

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									=  logging.get_logger(__name__)






class 				A_					(						snake_case__   ):
     _lowercase     :	PyTorchBenchmarkArguments
     _lowercase     :	PretrainedConfig
     _lowercase     :	str        =      "PyTorch"


     @property
     def     UpperCAmelCase    (				self     :						int   )				->      List[Any]:
      return torch.__version__


     def     UpperCAmelCase    (				self     :						List[Any]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						int   )				->      float:
      __lowerCAmelCase:  int        = self._prepare_inference_func(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )
      return self._measure_speed(_inference   )


     def     UpperCAmelCase    (				self     :						List[str]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						int   )				->      [Memory, Optional[MemorySummary]]:
      __lowerCAmelCase:  Optional[int]        = self._prepare_inference_func(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )
      return self._measure_memory(_inference   )


     def     UpperCAmelCase    (				self     :						Union[str, Any]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						int   )				->      float:
      __lowerCAmelCase:  int        = self._prepare_train_func(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )
      return self._measure_speed(_train   )


     def     UpperCAmelCase    (				self     :						List[Any]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						int   )				->      [Memory, Optional[MemorySummary]]:
      __lowerCAmelCase:  Dict        = self._prepare_train_func(UpperCAmelCase     ,    UpperCAmelCase     ,    UpperCAmelCase   )
      return self._measure_memory(_train   )


     def     UpperCAmelCase    (				self     :						Any     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						int   )				->      Callable[[], None]:
      __lowerCAmelCase:  Optional[Any]        = self.config_dict[model_name]

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

      __lowerCAmelCase:  List[Any]        = (
          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:
        __lowerCAmelCase:  Any        = config.architectures[0]
        __lowerCAmelCase:  Union[str, Any]        = __import__('transformers'     ,    fromlist=[model_class]   )
        __lowerCAmelCase:  str        = getattr(UpperCAmelCase     ,    UpperCAmelCase   )
        __lowerCAmelCase:  Union[str, 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:
       __lowerCAmelCase:  int        = MODEL_MAPPING[config.__class__](UpperCAmelCase   )

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

      # encoder-decoder has vocab size saved differently
      __lowerCAmelCase:  Dict        = config.vocab_size if hasattr(UpperCAmelCase     ,    'vocab_size'   ) else config.encoder.vocab_size
      __lowerCAmelCase:  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()

      if self.args.torchscript:
       with torch.no_grad():
        __lowerCAmelCase:  Optional[int]        = torch.jit.trace(UpperCAmelCase     ,    UpperCAmelCase   )
      else:
       __lowerCAmelCase:  List[str]        = model

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

      def encoder_forward():
       with torch.no_grad():
        __lowerCAmelCase:  Tuple        = inference_model(UpperCAmelCase   )
       return outputs

      __lowerCAmelCase:  Optional[Any]        = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
      return _forward


     def     UpperCAmelCase    (				self     :						Optional[Any]     ,    UpperCAmelCase     :						str     ,    UpperCAmelCase     :						int     ,    UpperCAmelCase     :						int   )				->      Callable[[], None]:
      __lowerCAmelCase:  Dict        = self.config_dict[model_name]

      __lowerCAmelCase:  List[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:
        __lowerCAmelCase:  int        = config.architectures[0]
        __lowerCAmelCase:  Dict        = __import__('transformers'     ,    fromlist=[model_class]   )
        __lowerCAmelCase:  Any        = getattr(UpperCAmelCase     ,    UpperCAmelCase   )
        __lowerCAmelCase:  int        = 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:
       __lowerCAmelCase:  Union[str, Any]        = MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCAmelCase   )

      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:  List[str]        = config.vocab_size if hasattr(UpperCAmelCase     ,    'vocab_size'   ) else config.encoder.vocab_size
      __lowerCAmelCase:  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():
       __lowerCAmelCase:  Union[str, Any]        = train_model(UpperCAmelCase     ,    labels=UpperCAmelCase   )[0]
       loss.backward()
       return loss

      def compute_loss_and_backprob_encoder_decoder():
       __lowerCAmelCase:  Any        = train_model(UpperCAmelCase     ,    decoder_input_ids=UpperCAmelCase     ,    labels=UpperCAmelCase   )[0]
       loss.backward()
       return loss

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


     def     UpperCAmelCase    (				self     :						Any     ,    UpperCAmelCase     :						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(
            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
       __lowerCAmelCase:  List[str]        = 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     UpperCAmelCase    (				self     :						Tuple     ,    UpperCAmelCase     :						Callable[[], None]   )				->      [Memory, MemorySummary]:
      try:
       if self.args.trace_memory_line_by_line:
        __lowerCAmelCase:  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.'   )
         __lowerCAmelCase:  str        = '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:  List[Any]        = nvml.nvmlDeviceGetMemoryInfo(UpperCAmelCase   )
         __lowerCAmelCase:  Tuple        = meminfo.used
         __lowerCAmelCase:  Any        = Memory(UpperCAmelCase   )
         # shutdown nvml
         nvml.nvmlShutdown()
       else:
        # cpu
        __lowerCAmelCase:  List[str]        = measure_peak_memory_cpu(UpperCAmelCase   )
        __lowerCAmelCase:  Any        = Memory(UpperCAmelCase   ) if isinstance(UpperCAmelCase     ,    UpperCAmelCase   ) else memory_bytes

       if self.args.trace_memory_line_by_line:
        __lowerCAmelCase:  List[str]        = stop_memory_tracing(UpperCAmelCase   )
       else:
        __lowerCAmelCase:  List[Any]        = None

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








def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 return "".join(chr(ord(SCREAMING_SNAKE_CASE						) + 32						) if 'A' <= char <= 'Z' else char for char in word						)


if __name__ == "__main__":
  from doctest import testmod

  testmod()





