)


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				self._prepare_train_func(__lowercase   ,			__lowercase   ,			__lowercase			)
     return self._measure_speed(_train			)


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				self._prepare_train_func(__lowercase   ,			__lowercase   ,			__lowercase			)
     return self._measure_memory(_train			)


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				self.config_dict[model_name]

     if self.args.torchscript:
      UpperCAmelCase__ 						=				True

     UpperCAmelCase__ 						=				(
         hasattr(__lowercase   ,			"""architectures"""			)
         and isinstance(config.architectures   ,			__lowercase			)
         and len(config.architectures			) > 0
     )
     if not self.args.only_pretrain_model and has_model_class_in_config:
      try:
       UpperCAmelCase__ 						=				config.architectures[0]
       UpperCAmelCase__ 						=				__import__("""transformers"""   ,			fromlist=[model_class]			)
       UpperCAmelCase__ 						=				getattr(__lowercase   ,			__lowercase			)
       UpperCAmelCase__ 						=				model_cls(__lowercase			)
      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__ 						=				MODEL_MAPPING[config.__class__](__lowercase			)

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

     # encoder-decoder has vocab size saved differently
     UpperCAmelCase__ 						=				config.vocab_size if hasattr(__lowercase   ,			"""vocab_size"""			) else config.encoder.vocab_size
     UpperCAmelCase__ 						=				torch.randint(__lowercase   ,			(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__ 						=				torch.jit.trace(__lowercase   ,			__lowercase			)
     else:
      UpperCAmelCase__ 						=				model

     def encoder_decoder_forward():
      with torch.no_grad():
       UpperCAmelCase__ 						=				inference_model(__lowercase   ,			decoder_input_ids=__lowercase			)
      return outputs

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

     UpperCAmelCase__ 						=				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
     return _forward


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				self.config_dict[model_name]

     UpperCAmelCase__ 						=				(
         hasattr(__lowercase   ,			"""architectures"""			)
         and isinstance(config.architectures   ,			__lowercase			)
         and len(config.architectures			) > 0
     )
     if not self.args.only_pretrain_model and has_model_class_in_config:
      try:
       UpperCAmelCase__ 						=				config.architectures[0]
       UpperCAmelCase__ 						=				__import__("""transformers"""   ,			fromlist=[model_class]			)
       UpperCAmelCase__ 						=				getattr(__lowercase   ,			__lowercase			)
       UpperCAmelCase__ 						=				model_cls(__lowercase			)
      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__ 						=				MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowercase			)

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

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

     # encoder-decoder has vocab size saved differently
     UpperCAmelCase__ 						=				config.vocab_size if hasattr(__lowercase   ,			"""vocab_size"""			) else config.encoder.vocab_size
     UpperCAmelCase__ 						=				torch.randint(__lowercase   ,			(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__ 						=				train_model(__lowercase   ,			labels=__lowercase			)[0]
      loss.backward()
      return loss

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

     UpperCAmelCase__ 						=				(
         compute_loss_and_backprob_encoder_decoder
         if config.is_encoder_decoder
         else compute_loss_and_backprob_encoder
     )
     return _train


    def 		A__      (      self   ,			__lowercase			):
     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(
           __lowercase   ,			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__ 						=				timeit.repeat(
          __lowercase   ,			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(__lowercase			) / 10.0
     except RuntimeError as e:
      self.print_fn(F'''Doesn\'t fit on GPU. {e}'''			)
      return "N/A"






    def 		A__      (      self   ,			__lowercase			):
     try:
      if self.args.trace_memory_line_by_line:
       UpperCAmelCase__ 						=				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__ 						=				"""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__ 						=				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
        UpperCAmelCase__ 						=				nvml.nvmlDeviceGetMemoryInfo(__lowercase			)
        UpperCAmelCase__ 						=				meminfo.used
        UpperCAmelCase__ 						=				Memory(__lowercase			)
        # shutdown nvml
        nvml.nvmlShutdown()
      else:
       # cpu
       UpperCAmelCase__ 						=				measure_peak_memory_cpu(__lowercase			)
       UpperCAmelCase__ 						=				Memory(__lowercase			) if isinstance(__lowercase   ,			__lowercase			) else memory_bytes

      if self.args.trace_memory_line_by_line:
       UpperCAmelCase__ 						=				stop_memory_tracing(__lowercase			)
      else:
       UpperCAmelCase__ 						=				None

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






import math



class 						a_			:



      """simple docstring"""







      def __init__( self	:				str     ,snake_case	:				List[Any]     ,snake_case	:				Union[str, Any]     ,snake_case	:				Optional[int]							):
           SCREAMING_SNAKE_CASE 		=x
           SCREAMING_SNAKE_CASE 		=y
           SCREAMING_SNAKE_CASE 		=z


      def __repr__( self	:				Any							):
           return f'Point({self.x}, {self.y}, {self.z})'







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2     )     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

